Question Everything

Economic dynamics - and the real danger

This is a summary paper that I have written to explain the findings from my computer model of an abstract economy's dynamics when it is run mostly on fixed, finite fuel sources such as fossil fuels. This paper is a little more formal than my previous blog on "It's the net energy stupid!" and I will be submitting it for publications at one of the energy-related web sites for wider dissemination. But I thought I'd give my readers a first peek.

The Dynamics of an Abstract Economic System

George Mobus

Associate Professor, Computing & Software Systems, University of Washington Tacoma, Institute of Technology

This is a preview of some of the ideas being developed in a paper by Mobus & Charles Hall being prepared for publication. This version only gives the background and main conclusions to be found in that paper, oriented toward general readers. The author wishes to thank Professor Charles Hall and his students for a very enlightening sabbatical at SUNY-ESF where the ideas for this model effort emerged. Any defects in this version are the fault of the author alone.

The purpose of this contribution is to introduce the concept of an abstract economy in order to explore the general dynamics of economic systems without concern for distracting details that do not alter those dynamics in general, but can cause confusion in trying to understand a more general theory of economics grounded in biophysical science.

An economy is a system that takes energy and raw materials as inputs, involves internal work processes that create and maintain physical structures, and expels degraded materials while losing waste heat to the environmental energy sinks surrounding it. This abstract description needs some more detail but should remain sufficiently general so as to cover a wide range of dynamic systems from living organisms to ecosystems to the societal economies of the world.

The structures that are produced by the work processes include biomass (reproduction and growth) and many kinds of artifacts. Collectively I call these structures — assets —, meaning that the structures are considered to be of some kind of worth to the agents participating in the economy. Assets are further divided into long-, intermediate-, and short-term. These temporal categories correspond with examples such as:

• infrastructure, housing, capital buildings and equipment, mass transportation [ten year or greater life spans]
• artifacts such as clothing, personal transportation, furniture, process designs [less than ten years but more than one year]
• food, fuels in stock, supplies [less than one year]

Biomass of humans and pets constitute a separate category of assets. The time scale of human life and reproduction determine the number of individuals within the population (how the biomass is divided into units). As crass as that may sound, the reality is that human beings are generally considered assets by most prevailing value systems. Of course, under the right conditions, all asset types may become liabilities, as when they become obsolete.

Artifact assets also include informational and knowledge recordings such as computer programs used to control work processes (as well as the very real knowledge encoded physically in brains of people). These artifacts are as much physical assets as any other, but they have somewhat unique properties, e.g. being essentially infinitely reproducible at very low energy/material costs. Their value, however, lies in how they affect the movement and interactions of physical structures in the accomplishment of useful work. Notice that missing from this list of assets is the financial instruments typically given asset status in our current economic models. I will address this later after describing debt financing below.

Figure 1, below, shows a diagrammatic representation of an abstract economy with a net accumulation of assets over time as long as there are energy and material inflows. In theory the materials could be recycled if there were enough energy available to recover wastes, refine and reshape them, etc. In practice there is never enough energy available as reflected in the cost limits for recycling operations in the real world. Also, the issue of economic growth (asset accumulation) requires new material resources continually being input into the system.

Figure 1. An abstract economy. Work processes use energy and raw materials to produce assets, some of which are consumed by processes that constitute the major body of the system, some of which will be reinvested in the work processes. Information (thin arrows) flows between the three subsystems.

The dynamics of asset production and decay are governed by the laws of thermodynamics. The general conservation law applies to both matter and energy. But the second law, in particular, has ultimate influence over the efficiency and effectiveness of work processes as well as the entropic decay of all forms of assets. Each asset type can be characterized by its own production and decay constants, but each follows a similar formulation.

A_(t) = A_(t-1) + k_pNE_(t) - k_dA_(t-1)

where:
A is the level of asset accumulation
NE is the net energy available for economic work (e.g. gasoline refined from oil)
k_p is the constant of asset production
k_d is the constant of asset consumption + entropic decay
both constants are << 1.0 and k_p > k_d
and t is the discrete time index

As it happens, since all asset types follow the same dynamics, it is possible to aggregate all assets using a weighting method to aggregate constants so that A_t represents the sum of all assets at time t. Here the dimensions of assets are given in embodied energy units, or emergy. The constant k_d represents both consumption processing and natural decay due to wear and entropy.

A key question that needs to be addressed is: given our current heavy reliance on fossil fuels for more than 80% of our energy inputs, what happens in an economy that is growing when the resources are depleting? As most readers of The Oil Drum and other energy related sites are aware, the focus of analysis has been on the dynamics of peak oil, as per M. King Hubbert, and concern has been for the impacts of declining oil supplies after the peak on the economy. The model that I have been working on, and am in the process of refining with Dr. Charles Hall at SUNY-ESF, suggests that this is actually not the relevant focus in terms of economic capacity.

Many people have realized, either intuitively or logically, that what matters insofar as economic activity is concerned is the net energy — the energy available after extraction of the gross energy (in this case crude oil) and refinement or conversion to a form useful in doing work. Energy must be reinvested in energy extraction, transportation, and refinement in order to gain net energy that can be available for asset production (other than assets needed for energy extraction). Thus there is an energy return on energy invested (EROI or EROEI) that must be taken into account in determining if an economy is viable, stable, or growing. Essentially, when the energy costs of extraction and refinement are subtracted from the gross energy extracted, the net is all that society can use for its work processes. Energy costs are similar to monetary costs of doing business. There are actually energy investments, as for example the embodied energy in off-shore drilling rigs, and there are energy operating costs, e.g., pumping oil. In addition there are also on-going maintenance costs to keep the infrastructure in working order.

The "Best-First" principle states that we human agents will always attempt to extract the easiest-to-get resources first. For oil and other fossil fuels this boils down to geophysical facts about the depth and quality, and geographical facts about distribution of field sizes and locations. We have tended to exploit the largest, easiest to get to fields, most of which are now in declining production. What this translates into is increasing amounts of energy must be used to find and extract the oil (read all fossil fuels), hence net energy is less per unit of energy extracted. The increases in energy costs as a result of the Best-First principle are shown in Graph 1, along with gross and net energies. This model is based on an increase in extraction difficulty as a result of depletion. It does not depend on an explicit logistic function and can be seen to not be symmetrical about the peak of gross energy extraction.

Graph 1. The net energy available to the economy is the gross extracted energy less the energy cost of reinvestment in extracting the next units of energy. This graph assumes exponential pressure in the growth of extraction. The reality of a finite resource turns this into a logistic-like curve.

The mathematical basis for this model will be covered in the paper by Mobus and Hall, in development. Sensitivity analysis, using various rate constants, shows that the model has a very stable form in terms of relations between the three curves. Lower rates of cost growth increase the values of net energy, of course, but do not change the overall dynamics of the system.

Of particular note is the fact that the net energy curve peaks before the gross energy curve. In this model the time is about thirty years, but note that energy units and time units are somewhat arbitrary as this is an attempt to ferret out the dynamical behavior of an energy system based on a fixed, finite resource. Why this is important is that for many years now, we have been focusing on peak oil or peak gross production as the most important inflection point. But the economy runs on the net energy. If peak oil has already occurred, then the peak of net energy production has long since passed. I think this is the most cogent point of this exercise.

Secondly, note that the curves for gross and net are not symmetrical about their respective peaks. This is a result of modeling the physics of resource extraction based on the Best-First principle. The pre-peak scenario aligns well with Hubbert’s theory. But the post-peak scenario shows a much faster decline rate. Since this model does not assume anything about peaking behavior and is based entirely on physical relations between extraction work and production rates, it might behoove us to think more carefully about the consequences of post-peak behavior such as this. It is true that under a profit motive for extraction, there would not likely be a continued all-out effort to extract the resources, thus possibly leading to a more extended post-peak scenario. We already appear to be in a bumpy plateau owing to the feedback from an unstable economic system. It is possible that such a plateau and other feedbacks from the financial system might extend the period of post-peak and modify the decline rates as a result. However, this will certainly come with a heavy price in terms of jobs and wealth production as it implies a greatly diminished economy.

Let us then see what the model predicts about wealth or asset production. Graph 2 shows a basic scenario for the production and accumulation of assets (in emergy units) given the above energetics.

Graph 2. Assets are produced as a function of net energy according to the equation above. This graph is for the aggregate of all asset types as described above. Assets decay or are consumed at a rate less than production as long as net energy is in its pre-peak phase. Assets continue to accumulate and the peak of asset accumulation comes after net and gross have both peaked.

The three colored regions between net energy and assets represent three distinct phases in economic activities. The green region, labeled ‘debt payback feasible’ represents a phase in which net energy is growing faster than production can add to the asset base by the above equation. During this time it is easy to imagine how expectations that future periods will always have a capacity for growth in asset production arise and lead to the institutionalization of debt financing. From the perspective of agents making decisions about how and when to create new asset wealth, the fact that there will be more such wealth in the future can be taken for granted. Debts can presumably be paid back with interest. The form of modern capitalism relies on such an assumption.

Though money is not explicitly modeled it can be thought of as convenient tokenization of energy units that facilitates trade and represents a claim on future work. In that sense, debt instruments can be denominated in such tokens in order to represent future amounts of work that will be done to service the debt and pay some presumed future profit in the form of interest on the loan. Money is the main form of information flows (thin arrows) shown in Fig. 1. It circulates between the processes and the asset stores to regulate what gets built and when. Financial instruments such as bonds, stocks, and other forms of future obligations are superimposed on this information stream as a way to push promises of future work further out in time. So far as agents creating these instruments without foreknowledge of the limits to growth, this seemed perfectly reasonable in the green region.

The yellow region represents a phase in which net energy is decelerating and converging with still growing asset accumulation. This convergence and ultimate cross-over is a period in which the potential to pay back loans, based on future work, is coming to an end. Once net energy is no longer in exponential growth, the expansion of assets also goes into deceleration. Agents within this phase may or may not recognize the problems associated with decelerating work capacity. One might speculate that such agents, having been so successful at using debt-based financing for wealth production in the past (green phase) would be tempted to elaborate more abstract debt instruments in an attempt to continue at least the appearance of wealth production growth. Various forms of financial bubbles might appear followed by collapse since it would become progressively harder to create real physical wealth (assets) due to decreasing available net energy. For agents that sensed a change in the general milieu there was incentive to change the organization of work processes in order to reduce costs — ultimately energy costs of production — such as moving manufacturing to regions of cheaper labor. The yellow region represents a period of adaptation, where possible, to decelerating net energy flow.

Eventually, however, once net energy production falls below the asset accumulation curve, the capacity for paying back debt-based obligations is no longer. Indeed, in the red region it is not even possible to create legitimate debt instruments since all net energy production should be now going strictly to maintenance work. No new assets can be created without ignoring the maintenance of current assets (and the concentration of wealth in the hands of the most powerful agents) and all work is geared toward fighting entropy and struggles to supply consumption processes (one imagines this to be chiefly oriented toward maintaining food production).

The vertical line marked ‘today’ is an arbitrarily placed marker for peak oil, presumed to have already happened (reports on various energy web sites strongly suggest this). If this is the case, then we are already into the red region where it is impossible to create debt-based financing (legitimately) since there is absolutely no possibility of paying off that debt with future work resulting in greater asset accumulation. Is it possible that this is exactly the problem we are seeing in our heavily debt reliant economy today? Our financial systems have clearly gotten out of sync with our real asset producing economy. We are in the throes of debt-unwind and very possibly massive defaults as nations, corporations, and individuals (who have no jobs) are incapable of promising to work more in the future to pay back their obligations. Those who would loan money (claims on assets) to those who propose to create new wealth would do well to reconsider since the model suggests that it will be impossible to even get back the principal, let alone the interest.

Of course, the real global economy is not 100% reliant on fossil fuels; though the mix of other sources makes it difficult to incorporate into this model except possibly by actual data (e.g. nuclear power production started after the mid fifties and has not grown, globally, in proportion to fossil fuel use). Conceivably, where the real economy is hobbling along and not seeming to crash as expected from this model, it is due to more stable energy production from, for example, hydroelectric, sources. Also much growth, especially in electricity generation, has been due to coal rather than oil. But a necessary caveat here is that the extraction of coal is fully dependent on the availability of diesel fuel for transportation and excavation (as in mountain top removal). So I would expect the peak of net energy from oil is already having a deceleration pressure on net energy from coal. It may be too early to say what the dynamics will look like if natural gas proves to be a viable alternative to either coal (electricity) or oil (transportation and heating). If the current claims about recoverable resources (at least in the US) pan out, it could both extend the energy curves and delay the overall aggregate fossil fuel peaks for some time. Nevertheless, the basic shape of these curves will be the same. If we do end up buying more time with natural gas we would do well to use it wisely.

It takes energy to do work. And work, constructing real assets (vs. paper assets), is necessary to create real wealth in order to retire debt obligations. If this model of an abstract economy, fueled as in this case by a fixed, finite supply of energy, truly captures the essence of the dynamics of a real economy then we should consider carefully the meaning of the earlier peaking of net energy flow as well as the potentially dangerous rapid decline in post-peak production. And we should certainly consider the implications of the three phases of economic growth, deceleration, and final decline as shown in this model. Peak oil may only signal that we are already past the point at which our economic systems can function as wealth accumulators instead of signaling that the trouble is about to start.

Abstracting labor

Let me introduce Steve^* the worker. Steve is a typical human being who will take a role in the center of all of our abstractions. This is a person-centric view in accord with our Energy Cocoon. Here we see Steve as having a job for which he is paid a wage, some means of turning around and purchasing things he needs and wants for himself and his family. That is, he is paid if he does a good job.

Steve's job entails operating a machine that transforms inputs of energy and raw materials into a finished product with embodied energy value. Steve is skilled in that he needs to observe how the machine is performing and issuing commands to the machine to make sure it is producing an optimal product. This is important because the Boss is monitoring the product quality in order to make sure the customer is getting a good product. The customer will pay for a good product, which is income to the company (as represented by the Boss). In turn, the Boss pays Steve a wage that he, in turn, can use to purchase necessaries like food. Steve needs food to be able to perform his duties (which entails living at home and supporting a family). Both machine and Steve give off waste heat and waste materials. We won't pursue that notion any further.

Note Steve's boss. Don't make too much out of the fact that the boss is frowning. Bosses always seem to frown. His job is to monitor the quality of Steve's work, sell the product to a customer, and receive an income (payment) from the customer. He keeps some for running the rest of the operation, some for himself, and pays Steve a wage along with giving him 'directions' or commands regarding any quality or rate of work issues. Like Steve the worker, the boss needs to eat and eliminate wastes too.

This diagram represents a more modern view of labor. The machine could be anything from a mill, a press, to a computer. It could even be a whole department of a more complex company. The central issues are:

• the machine accounts for a larger percentage of energy/material throughput than does Steve
• even so, Steve has an energy/matter flow through his subsystem
• Steve's role is to manage the machine according to some preordained quality attributes
• the machine acts as a work amplifier with Steve providing the control current

In olden times, before machines that took significant external energy flows to operate, a worker in Steve's place supplied the majority of energy and his efforts went to applying one or more hand tools (machine) to the production process. And he still needed skill to make sure it was done correctly. The situation is captured very well by the story of the weaving loom (see: Wikipedia article). Before water mills were employed to provide the working power to looms, women (generally) had to operate a foot peddle to move the arms of the loom separating the threads. I suspect it was hard work. Later, water wheels provided motive power and the machine age using external energy was being born (same story for windmills in Holland, and even earlier uses of animals to walk around a central hub creating radial force to mill grains, etc.) Things really got going when Frenchman Basile Bouchon invented a paper roll control device that was used to manipulate the threads without human intervention, which led eventually to the Jaccard loom, controlled by punched cards using essentially the same principle but with more reliability. Incidentally, the Jaccard cards were the predecessors of the IBM punch cards used in the earliest computers!

If you read my blog post, "What is value?" you may recall the labor theory of value, which supposes the value added to a product comes from the efforts of the laborer. In olden times that was the case. Now, what are we to make of Steve's situation when the means of production are more largely represented by the machine and its ability to use external energy, like electricity, to do the heavy lifting? [Note the similarity to of the above figure to Figure 1 in the prior post. One of these days I will consolidate the different views!]

Suppose, for a moment, that the product being produced by Steve (with machine) is essentially what it was when Steve's long-ago predecessor made it by hand. Then it is clear that the addition of the machine and its energy input is what we mean by improvement in technology and productivity. Basically Steve can make many more widgets per unit of time than even a small army of workers could have done in the olden days. Steve needs a different set of skills to control the machine than was needed to pound the hammer to form the widget. But it is possible that the skill level and complexity is about comparable to what his predecessor needed. What we have done is to substitute a machine and new skills in operating it for that small army of workers producing by hand. If we hold the volume of production constant, i.e.., same number of widgets per unit of time as in olden times, have we merely substituted one form of input energy (external to the machine) for an equivalent amount of input to the workers in the form of food? It is an interesting question. [Another interesting question is what happens when the skills needed to operate a machine are significantly less than in olden times? Bosses still lay claim to the worker's time, but the effects on workers can be demoralizing and sometimes dehumanizing. I'm reminded of Charlie Chaplin in Modern Times.]

One of the things that has motivated bosses to invest in machines that effect this kind of substitution is the perception, right or wrong, that the productivity of a machine is preferable to having a small army of workers. According to financial analysis, it is cheaper and therefore better for profits (I'll ignore some other very valid arguments such as the fact that an automatic machine often improves quality while increasing the rate of production). Investment in a machine can be shown to payback at a better rate than if the boss continued paying out wages to the displaced workers. Yet I wonder if this analysis only works in the case of a growing business and economy. Isn't it the case that the best justification for automating processes is that you can produce more per unit time and thereby increase your sales? Buying another machine is certainly preferable to hiring many more workers who might or might not be adequately trained. It seems the whole growth orientation is a stronger incentive for bosses to lay out the investment in capital equipment of this sort.

Back to Steve. What should the Boss pay Steve? Surely he cannot earn the same wage as was earned by the small army of his predecessors even though he is producing the same number of units per unit of time. There would be no incentive for the Boss to invest in the machine if he ended up paying out that much money still. So Steve will make something considerably less than the sum of what his predecessors made, the difference being accounted for in the investment in (and maintenance of) the machine plus, more than likely, a nice increase in profits, a point that has always stuck in the craw of Marxists.

What Steve makes is largely a function of what Steve needs to earn for himself and his family. But, obviously, there will be a lot of room for interpretation of need here. This is actually one of the areas where market mechanisms have worked rather well historically, except when Bosses, or employees like Steve, get greedy! Steve may have had to invest some of his time and energy in learning the skills needed to run the machine. He will tend to put a, perhaps, higher premium on the value of that investment in his salary calculus. At the same time he will be competing with many other potential workers who could try to under bid him to get his job. But if he is highly skilled the Boss is going to think twice about firing Steve and hiring someone else whose skills are unproven. Uninterrupted quality production is generally worth more than the differential between what Steve is making and what someone else would make. Generally, through the jostlings of the labor market a 'fair' wage is struck between Steve and the Boss and both are satisfied with the arrangement.

Unless, of course, one or the other, or both do get greedy, which is not uncommon. Human nature, being what it is, is easily tempted to try to eek out that little extra. The Boss may want to replace Steve with cheaper labor or Steve might demand more wages regardless of any actual increase in his contribution to value added (there is always the cost of living/inflation argument; but what most people don't give much thought to is the fact that when workers get raises, to supposedly cover the cost of living increase, they are simply contributing to inflation!) We can think of all kinds of fly's that get into the ointment here. The system is already sufficiently complex that information might not be available to all parties. And there is that human nature element to contend with. The only worker not causing trouble is the machine. That is another reason Bosses like them.

Somehow, through the magic of accounting (both financial and managerial) these issues get sorted out. The Boss won't stay in business if his wage costs exceed their fair proportion of total costs. Unfortunately, cutting costs by eliminating workers is the easiest way to get control of costs. If the company goes out of business, no one wins. The accountants keep careful track of costs and income to make sure the arrangements all work out. Unless they get creative like the guys at Enron and Arthur Anderson!

But let's, for the moment, say that generally everything tends to work and Steve gets paid something reasonable, the Boss makes a reasonable profit, and the customer is happy with the widget. A remaining, very important question is: "What is the value added by this whole arrangement?" In other words, how shall we establish the price of the product? This is where the market isn't always our friend. In a barter system, in days of yore, everyone had a pretty good idea of what things were worth to them relative to the value of what they produced for trade. One cow should equal two goats. The value of the products were transparent and while it was still human nature to dicker over details and try to fudge a bit of profit, in general both parties came away relatively happy with the trade. Even when comparing apples with oranges, or rather arrow heads with beaver skins, people could gauge their own efforts, and their need for the other product and come to a sufficiently equitable agreement.

But the situation gets more clouded when external energy, machine work, and machine operation skills come into play. Machine skills necessitate specialization and thereby preclude a general understanding of effort expended by the laborer in making a product. On top of that, as soon as bosses came into the picture, ostensibly to organize the company and coordinate the activities, the value added to create more complicated products became increasingly hard to judge. [Incidentally a very similar argument can be made about modern public education. Teachers aren't exactly using ordinary machines so much as new complex organizations (schools) to produce their 'products'. It is even more complicated to determine what the quality of the product is (standardized tests, in spite of everything people want to believe, tell you nothing about the value of the education received) and what is fair compensation to teachers for their efforts. I, of course, have my biased opinion on the matter!]

Today, how do you know how much you should pay for an iPod^TM? All you really know is that you perceive that you have some discretionary income to throw at entertainment valued products and services. And there is the market. Somehow, magically-seeming, a price is set and people flock to buy at that price. The bosses attempt to keep prices reasonable so as to optimize sales volume and profits. If competitors come into the market that is supposed to drive prices down (but what about quality issues? Bosses still like the same profit margins, so something has to give.) But the reality is that no one has a real clue what anything is worth any more. Its all a big game we play and there are some real winners (windfall profiteers) and many losers (also known as consumers).

Where is Steve in all of this? Well the boss has to figure out how to fight off competition and lowering prices is a handy tool. Unfortunately that means margins are going to be cut. One solution is to grow the capacity by adding another machine and one additional laborer (of course it isn't at all that simple, but this is an abstraction after all). The increased volume at a slightly lower margin can compensate for the profit losses per unit. This is the growth strategy. Another strategy is to convince Steve that he is paid too much for the current market conditions and for the good of the company as a whole (these days meaning the bosses bonuses) he needs to take a pay cut. More often than not that is a crock. And Steve generally knows it.

Where does all this leave us? What I have tried to do is give a small demonstration of how our modern complex, machine-based production of goods and services has blurred the image of value, costs, and trade. Economists recognize this blur and seem to be satisfied with letting markets tell us what things are worth — a very lazy approach in my view. We need a tool to help us do the hard work of understanding. If we harken back to a simpler time and construct an abstraction of workers and bosses that captures the relevant relations between energy, work, skills, materials, tools, wastes, and products, and then use that abstraction to study modern work processes, especially incomes in terms of needs, wants, and fairness, we might stand a chance of grasping our current situation much better.

That's the idea anyway. We are not much different beings than we were thousands of years ago, before the agricultural and industrial revolutions. Our needs are the same even if our opportunities for enjoying the benefits of the machine age have grown immensely. So our psychological proclivities today are about what they were then, but with, perhaps, some new temptations and certainly new challenges for understanding. But in the end, the energy and material flow budgets have to balance out. What someone gets from the pool of assets our economy creates probably should be based on their contribution to the work processes they control. The boss probably does have a more complicated job than does Steve. So it doesn't seem untoward that he make some more income than Steve. But it is unlikely that the bosses value added to the whole operation is hundreds of times greater than is Steve's. What the abstract Worker Steve (and his boss) can do is show us what we need to look at in analyzing work flow and compensation flow. It also gives us a handle on valuing products from the standpoint of external energy inputs through machine labor. In a future post I will take a closer look at the role of worker skills and investment in training a bit more closely as we try to figure out what it means to be paid fairly for one's work.

Our current economic views have over abstracted to the point where we have no clear insights into costs, prices, values, and what information flows would provide those insights. And that over abstraction has gotten us into a lot of trouble, both socially and ecologically. This simple abstraction takes us back to fundamental relations that have never changed over history. It is another example of a systems approach to understanding our world.

Next, I will examine Steve using some of his income for purchases, finding out he doesn't have enough saved from prior earnings, and goes to a banker because he really wants a boat.

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^* No particular significance to the name Steve. It just popped into my head and sounded good. Of course who knows what devious thoughts are going on subconsiously?

The Science of Systems 11

Systems Science 11 — Bio-Systems Examples

What is Life?

Erwin Schrödinger asked that question in 1944, one year before my birth. Of course he was by no means the first person to ask it in scientific query. But he was the first to link living processes to the ideas of thermochemistry and our modern notions of organization and order. He called life negentropy or the negative of entropy, implying that living systems had the capacity to self organize and maintain states far from equilibrium in spite of the Second Law of Thermodynamics. What some scholars had, in previous times, called the 'vital force' Schrödinger correctly identified the basic principles that underlie the motivation of living systems, the flow of energy through the system.

Inspired by Schrödinger, years later (1968), Harold J. Morowitz (sadly no Wikipedia entry to my dismay) extended this thinking in what I personally consider a seminal contribution at least as important as that of Ilya Prigogene's with respect to chaos theory. Whereas Prigogine posited some emergence of order out of an otherwise chaotic process, Morowitz provided a precise mechanism for how this might happen. Far from deterministic, in the ordinary sense of that word, Morowitz provided the low level details of how a stochastic process could be driven far from equilibrium and provided a deeper understanding of the means by which Prigogene's dissipative systems could produce higher levels of organization and maintain a 'vital' process. It was not so mysterious after all. In the hands of Morowitz we see how the flow of energy through a well defined unorganized but bounded system will drive that system toward higher internal organization without it heating up to a point where whatever structure obtains is obliterated (via dissipation). This seminal work is Energy Flow in Biology, and in my personal opinion it ought to be required reading for every biologist who claims to understand evolution and organization (see this Amazon.com page for a list of Morowitz's other books).

Living organisms, from single celled to human beings, are nearly incomparable in terms of complex, organized systems that appear to be tractable in terms of what goes on within the observable boundary of that system. They are not closed to either energy or matter. Yet they are sufficiently like Mech-Systems that we are able to study the internal workings and hope to have some decent ideas about how they work. Nor are they closed to information flows and that too makes them more interesting than Mech-Systems, but not as ultra-complicated as an Eco-System. Indeed, individual organisms along with their populations comprise the living aspect of an Eco-System. But unlike the latter, a Bio-System has evolved with elaborate control structures, a hierarchical control system, that provides for stability internally that is not always present in an Eco-System.

In this installment, I want to explore, through some examples, the principles of organization and dynamism for Bio-Systems, showing where they inherit some characteristics of Mech-Systems (origin of life) and where they behave, at times, like Eco-Systems (neural Darwinism and learning). But mostly I hope to show how, via the mechanisms of hierarchical control, an organism achieves a relatively long life of stability after development and manages to survive and procreate as a result of the control of energy flow through the body (cell).

Where Did Life Come From?

One of the greatest questions of all time. At one point early in Earth's history, there were no living things. Then, about 3.8 billion years ago, primitive organisms emerged, swimming in ancient oceans and kicking off the Darwinian process of variation in form followed by selection for fitness. Today the origin of life (abiogenesis) is still shrouded in mystery, not because we haven't got an idea of how it might have come about so much as the fact that we have a number of competing ideas and are in the process of testing them.

There are several clear, key elements that must be present for a system to be alive. Typically life is defined by this list of characteristics (see Life). But Schrödinger, Morowitz, Prigogine and many more have wanted to define life as a unified process. One of the most ambitious attempts to describe this process in a concise, coherent way was developed by Chilean biologists Humberto Maturana and Francisco Varela. They introduced the concept they called autopoiesis in 1973. More on this later.

The key to understanding the origin of life is to describe how a prebiotic chemical mixture could give rise to something like an autopoietic system, in essence a cell. Morowitz has suggested that there are clues hidden in modern metabolism, sort of like fossil chemical reactions that have been conserved from the earliest times because they are so good at producing a stable but once primitive metabolism.

The first requirement for some aggregate of chemicals to be alive is that the system be bounded. Fatty membranes seem to form relatively easily when the right molecules are available in an aqueous solution. These membranes can act to contain larger, less portable molecules while being permeable to smaller, component molecules. The second requirement is for an appropriate energy gradient to exist wherein the system can capture high potential energy from the environment and use it internally to do the work of creating (and breaking) chemical bonds in the process of maintaining the dissipative structure, the structure that then emits low potential heat and waste products. One of the reasons I favor a metabolism-first model (see this section of the abiogenesis article) is that appropriately coupled energy flow mechanisms internal to the system have to have been in place in order for RNA synthesis, as well as other complex molecules, could be achieved. Someday I will take a closer look at this, but my hunch, for what it is worth, is that the origin of adenosine triphosphate came very early (or at least adenosine diphosphate). ATP is an energy currency (carrier) for cellular metabolism. The third phosphate bond is very high in energy and can easily be broken in reactions, releasing that energy to drive syntheses of various kinds. Moreover, adenosine is one of the four code molecules in RNA and DNA (the others being guanine, cytosine, thymadine, and uricil in RNA in place of thymadine). This looks suspicious to me! And, if it weren't enough that adenosine is a key player in energy flow and information coding in genes, it is the end molecule in the CCA terminal of transfer RNA the molecule responsible for bringing amino acid molecules to the ribosome where protein synthesis takes place. Adenosine seems to show up in some very interesting places in the metabolism of cells (see also cyclic adenosine monophosphate for yet another intriguing role for the adenosine molecule!)

A second requirement is that the aggregate of chemicals have achieved some form of collective autocatalysis, especially a web with internal autocatalytic cycles that forms the basis of autopoiesis. Again, this suggests to me that a primitive metabolism, bounded by some form of membrane, emerged early from the pre-biotic soup and gave us the origin of life. Once these pre-bacterial forms found ways to encode their own structure (perhaps in RNA first) and maintain that code in primitive chromosomes life was on its way in terms of Darwinian evolution. Yet another step on the way to eukaryotic cells is suggested by Lynn Margulis' theory of endosymbiosis, wherein early bacteria having various 'talents', like photosynthesis, formed symbiotic relations with other bacteria that had, essentially complimentary talents, such as digestion of large molecules, to form cells with organelles.

The main point, however, is that energy flows into the system are captured (by photosynthesis and respiration in modern plants and animals), disbursed to working areas (metabolism) and the system is able to move things around, maintain and repair itself, and, well, live. Some time after primitive RNAs that actually coded for functional components, proteins that worked as catalysts (enzymes), and in turn, for structural components such as organelles and membranes, Darwinian evolution got a purchase. The rest is biological history.

What stands out in the story of the origin of life is the way in which the right combinations of Mech-Systems, interacting among themselves, can produce a system capable of interacting with other similar systems. That combination, capable of a degree of adaptability (later) transitions from non-life to life, an emergent property of all such systems. We'll take a peek under the hood of a living system to see how these play out.

Homeostasis and Autopoiesis

Homeostatic processes are at the core of living systems. You may recall from Systems Science 7 — Cybernetics: The Science of Control that homeostasis is a first order cybernetic system, feedback control. Homeostasis is generally a little more complex than I described it there. In metabolism there are critical factors, like pH (acidity), calcium concentrations in the protoplasm and so on. In multicellular organisms like us, there are larger scale factors that require managing within critical ranges. For example body temperature for us hot blooded mammals can't be allowed to get too low, nor can it get too high. There is an allowable range of temperatures (or any of the factors) that the body (cell) can tolerate and adjust to. But then there is a range outside of that usually narrow range where action must be taken to counteract whatever is causing the deviation from normal, what I will later call a deformation. Then there is a slightly wider range of values in which damage can be caused, the deformation reaches a breaking point. The homeostatic mechanism is the cybernetic controller that works to keep the values within the safe range. Its job is to prevent damage and if the system gets too far outside the safe zone, the mechanism must be able to muster more resources to wrestle the system back into alignment. For example in the case of body heat, if you find yourself exposed to cold temperatures and your core temperature is starting to go down (even a little bit) your muscles are commanded to shiver to produce heat. Your behavioral system also takes note and you move around attempting to get out of the cold. The shivering is an automatic operational control. The getting out of the cold is a higher level tactical control.

The homeostat (short for homeostatic mechanism, the controller in Fig. 1 of SS-7) has to be constructed (from genetic information) and maintained. So other systems are responsible for keeping the homeostat charged and ready for action. In fact there is a whole complex web of mechanisms that are there to keep the system, all of its various homeostats, operating within their proper ranges. Success is health. Failure is disease and possibly death. This larger complex of mechanism includes subsystems whose job it is to repair damage done to the system as a result of a homeostat's inability to maintain proper values. These subsystems are held in reserve, so to speak, against the need to do repair work, but obviously the system would prefer (if it had a brain!) to not have to activate them.

This larger network is the self-maintaining autopoietic system described in the Wikipedia article. The autopoietic network surrounds the homeostats and acts generally as the logistical control for all the lower level operational subsystems (and their homeostats). Actually it is more complicated than that might sound since every subsystem has its own control parameters, even the autopoietic components, so in a sense every subsystem is supporting every other subsystem, even if indirectly. Nobody ever said understanding the details of a living system would be easy!

Fig. 1 shows a highly simplified diagram of a system with autopoietic maintenance surrounding a homeostatic core process.

Figure 1. A homeostatic core is surrounded by multiple mechanisms for maintaining and repairing structures. Each such component in a complex network has its own homeostatic control.

The "maintenance processor", actually many such processors might be involved, actually has an internal structure similar to the example homeostatic core process. However, in its case the environment IS the pictured homeostatic core process. A distortion or disturbance in the pictured homeostatic core process acts as the stimulus to the maintenance processor and its homeostat kicks in to cause the maintenance work to commence (shown as the black & white arrow pointing toward the homeostatic core) as its response. Inside a living cell and in your body there are literally thousands of low-level mechanisms interoperating to maintain the system in its function within its optimal ranges of factors.

Under nominal environmental circumstances (the right environment for the particular organism) every thing works in concert and the organism is "happy". But what happens if one or more environmental factor gets out of a comfortable range? This is going to trigger the appropriate control mechanism to counteract the disturbance. The autopoietic system will channel resources to meet the emergency and with luck and good behavior the system may be able to reestablish ideal conditions. However, should it fail to counter the effect before damage is done, then other mechanisms will be brought on-line to repair the damage done. If the damage overwhelms the system's capacity to repair, then, of course death follows. Interestingly, as we will see, in the non-death scenario this is the basis of adaptivity. Here is the argument.

The autopoietic system maintains an energy budget for directing internal energy flows to where they are needed to do work. This is part of its logistic control function. The budget is flexible in that flows can be adjusted on demand and within reasonable ranges, much like the ranges of deforming stimuli we discussed above. So, for example, if there is an increase in demand to move molecules within the cytoplasm, more ATP can be routed to the actin-based fibers responsible for the movement. Thus they can respond to the demand. This of course would be at the short-term expense of some other subsystems that might get less ATP as a result. More importantly, the autopoietic system can keep track of these shifting demands over time and make longer term adjustments based on longer term demand.

A good example of this adjustment based on longer term demand is your muscles. Suppose after a lifetime of being a couch potato you decide one day to get into shape by working out, maybe lifting weights. Initially your muscles are not competent to handle the weight lifting without some pain (and some damage), even if you start easy and work your way up. If you only tried the weight lifting once and then quit, your body would not do anything except complain with pain or stiffness the next day. It would have certainly routed energy into the used muscles to accomplish the goal at the time. But then it would go back to the 'regular' energy budget and that would be all there was to that.

But if you work out regularly, your muscles send signals back to your growth control centers expressing an on-going need for additional resources, both real time energy input and reinforcements in the form of more muscle cells. Your muscles actually retain a kind of memory of the effort you have been expecting over a longer period of time and training. So the body responds by coordinating several processes. Your diet may start to include more protein, for example. Your growth hormones for muscle bulk may signal muscle cells to replicate to bulk up your muscleclature. You basically build a physique that is capable of responding to the long term demands that have been put on it. You adapt structurally within the limits possible for doing so. The autopoietic system has responded to the longer term demand by marshalling resources to build more prepared structure. This so that the system is better able to respond in a faster time and avoid damage. Of course a new balance in the energy budget is needed; more energy will be routed to maintain the new, larger subsystem. But the tradeoff between energy needed to maintain the subsystem and the energy needed to repair damage, and even to respond to the demand is quite favorable.

Here is a paper I did some years ago, "Foraging Search: Prototypical Intelligence", which lays out the case for why learning evolved in the brain based on these energy budget arguments. If you look at the graphs you will see an interesting aspect of adaptivity (in the case of neurons, adaptivity is the change in synaptic efficacy that leads to changes in behavior). The first graph shows a non-adaptive stimulus and response along with accumulations of energy costs resulting from three different components. Costs, in this sense are based on the notion of opportunity costs. The energy is used to take care of the response, but it might have been used elsewhere had the stimulus not occurred. The first component is the energy cost to respond to the stimulus. Without going too deeply into the biochemical aspects of this, it is the case that the more prepared a response subsystem is, the less total energy it will require to respond to the stimulus! This is because of the lag time between the onset of the stimulus and the time at which the response becomes strong enough to begin counteracting the stimulus. A more prepared (stronger) response subsystem can react sooner and with greater force, diminishing the time lag and the total amount of work that needs to be don to respond. Thus, a minimally prepared subsystem will end up expending more energy for a given stimulus-response. The basic idea in structural adaptation is to minimize this cost.

The second component of energy costs is for repair of damage. In principle, if a subsystem is better prepared, not only will it minimize response costs, but also quell the stimulus sooner and avoid or minimize damage repair expenses. Finally there is the energy cost of maintaining the structure at an elevated level of preparedness. Bigger muscles require more food, even when you are not working them hard. However, this cost is only incrementally higher than at, say, a baseline level of preparedness. So while it is higher over time, it is marginally higher compared with the costs of response and damage repair.

A key idea here is that it is the long-term frequency of stimulus events that determines the degree to which the response subsystem needs to build up. If you were to lift weights only once per week, or stopped after working out every day for one week, your muscles would not bulk up to the Schwarzenegger level or even look that different from your couch potato days. But if you worked out, say, three times a week, for months on end, you would expect to see some more 'definition' in your muscleclature and muscle tone. If you worked out as much as Arnold did in his youth, maybe you too would look like the Governator (or more like the Terminator; age has its costs to pay!) The more frequently a stimulus occurs, and the longer the time frame over which it maintains that frequency determines how much adaptation is required to be 'ready' and 'able' to respond.

Anticipatory Systems

As I show in this paper, the benefits of a memory system based on building adaptive (bigger, more powerful) structures, is a system that can respond to the first hint of a stimulus and by earlier quelling of the stimulus, reduce costs considerably. But that isn't all that can be accomplished, at least in neural tissue.

Neurons, at their synapses, have evolved long-term associative memory networks (for more details see my paper, "Toward a Theory of Learning and Representing Causal Inferences in Neural Networks". Associative memory traces allow a neuron to create, essentially, a causal linkage between a non-deforming stimulus, such as the sight of a predator, and a deforming stimulus, such as pain from a previous, non-lethal encounter with that type of predator, so as to anticipate the outcome of letting the deforming stimulus occur. The third graph in the first paper shows that having such an anticipatory stimulus causes the system to preemptively respond such that repair costs are not incurred and response costs themselves, which are proportional to the stimulus strength, are very much more minimized. Thus, the system's total costs have been reduced at not much greater cost of maintaining the larger response subsystem and the associated anticipatory response subsystem.

While this is most visible in neurons, brains, and behavior control, the same general principle applies to all adaptivity in Bio-Systems. At the end of the Foraging paper I present the case for why Bio-Systems (animals in this case) need to evolve anticipatory adaptive response. The case hinges on the argument that the real world is non-stationary. That means that things are always changing. By this I don't just mean things are moving, or that the environment is subject to cyclical behavior (like the seasons). Rather this refers to the fact that totally new conditions can come into effect in an environment (when I get to Eco-Systems I will develop this concept much more thoroughly). Completely new stimuli may be encountered. But more often, new causal relations between old stimuli may arise. A Bio-System has to be able to adapt to these new arrangements and causal relations. It does this by always being able to flexibly adjust the energy budget and 'learn' the new associations. It must forever be able to readjust as needed over varying time frames.

The Limits of Adaptivity, Growth, and Development

One of the main things that differentiates Bio-Systems from Eco-Systems is that the former have internally imposed limits on their capacity to modify themselves in light of radically new stimuli. They are bounded by internal controls that limit their adaptivity as well as their capacity to grow or develop. While our genes provide us with considerable flexibility in terms of how variant we are in our development, and how much we can learn, they still put constraints on us in all of these ways. Of course, even Eco-Systems have limits when considered as a stable system. An old growth, climax forest can be utterly altered in hours from a bad fire. However, Eco-Systems are not closed to the invasion of other species and life on Earth has withstood numerous massive die-offs yet managed to adapt and life continued to evolve. You can't say the same for an individual, a Bio-System. [Note that individuals are very often colonized by all manner of parasites, forming a kind of Eco-System! But to the individual so colonized, the effects of the activities of these foreigners is part of the environmental forces acting on it.]

There are obviously good reasons why Bio-Systems are self-limiting. First there is no reason that a Bio-System should have to adapt radically and constantly, taking on virtually any form needed to survive. The environment doesn't change that fast. Individuals are part of a population and whatever changes do occur don't necessarily impact all individuals. Further, because of the way genetics works, there is generally a reasonable amount of variability of traits and behaviors within a population such that if some individuals are affected by an environmental change, not all will necessarily be negatively affected. That is natural selection at work. The one rule you can count on in Bio-Systems is that no such system will expend energy on some capability that isn't absolutely necessary for survival.

Energy arguments can be brought to bear on growth limits as well. Bio-Systems, with a few exceptions that are reasonably understood from an evolutionary point of view, are programmed to grow to a needed size relative to their eco-niche and then stop. The same does not apply to populations of Bio-Systems, however. Populations sizes are controlled, as a rule, by Eco-System internal dynamics that I will cover in that installment. But at the individual level, growth is carefully regulated by the logistical control system so as to keep any single individual within a population from dominating completely and possibly wiping out other members. Populations need to maintain variability and so domination by one fast growing individual (or even a few) would be counter to the long-term health of the species. Hence, individuals are restrained and kept within an admittedly variant range, but with a relatively small variance.

Finally, it would be completely self-defeating for an individual to undergo form and behavioral modifications that effectively made it unrecognizable to other members of its species. In the end, given that the only means of continuation of Bio-Systems' patterns is through reproduction, and that requires successful mating, con specifics have to be recognizable to one another. Neighboring Eco-Systems hardly care at all about what the others look like, or what their composition might be. But individuals need to mate with similar individuals in order for the species to continue. This might seem like a silly thing to be concerned about. But back in the beginning of life, it is very likely that the first proto-cells and very early cells that invented sex or sex-like reproductive strategies had more capacity to take on new forms since they had not yet evolved internal control systems to ensure species continuity. Of course, those that changed radically enough to not be successful in reproduction soon disappeared from the scene leaving those that could maintain species-specific forms and behaviors. Evolution is the great leveler.

Why Reductionist Methods Work on Bio-Systems (and are hard in Eco-Systems)

I'd like to end this installment with an observation about some methodological aspects of studying Bio-Systems, especially versus Eco-Systems. The relevance to the latter may need to wait until I've finished that installment to be fully appreciated. But I'll at least introduce the ideas here.

The fact is that reductionist science has been wildly successful at producing our knowledge about Bio-Systems and natural Mech-Systems (although engineering of man-made Mech-Systems may involve some reductionist thinking). The reason is that these systems tend to have more definable boundaries and boundary transfers with functional attributes that are relatively easy to measure. Put bluntly, you can dissect a representative system and find its parts and their relations to other parts. It may take advanced technology (e.g. electron microscopes) to get there, but, in principle, all functions are measurable.

This is a pretty strong statement, to be sure. But I think if you explore the history of biology you will see repeated examples of functional decomposition illuminating the inner workings of system after system. The use of model building as a way to study whole systems dynamics, has been less useful in understanding Bio-Systems as it has in Eco-Systems precisely because access to the system internals is so readily available given that you have the dissection tools to do it. And the history of biological science is that when you don't have the tools, you either put off the questions or look about to create the tools. The invention and development of imaging technologies for brains (and other working tissues inside live organisms) is a case in point.

It isn't uncommon to hear a critic of reductionist methods complain that reductionism fails to explain the whole organism. The mantra one most often hears is something like "The whole is greater than the sum of its parts." It all depends on what you mean by the word 'sum'. If you mean that if you just throw a bunch of parts together they will fail to produce a living system, then DUH! Bio-Systems and natural Mech-Systems are the sum of their parts AND the functional relations between the parts. And reductionist methods have done a splendid job of discovering and explaining all of that. [BTW: if you read the article, above linked, be sure to note the difference between reductionism in science and the philosophical notion that reductionism means 'reducing' explanations of complex systems to simpler principles (rather than parts and relations), e.g. reducing biology to "mere" chemistry. Of course, as we saw in the origin of life above, you have to be able to explain the biological phenomena BASED on chemistry, but this isn't the same as reducing a complex system to just its chemistry. I'll probably write more about that issue at some point in the future since it is such a common argument leveled at science, motivated, I think, by a desire to knock science off its high horse as the best method to obtain knowledge. As you might guess, I have very little patience for those arguments!]

With Eco-Systems the situation becomes incredibly much more difficult (and remember, Eco-Systems include societies and economies, etc.). The very fact that Eco-Systems are highly variant and non-stationary in nature makes attempts to understand relations between components by taking such a system apart problematic. Relations may be continually changing over time in non-stationary ways. Sampling the components itself is messy and hard work. The science of ecology is still grappling with reductionist methodology and probably always will. Where the Eco-System includes humans sampling becomes even more problematic for ethical reasons (of course this can apply to Bio-Systems studies that involve testing on animals). Sometimes it is the case that attempts to dissect parts of an Eco-System do destroy the sought relations and that fact may go undetected by the scientist. Fortunately, another methodology involving the construction of models (mostly computer-based) can be brought to bear on Eco-Systems. This is generally regarded as an integrative or whole-systems approach, essentially the reverse of reductionist approaches. But it is also true that models need to start with some basic understanding derived from field studies. The model must try to capture what is known about the system from reductionist studies, but at some point it can become a powerful tool, if used correctly, in positing hidden relations and suggesting places to look for previously hidden relations. I will likely do an installment devoted to computer modeling in the future to explain this tool.

While I said that modeling has been less used in Bio-Systems studies, that is starting to change. Today very powerful computer models are used to study genetics, metabolism, and neural networks just to name a few. These models are used in a manner similar to how they are used in studying Eco-Systems. That is they can be used to develop hypotheses that can then be tested in the reductionist sense. Thus there is another region of overlap between Eco-Systems and Bio-Systems. Next we'll take a look at this from the other side, that of Eco-Systems.

A paradox

A paradox, a paradox, a most intriguing paradox

      The Pirates of Penzance, Gilbert and Sullivan

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Straight forward logic would seem to dictate that if you improve the efficiency of a given work process within a system then the whole system should enjoy the greater efficiency. Right?

Or we might reasonably assume that developing new ways to produce energy would on the whole increase the availability of energy.

These are slightly different ways of saying that a local optimal solution to a problem should contribute to the overall optimality of the global system. It turns out that this is mostly magical thinking.

In my last blog I said something to this effect:

As in all network of processes relationships, a local optimum does not imply a global optimum. What looks good at one node in the network, the choice to go to that other node instead of any other choices just because it looks like the best outcome can have disastrous impacts if all of the subsequent choices lead to extremely high costs.

I finally got around to reading Leonardo Maugeri's October, 2009 article in Scientific American called "Squeezing more oil from the ground" and I couldn't help but realize that this kind of thinking dominates the technological cornucopian perspective. In the article Mr. Maugeri promises us that there are much greater reserves of oil left in the ground than had previously been believed to be recoverable. And, that "new technology" will make that oil recoverable (real soon now). Hence, all this fuss about peak oil is something we can ignore for at least another couple of decades. While he does point out that these technologies are costly, what he never gets around to pointing out is what the actual total cost, in energy units, are realized in developing and deploying them. He leaves the reader to assume that because these technologies provide greater extraction efficiency (as defined by the recovered volume of oil) that we are all better off as a result. What he doesn't ask is: What is the net energy gain after all energy costs are taken into account. This wasn't an oversight or a belief that the average SA reader wouldn't be able to understand the nuances of energy return on energy invested. He simply doesn't think like a systems scientist.

It is extremely typical to apply the greedy method of thinking to what you believe is profit making decisions. You are at a node in a decision network. You can't see anything except the next possible moves you could make (decision taking) and have an estimate of the costs of each move. So you choose the one with the least cost. But suppose this is just one step in a complex web of possible (and possibly interconnected) steps along a path to a specific destination or specific result. If there is a longish chain of steps between you and your destination, and all you know is the cost of the available steps from where you currently sit, you could be tempted to apply the greedy method at each node in the network. But if you do, you are in for a nasty surprise.

This figure is a simple representation of a decision network. You are on the node with the 'X' and you have to figure out which way to go next; what the next node you should go to would be. All you have in the way of information about this network is the cost of each outward bound step. In the figure these are 5, 10, 50 and 20. What you don't know is what the costs out of the node that you end up choosing. Once you choose, you cannot go back. Hell, you don't even necessarily know if your choice will lead you to your desired end point (notice the upper left node?)

Notice that there is an actual cost associated with every link in all of the possible paths from where you are to where you want to go. But when you are standing at the 'X' node you can only see the costs of going to the next step. You have no god's eye view of this 'problem'. Unfortunately this is how most of our real world problems actually work. We have limited knowledge to work with, and have to make decisions with limited information. What do we do?

Clearly in this example picking the path with a cost of 5 is a disastrous choice. Picking the one with a cost of 10 is no better. The path starting with a cost of 20 looks undesirable from the location at 'X' but as can be seen from our special god's eye view, it turns out to be the best choice, cost wise.

A lot of energy efficiency problems have a similar structure. I mentioned my experience in the 80s in solar energy where the energy required to make the flat plate collectors could have easily been more than the energy gain for the local householder. The latter enjoyed a lower heating bill but at the expense of the rest of us in terms of total available energy. The problem there was the cost-price distortions imposed by governmental and market convolutions that decoupled energy costs from dollar costs. The latter did not accurately reflect the former, otherwise, no one would have bought flatplate collectors to heat their houses.

Just to give an example of what information distortions can do to decision making take a look at this next figure.

The bolder lines and numbers represent an information feedback chain that accumulates perceived costs according to the number of steps in the path. Think of these as 'prices'. Note that they accumulate at each step and that they are artificially maintained at 10 added per step. Suppose you now had the information shown (only two examples) in bold numbers at the 'X' node. Which path would you choose? Once again you would be fooled into taking the wrong path even though it is cheaper in terms of feedback numbers, i.e. price. As far as you can discern, that price is really less than the one for 60 and since this represents prices accumulated all the way back to the destination, you are perfectly rational to assume this is the best, cheapest path. But the underlying costs are what really count. Believe it or not, there is a lot of this dollar price distortion versus energy costs so that our local buying decisions do not fairly reflect the true costs being borne.

In the case of increasing, or at least maintaining, oil flows by adopting new technologies we have a similar problem. Secondary extraction techniques, for example, involve pumping CO₂ or CH₄ under high pressure down into the wells to increase flows. Something similar is done by pumping water (sea water in Saudi Arabia) into the well. Oil floats on water so this boosts production. What the oil engineers report as the costs of doing this is the dollar amounts used to pay for the gasses and electricity to pump. This works out OK as long as the gasses don't have to be shipped long distances. None of the oil pumping operations puts a price on externalities, like toxic spills. In fact much more energy (hidden in the assets section of the balance sheet!) was used up just to set up the infrastructure needed to perform these operations. It would take a small army of accountants backtracking through the capital equipment assets to derive what energy was actually consumed, but I assure you it would amount to a great deal more than shows up in the reported costs of operations of secondary extraction.

Also consider the energy costs of exploration. How much of that shows up in the real costs of keeping the oil flowing? Or the drilling in deep water or frozen tundra? They say it is expensive, sure, but we keep the same number of barrels of oil flowing. We never get an accounting of the net energy gained.

Another case in point is that of the use of cadmium telluride in a 'newer' technology for photovoltaics. What the enthusiastic purveyors of this technology will gush about is the increased efficiency at collection and conversion, at, now get this, lower prices than conventional polycrystaline silicon cells. What is left out of the equation is the fact that tellurium is rare and will eventually prove to be extremely energy costly to produce, or that cadmium is an extreme poison that needs very careful processes for recycling to prevent toxic accidents. All of this takes a lot of energy but is not currently reflected in the traditional economic sense in the prices.

A large part of the seeming cost savings come from the process efficiencies of manufacturing the cells (supposedly). But those efficiencies were gained by developing newer plants and equipment. All very good on the surface, but what lies beneath. How much additional energy went into the capital goods so that we could get a cheaper production process?

Supposedly, over a very long time scale, when all of the capital has been properly amortized and the competitive pressures on pricing have been stabilized, eventually the long-term price of a product like this should reflect all but the true externalities (the costs of environmental damage resulting from operations), that is the costs the companies can avoid paying [There is a move afoot in accounting to report what is called in financial statements. The purpose of this is to account for potential liabilities companies accrue and might end up having to pay for, like fines to the EPA. Well, that will be the day!]

Unfortunately it isn't just energy problems that are of this local optimum, global sub-optimum ilk. They seem to come up over and over again when someone attempts to solve a local problem but ends up having global consequences. Food aid might fit in this category according to some critics. For example consider the case where grain surpluses are given to African villages who come to rely on the donations (especially if development efforts are hampered by poor soils or lack of water for irrigation). Then one day along comes corn ethanol, profit motive and all, and the aid gets cut because we Americans want to keep gas prices low.

Let's face it. We want to eat our cake and have it at the same time. We will almost always opt for the greedy choice when we have to decide from a local (non-systems science) perspective. And as far as economics is concerned, that is always.

We will try to build high-speed rail systems because that will get us to where we want to go faster (time efficiency). But it never occurs to us that moving mass at a higher speed uses exponentially more energy. We would do well to just fix and improve our existing rail system. But from the non-systems perspective, that doesn't solve our immediate problem. So...

This is one of the (many) reasons I have little hope for Homo sapiens as currently configured. Systems thinking is long-term and strategic. It isn't the greedy solution to a logistics problem. We're good at the latter and piss poor at the former. I still think its a matter of evolution of eusapience.

The Science of Systems 10

Systems Science 10 — Mech-Systems Examples

The Boundary Problem

Before we get started on some real world examples there is a small matter to warn you about. In the third installment, 3. Organization and Its Conceptualization, I noted that systems have boundaries, even if they are hard to distinguish. For the purposes of that subject I left it at that. But as we start to study real world systems we will find very quickly that there is a problem associated with the identification of a boundary that arises not from the notion of "systemness", but rather from the very act of studying or observing a system from the perspective of a conscious, thinking being. The problem arises because systems are always subsystems of some higher-order system, hence embedded within an environment containing other systems. Indeed, some of these other systems might have some close association with the system under study in such a way that they cannot be easily distinguished from it. Hence, we might have some difficulty isolating 'our' system for study. A lot of work is involved in resolving this issue.

It gets even more complicated when a system is highly interactive with many of its environmental components. If the system's boundary is indistinct it may not be possible to say definitively what should be included in the system and what should not. We run the risk of choosing a boundary that might include some components that appear to be members of our system of interest but are not actually essential functional members. They could have just as easily been left out, the boundary drawn between the components of our system and the other one correctly. But how do we know? The boundaries we "choose" consciously are just that — choices, not necessarily physical dividers. Or at least it seems that way.

The real problem is that the whole idea of systemness starts to look a bit arbitrary when we run into these kinds of situations and, in the end, choose a boundary based on our preferences or biases. If systemhood is an arbitrary category, decided one way by one investigator, and another way by a different investigator, our whole basis of systems science could start to look useless! Indeed the main criticism of systems science is that too often boundaries are chosen arbitrarily, thus negating the whole validity of the systems concept.

Our intuitions, not always a great guide in science unfortunately, tell us that there is a reality to the notion of a system, and fortunately, in this case, they serve us well enough. Aside from the many clear examples of systems with boundaries that are easily discernable and provide a unique individuality to a system under study, there are methods of analysis that allow us to resolve the boundary problem in those cases where we are having difficulty. As we explore various kinds of systems in these next few installments, we will come across examples of ill-defined boundaries that will give us pause. But we will also see that some of these methods can be used to help out. In one of the last installments I will actually focus on some formal methods that are used in systems analysis and provide a treatment of how this boundary problem can be solved there. For now, when we come up to the problem in these examples, we'll use a less formal approach that will anticipate the formal methods later on.

Starting Simple

It's best to start out with some simple examples that capture the main principles of systems science and then work our way toward greater complexity. We'll start with Mech-Systems, both human-designed and naturally occurring systems since these are among the simplest of all 'interesting' systems. It is fitting to start with human-built systems since some of our earliest recognitions of systems principles started with studying our own machines, usually with the intent of improving designs.

Below I will start with a very simple object that has 'behavior' and delineate the basic principles that I've written about previously. Additionally, we will see how this machine has been 'modularized' and incorporated into larger, more complicated machines, demonstrating the principle of emergence even in human-built systems.

Deterministic Black Boxes

Suppose we have a box sitting on a table with three wires sticking out of it [in truth there will be two more wires sticking out to supply operating current to the device. These are the energy flow input and output while the three wires are signals — more on this later]. Two wires are input, in that they receive a voltage from some other source (two sources generally), and the other wire is an output. Figure 1 shows such a box in four different states of behavior given the indicated inputs. A zero indicates no voltage input while a one indicates some voltage input. Similarly a zero output means the box emits no voltage while a one means it is emitting a voltage.

Figure 1. A black box with two inputs and one output. This device is performing a logical AND operation. a) two zero inputs produce zero output; b, c) a single input of one on either input lines still produces a zero output; d) two inputs of one produce a one output.

In a real sense it doesn't matter what is inside this box, or even that the inputs and outputs are necessarily voltages. This could just as easily be done with water pipes or mechanical levers as long as the inner workings of the box are compatible with the nature of the inputs. This black box performs a simple Boolean logical operation called AND, and the device is called an AND gate. The AND behavior rule is this: the only time the output will be something rather than nothing (e.g. 1 instead of 0, or also couched in terms of 'true' rather than 'false', simply a convention of semantics) is when both inputs are something (not nothing) at the same time; at all other times the output is nothing. Or in terms of voltages on wires, if both inputs carry positive voltages than the output will be a positive voltage; otherwise the output will be no voltage.

The four situations cover the entire range of input conditions and output results. If we were to observe such a box for a very long time, testing all of the four input choices many times, we would come to the conclusion that this device, however it operated internally, was an AND gate.

We might have a similar looking box and run the same set of input experiments but get the following results: 0,0 -> 0; 0,1 -> 1; 1,0 -> 1; 1,1 -> 1. Note that the first and last output behaviors are the same as for the AND gate, but the two middle behaviors are different. What is this box doing? It is a logical OR gate, producing a 1 output if either one or both of the inputs is set to 1. There is also a gate that performs an EXCLUSIVE OR, which simply means that one or the other of the inputs is a 1, but if both are set to 1 the output is 0.

Now lets say we have yet another black box with only one input wire and one output wire. This device has an odd behavior. When the input is a 1 the output goes to 0, and vice versa. This device changes the sense of the input and is called a NOT gate.

In all three of these boxes we've assumed more or less continuous operations with inputs switching from 1 to 0 or 0 to 1. The boxes themselves retain no memory of their prior states. It isn't in their job description. But another kind of box we have has a peculiar behavior in that it is capable of retaining a memory of the last state it was put into on its output. This box is actually a combination of two of the above box types wired together to produce a nifty effect. If we take an OR gate and wire its output to a NOT gate we get what is called a NOR gate (see the Wikipedia link for the symbol for this device)! Now we take two of these NOR gates and wire them in a most peculiar way. Please take a look at the diagram in this Wikipedia article on the SR LATCH. This one is a bit difficult to explain but the behavior is not. This device uses internal feedback between the two NOR gates to capture and hold a state that was set into it by the values on the two input wires. The state behavior is given in a table in the article. The two output lines are labeled Q and a Q with a bar over it (meaning NOT Q, which means it will have the opposite output from whatever Q has). Generally speaking we will only use the Q output. The way this device works is that if both input lines, labeled S and R, are set to zero, the output at Q remains what ever value it had previously been set to. In other words, it represents the memory of the last transition (from 1 to 0 or from 0 to 1). The operation table shows you what you have to do to S and R to get the Q line to be either a 1 or a 0. The fourth situation, where both S and R are 1s is not a viable condition, it is ambiguous, so this input situation is not like the AND or OR gates.

What is important to note in this example is how we built up a slightly more complicated box from combining several less complicated boxes — wiring their outputs to inputs. Also note that we have done this by design so we can't call this device a black box because we know how it works. This is, instead, a white box — its workings are fully disclosed (we might have called it a transparent box, but that is too many syllables!). One can transform a black box into a white box if one can dissect the black box without destroying the workings. For machines this is trivial and possible. Note that certain dissections of other kinds of systems may destroy its capacity to work and we will lose information about it by the act of taking it apart.

Putting things together in cool way

At this point we can start to do some very interesting things. Boolean logic, while interesting to a point, is very simple stuff. But being able to do arithmetic with an automated machine is starting to get really interesting (OK, so today we take calculators and computers for granted, but that doesn't mean people know much about how they work inside!) We have all the elements we need to do this with the above logic gates and the memory device, the latch. We can put them together to create a device that can add two single bit binary numbers together. Binary arithmetic is based on the ability to represent binary numbers (where the digits are only 0 and 1). A small example may suffice if you already understand positional value notation, as in the decimal system where the digit to the left is worth ten times the one to the right. It works the same in binary except that the digit to the left is double the one to the right. So in binary we have the following numbers:

Binary	Decimal Value
0	0
1	1
10	2
11	3
100	4
101	5
110	6
111	7
1000	8

Zero and one are pretty obvious. The decimal number 2 if formed in the same way you form the number 10 in decimal, by adding 1 to 9 (which gives a 0 in that column) and carrying the 1 to the next column to the left. For a binary representation of 2 we simply add 1 and 1, leaving a 0 in that column and carrying a 1 to the next column. Here is an example of adding decimal 3 and 4 together in binary format.
    0011
+  0100
---------
    0111

Starting from the right-most column, 1 plus 0 = 1, no carry; same in next column; and 0 plus 1 also = 1. Hence the answer, 0111, is decimal 7 as expected. By the way the leading zero has a purpose I'll get to next.

Now lets do something a bit more interesting. Add decimal five and decimal four, that is:
    0101
+  0100
---------
    1000
which is decimal eight.

The difference here is that when you add a binary 1 to another 1 the answer is 10, or you might say 0 carry a 1 to the next column.

So what does all this mean? We can put the component boxes that we've developed so far together in a way that allows us to add two binary numbers with four column just using logic gates along with a set of memory cells (latches) wired together to form what we call a register (a four-bit register). We need three of these, two to hold the operands and one to hold the result. The description of how this is accomplished is more than I want to get into here, so I will let the interested reader refer to the Wikipedia article on adder circuits. The main point I want to get across is that starting with some reasonably simple components we have built a device that can add two numbers together. The number of bits in a register is rather arbitrary. We showed a four-bit value, but in real computers the registers are generally 16, 32, or 64 (some 128) bits wide. As homework you should try to find out how big a decimal integer can be represented in a 64 bit register. There is also an interesting fact about how negative integers can be represented, but this isn't really a course in computer architecture so we will leave it at that.

But with the assumption that you can represent and add negative integers you suddenly have subtraction from a simple ability to add. And once you get subtraction you have all of arithmetic. Multiplication is just repeated additions: 2 x 3 is just 3 added to itself. Division is repeated subtraction, with the caveat that you need to make arrangements to handle a remainder value.

By now, though, I think you get the picture that more complex machines are built up from simpler machines, combined and recombined in ways that increase the kinds of functions that they can perform. An adder circuit, plus some additional logic gates can produce a device that we call an 'arithmetic-logic unit', which is at the heart of all computational devices. Along with a bunch more registers to store intermediary results and keep track of operating instructions and where we are in the program, and a set of control logic circuits to control sequencing through the program, you have a 'Central Processing Unit', the brain of a computer

Add huge banks of registers, called memory, wires to get the bits in the CPU to and from the memory, and some specialized logic/register devices for input and output and you have the modern computer. Simple, no?

A Computer

OK, maybe it isn't so simple when you look at the big picture (or inside a modern computer). At least it doesn't look simple. But in fact it is fundamentally very simple when you realize how it is built up by combining these few simple circuits in a hierarchically increasingly organized way. Note that the complexity of a computer comes not from having a vast number of different component types (unlike chemistry that has 92 naturally occurring elements to play with), but with the way the few components are recombined into a hierarchical structure. Each new level provides seemingly new functionality.

The computer is a wonderful example of both hierarchical complexity and evolution! Most of you can remember when the home computer was a largish box with small memories and primitive input/output options. Remember the days before the Internet? But engineers are always tinkering and thinking up new ways to recombine components to get more speed and function. Some of those designs work well and are picked up by the marketplace. Others wither and die, even though they worked in a technical sense, they weren't fit to survive the rigors of the market.

The computer and its related technologies illustrate even more interesting aspects of systems. The computer is a 'perfectly' deterministic machine (as long as the parts don't fail). If even one tiny bit, especially in the CPU, goes out, the whole machine crashes, even though that bit only talks to a few other bits in the machine (remember, mech-systems have much sparser interconnections among parts). Computers are examples of brittle machines. Engineers are beginning to learn from nature, biology specifically, and starting to look for ways to reduce this brittleness. For example, having multiple 'cores' or more than one CPU working in concert helps. If one CPU fails, the other can keep going. It may slow things down but there won't be a catastrophic failure of the whole computer. This is the result of redundancy. Each component is still brittle, but by having several available, if one goes down another can pick up the work load. In the future, engineers hope to develop less brittle, more 'adaptive' components. Redundancy can be quite costly, so having a component that may get sick but still perform acceptably by adapting to its hurt (like limping) could be much better.

Finally, while a computer might be a very deterministic machine, it turns out that we can now build elaborate, complex systems out of myriad computers, like the Internet, where overall behavior becomes stochastic. That is it becomes subject to the laws of probability. This is especially true for computer systems that interface with the real world through sensors and effectors, like robots. The real world is highly stochastic, so the states of inputs can never be sufficiently determined for the computer. This is how Mech-Systems can actually have more life-like behavior, or at least probabilistic behaviors. Even man-made machines are getting so complex that we cannot always be sure of their performance. It is an example of how perfectly deterministic subsystems can, when interacting through a noisy medium or interacting with external stochastic processes, become non deterministic in their overall behavior. It is not at all unlike the way non-living atoms, when combined in particular configurations and interacting with one another in a warm watery bath can produce the phenomenon we call life.

A Stream^*

If you've ever been trout fishing or even just hiking by a mountain stream you may have noticed that despite the swirling and splashing, first running fast, then slowing in tranquil pools, the stream has a very interesting organization. The energy flow that propels the water downward is the gravitational gradient. Water condenses high in the mountain, in the watershed, and collects in a low lying crevice in the topography. It then builds momentum flowing down the gullies and valley ways. Gravity and surface tension of water, along with the banks, help form the boundaries. They are not impermeable as water evaporates or splashes up on the rocks. Animals drink from the stream and carry the water away.

Energy also enters the stream in the form of nutrient runoff from the watershed. Falling tree leaves and other debris that serves as food for insects and they for fish. Clearly the stream's energy is dissipated in flowing eventually to a lake or river or to the ocean.

With the exception of the life that the stream may harbor, it is basically a Mech-System in terms of the mechanical and chemical interactions it has with the immediate environment. Over time the stream's course will change as it carves new channels or reshapes the banks. During heavy spring snow melts the banks may overrun accelerating this process, but there is a limit to the extent of overflow and the integrity of the stream system is maintained over time, as long as precipitation continues annually. The stream overflowing is an example of the difficulty in choosing boundaries mentioned at the very beginning of this installment. If you were to see the stream only during one of these events, you might choose the boundary as high above the 'normal' bank. Contrariwise, if you visited in late summer or early autumn you might choose a narrower boundary as the flow rate has shrunk to a trickle by comparison. This is why systems require careful observation over long stretches of time and under many different environmental conditions so that you can take all of these phenomena into account when choosing a boundary.

While it is easy to think of a stream as 'only' a Mech-System, it is an important example of one that forms the environment of living systems, Bio- and Eco-Systems. Indeed the interactions between the biome and the stream can have tremendous impacts on the evolution of the stream itself. For example, salmon streams are heavily influenced by the annual migration of spawning salmon up to the higher elevations where they die after spawning. This has an incredible impact on the chemistry of the stream as their bodies decay. So, while the running water itself is a Mech-System, taking a wider boundary view can readily convert the system of interest into a full blown Eco-System. This is an example of why there must be an on-going effort to observe all interactions at your chosen boundary to make sure you have captured all of (what for you) is relevant dynamics. This is not particularly easy to do for very complex systems, but it is the only way you will know. In a sense, we adopt the black box philosophy. We watch the chosen boundary very carefully and measure everything that goes in and comes out (Fig. 2). Careful metering of the inflows and outflows will tell us a lot about the choice of boundary we've made. But then, to really know, we need to take the white box approach to make sure we know that the system we are studying has the interesting internal dynamics as well.

Figure 2. The only way to be sure of the correctness of your choice for a boundary is to monitor it closely and instrument (if you can) the inflows and outflows. The little 'meters' set on the boundary to measure all of the relevant flows can give a quantitative method for determining the black box analysis of the system. The arrows leading out of the meters are data to be collected and analyzed.

A Hurricane

As a final example of a natural Mech-System, consider a hurricane or typhoon. These circular patterns of high winds are actually vortices in which there is a vertical temperature gradient from the warm ocean surface water to the upper cooler atmosphere. Once a tropical storm system forms, and there is also a pressure gradient (horizontal) heat can be conducted upward through the storm system in rising concentrations of moisture. The laws of physics regarding the formation of whirlpools, vertical fluidics acceleration, cause the system to form a vortex producing the winds. At the surface this increased wind speed enhances the evaporation and heat conduction even more. The heat is now being removed by convection from the ocean surface and convection is a much more rapid means to carry heat along a gradient.

The process is self-reinforcing as long as a temperature gradient exists (this is why hurricanes fall apart once they are over cooler land). Hurricanes are short-lived phenomena, compared with streams, but during their time, they develop considerably complex structures, such as the eye where no wind blows.

Note that the boundaries of a hurricane are actually harder to define than for a stream. One way, commonly used, is to mark the periphery where the wind velocity falls below a certain threshold. That is somewhat arbitrary, but it is based on years of observation and determination of just at what speed the winds are considered destructive. The air/water boundary seems easier to declare but with the wave action and evaporation pumping water into the air, it isn't all that easy to instrument!

Energy Flows and Dissipative Structures

These Mech-Systems, and all active systems, demonstrate a key fundamental principle in how systems organize and work. In every case the system exists within an energy gradient that "drives" the dynamics of the system. The components interact is constrained, that is lawful, ways, but it is the flow of energy through the system that makes things happen. Energy flows involve temporary residence, as it were, in internal micro-structures within the system's component interactions — the subsystems — and this energy, in potential form, can do very specific work before dissipating as waste heat. This is how the internal structure of systems is formed, how they become organized. In the case of the computer, or any manmade machine, new organizations come about through invention and product development rather than in situ. But in the case of a hurricane, organization develops as a result of energy flowing in real time.

As long as energy flows from a high potential source, through the system, to a low potential sink (the gradient) the system will tend toward greater organization of microstructures that best (are most fit for) dissipating heat by doing internal work. This will continue from the time the initially disorganized system first finds itself within the gradient, until a time when no better dissipative structures can develop. All the options have been tried by trial and error and no new structures emerge. This is the steady-state, which will be maintained so long as the same energy gradient applies. If the gradient intensifies or diminishes the system will respond accordingly. There will be much more to say about this energy flow phenomenon in the next two installments on Bio-Systems and Eco-Systems where the effects are truly astounding. For now, just recognize that dynamic Mech-Systems are just as subject to the organizing principle of energy flow as all other systems.

---

^* Thanks to Charlie Hall, at SUNY-ESF for the inspiration for this example. He is teaching a Systems Ecology course (I'm sitting in on it) in which the students took a field trip to a local stream where they took samples of all sorts of parameters. They brought their samples and data back to the lab where they are now in the process of building a systems dynamics model of the stream.

The Science of Systems 9

Systems Science 9 — Major Classes of Systems

I find it useful to consider systems in three major classes or types. There is a fourth type but I think of it more as a kind of an abstraction than a real system, at least at this stage of the universe's life. I'll explain that in a bit.

The three non-abstract classes are:

• Mech-Systems
• Bio-Systems
• Eco-Systems

Below I will explain each of these, but a rough idea here will lay some groundwork. First, the "abstract" type of system can be called the Chaos-System, or Unorganized-System. Think of a completely insulated flask containing gas molecules, say hydrogen as H₂. Allow the system to age for a really long time and according to thermodynamic theory inside the flask the temperature is the same everywhere and things are just bouncing around. Very boring! The reason this is abstract (even though you can imagine such a flask existing in reality) is that any attempt to peek inside to see if things are really chaotic would introduce an observer which would be at a different temperature that would cause the system to undergo changes (even a really minute measuring device would still cause a change!) Thus we can only use such systems as an abstract 'ground state' in a mathematical sense. Still it has its practical uses in being able to characterize real systems in relation to their abstract (theoretical) ground state. While an important issue, for purposes of this short version, I will leave it at that. Plenty of books on thermodynamics, statistical mechanics, thermochemistry, etc. lay it out in excruciating detail, so interested readers should refer to them.

My classification scheme (of what we should think of as 'interesting' systems) is based on several parameters or dimensions derived from the previous installment on complexity, etc. Leaving aside thermodynamic order, which is highest when the temperature is lowest, I consider four basic dimensions that help to classify systems. These are actually a pair of two dimensional spaces: competitive vs. cooperative component interactions and complexity vs. organization. I should hasten to point out that this is not the only two spaces one could conjure to elucidate system types. Indeed, it would be useful to consider multi-dimensional (hyper-)spaces to see if the analysis bears consistency when many factors are taken into account. But these two spaces should prove instructive for my present purposes. In the figures below you will see ovals representing the three types of systems. These are conceptual aggregations. The idea is if you were to take a survey of real systems according to some measures on these dimensions they would be placed within (mostly fuzzy) boundaries within the larger space. This is, after all, how we define classifications. Seemingly different systems might share similar measures on these dimensions and hence end up clustered within the 'glob' represented. I must emphasize that this is a hypothetical framework. As far as I know no one has done such an analysis rigorously. What I offer here is based on a very anecdotal survey of my own. But I think you will see the general merit of classifying systems in order to bring some organization to systems science, just as classifying plants and animals (systemics) helps organize our understanding of phylogenetic, behavioral, and physiological relationships between different flora and fauna.

Figure 1 shows a two dimensional space based on complexity (vertical axis) and organization (horizontal axis). The origin at lower left is labelled low for both dimensions, while the top and far right represent high levels of these dimensions. The three types of systems are shown as rough areas occupying somewhat overlapping regions of the space. I've included the Proto-universe (after the Big Bang), galaxies, and solar systems as kinds of Mech-Systems, which I will explain below. As represented here, Mech-Systems are toward the low end in complexity, having few components and few interactions. They can, however, be very high on organization. As we will see, human engineered machines fit this category nicely. Bio-Systems occupy the region in the middle, having medium to high organization and complexity both. Eco-Systems are generally very high in complexity, but not necessarily very ordered (we now know that even so-called 'climax' ecosystems are not long-term steady-state systems).

Figure 1. Complexity versus Organization as a way to classify system types. The overlapping regions implies that there are no 'clean' boundaries. Eco-Systems contain Bio-Systems, for example, a bee colony has aspects of both ecosystem and macro-bio-system.

The second two dimensional space relates the dominance in the tradeoff between competitive processes (component interactions) and cooperation. Bio-Systems rely more on cooperation than competition, though the latter is essential for certain biological functions. Mech-Systems are characterized by higher degrees of cooperation among the parts. Human engineered machines are the quintessence of such systems where absolute cooperation between the parts are essential. But we can also consider non-engineered, but mechanical systems, such as the Earth's crust or a mountain stream as a Mech-System with some amount of competition going on between the parts.

Eco-Systems tend to have more competitive interactions between 'components'. At the low end of an Eco-System we find populations of the same species, where individuals are in competition with one another for food and mates, but still manage to cooperate for the good of the species. At the high competition end we have a typical rocky intertidal zone where numerous species and individuals within a species are continually competing for real estate (which usually translates into food and mates!). A critical question that we need to address is: where are human societies placed in this figure? As I will argue, there are societies (examples) that run the range from relatively high internal competition to relatively high cooperation. But in general humans seem to center somewhere in the overlapping region of Bio- and Eco-Systems. I will address human society as a special case of Eco-System that may need to tend toward Bio-System if we are to survive in the future.

Figure 2. Cooperation versus Competition as a way to classify system types. Some kinds of systems based on this scheme are infeasible, they cannot have both high competition and high cooperation simultaneously. On the other hand, minimal interactions with some low cooperative and some low competitive suggests simply a disorganized, possibly chaotic system, as our abstract system mentioned in the text above.

Mech-Systems^*

This class of systems is characterized by the preponderance of cooperative interactions among the component parts. Additionally most components interact with very few other components directly. The most obvious members of this class are man-built machines. Machines are designed to fulfill very specific functions and their working components are designed to work together, cooperatively, to produce that function. However, if we are not too strict on the absence of competitive relations some natural systems can be fit into this class, especially atomic and molecular-scale chemical systems where quantum effects are not too noticeable. Even though DNA molecules are associated with living systems, the molecule itself is not living. It is, however, fairly stable (thermally) and rigidly structured in the right temperature and pH aqueous solution. It can be manipulated chemically in very predictable ways so that pico-machines can actually be built using molecules of DNA.

Machines are generally characterized by their degree of deterministic behavior. They can be counted on to do what they are supposed to do. The better the design and fit of all the parts, the more durable the materials used, the more reliable the machine is for a longer duration. But entropic processes still get their due. Parts wear out eventually and unevenly and can alter the performance of the machine in time. Energy is required to replace or repair the workings.

Machines are a good example of the difference between organization (in the sense of complexity) and thermodynamic order (third law of thermodynamics). Because machines are more likely to do what they are supposed to do, they can be said to have a high level of order, as if their parts are 'frozen' in their relationships to one another. In this regard they are like a crystal in which the bonds between components are 'frozen' and the number of states of the system are restricted.

Figure 3. A Mech-System is characterized by components with few interrelations with other components and those relations are "frozen" such that the machine produces predictable outputs. Here the whole machine is represented by a rounded-corner rectangle. Each component is a circle (thin line circumference). Lines between components (interactions/interrelations) are few and the overall structure is rigid, like a crystal.

As mentioned above we should not restrict the notion of Mech-Systems just to machines. Many natural systems have machine-like properties in that they have fewer interactions between components and those interactions are stable over time. In natural systems, however, many times these interactions are stochastic, that is they are probabilistic over time. One example is a waterfall. Over time the water pouring over rocks generally follows a pattern, even if the details are not specific. Waterfalls are chaotic systems with no drops of water following preordained pathways. The interactions between water flow and rock is that the latter gets gradually eroded so as to change the general pattern of the flow. So such Mech-Systems do evolve over time (actually not unlike the wearing out of mechanical parts in a human-built machine). Nevertheless, the system is fairly simple and organized. One might also argue that there is a balance between competition (erosion) and cooperation (channeling the general water flow).

Bio-Systems

Bio-Systems can be thought of as more complicated networks of quasi Mech-Systems (like the DNA example above) in which there is greater flexibility in the network of connections; they can vary over time. Clearly individual living organisms are the paradigm example of Bio-Systems, hence the name. As we will see, Bio-Systems are somewhere between machines and ecologies. In the latter case, relationships are constantly in flux and systems can only be characterized by changing network structures. In Bio-Systems there is less rigidity in interrelationships than in Mech-Systems. There are generally many more connections between components providing multiple redundant pathways for interactions. A key characteristic of Bio-Systems is the trade-off between cooperation of components (as in the machine) and competition between them under critical conditions.

Individuals, and in the case of eusocial animals, colonies, are composed of components that generally cooperate, or at least the dominant mode of operation is cooperation. But this is not a strict rule. During development there is often competition between cells as they differentiate and vie for location. In the cortical tissues of more advanced vertebrate brains, even in adulthood, there can be an on-going competition for processing power and physical space between modular functions. This is demonstrated most readily in the motor cortex where plasticity of the neural units hinges on use-it-or-lose-it rules and in the case of loss of a digit or limb results in the areas once dedicated to controlling that appendage's movements will be colonized by nearby areas controlling similar appendages.

Competition comes into play especially when the system is under stress from resource constraints or disease. Some non-critical subsystems are shut down or at least down modulated so as to allow resources to be routed to critical functions. This is true at the single cell level and at the whole individual level in multicellular organisms. In this case a logistical control subsystem is responsible for mediating the competition (regulation) to ensure that the critical function wins.

There are several other examples of competitive processes operating under normal circumstances within an individual's body but where logistic regulation prevents destructive competition. One example is opponent paired muscle sets that move joints or maintain posture. Muscles like the biceps and triceps operate in a dynamic tension to control the motion of the forearm. Neural controls, which include cross inhibition, make sure that these two muscle sets don't over-flex and produce damaging tensions around the joint or tear each other apart. Without such logistic regulation, controlled fine motion would not be possible. Another example is related to the neural circuit just mentioned. Throughout the nervous system (though generally more prevalent in brain stems) there are examples of circuits called 'central pattern generators'. There are many different versions and these circuits are responsible for rhythmic motions like walking/running gait control. They work by sets of neurons, generally through a sequence of other neurons, mutually inhibiting or exciting one another. Their outputs tend to resemble oscillatory excitations that are sent to specific effectors, like the logistic regulators for opponent paired muscles. They are especially useful for undulatory motion such as peristalsis in the digestive tract. In these circuits the neurons appear to be competing with one another for excitation (say through cross inhibition), but the phase differences created by the effective distance between cells and their terminating synapses allow the circuits to produce a cooperative output. [see also: Mobus, G.E. and Paul S. Fisher, "Foraging Search at the Edge of Chaos", in D.S. Levine, V. R. Brown and V. T. Shirey (Eds.), Oscillations in Neural Systems, Lawrence Erlbaum Associates, Publishers, Mahwah, New Jersey.]

Evolution has worked to produce individuals in which cells and tissues generally cooperate with one another in a well choreographed dance. Complexity is quite high compared with machines since many component parts interact in multiple ways with many other parts, the interactions are most often context dependent. The range of possible states is huge by comparison with machines, but it is not, technically, infinite. States are defined by molecular-level interactions within cells, so at a certain level of observation the system appears to be continuous rather than discrete. But if one examines the micro-states of cells, one discovers more distinct transitions as biochemical processes proceed. Still, for generally practical purposes the systems can be treated as continuous without (usually) loss of information. However, caution is advised in treating this too literally. A single micro-mutation in a DNA molecule in a stem cell or a germ cell can lead to considerable shifts in the future states of individuals.

Competition between certain components in a Bio-System are conserved through evolution because competition is useful at critical times in the life of individuals. Cooperative processes, via logistic regulation, dominate so that the individual acts much like a machine most of the time, at least during its adult, sexual stage. The parts work in concert until age or disease cause them to falter or fail. But, at the same time, their normal function is stochastic in nature, meaning that the variability of behavior of parts is higher than in a strict machine. Such variability stems from retention of the plasticity of sub-cellular and tissue-level components. This plasticity is what allows Bio-Systems to adapt to non-standard environmental conditions when necessary (and in brain tissue it is the basis for learning). The downside of maintaining such plasticity is that sometimes parts don't behave exactly as they should and because they are interconnected to so many other parts this can cause intrinsic (that is, not from the outside) malfunction. The parts may get 'out of synch' with one another leading to oscillatory fluctuations. Generally this is temporary and correctable. But with more complex Bio-Systems, more parts means more potential for things going wrong. Many genetic-based diseases fall in this category.

Bio-Systems are, thus, highly organized and the parts are highly interconnected making life a very complex kind of system. At the same time they are intermediate in terms of thermodynamic order between machines and Eco-Systems. Evolution of Bio-Systems has been described as keeping species on the boundary between order and chaos. They are just organized enough, and sufficiently controlled so that they are successful in meeting the challenges of living and procreating. But they are flexible enough and subject to just enough intrinsic fluctuation that they are not strict automata. They can deviate from the norm as a form of 'exploration' of the environment and of useful behavior space.

Figure 4. A Bio-System is characterized by more interconnectivity (greater organization) between components. Here components are represented as Mech-Systems but real Bio-Systems are more often hierarchies of Bio-Systems within Bio-Systems. The whole Bio-System is here represented by a thick line circle. More cogent characteristics, not shown in this figure, are the relationships in time between competitive and cooperative interactions.

Eco-Systems

The third class of systems is characterized by a high degree of complexity (generally) and a low degree of thermodynamic order. These systems are aggregates of multiple Bio-Systems from multiple 'species', often multiple Mech-Systems, and non-living components, having many more interactions, with much higher variation in both kinds of interactions and degrees of coupling strength in those interactions. More importantly there is often a much higher degree of competitive types of interactions, even between conspecifics, relative to cooperative ones.

Figure 5. An Eco-System is characterized by somewhat less, but highly dynamic and variable interconnectivity between components. Components are of every other type of system, Mech- and Bio-, and can also include nested Eco-Systems.

Here I look at two basic kinds of Eco-Systems, the standard idea of an ecosystem as studied in ecology, and a Social System, an ecology of social organisms. Of course the one we tend to be most interested in is the human social system.

Biological Ecosystems

Ecology is the study of natural environments and their component biological subsystems. In fact many ecologists have eschewed the systems concept in describing their units of interest because the term too often is associated with Mech-Systems (machines) and ecological systems are anything but like a Mech-System. However, as this scheme of classification implies, ecological systems include Mech-Systems, the non-living substrate and general physical environment, as well as Bio-Systems from viruses to megafauna and plants.

Ecologies are often studied from the standpoint of systemics (what species are present), dynamics (how they interact with one another — behavior), population dynamics (how species grow and contract), evolution (how some species diminish over time and other species invade, changing the dynamics), and, very importantly, the thermodynamics or energy flows from inputs (via primary producers) through trophic webs (primary consumers, carnivores, etc.) to microbial recyclers.

I don't propose to go into any greater detail in this section since so much has been written about ecology and there are still many fine systems theoretical perspectives in play in the field. If this ever becomes a book on systems science I would probably provide some of these examples plus relate various principles from prior installments to specific ecological principles to show how systems science (and not just Mech-Systems science) should still be the overarching framework for this field. This is especially important because of the relationship between the non-human ecology and human social systems. Though many wise people throughout recent history have pointed out that the human ecology has to be regarded as a subset of the larger ecology (of the Ecos) it is only now that we are about to disrupt that ecology by not recognizing this fact that truth dawns.

Social Systems

My argument is simple. Human societies and cultures, including all artifacts, behavioral conventions, institutions, and especially agriculture, are just as natural as any wild forest or coral reef. The only difference is that humans are clever enough to overcome any of the constraints of the larger Ecos that might have kept us from overrunning the world. In the process of expanding our territory out of Africa we have exploited other ecologies, other resources, and often destroyed them in the process. Humans have now succeeded in appropriating over 40% of the primary productivity of the planet for their own trophic uses, and there are no natural predators waiting to check our population.

Social Eco-Systems depend on the strong cognitive and emotional interactions between individuals making up the social group. Ultimately that is what binds us. As in a general Eco-System these interactions may be restricted to a few other individuals (at least at a time), but through chains of interactions with other groups, and elements of our culture (e.g. TV) we have evolved an extremely complex and generally organized ecology.

Eco-Systems are, however characterized as much by competitive processes as cooperative ones. In the case of biological ecologies, the shifting dynamics of a region allow for an evolution of natural feedback loops that constrain the components from 'taking over'. Human societies have also evolved to introduce some elements of regulatory constraints to help coordinate activities and keep competition from causing too much damage. But this evolution is really just begun. Governance and economic market activities are still relatively young inventions and even though we would like to believe we have developed the perfected form of both, in the form of democratic capitalism, there are still many flaws and unrestrained aspects as we have recently witnessed in the economic crises we find ourselves in now.

Humans continue to evolve, and assuming that there are human survivors from any potential cataclysm due to climate change and net energy decline I fully expect that it will be in the area of strategic thinking capacity (an actual development in the brain). Along with that evolution I expect that the social structures involving logistical and tactical coordination (in other words governance and markets) will also develop. For one thing we have learned that political process breaks down into ideology and dishonesty (with money involved). Politics has turned out to be a horrible way to form and run a government. And markets without appropriate regulatory oversight (along with foolish market participants) are disasters in the making.

The human social Eco-System is actually evolving toward being more of a Bio-System in which coordination resolves the issues of competition that cause Eco-Systems to be so dynamic and in a state of constant flux. Bio-Systems are stable over time whereas Eco-Systems can go through drastic changes from time to time. As an individual component in either of these two kinds of systems, which would you rather be? A Bio-System provides greater predictability, stability, assurance, etc. An Eco-System is a grab bag. As human evolve to have greater moral fortitude, altruism, empathy, and strategic perspective, they will work to evolve social systems that function more like Bio-Systems than Eco-Systems. But that is a long way off. The current species of human has only just awoken to the desire for Bio-System status. It will take a long time for this species to give way to the next, more sapient species that can complete that process.

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^* I use this hyphenated form in Mech-, Bio-, and Eco- systems to denote a generalized type of system. In fact other non-living systems might fit into the Bio-System class, and social systems can be analyzed as a form of ecosystem, hence the Eco-System class.

The Science of Systems 8

Systems Science 8 — Complexity, Organization, and Order

We've looked at the boundary of a system as a way in which one system is delineated or differentiated from the environment. The boundary 'contains' the elements or components of a system. The word 'system' itself implies some kind of internal organization or structure along with function. In this installment we will take a look at the internal milieu of a system and make some general claims about organization that seem to hold for many different kinds of systems at all scales and composition.

What we are trying to get a handle on is the concept of complexity as it applies to the internals of systems, and by our recursive definition of systems the complexity of relations between multiple systems. We have already examined some aspects of how systems evolve from a lesser organized state to one of greater organization. We've also hinted at how subsystems organize and give rise to wholly new functions and interactions with other subsystems. The latter is what we have called emergence in that new structures and functions emerge from lower level subsystems interacting in new ways that become stable, consistent with the energy flows through the overall system.

'Complexity' is a tricky word. Even among scientists who study complexity in its many guises there is not always agreement about what it means or implies. So I want to be careful to delineate how I am using the term and why. Specifically I do not want the word to carry too much burden of meaning. Some people look at highly organized systems with many interacting parts and call that complex. They are not wrong, but the word then implies not only numerous parts of numerous kinds, but also highly specific and possibly stable interrelationships between the parts. The problem arises when one looks at a collection of many component parts of many types and may not see any pattern of relationships that remain stable over time. Is this a lack of organization in the system or a lack of perception and understanding on the part of the observer? In Installment 9, Classes of Systems, I will explain this more thoroughly.

A similar problem exists with the term 'order'. This implies a stable, consistent, perhaps even rigid structuring of the component parts. Complexity, in the sense of organized structure, is sometimes equated with order, but this is not quite right. Order, at least in the sense of the Third Law of Thermodynamics, is of the type of a crystallized system. There are a few well defined structural relations that have become literally frozen in time and space. This can be achieved by lowering the system's temperature to a point where the internal thermal motion is minimized and the components settle into their minimal energy configuration with respect to one another. Or at least they are trapped in a local minimum, with a global structure that minimizes total entropy. The point, however, is that any complex system might be frozen into an ordered state but this wouldn't tell us much about how interesting the system is.

And I think the issue is one of how interesting a system might be. Crystals might be pretty, like a diamond, but they are not terribly interesting as objects go. At the other extreme a gas phase mixture of many kinds of atoms is similarly not very interesting if held at a temperature that prevents interesting chemistry from taking hold. In the first case of the crystal entropy is at a minimum but nothing is happening. In the second case entropy is at a maximum with too much happening, but none of it interesting. What we find interesting is the somewhere in between phase where things are happening, but more than just atoms colliding with one another. What we are really interested in is dynamic organization, that is, where structures can emerge and interact in new ways.

Complexity Defined

Lets start with a straightforward definition of complexity. Then we will relate it to the ideas of energy flows and emerging organizations. In the end, we will see that this is precisely the basis for what we call universal evolution.

I will start by describing a multi-dimensional space in which each dimension measures an attribute of complexity. The first dimension is simply the number of kinds of atomic components. The word 'atomic' here means components that cannot (or need not) be broken down to simpler forms, as in the original Greek usage — atoma. The number of kinds can easily be finite, as in the number of kinds of naturally occurring atoms in the universe. In fact, I sometimes think that an infinite number of kinds would be a meaningless concept. Of course the number of kinds might be extensible just as we have extended the number of kinds of atoms in the periodic table by nuclear chemistry. But this isn't essential to the basic definition.

A second dimension would be the numbers of atoms of each kind — their relative abundance in the system. In the case of the universe, for example, the atoms of hydrogen and helium are by far the most abundant proportional to carbon, nitrogen, oxygen, etc. This dimension is important, but by no means crucial to the potential that complexity offers. We know that the existence of carbon, for example, counts for a great deal when it comes to organization.

A third dimension measures the number of pair wise interactions that are possible between components. This dimension is somewhat more problematic definitionally. In regular chemistry, for instance, interactions are never purely pair wise (e.g. reactions that take place in solution). The best that we can say for this dimension is that it measures the number of rules by which two atomic components might interact in as pure a context as possible. For example one might define a set of 'standard' conditions and then enumerate the ways in which any two components might interact. For example, in chemistry and subatomic particle physics there are definite rules of binding the components together (e.g. number of electrons in the outer shell of an atom determines the kinds of chemical bonds). The need for standard conditions introduces one or more additional dimensions that measure the various aspects of the environment which 'condition' the interactions. Here we see that complexity itself becomes complex to define!

This four⁺-dimensional approach to measuring complexity is very general. It should be applicable to any level in the ontological hierarchy (quantum physics, chemistry, biology, etc.). The measure of complexity is thus not a single number but a n-tuple (e.g. a list of numbers and other m-tuples separated by commas, where the length of the list is the number of dimensions and the numbers are appropriately scaled). The actual value of n will depend on how many environmental conditions are involved in describing the interactions. A list containing any tuple with low values is likely to describe a relatively non-complex system. For example, the four-tuple, (2, (10^100,000, 10^20,000), 1, 5), would represent a rather impoverished system with only two component types, one interaction between them and five conditions, even though it has a huge number of components of the two types (the 2-tuple as the second item in the four-tuple). [Note: I cheated a bit here. The last number should actually be a 5-tuple itself, with each number identifying the range of condition parameters, but I thought that might be overkill for this example!]

The salient point is that it is not very useful to think of complexity in too vague terms. Or put another way, complexity is complex! The reason we want more precision in thinking about what it means to be complex is that we can then start to think about what sorts of interesting things a complex system can do under the right circumstances. To be definite, the circumstances I refer to is the flow of energy through the system. Depending on the situation at the boundary and the internal complexity of a system various conditions of energy flow can produce very interesting results. In fact, at some sufficient level of complexity and at just the right flow rate of just the right quality of energies, very interesting structures can form and move and further interact with one another. Emergence can take place. Selection can take place. And organization can take place.

The surface of the Earth receiving the influx of solar energy is such a system. As the planet formed from rocky, dusty, and gaseous debris circling Sol there was much disorganization but great potential in terms of the complexity of the system. Through the combined energy fluxes of sunlight and gravitational forces things happened. The Earth got interesting.

Energy Flow Jostles Components

Energy flow is, itself, a somewhat problematic concept. Energy isn't exactly something you can see or feel, except by its effects on matter. Perhaps the quintessence of pure energy is the ,a href="http://en.wikipedia.org/wiki/Photon">photon. The photon is neither a particle nor a wave, but both, more or less simultaneously. Photons move and mediate the electromagnetic force. They travel at the speed of light through a vacuum and interact with electronic particles like electrons and protons. In doing so they can 'move' the particle and this effect is what we mean by energy doing a kind of work.

Sensible heat (what you feel as heat) is a very low energy form of photon, in the infrared radiation range, jostling the electrons and atoms in your skin, in the molecules of water in your cells, mostly. So heat, the lowest grade of energy, has an ability to interact with matter, but depending on the relative temperatures of the heat source and the heat sink it may or may not do any work to speak of. The higher the temperature of the source, relative to the sink, the more 'pressure' will exist to push the energy flow through the intermediate system. The same principle applies to other forms of energy, e.g. when electrons themselves move and mediate the electromagnetic force (i.e. electricity). In that latter example the pressure is called voltage.

In a reasonably complex but not necessarily organized system with an external source of energy, say heat at a high temperature, and an external sink at a low temperature, energy will flow through the system (if the boundary conditions permit!) from the source side to the sink side, jostling any matter that is in the way. The important point here is that the jostling causes our 'atoms' to bang into one another as they become particles mediating a mechanical force. They also may have the capacity to absorb a bit of the higher potential energy in the form of potential to 'stick' together. Chemical bonds created by jostling are an example. But when the atoms stick together, they form more complicated, organized objects. More importantly, these objects may interact with one another in more complicated interrelations.

In addition to 'chemical' interactions the material is mechanically pushed, so to speak, toward the low temperature sink side. In the strictly thermal model we've been using the act of heating particles at the high temperature source causes the particles to initially spread out creating a high pressure zone at that end. At the low temperature end, the particles are cooled and tend to cluster closer together creating a low pressure zone. Matter moves from regions of high pressure to regions of low pressure. That is why we have wind! So the mass starts a more organized movement from the source end to the sink end. At that end, the cooler particles are displaced by incoming hotter particles and tend to move to the outside (not outside the boundary) of the column of hot-to-cold region moving particles. They, in turn continue to move along the periphery toward the hot end where they will get re-heated, setting up a cycle called a convection cycle. Of course a lot depends on the overall geometry of the system, but some form of organized motion is set up by this energy flow through.

So just with the flow through of energy from a high potential source to a low potential sink, and given a sufficient level of general complexity we have an increase in organization, both mechanical and chemical. And that organization will persist so long as energy flows. Note that the entropy of the whole arrangement, the meta-system of energy source, system (of interest), and energy sink is actually increasing as the potential between source and sink tends toward equilibrium. But locally, within the system (of interest) order increases and entropy declines. Turn off the energy flow, however, and the system will return to its original disorganized amorphous state.

Organization Emerges

A lot now depends on the types of components, their relative abundance, and the kinds of interactions that can take place between them. And some luck helps!

The process described above is what we call stochastic. The actual interactions that take place are a result of chance meetings. However, given that the process is strong and the complexity high, the chances for very interesting interactions is high. Some particle interactions or combinations are more stable under the given conditions of the system with its flow through of energy. It will have a characteristic temperature gradient from source to sink. The same applies to pressures and composition gradients. The stable combinations will tend to persist, to be robust along the gradient and in being so change the relative concentrations of 'atoms' within. Combinations that are not stable will end up coming apart and their components will return to the population of particles. Though even the most stable combinations will eventually come apart just due to chance jostling of the wrong kind. However, this happens much less frequently than the coming apart of not-so-stable species. The internal environment and dynamics of the system tend to select for certain stable forms.

Now, again if the system is sufficiently complex, it will happen that some of these stable forms interact with one another in very interesting ways. They can mutually reinforce or benefit one another. For example species A might 'help' species B by having a strong affinity for one or more of the component parts of B but is able to give the parts to the formation of B without itself being destroyed. A simple example is that of a catalyst in chemical reactions. A particularly important form of catalysis is cyclical where A helps B and B helps A. Such a reaction can easily dominate the system leading to a predominance of A and B. More complex loops of mutually reinforcing species are possible. Indeed the origin of life is now believed to be wrapped up in these mutual 'autocatalytic' loops involving precursors of RNA and proteins.

The important thing to recognize in all of this is that the generation of greater organization is driven by the flow through of energy but is also dependent on some elements of chance. For example of two equally potent autocatalytic processes that might have emerged, would be competitive for components, and are equiprobably, chance will decide which one forms first. And that chance formation might make a great difference in what evolves next.

Evolution

So here we are at the beginning of something really interesting. The system (the Earth) is driven by several energy flows. The one with the most evident impact on the emergence and evolution of higher and higher structure is the Sun. Light energy drives life, for the most part. Gravitational energy in the form of creating high temperatures at the core of the planet (and in the mantle) and the tidal dynamics caused by our moon, have certainly contributed to helping organize the way things developed. The fact that the Earth rotates daily means that the high energy input source and the sink (deep space) alternate so that energy flow is a kind of pulsed (sinusoidal) affair at points below the arctic and antarctic circles. At lower latitudes near either pole the pulses are seasonal rather than daily! Nevertheless, life has evolved on this planet in a drive toward increasing organization and order changing the high entropy complexity into low entropy complexity over the past 4.5 billion years.

Another way to reflect on the nature of organized complexity is our old friends information and knowledge. Going back to "Systems Science 5 — Information and Knowledge" we can see that the structure (knowledge) and the generation of messages by the dynamics of those structures (information) are another way to measure complexity.

We've now built a basis for understanding general systems. We have several 'tools' for describing systems and their dynamics and evolution, at least qualitatively. The reader, I hope, appreciates that there are more formal and mathematically sophisticated ways to handle these concepts. But for purposes of introducing the general reader to the concepts of systems we have made some good progress.

In the next installment I will be turning to the taxonomy of systems in preparation for starting to apply systems theory to the real world. We have to be able to differentiate aspects of real-world systems in order to understand some additional important phenomena, but also to apply our tools to systems we encounter in life. My intent is to show that a grounding in systems science will allow anyone to tackle understanding diverse and seemingly disparate objects and processes in this world.

What is "sustainability"?

The Magic Word

I have been thinking about this word, sustainability, a lot lately. How could one not? It is used everywhere these days. Sustainable development, sustainable energy, sustainable growth! Everyone, it seems, is on the sustainability bandwagon, and I have been no exception.

I developed a model which I call that sustainability criterion for renewable energy production. The criterion is simple enough, a renewable energy production facility (e.g. solar, wind, etc.) is sustainable only if it produces enough excess energy above that required for economic work to replicate itself before its useful life is ended. Of course this doesn't meant that every solar panel has to somehow reproduce another solar panel every, say, 20 years. What it does mean is that in the aggregate of all renewable energy producers and the physical plants that produce those producers, there has to be enough energy produced to produce the next generation of energy producers. On the whole, then, the combined energy outputs of all renewable sources has to serve the economic work process plus provide energy to run the processes that continue to produce the energy capture and conversion equipment.

Currently solar panels and wind mills/generators are manufactured using existing energy infrastructure which mostly comes from fossil fuels. Eventually, when the fossil fuels are no longer available all of these energy producers will need to supply their own reproduction energy just as biological systems must do. To be sustainable, in the sense that life has sustained itself on this planet for billions of years by capturing more energy than just what was needed for maintenance, our invented energy capture/production equipment will have to do the same.

This is a systems-based definition of sustainability. Virtually every profession and perspective will define the term somewhat differently based on their particular view of things. For example, a sustainable business model will reflect the values and beliefs held by capitalists who foresee sales and profits going on and on into the far future. You know, like General Motors! I've even lately heard the term used in the context of education — sustainable education invokes the notion that we can keep the machinery of school-based education (teachers and students) going on forever, with good 'outcomes'.

'Sustainable' has become one of those words that can mean pretty much whatever we want it to depending on who is using it and under what context they're doing so. Too many of these implied meanings seem contradictory at times. At some point it becomes a meaningless word.

The dictionary definition basically says that it means to support or keep going. No real time frame is mentioned. I think most people apply this general concept to whatever is being claimed is sustainable. We'll keep the business going. We'll keep the education system going. And so on.

But there is also a hint of the notion that we won't be able to keep the business going unless we do something special to the model (process) that makes it sustainable. We need to change something about education in order that it becomes sustainable. Our energy systems and economy need to be altered so as to be sustainable long into the future. And that, it seems to me, is where things get pretty fuzzy.

I'd suggest we take the systems perspective on this issue. In this approach we view that which is to be sustained as a process. It has boundaries through which inputs and outputs travel. It gets energy, materials, and information from its environment and pumps out heat and wastes and 'products'. In most cases the question resolves to: "can this process continue to pump out that product as long into the future as we wish?" From this perspective it is clear that three things need to be settled before an answer can be gotten. First, the inputs are resources and we need to know if there are essentially endless supplies of these. Second, are the internal workings of the process such that the ratio of product to waste (and energy to heat) is maintainable for all time? Are there possibly flaws in the inner workings that will cause a steady degradation of output over time? Third, will the environment continue to carry off the product(s) and wastes so that they do not build up and clog the flow through?

This may seem like an oversimplification of the whole problem, but it is only a matter of accounting and measuring in reasonable units of time. Every process has these characteristics even if there are millions of inputs and outputs; even if the inner workings are mind bogglingly complex and stochastic. All of that may make the question harder to answer, but it does not alter the fact that an answer is possible. And, necessary.

Several things can impact the long-term sustainability of a process. If the resources are finite then the draw down of them clearly makes the process unsustainable. In some cases, alternative inputs might be substituted, but if they too are finite then this just delays the inevitable. If the process is one that grows, then several things can go wrong. The growth itself may involve slight errors in replicating key sub-processes causing the internal workings to go awry. If the rate of influx of even an infinite resource is fixed then at some point the growth will outstrip the resource input and the system will neither be able to continue to grow, nor will it be able to have any kind of margin of error, so to speak, should something go wrong. A related problem is if the growth of a process means an increase in product/wastes output relative to the absorbing rate of the environment then the process can be choked in its own output.

If we take this systems approach to characterizing the meaning of sustainability then it becomes clear that claims of sustainable this or sustainable that need to be verified by demonstrating that all of the factors above are met. This will take research, careful observation, careful measurements, and careful use of language! Of course the bandwagon is rolling already. The word, like the word "green" has taken on a life of its own devoid of any thoughtfulness and so it is unlikely that the powers that be will insist on some kind of standard usage that includes validation of claims made. Sustainability is a fad, so the likelihood that it will be a terribly useful guide to policy decisions is rather small. Just label something as sustainable and everyone will applaud.

Of course, in the really long perspective nothing is sustainable! If sustainable means keeping something the same forever (or a really long time) it is actually an unachievable status in the real world. Everything is changing and evolving. I mentioned that life has sustained itself through the trick of capturing more energy than it needs to just maintain. That is how it grows. But the life on Earth today is vastly different from life a half billion years ago. What has endured is the energy flow through driving the internal workings. Those, in turn have been subject to copy errors and because all processes (cells, for example) are embedded in complicated environments that put stresses on them, they are subject to selection for those errors that happily confer some advantage. Life is sustained as long as energy flows, but species, and higher taxonomic categories as well, are not sustainable. Indeed they should not be sustainable in the very long run.

Our current social systems, our economic and political models, even our organizational forms and family units, are presently undergoing huge stresses from an environment we ironically had a great deal to do with creating. Nothing that we have or are is truly sustainable. Everything, including us, will change or go extinct in the long run. And the long run may not be as long as most of us think.

We cannot sustain our current version of social order and economic activity. We look for ways to fix the inherent flaws (like increasing regulation over capital markets) to make what we do now sustainable. But it is largely a vain effort. We are all inherently conservatives who want the world we have known to stay the same. We want to go on building so-called wealth and having kids to give it to. We want everything to be as it was for our lives. We believe our experiences were valid and should be sustained for all time. But we are wrong.

We should not be looking to sustain what we have, just for the sake of keeping it going. We should look for ways to stabilize the parts that work, to buy time and keep the change from being overly destructive. But we need to embrace evolution. We need to let go of institutions that are dilapidated, that are actually causing us more pain than good. What we need to do is look at our processes as described above and make wise decisions about resource flows based on what is and is not renewable. We need to evaluate the internal workings, our economic models and our population size, relative to both the resource issues and the piling up of wastes at our feet and in our water.

Life abides. It is sustainable. Processes change. We should recognize this and live by it. We should not believe that we are going to keep everything exactly the same for the indefinite future. That doesn't mean it is OK to be a destructive force or cause damaging changes (as with our over production of CO₂). But it does mean we have to think through our institutions, our processes and our values guided by nature's design and be able to change those as seems wise, rather than slavishly follow some vague concept called sustainability out of a foolish desire to preserve a status quo. Right now people are wasting valuable time trying to figure out how we can generate liquid fuels out of vegetation in order to keep driving our cars. They talk about sustainable fuel production trying to make the idea of maintaining our lifestyles seem reasonable. They want to build giant solar arrays that will rob the Ecos of some of its fair share of light so that we can maintain our consumption of electricity (indeed without addressing the population growth this is more than just maintenance). Human life, in this mode of thinking, is not sustainable. And if we persist, neither might be a livable planet.

Boundary Conditions

There is considerable effort underway to figure out what is going on in the economy. Everybody, rightly, wants to understand why the economy is tanking and why the financial system is out of control. And many economics analysts are working assiduously to answer these questions.

Unfortunately most of them will not be able to see the forest for the bark on the particular tree that they happen to specialize in. Understanding what is happening to the human economy is actually fundamentally simple. We need only look at the ultimate boundary conditions of the human economy to grasp what is happening.

The boundary of the human economy (as distinct from the whole Ecos) is comprised of the interface between the economy and the sources of energy and material and the sinks for wastes. Figure 1 shows the whole economy and this interface.

Figure 1. The boundary conditions for the human economy. Energy influxes from real-time and fossil sunlight drive the economic engine. Energy is needed to obtain usable energy as well as usable materials. Both the material resources and the fossil fuel resources are finite. Some material may be recycled internally but energy cannot. All energy influx eventually exits the system as waste heat. Much of the material similarly exits the system as waste (some being toxic). The major elements of the economic engine are shown internal to the boundary.

What is instructive in this viewpoint is that everything that can happen in terms of work and the production of human-defined wealth is limited by the flow of energy and its availability to do human-defined useful work. The system is limited, no matter how efficiently energy and materials are used, by the total flow rate of energy. As the figure suggests, the influx of real-time solar energy (driving photosynthesis, the hydrological cycle—hydroelectric power, and the atmospheric and oceanic dynamics—wind and currents) is approximately stationary over time. Hence, while we may be able to increase some of the capacity to convert raw sunlight into useful form (e.g. photovoltaics, wind turbines) the scaling up of this approach is problematic.

Energy obtained from fossil sunlight is many times more potent than what can be gotten from direct conversion of real-time sunlight. It is concentrated. Unfortunately it is also a finite resource. The evidence has been mounting that shows that we have reached a peak of feasible production of conventional oil. Thus energy gain from oil reserves (the bank) will begin to decline. Compounding the problem is that it takes energy investment to obtain energy returns. The only returns that count are the net energy available to do useful work. As the finite resource diminishes it also gets harder to extract — it takes more energy invested to get the same unit of energy return. This precludes growth in energy available meaning that less and less work will be done per unit time in the future. Less work, less wealth.

As long as the human economy could extract energy from the fossil fuel bank in ever increasing amounts, year over year, all of the internal subsystems in the economy, the machines, the structures, the photosynthesis (agriculture and forest products), the solar power (hydroelectric dams), and even the biomass of human and animal labor could grow and develop. Similarly, the institutions, the control hierarchy, and other social systems could expand to meet the needs of maintaining organization within the economy. But as we enter an era of contracting supplies of net energy available to do all that work of expanding the economy the economy itself will have to contract. Subsystems within the economy, those listed and in the figure, will need to contract in proportion to the whole and variously relative to one another in order to maintain some kind of orderly process. That will depend on how rational and wise we humans are going to be in approaching this condition.

I have already written about one formula for achieving this end. That would include a serious reduction in producing those machines and structures that serve only luxury and aggrandizement functions along with increased attention to increasing efficiencies in both of those areas. In addition we will need to restructure our institutions to be both more efficient and more effective in managing our resources. We will need to channel more of our energy saved from these efforts into the construction of solar power conversion equipment (augmented I suspect by nuclear power) and the better management of photosynthesis (e.g. more efficient use of water). On that last point, this does NOT include using photosynthesis in a vain attempt to keep our transportation fuels at a level necessary to support the unlimited personal transportation system, which, in turn, supports the unsustainable living arrangement known as suburbia.

And that brings me to the 'labor' subsystem. Of course I mean the population of people (and farm animals). Contraction of energy implies contraction of the population as well as contraction of the material consumption standard per capita. Trying to maintain the current population size while the machines, agriculture, structures, and institutions (which supply jobs) contract means that every individual will become poorer over time. Unless we address the size and scaling issues of population along with the other components, and set about judicious (and ethical) population reduction measures we will simply be inviting social strife and upheaval as people perceive their losses and get angry.

An economy that is in contraction is said to be in depression. We've had ample examples of uncontrolled contraction throughout history. The big difference between these previous cyclical depressions (and recessions) and our current situation has been that previously there was always more useable energy to be gotten easily. Eventually, when investment and consumer confidence started to build, the energy to do the work was there for the taking. The current situation is fundamentally different. Net energy for useful work is starting to decline meaning that less work can be accomplished. While in an era of profligate energy increasing we could readily expand and invest in development. The economy could generally grow. We could, in effect, borrow against expected future energy increases to build stuff that we didn't really need (but wanted) and did not see that the bank would eventually run out.

Now it is time to pay the piper. With each unit of energy from whatever source requiring more energy to obtain with each passing year, our net useable energy is headed downward. That alone forces us into contraction. Since we are so used to thinking in terms of progress and growth, it will be hard to mentally accept this completely new situation. We could easily fall into despair and view our condition as something like a permanent depression. But it doesn't have to play out that way. We could look upon this as an opportunity. We already know that our economic activities, especially the burning of fossil fuels, is threatening our world. Global warming, declining soils and water tables, waste chemicals that are toxic and have unknown long-term consequences, and many more human-caused problems are upon us. We are seriously considering the collapse of civilization on a global scale, with nowhere else to escape. So a forced contraction of economic activity would seem to be a good thing for the entire Ecos, including ourselves. We need not be depressed. The contraction could be managed if we are willing to take the necessary actions and act wisely. Part of that wisdom includes acting in a timely manner. The global threats seem to be accelerating so time is of the essence.

There will be no increase in the influx of solar energy to the earth. There will be a continuing diminishment of energy from fossil solar energy as the oil, coal, and gas supplies are depleted. We may be able to increase our supply of electricity marginally with increases in solar conversion capital and infrastructure, but doing so will be tricky if we are to avoid robbing the rest of the Ecos of its fair share. Until and unless we can harness nuclear fusion, it is looking more and more like our future will be one of contraction until we find the right steady-state equilibrium point where our energy uses exactly match our renewable supplies. Only then will we have a long-term sustainable economy.

Humans are not guilty of sinning just because we've had a helluva party. We have been doing exactly what any living thing would do under the same circumstances. We've expanded to fill the world with minds and gadgets and, most importantly, knowledge. We are biological after all. Life is mandated to grow and complexify. There is no need to feel guilt or sorrow about the mistakes we've made in judgment, so long as we have learned from those mistakes and are now ready to act like adults, take responsibility, and do what is necessary to restore balance between ourselves and our world. The world of tomorrow will not be the same as it has been for our history. We have already wrought irreversible changes that will require us to adapt. We have already altered the biota and we don't know what the impact will ultimately be for life on earth. But one thing we do know is that such changes have happened before on this planet. And each time life has emerged with some new, more complex and organized structure. I suspect that absent a planet killing meteor or catastrophic solar event evolution will continue in this pattern. There will be a better tomorrow.

Meanwhile we need to suck it up and get to work preserving the worthwhile and learning to live within our means (energy flow). I don't mind telling you I have my concerns. My work on sapience suggests that wisdom is in very short supply on this planet. We may not be able to muster the judgment and moral reasoning needed to make good decisions and get the majority to recognize them as such. In the end we as a species may just be too selfish and self-serving to do the right things. In which case, that better tomorrow will be without human representation.

The Science of Systems 7

Systems Science 7 — Cybernetics: The Science of Control

In the prior post I asserted that what exists are systems, or unities, entities, networks, comprised of aggregates of components. Over time, and under the influence of energy flow, these components tend to self-organize into structures and processes in which energy flows in and dissipates out while doing work to maintain the internal organization. Living systems are the epitome of this system-as-process ontology.

One might well ask how a process, a dynamic structure, can maintain its integrity over time, especially when energy flows might vacillate (such as in the diurnal cycle) and material flows, for resources, are uneven. A system becomes long-term steady-state or stable when part of the internal organization acts to control the overall function of the process, to keep it doing what it should be doing in order to remain stable and adapted to the external conditions.

Cybernetics is the science of control of systems, processes, such that they continue to function in consonance with the environment in which they evolved. Once a network of functional relationships has been evolved, and under conditions of reasonably stable (if slowly varying) energy flow, a control subsystem can act to maintain functional stability over the long haul.

Within-process control is achieved by the use of the feedback of messages to a control actuator that can have causal influence over some key elements of the internal system network, usually through amplification, such that the system as a whole changes its internal flow rates and/or configurations in order to maintain stability. Once evolved, a process' internal network represents the expected state of affairs, under normal operating conditions. When, however, conditions change enough to make a difference in the overall performance, relative to that standard, then the messages being fed back to the system are informational. That means the system has to change something in order to get back into stable operation.

Feedback and Closed-loop Control

Remember that one way to define information is "...news of difference that makes a difference." That is exactly the way it is used to manage processes. In a simple cybernetic system a transducer measures some quality parameter of the process output (see Fig. 1). The resulting message signal is compared with a reference standard or 'set point' value for the parameter. Whatever difference exists, positive or negative, between the set point and the actual real-time value constitutes a signal of error. In other words, the resulting message tells the system news of difference from the expected, zero error. To complete the loop and provide control, a controller must act, as noted above through amplification, to counter the error.

Figure 1. In this view an entity is comprised of a work process that converts inputs into outputs, one of which is considered 'valuable' (possibly by another entity) or represents the long-term health of the process. The stability of the process is accomplished by measuring the output value and comparing it to the value of the desired or target value (also called a set point value). Any error in the actual measured value is fed back through a controller, a subsystem that decides on a counter action that is initiated through some kind of amplifier.

The core adaptivity of a living system is actually its ability to control the internal structure of its basic work processes (e.g. physiology) through homeostasis. This is achieved by feedback control as shown above.

Coordination Control

Figure 2, below, shows a more complex situation where three entities are organized such that the product outputs from two of them are resource inputs to the third. The latter uses these inputs to produce a 'final' product output. All three entities need to cooperate in order that the final product is of optimal quality. Internally, cooperation is aided by a new layer of control using essentially the same principle of feedback control but with a twist. A new kind of controller, a coordinator, is introduced. This controller uses error information from all three entities along with a general model of how to coordinate the behaviors of the three to achieve the minimum error from the final product.

I am not introducing a mysterious object here. The coordinator controller actually developed from another entity that simply specialized in the task of processing messages — a kind of computer, in essence (see below for discussion of specialization). Messages, remember, are just very low power flows of energy or energy and small bits of matter. They are modulated into a communications channel between two entities and convey information when the receiver has a low expectation of the precise message form received. The signals (thin arrows) in Fig. 1 represent transduced messages. The error signal provides the controller with information when there IS an error.

Figure 2. Coordination between multiple entities by a higher level controller, a coordinator. This controller uses a model of the optimal performance of all three entities to generate control signals that (as shown) can modify the target values of each entity in such a way as to force the individual controllers to adjust their entity's process.

The trick, as shown in the figure, is to adjust the target values slightly in order to take advantage of the existing individual controllers working on their own process. This works by changing the error signals and prompting the controllers to adjust. Of course this scheme only works if the target value can fall within a range of acceptable values (see reference to homeostasis above). It also depends on a reasonable amount of stability (predictability) in the initial input resources. There must exist a 'feasible' model solution to variations in these inputs that will maintain an acceptable range of output values.

The model used by the coordinator fills a similar role as the targets did for the individual controllers. And, just as the targets were 'given', the model is pre-established and essentially fixed. As the complexity of the network (number of cooperating entities) grows the model given must be more complex as well (sometimes even more complex than imagined). Simple networks such as depicted in Fig. 2 are generally quite brittle. That is there are few feasible solutions to optimize the output for wider ranges of distortions in the inputs. Such systems would be completely at the mercy of the environment with minimal capacity to adapt to these variations.

Note that the system is portrayed as inputs coming in from the left and products and wastes exiting on the right. While this might seem stylistic, in fact all systems can be organized as networks with this general arrangement. We can see, in this arrangement, that opportunities for increasing complexity, adding more entities, exist in terms of increasing breadth (appending new entities in parallel with existing ones) or increasing length (appending new entities at the input or output ends). Several advantages accrue with both of these possibilities. For one thing, it is possible to introduce redundancy. Suppose we simply replicate one of the input entities and add it below one of the existing input entities routing its product to the output entity as a backup input. Now if its companion input entity should have difficulties obtaining acceptable resources, perhaps the new one can make up the difference. See Fig. 3 below.

Figure 3. A system can become more complex by adding entities to the breadth. In this example an input entity has been simply 'copied' and used as a redundant or additional input processor. The coordinator must be extended to handle the new addition, which will change and complexify the model.

The system can also extend or lengthen by virtue either of copying something (either input or output) as in the broadening example, or by capturing an entity that was previously independent that produces one of the inputs, say, that the system needs (Figure 4). Of course capturing a new entity on the output side can effectively change the nature of the whole system.

In both broadening and lengthening a system it is necessary to extend the coordinator and its model to cover the new functionality.

Figure 4. In this example the system is lengthened by 'capturing' a previously independent entity that produces one of the critical inputs to the system, thus helping to ensure stability of that input. As above, the coordinator has to be modified to incorporate this new configuration into the model and control processing.

The living systems version of lengthening is exemplified by the endosymbiotic theory, which, for example, provides an explanation of how eukaryotic cells emerged by symbiosis among bacteria-like simple cells.

Imagine that each entity here represents some relatively simple bit of metabolic activity, say, for example, early bits of what we know now as the Kreb cycle. Various bits might have evolved chemically semi-independently. They would have been loosely coupled in the primordial soup, perhaps each with their own unique primitive membrane acting as a structural substrate. At some point the now ubiquitous phospholipid bipolar membrane may have allowed the uniting of these bits and pieces producing a tighter coupling and allowing the cycle to form and evolve.

Coordination control involves two basic types of control processes. One is the coordination of the entity with its environment, that is, its sources of inputs and output sinks. In the example above of lengthening the input chain by 'capturing' a source, by establishing a tighter coupling between the source entity and the coordination control of the main entity, the entity is changing the ways in which it acquires resources. This is a tactical move to ensure resource availability. Tactical control involves maneuvering with respect to the environmental entities so as to ensure resource availability (in a timely manner) and to ensure products and wastes are appropriately transferred to the environment. This latter is every bit as important as ensuring resources since the buildup, even of products but especially wastes, can be as detrimental to the entity as loss of resources might be.

The other form of coordination control that works in tandem with tactical control is logistics. Logistic control involves internal routing of materials and energies to ensure that each operating unit is adequately supplied at the right time and wastes are eliminated. In a sense, logistical control takes over the management of what had been (in earlier versions of the entity) the effective tactical control employed by each operational unit when they were not as tightly coupled. The logistical controller is the mechanism that promotes the tighter coupling and relieves the operational units from needing to supply as much energy to independent tactical control.

Both tactical and logistical controls operate over somewhat longer time spans than operational or real-time control. That is they tend to aggregate error data over time and use time-averaged values for computation of their control outputs to the operational units. This is primarily because it takes time for trends to evolve that would mandate control decisions. Acting too quickly to short timescale errors could have a destabilizing effect on the operational units.

In living systems, especially cells, very complex networks of sub-processes require intricate coordination controls for both getting food and other resources (tactics) and maintaining internal flows. Some operational-level units are tasked with repairing other units that tend to degrade due to thermal effects (entropy). They require energy and raw materials to maintain the integrity of the entire entity. There are also operational-level processes that construct other processes (another example of autocatalysis when one process constructs another process that, in turn, constructs new copies of the first!). The logistical control of such complexity is substantial, involving distributed, numerous feedback loops. The entire workings have been captured in the concept of autopoiesis conceived by biologists Humberto Maturana and Francisco Varela.

Planning Control

The highest level of control in the hierarchical scheme is planning. This kind of control requires that entities have the capacity to construct internal models that are changeable to reflect changing conditions. Derived evolutionarily from the model structures used in coordination control, planning models involve not only a model of internal process, but also a model (or models) of the external world. That is, the entity must be able to represent the relevant parts (other entities and physical conditions) of the environment and have a way to represent changes in those as a function of time. In deed, in order to make plans, for the future, an entity must be able to anticipate how the environment might change. The planning then involves taking anticipatory actions that will increase the likelihood of the entity thriving or avoiding damage.

Of course we are now fully in the realm of living beings. And I will restrict the discussion to animals since whatever 'planning' might be done by plants is rudimentary at best. Animals have achieved a high form of agency. That is, they are able to produce effects on the environment as much as be affected by it. They have varying degrees of autonomy in making decisions to act. Some decisions are purely reactive, based on evolutionarily hardwired responses. Others are more flexible in terms of taking more situational conditions into account before taking an action decision. Still others, in animals with more evolved nervous systems, take far more conditions into account, including historically learned propensities/exigencies, and internally generated motivations to deliberate prior to taking a decision. Humans, of course, seem to have reached the epitome of such decision processing.

When there is a dedicated, distinct third level of control in the hierarchy specifically for planning the long term behavior of the entity we call it strategic. Strategic means that the plans take into account the likely scenarios of what will happen in the environment, how other entities will behave and what conditions will prevail at some temporal horizon, scenarios of what will happen internally based on adaptive competencies, resources stored, possible weaknesses, etc, and the panoply of motivations, desires, requirements, and such. The strategic planning process is to take all of this into account and develop a program of goals to be used by all of the various tactical and logistical controllers.

Devilish Complexity

Complex organization (like a living entity) involves elaborate, intricate networks of many kinds of components interacting in multiple ways to produce a whole, temporally stable, system. And as I have pointed out in previous installments, systems are really systems of subsystems or networks of networks (of networks ...). The three level hierarchical control model described above can be applied to many subsystem levels within a very complex entity. That is, for example, all cells in the body have fairly elaborate coordination controls, as discussed above, but also some very primitive planning control! Every cell devotes some of its internal processing to anticipating changes in its environment in the form of adaptive responses with memory. Consider muscle cells, for example. They will adapt to frequent and persistent demand for twitching by 'bulking up'. When a person works out routinely, their muscle strength and endurance increases over what it would be for a couch potato. Moreover, the muscles retain a readiness to react to demands based on this training for some time into the future. In fact, even if one stops working out the muscles will remain at the ready for quite a while. Only after several weeks or months of failing to work out will they start to loose their readiness.

Living tissues have a built-in ability to retain a 'trace' of capacity to respond to environmental conditions. When put under demand, the tissues (cells) build up the capacity under a schedule (essentially a logistic curve). This is a primitive form of 'learning' and acts to anticipate future demand. So, in that sense, it is a primitive version of planning for the future.

Thus it can be seen that every complex subsystem of a larger, more complex system, can have its own hierarchical control structure. Perhaps a much easier to visualize example of this is a typical large business organization. Such organizations have different departments, like production, marketing, accounting, etc. Take accounting as an example subsystem. Within the accounting department, which functions as one of the main tactical (financial accounting) and logistical (managerial accounting) control systems for the entire organization there will be operational sub-units (e.g. bookkeepers), logistical sub-units (middle managers over bookkeepers), tactical sub-units (supplies requisitioners), and strategic planners (controllers, CFOs). And every department in the organization has a similar (self-similar) internal structure. Even the production department has its chief operating officer who does strategic planning for the production facilities.

With this kind of devilish complexity is it any wonder that individuals, who frequently are called upon to make strategic, tactical, logistical, and operational decisions both for their organizations but also for themselves, are too often caught in being confused about what kind of decision they should be making at what time? You see this confusion (and its aftermath) all the time in typical organizations. In general most people don't even explicitly understand what differentiates the various kinds of decisions and the timeframe over which they should be made (or what information goes into formulating a decision). This is one of those areas that I strongly feel systems science could positively affect. If people understood the differences in each of the types of control decisions they would be better prepared to make appropriate decisions. I have personally been involved in strategic planning committees where the participants too often strayed away from thinking about strategic issues and got into logistics or tactics without realizing that they were doing so. It is hard to make distinctions, especially when all subsystems involve some amount of all three kinds of control.

And, of course, there is the problem with governance in general. Governments are hierarchical control structures (see my blogs on Sapience, esp. page 2 where I ran a series on Sapient Governance discussing this). Most of the world democracies have achieved some amount of differentiation into the various controls. But too often confusion reigns. I would argue that even in the US, for example, the notion of truly strategic planning (foreign policies, environment, energy) is sorely lacking or very weak at best. In part this is because the political system itself gets in the way of doing meaningful strategic planning (the politicians need to be more mindful of short-term issues that will get them re-elected). But it is also due to both politicos and citizens failing to adequately distinguish the kinds of decisions and time scales involved.

Perhaps a better educated citizenry, with respect to at least this part of systems science, would help move us toward better articulated decisions that address all of the relevant control aspects of modern civilization. It certainly couldn't hurt.