##1. Integrating Economic Gain in Biosocial Systems.pdf

7/27/2019 ##1. Integrating Economic Gain in Biosocial Systems.pdf

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& ResearchPaper

Integrating Economic Gain inBiosocial Systems

Timothy F. H. Allen1*, Joseph A. Tainter2, John Flynn1, Rachael Steller1,Elizabeth Blenner1, Megan Pease3 and Kristina Nielsen1

1Department of Botany, University of Wisconsin, Madison, WI, USA2Department of Environment and Society, Utah State University, Logan, UT, USA3Gaylord Nelson Institute for Environmental Studies, University of Wisconsin, Madison, WI, USA

The concept of gain and profit is introduced into ecology as a way of summarizingstrategies of biological and social structures. High gain systems can be predicted by flux asthey take in fuel at a rate. Low gain systems must refine low-quality materials in order toacquire fuel. Low gain systems are predictable from their plans and coded behaviour.Changes from high to low gain mode andvice versarepresent a reordering of a hierarchyover time. We use a catastrophe pleat as a way to model high gain collapse. Collapse isavoided in low gain by planning. At first, planning enlarges the system through econ-omies of scale, but eventually resources are of such low quality that gathering and refining

begins to limit size. We also model shifts from high to low gain with a response surface ofresource used against adaptation. There are peaks of high and low gain adaptation withwhich we analyse termite evolution. Positioning systems on the surface is complicated byvalid alternative interpretations of system behaviour. Each low gain phase starts with a

burst of high gain so it is possible to reinterpret successive moves to the lower gain asfractal shifts that link successive response surfaces. We propose mathematical protocolsfor capturing the response surfaces. Copyright # 2010 John Wiley & Sons, Ltd.

Keywords anticipation, biosocial systems, collapse, ecological economics, efficiency, hierarchytheory, high low gain, resource use, termites

INTRODUCTION

Managing complex systems is a deeply reflexiveprocess. There are models within models, changewithin change in appearances as well as change in

the substance over time. Contemporary problems

often invoke biosocial systems that are highlyorganized through models that the system itselfpossesses independent of any models brought tothe situation by the investigator. The bankingcrisis, climate change and pandemics all havemodels their own. Clearly bankers have theirpreferred condition, but also influenza has astrategy fixed by natural selection. We model

Systems Research and Behavioral ScienceSyst. Res.27, 537^552 (2010)Published online 23 August 2010 inWiley Online Library

(wileyonlinelibrary.com) DOI:10.1002/sres.1060

*Correspondence to: Timothy F. H. Allen, Department of Botany,Birge Hall, 430 Lincoln Drive, Madison WI 53706-1313.E-mail: [email protected]

Copyright# 2010 John Wiley & Sons, Ltd.Received 3 December 2009

Accepted 11 March 2010


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systems that themselves invoke models andnarratives. Once the investigator arrives theseproblem systems will appear to be differentdepending on the level of analysis chosen bythe observer. Standard hierarchy theory hasaddressed this issue fairly adeptly, but there is

yet another layer of uncertainty that has receivedscant attention. Observed hierarchies change theirlevel structure and organization as they passthrough time. Gustafson and Cooper (1990)complained that Allen and Starrs (1982) versionof hierarchy theory made much of verticalhierarchies (discourses in levels of analysis) butonly gave passing reference to horizontal hier-archies (change in relationships between levelsover time). Hierarchies represent a way ofexpressing organization and so hierarchies chan-ging over time should reflect changes in organ-

ization. Recent developments in hierarchy theoryworking in the arena of ecological economics andresource use have found regularities in hierarch-ical change. This paper will show how new ideasin resource capture, deployment and profitaddress Gustafsons complaints while they opennew doors on managing complexity. All this willhelp us deal with unruly complexity in contem-porary challenges.

Here we present advances in the conceptual-ization of resource capture and deployment tomake profit so as to bring hierarchical perspect-ives closer to mainstream prediction and man-agement. The perspective we take on organismsfocuses on what they have to do to stay inbusiness. Players in evolution only win in termsof being able to keep playing, that is stay inbusiness. Wholesale importation of evolutionaryideas into social circumstances has a spottyrecord including social Darwinism. Even so,casting evolution in terms of making a profitwhile playing the game allows a unity of conceptacross biological and social systems. Organisms

and social systems both persist by gaining fromcapturing resources and then doing workthrough degrading those resources, usingresources as fuel (Kay and Schneider, 1994).Profit and Energy Return on Investment (EROI)are common parlance in economics but they havealso appeared in biological systems under therubric of high and low gain (Allen et al., 2001).

Gain explains systems behaviour as they captureand convert resources into useful work forthe system (Allen et al., 2001; Tainter et al.,2003). The principles for biosocial systemschanging over time amounts to changes betweenmodes of gain. High gain systems have access to

resources of sufficient quality so that inputs canbe used directly without refinement. Meanwhilelow gain systems capture greater quantities oflow quality resource, but must refine inputsbefore they can be used (Allenet al., 2001; Tainteret al., 2003).

The notions of high and low gain divide on thedistinction between flux versus coded constraintson that flux. If you can see the flux you cannot seethe constraint on the flux and if you can see theconstraint the dynamics are stopped by it andyou cannot see the flux (Allen, 2010, this volume).

That mutual exclusion causes high and low gainactivities to represent non-overlapping aspects ofresource use and allocation. Uncomplicated byrefinement and efficiency, high gain aspects ofsystems can be predicted by resource flux: therate at which resource is captured and spent. Theequations of thermodynamics generally apply tothe behaviour of high gain systems. Thermodyn-amics is not efficient or otherwise, the flux justhappens according to the second law of thermo-dynamics giving most probable outcomes. Highgain is therefore not about efficiency, it is aboutwhat is available, how availability changes, andwhat fluxes are involved. In contrast to high gain,low gain does invoke notions of efficiency and somoves beyond simple energetics. Efficiency givesprivilege to some preferred state that invokesvalues embodied in the models possessed by theobserved system. A plan to economize oroptimize constrains consumption below the rateof raw thermodynamic flux, giving privilege tothe continued existence of fuel in store, some-thing that might be desirable. The efficiency of

planned action is the device we use to predictunder low gain. Plans may control dynamics thatoccur at given rate, but plans do not themselvesoccur at a rate. The necessary separation of highfrom low gain is such that one can see and predicton either rate-dependent behaviour or rate-independent constraints, but not both at thesame time.

Copyright 2010 JohnWiley & Sons, Ltd. Syst. Res.27, 537^552 (2010)

DOI:10.1002/sres

538 Timothy F. Allen et al.

RESEARCH PAPER Syst. Res.


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LEVELS OF ANALYSIS AND SHIFTINGPREDICTORS

Hierarchies are often addressed vertically bygiving one level privilege as others are con-sidered tacit relative to that focal level (see

Needham, 1988, for tacit versus focal attention).A complication is that the observer or analystoften chooses to change which level of thehierarchy is focal. As a separate issue, even asthe focal level is held constant, an observedhierarchy may itself show reorganization overtime as relationships between levels shift. If thelife in a small, closed microcosm contains just arat, the animal will die of suffocation, whereuponthe living component becomes bacterial. Life inthe microcosm may remain focal but that life haschanged completely. By contrast, changes may

appear because the observer focuses on some-thing other than the life in a microcosm. In yetanother way, for biosocial hierarchies to manifestchange in appearance sometimes the order of theobserved levels can change, as when individualworkers unionize and so gain wider control.Changes over time often lead to modifications inthe hierarchy, which is itself already given tochimeric transformations in appearance acrosslevels of analysis.

All biosocial systems involve flux, whichembodies high gain, as well as coded plans,which manifest low gain. With both sidesrepresented (Figure 1) such systems are notintrinsically or materially high or low gainindependent of level of analysis. If one changesthe boundary of a system while keeping all elseequal often the other type of gain will appear. Forinstance, filling an automobile with gasoline is ahigh gain activity because ready-made fuelcomes into the car from the environment. Thefuel is consumed according to available supply.On the other hand, one can expand the bounds of

the system to include oil rigs, tankers and oilrefineries. While crude oil has high caloricdensity, it exists at a less pure lower grade thangasoline; high and low gain are comparativeissues so in this argument crude oil is low grade,its high embodied energy notwithstanding.Gassing up the car is the endpoint of a longlow gain process of capturing lower grade

material and refining it to make gasoline(Figure 2). Allen et al. (2009) went through thesame argument for nuclear power plants beinghigh gain as they consume fuel rods but low gainif the system is bounded wider to include mininguranium ore and processing it to get the isotopeto make the fuel rods. With both flux and

constraint present neither has intrinsic privilege,but one must be chosen for predicting the systemin a given analysis.

The above is a general condition because theequations of thermodynamics are not limited tothe flow of heat or energy. Gold can power a socialsystem in a way that is analogous to how heatpowers a mechanical system. In one sense gold

Figure 1 There are two basic parts to biological and socialsystems: thermodynamic happenings and coded limitations.The coded information amounts to plans which are executedin some sort of construction process. In biological systemsthe codes might be embodied in DNA, hormones or even

mating dances. The construction might be protein synthesis,but could equally be making a nest for a bird. In socialsystems, there are many modes of construction, all involvingplans. The whole constructed material system is an updateon the narrative that the whole tells its mates, predators andprey. The scientist observes the updated whole with itsextended story. The scientists tell stories about those storiesamongst themselves. The construction may not live up to theplans such that the system must become something else moreefficient by creating a new plan. Economists let their systemscontinue to tell their respective stories as they watch adjust-ments in plans as the system repeatedly becomes moreeconomical, more flexible or bigger. Ecologists and biologistsin general do not wait for their systems to update, and merely

note that the old plan fails to work as resources are used up.Economists expect adjustments to be only temporary andsimply note that scarcity increases costs that demand ever

more efficiency


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Economic Gain in Biosocial Systems 539

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only commands the things that power socialsystems energetically, but does not power societyin a directly energetic fashion. But in society thecurrency is often literally currency and that is whatmatters. Work is done under the application of thesecond law of thermodynamics, which applies notjust to energy but also to the concentration ofmaterial. Gold can be one such material concen-tration. The equations that describe how spendingriches gets things done can take the same form asthe equations for how heat drives a steam engine.The fuel is concentrated and represents the top endof an energy gradient. As fuel is burned, the heatfrom it becomes diffuse and the temperature goesdown. In all this, motive power can be extracted,perhaps to drive a locomotive. Gold in a treasuryequally represents the top of a gradient wherevaluable material is concentrated. While gold itselfis not readily seen as having embodied energy, itssymbolic effect can get work done. The gold isspent to get work done and in the process itbecomes diffuse, distributed across the pockets ofthose who did work and got coin in recompense.

ECONOMIES OF SCALE: RESOURCECAPTURE, TRANSFORMATION ANDSYSTEM BEHAVIOUR

An important property of low quality resource isthat it may not exist concentrated locally but is

more abundant across the whole landscape thanis the local higher quality counterpart. Thisgreater global quantity of a resource comparedto the aggregate of local hot spots is onemanifestation of the second law of thermodyn-amics. Left alone most resource will become

more diffuse. Think of gold nuggets. Quickly thenuggets are all gone, and miners must turn togold dust. The critical observation is that there isand always was much more gold in the area asgold dust than as gold nuggets. With the rightlow gain refining techniques, panning for golddust is profitable and more gold is removed asdust than ever was as nuggets. Beyond that, thereis, and always has been, more gold in the diffusestate than there ever was as nuggets or gold dust(Figure 3). More diffuse material almost alwaysexists in greater total amounts.

Since the coming of the Industrial Age, wehave come to take for granted great productionand work output. Part of industrial productionrates comes from the huge motive power of fossilfuel, but that is not the whole story. A separate

Figure 2 Whether pumping gasoline is high or low gaindepends on the level of analysis. If the boundary is the fuelentering the car, then the system is high gain. But if the systemis bounded to include oil drilling, transportation and refine-ment, with crude oil as the original input, then it is low gain

Figure 3 As the quality of a resource declines, that which isleft occurs in a more diffuse form. If, however, a system canavail itself of the resource in that diffuse state it will findmore resource available than there was for systems that

previously used up the quantities of high quality hot spotsof resource. It will be more expensive to gather and processsuch low quality material, but the sheer quantity of resourcein that diffuse state can more than make up for the extraeffort. There are economies of scale that can, and usually do,make a bigger profit for systems that use poor qualityresource. Most of the gold in the world that can be minedis in the form of gold dust, but there is even more gold in a

form that is too diffuse to capture


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issue is the creation of interchangeable parts,such that each item, say a gun, can be massproduced. We easily forget that before therevolver, all guns were made once off as uniqueitems. The parts were made to mesh withthe other parts on that particular gun only. Once

the parts became universal to all units of thattype, say in a Smith and Wesson revolver, theneach part could be mass produced. The criticalissue was not so much the power of fossil fuel inindustry as it was the economy of being ablesimply to turn a handle to achieve hundreds orthousands of usable parts. This is generally calledthe economy of scale. Making lots of things thatare the same costs much less than making a lessernumber of things that are different. While thearchetype of the advantages of moving upscaleoccur in industry, economies of scale turn up in

many settings, as when a zooplankton animallearns to ignore all but the commonest phyto-plankton food source, thus significantly increas-ing efficiency of handling. If the system can dealwith the lower quality matrix, then it makes a bigdifference that there is more total quantity of stuff

that is good enough. Economies of scale associ-ated with the huge amounts of diffuse resourceare what pay for the increased effort of chasingand refining diffuse material. So long as a verydiffuse version of the resource can be processedefficiently to make a workable fuel, then

economies of scale can make the difference inprofitability of actually using such processes.

The door to the use of poor raw materials isopened by increased efficiency of processing.Matter used as inputs, such as ore, is degraded byextracting the valuable material from the rawinput. Low gain systems get more out of a giveninput of ore or energy of a certain quality than doequivalent high gain systems. Low gain isassociated with the deep degradation of inputsto generate very low quality effluent, say ash orslag (Figure 4). Deeper degradation of ore means

that more metal or whatever is extracted, and sothere is less metal in the degraded slag. At theother end of the fuel cycle, burning fuel generateseffluent at a lower quality state, with less energyin it. Deeper degradation of fuel means taking theproducts of combustion to a lower level state.

Figure 4 High gain takes in high quality material and degrades it without effort put into being efficient: profligateconsumption. Low gain efficiently degrades inputs to get more work out of them. The option may then be open for takingin lower quality of inputs. There is not only more quantity of raw lower quality inputs, but they also contain potential forproducing a greater quantity of refined material that is of the same quality as the high gain inputs used directly as fuel.Degradation is separate from dissipation, which is input quantity times degradation. Low gain in the end gets more work done

by increased degradation opening the door to greatly increased dissipation


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Carbon monoxide comes out of the tail pipe of anautomobile, but that gas still has energy in it, itwill burn. More efficient engines will burn thatcarbon monoxide or its precursors. The mostefficient burning goes all the way down to carbondioxide and water, the energetic dead state of

oxidizing carbon. A biological example here iswhen respiration takes sugar down to carbondioxide and water as opposed to fermentationthat has alcohol as its effluent with potentialenergy still left over. All work involves a processof degradation, but the dead state at the bottom ofthe gradient in biosocial systems is not fixed andis actively manipulated to achieve advantage.Yeast, for instance can change the dead state fromcarbon dioxide to alcohol depending on theabsence of free oxygen that would otherwiseallow full respiration to carbon dioxide. A

characteristic of life is that, through increasingefficiency, life can change the dead state; thechemical energy embodied in alcohol notwith-standing, for yeast, alcohol exists in a dead state(Fraser and Kay, 2004).

Increased degradation of inputs may producemore work directly, but there is another route tomore work. This second path depends onincreasing the amount of fuel put into the system(Figure 4). Both deeper degradation andincreased inputs can get more work done. Lowerquality inputs are more abundant in the environ-ment than higher quality inputs, and therein liesthe full potential for low gain systems to achievemore work than equivalent high gain systems.The availability of that extra input pays for theincreased costs of deeper processing as well asthe cost of processing greater quantities of inputs.This is a version of the economy of scale. Theincreased expense of processing is covered by theability to process so much more. Processinglarger quantities at a workable profit giveseconomy of scale.

ECONOMIES OF SCALE VERSUSDIMINISHING RETURNS: SOMEBIOSOCIAL EXAMPLES

Tainter (1988) notes that societies increase incomplexity (differentiate in structure and

increase in organization) as a means to solveproblems. The solutions to challenges ofteninvolve such things as new activities, moreelaborate technologies, new institutions andsocial roles, and gathering and processing moreinformation. The typical evolution of problem-

solving systems is simpler to more complex, asthe simplest organizations and technologies areadopted first, while those that come later tend tobe more elaborate and costly. Moreover, ascomplexity and costs increase, there are dimin-ishing returns to problem solving. The expense ofthe previous easy solutions becomes the contextof the hard problems and that expense remains asan ongoing cost that generally accumulates.Diminishing returns on complexity introduceinflexibility, greater cost and loss of resilience. Inan analogy to a boat there is loss of freeboard as

the vessel is laden and sits lower next to thewaterline. Flexibility is lost to societies as theybecome burdened by the elaboration that comeswith complexity (Figure 5). A counterpoint toTainters issue is the economies of scale discussedabove, but such economies are not alwaysavailable. For instance the raw input might bepeasants to be taxed. Peasants are low quality

Figure 5 The flowchart here explains the diminishingreturns on problem solving via internal complexification.Economy of scale, if it is present, can pay for the extra effortof deeper degradation. In this flowchart there are no sucheconomies of scale and so the internal complexification solvesproblems but with diminishing returns. Complexificationhere costs dearly. The detour to Simplify to cut costs is theonly counteracting force available, but it is very rarely

employed


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relative to looted wealth, but there are lots ofthem. After the switch to taxation there may beneed for greater production and that woulddemand deeper extraction of wealth throughhigher taxes. The problem is that the taxes stillcome from the same people who simply bear a

greater burden of taxes. The mass of peasantsrepresents an increase in means of extractionover looting scarce gold so there are economies ofscale in the move to taxation. However there is nosuch thing as lower quality peasants of whichthere are more to tax. Tainters diminishingreturns arise when scaling effects are not there topay for complexification. There are simplydiminishing returns at a fundamental level ifeconomies of scale are absent. The wholeenterprise becomes less worthwhile.

The shift from high to low gain occurs in the

context of resources of a given quality beingdepleted. In general the easiest best qualityresources are used first either by the system inquestion or its competitors. In any materialsetting there is an array of resources of variousdegrees of quality, each with its particular cost ofgathering and processing. Any system addres-sing high quality resources cannot afford to beprudent and plan for the future because it is incompetition with, and will lose to, other systemsthat do not waste effort planning and executingefficient use. Low gain systems exist in a differentresource environment under alternative selectivepressures.

The general pattern is depletion of the highestquality resource leaving successively lowerquality. Without high quality resources highgain systems disappear. Often they have evolvedto go extinct locally only to return in some otherplace or at some other time where high qualityresource is present. This is the ruderal strategy ofGrime (2002) or the r-selection of conventionalpopulation biology and evolutionary biology

(Southwood, 1976). At a higher level of analysis,vigorous exploitation under local high gainextraction is part of a larger pattern of exploita-tion where temporary retreat is an active strategyto keep primary production of a renewableresource in high gear. High gain exploitationand retreat is interpretable as a low gain strategywhere the average production of the whole area

is increased by keeping all parts in maximumgrowth in various stages of recovery. This isOdums maximum power principle (Odum andPinkerton, 1955), where systems pulse in anorganized way so as to allow more production inaggregate. There are thus alternative expla-

nations depending on the scale at which thesystem is bounded.

The depletion of a resource base may be seenthrough the lens of a ratio of how much highquality resource there is in hotspots relative to theaggregate for low quality resource over the wholeregion. By developing a use for low qualitymaterials, low gain systems take advantage ofsituations where the ratio of local high quality toglobal low quality resource is smaller. At first thedecline in the ratio expresses loss of the best hotspots capable of supporting a high gain system.

But when most of that is gone, low gain systemsdrive quality even lower. The ratio keeps goingdown because of the enormous potential forproducing refined material from massiveamounts of poor quality raw resource. As oilbecomes a difficult resource, gas guzzlers simplygo extinct, while hybrid efficient cars suck up thelast of the petroleum. While the first shiftsremove high gain potential, the later shiftsmanifest the consequences of the economies ofscale in processing huge quantities of ubiquitouslow quality raw resource.

MOVING ALONG THE LOCAL TOGLOBAL RESOURCE RATIO

Termites make a very good example of how shiftsover evolutionary time follow the patterns ofhigh and low gain. Primitive termites eat goodwood in which they live. In the end they literallyeat themselves out of house and home. Thesehigh gain termites are forced to reproduce and

move to a new site where new good woodprevails (Thorne and Traniello, 2003). The forcedmove amounts to a high gain collapse. Moreadvanced termites eat a wide range of organicmaterials from the environs (Wilson, 1971). Asopposed to the moderate colonies of high gaintermites, the large low gain termite colonies livein huge ventilated mounds built of saliva


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cemented feces. They still eat woody material butthey do so by gathering dead and rotting woodfrom the landscape around them. The large sizeof these colonies is characteristic of low gainsystems. Lower quality woody remains exist inlarger quantities over an area than does good

wood. Good wood is a local resource, focused onindividual chunks. Gathering woody materialthat is diffuse is clearly low gain and offerseconomies of scale.

Some sixty percent of all termite species go onestep further and do not eat wood at all. For atermite, if dead rotting wood still has fortypercent of goodness compared to good wood,there is still five percent goodness left in the soilafter the wood has rotted into the soil. In contrastto large low gaining termite colonies, the coloniesof soil eating termites are generally very small

consisting of twenty to thirty individuals, most ofwhom live outside the colony itself. In thesehighly derived colonies (Donovan et al., 2000)only the royals and the brood occupy the physicalcolony. While the colonies are small, soil eatingtermites as individuals are very large, whichappears to be an adaptation to keeping the lowgrade material food inside the animal for a longtime, long enough to digest the recalcitrantcarbon in soil.

The termites that eat soil introduce a new turnin the progress to ever lower resource quality. Ingeneral, in a move to a lower quality resource,there are increases in the total quantity ofresource that can be extracted and made intofuel (e.g. large termite colonies; Figure 3).However, when there is very little goodness inthe extremely low quality inputs, the ubiquity ofsuch material cannot compensate for the massivecost of eating and processing such huge amountsof material (Brauman et al., 2000). We see theextreme low quality of soil as capping theamount of energy available over the time

available to consume it. True most of the carbonof ecological significance in the world isembedded in soil, but termites are not in aposition to eat enough soil to make up for soilbeing such a poor carbon source. Unable to takeadvantage of the gigatons of carbon in theworlds soil, soil eating termites might as wellbe exploiting a very limited resource. The

counter-example here is bacteria that also eatsoil carbon but, with their very fast growth rates,are as ubiquitous as their resource.

The strategy of soil eating termites is differentin that these super low gain systems downgrade.In Figure 5 there is a detour to simplification to

overcome the costs of diminishing returns. Asimilar thing happens in evolution of somefamilies of plant. Once small rudimentaryflowers have been evolved then cutting downon their number and simplifying is an option.Duck weed, a flowering plant, is so rudimentaryas to invite being mistaken for an alga, although itis related to the corpse flower with inflorescencesover 3 meters tall. Simplification is a viable optionin both biological and social systems. When, forexample, the Byzantine Empire lost half its landto the Arabs in the 7th century AD, the emperor

Constans II (641668) dismissed its standingarmy in favour of a militia (Treadgold, 1995).Instead of receiving full salary from the empire,the militia generated most of their income fromfarms given to them, and fought better to protecttheir personal land holdings and families.Byzantine society simplified overall, with lossof urbanization, literacy and numeracy, but thishyper low gain empire resisted Arab invasionmore effectively than before. The contemporaryFirst World forced to move to renewable energymay, like Byzantium, have to be active in itssimplification on the consumption end of theequation. When the resource becomes so diffuseas to be too expensive to exploit it fully,retrenchment is an option.

FORMAL MODELLING OF PROFIT INSYSTEMS: A CATASTROPHE MODEL

We have two expressions of the dynamics fromhigh to low gain, and on to super low gain. First is

a figure that shows a folded pleat in a responsesurface (Figure 6). The example of termiteevolution is a good way to explain this figure.The space of the pleated surface would indicatesome sort of cubic equation. The fold indicates aninstability, which is the collapse of high gainresource exploitation. At the fold there areinsufficient resources available to pay for getting


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more efficient, and so a budgetary shortfallcauses a discontinuity in availability of viableresources. The vertical axis of the figure indicatesa dimension of high gain to low gain resourceexploitation in terms of resource use andefficiency. The tendency is always to movetowards low gain because better qualityresource that has been harvested already cannotbe regained. In the face of a new resource therecan be a reset to high gain, although it will be interms that reflect use of a new type of resource, aswhen Britain moved from burning wood to coal.

The surface across which system behaviourmoves has two components. One is the decline inresource quality as the most concentrated pock-ets of resource at any time are used. The systemtherefore always moves downhill and towardsthe front of the figure, either going extinct ormoving right/backwards into the figure, becom-ing more efficient to avoid running out of

resource. The other dimension of the plane issomething like complicatedness of the system asa reflection of devices for deeper degradation ofresource. The first part of the journey over thesurface is high gain. As resource is used with noadaptation to decline in quality, the system

simply heads down hill towards the discontinu-ity of the fold and eventually over the cliff as thesystem runs out of resource of sufficient quality.It collapses at point A (Figure 6).

A more prudent path would move down theslope but also immediately towards the back ofthe surface by increasing efficient use of theresource. In this way the prudent track avoids thecliff. This might seem a sensible precaution, but nosystem ever takes this path because in a high gainenvironment anything adopting prudent con-sumption would be out-competed by others

employing profligate use of the resource. Humanenvironmental managers and those with greenpolitics often urge prudence and conservation insomewhat self-righteous terms. But human popu-lations never take that advice because they are notprepared to make sacrifices. Prudence costs andconsumers are unwilling to deny themselves onthe basis of mere abstract predictions at the timethe ecologists urge restraint. Theoretical outcomesare ignored. Tainter (1988) points out that societiesincrease in complexity as a problem-solvingstrategy, and at some point experience diminish-ing returns on this effort. The end process is oftena collapse, unless new energy subsidies can befound. Complexification to solve problems worksso long as there are economies of scale. But sucheconomies are unpredictably simply not there.Without economies of scale, complexification isalways a temporary benefit because diggingdeeper without the help of economies of scaleappears to happen consistently over the mid-term.Absent economies of scale accumulated stress andcosts lead to long-term demise.

Ravetz (2006) is confident that we cannot getpeople to respond to abstractions that confidentlyannounce that problems are on the way. Thepractical action he suggests is to clear the decksso as to open a path to frugality and restraint oncethe actors can see a disaster in the offing. Asudden awareness anticipates the second trackon the surface that skids around the corner to

Figure 6 Termite evolution from high to low gain on apleated surface. Starting up in the top left of the surface,high gain termites eat themselves out of house and home,collapsing at point A where the colony must reproduce andmove. The shift to low gain always occurs only as theinstability is imminent. The course correction at the lastminute avoids collapse of the resource base by becoming

suddenly much more efficient. The correction may avoidcollapse by reaching the continuous surface at point B. Thealternative route to B is the dotted line of prudent planning.No system ever does that because it is out-competed by high

gain rivals, and there is no incentive to economize anyway.Point B is transitional. It leads to low gain efficiency andincrease in size due to economies of scale at point C.Burdened with much infrastructure the low-gainers at Ccan become too large and demanding, in which case they fallover the front side of the surface. The super low gain strategyof the soil eaters goes to point D with deep adaptation and

energy limiting super low gain resources


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point B in Figure 6, just before the system wouldotherwise go into high gain collapse. The move toincreased efficiency is last minute and may ormay not work. At a lower level of analysis this isan expression of Tainters observation thatplanning and problem solving is always last

minute and short term. Some systems fairlymuch drive straight over the cliff. An example ofthis noted in Allen et al., 2003 is the AbbasidCaliphate of AD 7501258. But other systems domake the turn to survival and low gain efficiency.The early Roman Empire, as it neared the end offeasible expansion, centralized power in the latefirst century BC in a single ruler whose govern-ment ran the empire, not on looted wealth, but onyearly solar energy transformed into agriculturalproduce. In the late third and early fourthcenturies AD, after a long period of invasions

and civil wars, the empire subdivided itself intomany smaller provinces to deprive governors ofthe resources needed to rebel. The same large-scale reorganization applies to large termitemounds that gather poor wood. In the samestroke both Rome and poor wood termitesincreased in size because of the economies ofscale that come with more efficient degradationof resources. Those systems, and many others,make it safely to point C in Figure 6. In the endsuch systems fall over the front of Figure 6 whenthey can adapt no more, being locked intoexpensive efficiencies that made them large.The Western Roman Empire collapsed from fiscaldistress brought on by increasing complexity anddiminishing returns in problem solving, notwith-standing increasing efficiency in use of low-gainresources.

There is still a solution to the problems of oldlarge systems. That is seen in the track in Figure 6in the move across to the bottom right to point D,where increases in efficiency invoke growingsmaller or at least simpler by economizing. These

systems may be fragile because the resource baseis so poor that it limits production. Soil eatingtermites are so fragile as to be restricted totropical clemency. But some downsized orsimplified systems may last a long time, as didthe recovered Byzantine Empire, and as has thesimple form of Lemna, the downgrade evolvedpond weed.

FORMAL MODELLING OF PROFIT INSYSTEMS: AN ADAPTIVE MODEL

With all the ambiguity in scale and shiftingsignificance in resource use, we need to be able toidentify unequivocally what is the system under

consideration. We tie that identity to the systemas it uses resource as fuel. All biosocial systemshave to burn fuel and it is an unequivocal act: fuelin; fuel spent; work done. The system using fuelis firm because the more ambiguous aspects ofresource use all end up generating fuel forburning. With regard to actually using materialto do work, it makes little difference whether thesystem takes in fuel more or less directly or has tomake the fuel from low-grade material. Fuel goesinto an automobile unambiguously, while howfar down the supply chain we look can be

equivocal. The different specifications of systemboundaries are chosen by the analyst and are notset in nature, but they do make a difference as tohow one predicts system behaviour even ifbounding the system does not determine whatone is in essence talking about.

We have shown above that depletion ofresources turns on the degree of availability.First local hot spots are used leaving only morediffuse versions of the resource in the wild. Thedriving variable we use in our analytical space isthe ratio of local to global resource in the vicinityof the system exploiting that resource (Figure 7).

The second variable in our model is adap-tation. Wrights (1931) adaptive landscape is theinspiration for peaks of adaptation on ouradaptive landscape. Since we are examiningadaptation across a response surface, adaptationat one place on the landscape may be different inquality from adaptation at another spot on thatsame surface. For instance, should a naive systemadapted to some other circumstance encounter ahigh gain resource that it can use, the old

adaptations become superfluous and so in timewill be shed (starting at point A in Figure 7). Anexample here might be zooplankton feeding on avariety of prey items in an indecisive way.Commonly these animals go on to identify whatis the commonest food source and then ignore allothers. A zooplankter feeding on just one type ofparticle can be seen as being in high gain mode as


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it lets the potential in other resource particlespass by with no attempt at capture.

The third variable is degree of adaptation,which we take to be the length of time that theadaptation predicts the future. Adaptation has acomponent of anticipation in it (Rosen, 1979) andthe high gain system anticipates that theabundant high quality resource will continueto exist; tomorrow will be like today. This is oftenvalid, at least for a while, since the best predictorfor what will happen immediately is usually thesituation that prevails in the present. Of course asystem well adapted to a high gain situation willhave adaptations to profligate use of a resource,but we assess that as a situation with less

adaptation because the anticipation is short term.In contrast to the high gain prediction there is alow gain prediction based on frugality. Frugaluse of a low quality resource places dependenceon a resource that is larger and so will last longer.Low gain is adapted to a situation that willpersist, and therein lies the longer term predic-tion. High gain systems are locked into their

short-term predictions, and so cannot work overthe long term. We see adaptation to poorerresource quality as a longer term anticipation thatrequires the system to be more highly adapted asa specialist system. Adaptation to a long-termoutcome often requires more control, such as acapacity to degrade recalcitrant material. Tainteret al. (2003) discuss ants that farm fungi in termsof a low gain adaptation. The long-term predic-tions by Atta leaf cutting ants are that leaves willalways be there. But that prediction only pertainswhen a very specific strain of fungus is entrainedby the ants with exquisite care so as to achieve thedeep degradation of the leaves, converting theminto large quantities of fungus (Wilson, 1971).

Elaborate specializations usually lead the systeminto a longer term future.There are two peaks on the adaptive landscape:

one is high gain (HI) and the other is low gain(LO; Figure 7). The high gain peak is situatedwhere local resources are very high quality,demanding essentially no refinement or proces-sing. Such a system is focused, but careless in its

Figure 7 An adaptive landscape showing the peaks for high and low gain situations. The dynamics never move back towardsthe back left side because local hot spots of resource are used irreversibly. Adaptations can, however, move right parallel to theback left edge. The high gain peak is set in readily available patches of excellent quality resource. As resources are used, high

gain systems that do not adapt move to extinction at B, down the back right edge of the surface. Adaptation to lower qualityresource must wait for lower quality to begin to prevail at point C. From point C adaptation to lower quality resource moves thesystem across to the high saddle, which is the passageway to the low gain peak. Further efficient degradation may arise from yet

greater restrictions taking the system to the austerity of point D


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use of resources. Contrary to the predictions ofhigh gain systems, quality resources are in factused up: bad news for the high gain system. Thedecline along the back right edge of the spaceleads to the collapse of the high gain system atpoint B in Figure 7. There quickly comes a point

where demise is locked in, as the resource basepasses the point where there is any solution to thehigh gain resource problem. A high gain strategyis on a one way street to demise (point B inFigure 7).

An important rule is that selection cannotmove the system towards deeper long-termanticipation if the resource is still local andabundant. Any system that becomes more frugaland long term adapted too early will simply bedriven extinct by high gain competitors (justabove point C in Figure 7). Such attempts by

some management effort to address long termplanning will meet Jevons paradox (Polimeniet al., 2008). In the 19th Century Jevons (1866)noted increased efficiency of steam enginesactually increased coal consumption rather thanconserve coal (Allen, 2010, this volume). Jevonsparadox will manifest itself when human designtries to look prematurely to the long termthrough increased efficiency. All that happensis the efficiencies are folded into continued shortsighted consumption. In nature evolution simplywill not go into Jevons paradox because there isno selective advantage. Humans often sanctimo-niously bemoan waste and imprudence, andpreach about conservation, but human systemsmore or less never take the prudent path alongthe front left edge of Figure 7.

In Figure 7 there is an entry point at point Awhere a naive system faces a potentially highgain resource. It appears that something quitelike wood eating cockroaches gave rise to thetermites (Nalepa, 1984). We can think of woodeating cockroaches as naively muddling through

with wood as a resource, while not taking fulladvantage of it. Were a cockroach-like creatureable shed it adaptations of loose colonies it wouldmove to become over long term evolution someversion of a primitive termite (positioned at theHI peak of Figure 7).

At first the decline of the high gain environ-ment is not deep enough to get past Jevons

paradox. But soon high gain depletion ofresource puts selective pressure against theprofligate resource use. There is selective pres-sure to evolve a scheme that looks to longer termresource use. We have shown this in Figure 7 as adeparture from the path of high gain collapse

about half way down (point C). In that environ-ment where high gain resources are rapidlydisappearing there is strong selective pressure todegrade deeper making lower quality inputsmore valuable. The move is from the back rightside of the surface towards the front left edge inFigure 7. As the system increases its capacity todegrade inputs, the economies of scale discussedabove come into play. Accordingly a newadaptive peak is achieved and the system enterslarge scale, low gain. The big termite colonieswould be this phase. A relatively large biological

or social system, be it a large ant colony or anexpanding human empire, invites an explanationof a low gain adaptive peak. While most termitebiomass as a whole is embodied in large low gaincolonies, 60% of species of termites eat soil (PointD in Figure 7). This end phase of termiteevolution gives small, almost token colonies. Insocial systems this end phase would invoke theloss of high complexity, as in the Byzantinerecovery or the rump of the Western RomanEmpire in Western Europe. In Figure 7 all thesesimpler systems have overshot and come off theback side of the high low gain peak, moving on topoint D.

MODELLING STRATEGIES

There are various strategies for modelling themovement across the surface of Figure 7. Onecould be the use of a two dimensional Markovchain. The edges of the smaller squares on thatfigure could be associated with a chance of

transition to the adjacent square on that side.Back left edges of all squares in Figure 7 wouldhave a probability of transition set to zero, sincethere is no way to reverse the decreasing ratio oflocal to global resource. These zeros spell out thenecessary demise of high gain systems gone toofar past the low gain peak, either on the high gainside or the super-low gain overshoot. At the point


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of Jevons paradox just above point C in Figure 7there would be no probability of moving awayfrom the high gain edge. But at point C itself, afairly high probability of moving towards the lowgain peak would emerge. All this being said, aMarkov model could capture the movement, but

would mostly be a bookkeeping exercise offeringlittle analytical insight.

An alternative modelling scheme might usedifferential equations. Various relationshipsbetween the three dimensions might be captured.The appeal of this investigative strategy mightbe insights from the form of the equations.Analytical methods do offer such perks. It wouldalso be possible to check structural stability as theequations are jostled with small changes in theirstructure to see what happens to the surface.Holling and Ewing (1971) found that changing

the tightness of the coupling of host parasiteequations gave insights into the stability proper-ties associated with increased efficiency. Perhapsa differential equation approach could givefurther insights into the subtle relationships inresource use and efficiency.

Figure 6 obviously invites a topologicalapproach to modelling resource use. Topologycan be very graphic and general in the insights itgives. Topological models announce instabilities.There may be instabilities that have escaped ourlogic and interpretation of examples, and atopological analysis might find them. Topologysays things like, If the system keeps going in thisdirection it will sooner or later encounter thisinstability. All this is helpful but topology willtell us little about where in particular theinstability is and what are the details of goingover the edge. By contrast, differentialequations do not anticipate instabilities well inprinciple, but they do give a good account of thedemise as it is happening in the part of the spacewhere the surface folds.

INTERACTING HIERARCHIES

Thus far, we have shown a space in whichcomplex systems change in a patterned fashion.A complication is that different systems withdifferent models and narratives are going to read

the very same place at the very same time asbeing different. In real time, in a habitat,biological or social systems interact amongstthemselves. How that interaction works outdepends on how the various exemplar systemsinterpret and respond to the narrative of the other

systems. The different narratives will cause therespective systems to read the selfsame materialsituation differently. Biosocial interactionamounts to the interaction of different narratives.The outcomes of such interactions are not readilypredictable from comparing high and low gain inprinciple because the detailed happenstance ofthe interaction is what counts. Harper performedexperiments where he observed the populationparameters of aquatic plants, which would bereadily interpretable as being characteristicallyhigh or low gain: instantaneous growth rate

and biggest daily increment can be easily seen ashigh gain characteristics, whereas maximumstanding crop suggests a low gain adaptation(Clatworthy and Harper, 1962). In our situationthese parameters would be the parameters ofefficiency and capacity to degrade resources.When Harper set his different species incompetition, none of the parameters of growthin monoculture predicted the outcome exactly.The winner simply had a trick up its sleeve thathappened to beat the losing competitors. Themessage in the present argument is that, generaltrends notwithstanding, the winner in a contestof high versus low gain strategy will come fromthe incidental interactions of narratives in theparticular situation.

The formal space we have erected is verylabile, not just because of the differencesbetween the species in play, but also becausethe scientist may validly interpret the behaviourof a certain species in several ways. As analternative to the discussion of high gain above,we can see cockroaches themselves as the high

gain players. Cockroaches are much less efficientthan termites in exploiting wood, and so appearto be making much less of an investment. Lowerinvestment and inefficiency in digesting recalci-trant resources might be seen as more high gainthan termites in good wood, inefficiency andwaste being high gain hallmarks. That wouldmove wood eating cockroaches to the top of the


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high gain peak in Figures 7 and 8. The increasedcapacity to degrade wood with more sophisti-cated colonies and gut flora in termites could betaken, not as opening the door to high gainexploitation of wood, but as specialization inincreasing efficiency of consumption in a move tolow gain exploitation of wood more efficientlythan roaches. The roaches and termites are doingthe same thing we described before, but we aregiving it a different interpretation. On theresponse surface, this argument moves themfrom the one adaptive peak to the other(Figure 8). Thus the position of a given species

occupies in our space depends very much howwe interpret the other species or the othersocieties to which comparison is being made.

The intellectual manoeuvre of moving cock-roaches from entry status to high gaining exposesa more general pattern. If the formerly high gaintermites move to the low gain peak, one can askwhat happens to the efficient wood eaters that

gathered low quality wood in a manner that putthem on the low gain peak in the previousinterpretation. The answer provides a generalinsight. Their low gain position on our landscapeunder the shift in the observers viewpoint hasbeen usurped by the termites that live in good

wood (who were formerly seen as high gain).Imagine that the backside of the low gain peak isreplaced by a smaller landscape that is self-similarin a fractal manner to the whole landscape. Thelow gain peak on the whole landscape simplymaps onto the high gain peak of the new smallerlandscape, and a new smaller low gain peakappears closer to the viewer of Figure 8. At theoutset of every low gain cycle, is a high gain peak,where those players who first enter the new lowgain universe have an easier time of it than do thelater players. Thus the beginning of a low gain

cycle may be high or low gain depending on thelevel of analysis (Allenet al., 2009). The low gainpeak on the larger landscape doubles as a highgain peak on the smaller fractal landscape at thestart of a new low gain cycle. The issue is that therelationship between cockroach ecology andtermites that eat good wood is in different termsthan the relationship between termites that eatgood wood and those that eat bad wood. Thefractal pattern is smaller because the return on agiven unit of consumption is smaller, even thoughin aggregate efficient more low gain consumptionas a whole actually captures in the end morequantity of resource. This process could go onindefinitely as yet smaller fractal landscapes aregenerated. In this scenario, the super-low gainstrategy of eating soil, occupies the low gain peakon a third even smaller fractal landscape. Fractalsystems represent some fundamental process thatpropagates up scale in terms of efficiency and mayyet be traced down scale in the fractal pattern. Thelower the peaks, the lower return per unit ofactivity as the system becomes more low gain,

despite sometimes there being an increase in longterm resource capture. The general relationshipthat is propagated is the set of high to low gainrelationships that may apply across a range ofdisparate species comparisons.

In social systems there is not natural selectionper se,and so we have more opportunities to seenaked encounters of different resource strategies

Figure 8 When cockroaches are seen as high gaining, theformerly high gaining termites are recast as being efficientwood eaters, and indeed they are relative to cockroaches. Thismoves those formerly high gaining termites to the low gain

peak. The formerly low gain termites that eat rotting woodare then displaced. They appear on a fractally smaller land-scape that presents the whole landscape again on the backsideof the low gain peak but smaller. The high gain peak of thenew small landscape maps onto the old low gain peak. Therelationship between cockroaches and good wood termites isthen simply different in fractal terms from the relationshipbetween high and low gain termites. One could iterate

further smaller landscapes by addressing soil eating termitecoming off the low gain peak of the landscape up one

fractal level


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within one species. If there is head to head use ofan abundant resource then the system that ismore opportunistic and profligate in its use ofresources will generally win out. On the otherhand, low gain players such as peasants canovercome high gain authority, but there must be

lots of them and they must be well led, that is bewell organized (Wolf, 1969). High and low gaineach come with their own costs and it depends onthe details of the encounter and the quirks of theinteraction of the respective narratives as towhich one wins. There is slack and the playingfield is heterogeneous and so one can expectmany ways to get by in a given environment.Even though many outcomes are possible,understanding high and low gain and theirpatterns of transition clarifies most situations.

CONCLUSION

It appears possible to model the transition fromhigh to low gain, but the space is difficult. First,the importance of the decline in local quality(steepness of the slope down) is different all overthe surface. With greater usable resource, a lowgain system consumes on the different timeframe of depletion, relative to a high gain system.There appear to be important places where valueof a resources declines steeply for some systems.Meanwhile elsewhere the surface may be fairlyflat such that changes on the surface matter little.We have tried to capture some of that by thecurvature on the surface. A second wrinkledepends on assertions as to where on thelandscape are the players in any comparisonmade across the surface. Above we calledcockroaches eating wood naive entrants to thespace of using wood, but also saw themalternatively as high gainers, so that the samespecies in the same environment may be seen

differently. The evolution to become termites is indifferent terms than the evolution inside termiteecology. The space appears quite local, indicatingwe have to be very specific as to what is beingnormalized relative to what.

Another challenge in the modelling effort is thethree aspects of resources processing and use:capturing; refining; using (burning). There are

efficiencies associated with all of those phases sothat efficiency is not just one thing. Very lowquality must be captured in great quantities, butonce they are captured there is more that isusable in them if only the system can refine theinputs sufficiently. More efficient degradation of

inputs to capture usable material or energy opensthe door to economies of scale as poorer qualityinputs become worth gathering. Such economiesof scale pay for the increased demands ofefficiency, allowing the system to expand. Deeperdegradation of the same quality input gives moreusable material but only sometimes enough moreto pay for the deeper degradation. In biology,evolution must always be in the black at somelevel, otherwise there are no selective pressuresfor increased efficiency. Research and develop-ment (R&D) as a conscious investment affects the

time to start making profit in social systems, butone can get too clever by half. The door is open inthat situation for failure of return on investmentand viscous diminishing returns. It is diminish-ing returns on high complexity and low-gainefficiency that causes complex societies tobecome vulnerable to collapse (Tainter, 1988).In the end, increases in efficiency in one realm donot match demands for efficiency in other partsof the capture/refine/burn triplet. Great effi-ciency in degradation, as occurs in soil eatingtermites, cannot be matched by the huge expenseof processing such huge amounts of material.

The ultimate utility of this whole scheme is inhow it informs the investigator of a set of options,first for change inside a hierarchy, and second forchange in an interaction between hierarchies,between players in a resource space. While themodelling appears tidy and reasonable, in theend there is the messiness of particular details ofspecific interactions in particular times andplaces. At this point, we appear to have aframework wherein we can do the natural history

of biological and social systems with regard toresource use. After some further research effort,we will learn what to expect from putting specificsystems onto our resource surface. The nextadvance will take advantage of those expec-tations. At this point we have a primitiveunderstanding of resource use, but at least wehave a framework in which to inform ourselves.


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##1. Integrating Economic Gain in Biosocial Systems.pdf