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Fostering Industrial SymbiosisWith Agent-Based Simulationand Participatory ModelingDavid F. Batten

Keywords:

coevolutionary learningcompanion modelingcomplex systems sciencecomplexityeco-efficiencyindustrial ecology (IE)

Summary

The sciences of industrial ecology, complex systems, and adap-tive management are intimately related, since they deal withflows and dynamic interdependencies between system ele-ments of various kinds. As such, the tool kit of complex sys-tems science could enrich our understanding of how industrialecosystems might evolve over time. In this article, I illustratehow an important tool of complex systems science—agent-based simulation—can help to identify those potential ele-ments of an industrial ecosystem that could work togetherto achieve more eco-efficient outcomes. For example, I showhow agent-based simulation can generate cost-efficient en-ergy futures in which groups of firms behave more eco-efficiently by introducing strategically located clusters of re-newable, low-emissions, distributed generation. I then explainhow role-playing games and participatory modeling can buildtrust and reduce conflict about the sharing of common-poolresources such as water and energy among small clusters ofevolving agents. Collective learning can encourage potentialindustrial partners to gradually cooperate by exchanging by-products and/or sharing common infrastructure by dint of theirclose proximity. This kind of coevolutionary learning, aided byparticipatory modeling, could help to bring about industrialsymbiosis.

Address correspondence to:Dr. David F. BattenCSIRO Marine and Atmospheric ResearchPrivate Bag 1, Aspendale, Victoria 3195,[email protected]/index.php?page=profiles&profile=10

c© 2009 by Yale UniversityDOI: 10.1111/j.1530-9290.2009.00115.x

Volume 13, Number 2

www.blackwellpublishing.com/jie Journal of Industrial Ecology 197

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Introduction

Traditionally, business, the community, andthe environment have been viewed as separatesystems, operating independently of—even in op-position to—one another. For example, indus-trial systems tend to emphasize the independenceand competitiveness of firms, whereas ecologicalsystems emphasize interaction and interdepen-dence. Awareness of the real interdependenciesbetween these systems is increasing, however, andthis highlights the need for a systems-orientedframework that protects the natural environmentwhile improving business performance. Industrialsymbiosis is evolving to unite the needs of indus-trial and natural systems. By working together, acommunity of firms can enjoy a greater collec-tive economic and environmental benefit thanthe sum of the benefits each would realize if itstrove to optimize its own performance alone.

A close relationship exists among industrialsymbiosis, complex systems science, and theadaptive management of natural resources. All ofthese involve interdependencies between systemelements of various kinds. Our world’s ecosys-tems are inherently difficult to manage success-fully because of the complexity and uncertaintyassociated with their ongoing evolution. Muchof this complexity and uncertainty stems fromhuman sources. For example, economic agentsin a market or social agents in a community arecontinually making decisions about whether andhow to work together in a competitive or a coop-erative manner. What they decide has importantimplications for the biosphere in which we alllive. Thus, it is imperative that we expand ourlimited knowledge about how and why peoplechoose to interact with each other in one way oranother and how their collective decisions affectour biosphere and its ecosystems as time passes.

Fortunately, complex systems science can helpto deepen our currently primitive understandingof how human ecosystems might grow and changeover time. Complex systems science is sufficientlydifferent from normative science that we mayview it as a new kind of experimental science.Traditional observation and experimentation arestill alive and well, of course, but they have beenjoined by a new, experimental breed of computa-tional science known as simulation.

In this article, I illustrate how two tools ofcomplex systems science—agent-based simulationand participatory modeling—can help researchersto identify how various participants in an indus-trial ecosystem could work together to achievemore eco-efficient outcomes. For example, I showhow agent-based simulation can generate alter-native energy futures in which groups of firms be-have more eco-efficiently by introducing strategi-cally located clusters of renewable, low-emissions,distributed generation. Also, I explain how role-playing games (RPGs) and participatory model-ing could support some of the broader goals ofindustrial symbiosis—helping to identify, buildtrust among, and evolve small clusters of agentswho gradually discover the mutual benefits ofcollective learning, exchanging by-products andsharing common infrastructure by dint of theirclose proximity.

Like Axtell and colleagues (2001), I arguethat the general problem of agency—the be-havior of intelligent individuals facing boundedrationality, imperfect incentives, and limitedinformation—lies at the very core of industrialecology (IE). The phenomena studied in IE orig-inated from the recognition of some economicexternalities and departures from socially opti-mal behavior by industry and consumers, due toinappropriate incentives, distorted markets, andimperfect regulations. Two important potentialroles for agent-based simulation and participatorymodeling in IE could be, therefore, (1) to explic-itly explore alternative (e.g., eco-efficient) rulesand incentives that behaviorally realistic clus-ters of agents could face in empirically credibleenvironments, and (2) to instill trust and coop-eration among agents who possess and exchangeonly limited information.

In the next section, I examine some method-ological aspects of agent-based simulation as aforerunner to looking at ways industrial ecologistsmight exploit such tools to identify eco-efficientclusters of industrial symbionts. In later sections,I treat industrial symbiosis as emergent system be-havior of a complex industrial system and thenturn to a discussion of what kinds of electric powerand water systems may suit clusters of potentialindustrial symbionts. Because electricity marketsare complex adaptive systems, agent-based simu-lation can provide a flexible means of exploring

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the influence that the repetitive interaction of acluster of market participants exerts on the evo-lution of the system as a whole. For such explo-rations, I make use of an agent-based electricitymarket model known as the National ElectricityMarket Simulator (NEMSIM). Although NEM-SIM was designed for Australia only, my resultssuggest that greater use of distributed generationwould be consistent with the ambitions of indus-trial ecology.

Recently, French and other European re-searchers attempted an iterative, participatoryapproach to multiagent problems, using bothRPGs and participatory agent-based simulation.The aim was to build trust among a group of stake-holders and improve their understanding of thealternative evolutionary outcomes that are possi-ble when they interact with each other and theirenvironment. Sometimes known as companionmodeling, this approach ensures that stakehold-ers participate fully in the construction of modelsto improve the models’ relevance, establish trust,and increase model use for the collective assess-ment of scenarios. This facilitates shared learningand collective decision making through interdis-ciplinary and “implicated” research to strengthenthe adaptive management capacity of local com-munities. An example is discussed later in the ar-ticle, suggesting that the field of industrial ecol-ogy could be better equipped to deal with theincreased complexity of integrated eco-industrialpark management problems if it makes greateruse of the companion modeling approach amongprospective park tenants and stakeholders.

A Primer on Agent-BasedSimulation

Since the release of Conway’s Game of Life,cellular automata (CAs) have been used as mod-els in many areas of the physical and environ-mental sciences, biology, mathematics, and com-puter science as well as in the social sciences.They are a useful modeling platform, becausecells on a grid that switch on or off accord-ing to states of neighboring cells can representa host of dynamic phenomena—individuals, at-titudes, or actions, for example. A CA modelsany world in which space can be represented asa uniform grid, time advances by steps, and the

“laws” of that world are represented by a uniformset of rules that compute each cell’s state fromits own previous state and those of its nearbyneighbors.

Once one wishes to develop automata to rep-resent human beings, who are far more complexin their internal processing and their behavior,one enters the world of agent-based simulation(sometimes called multiagent systems). Suchautomata are usually called agents, and there areseveral streams of thought on how the agentsshould be designed, built, and used. Althoughthere is no generally agreed definition of what anagent is, the term usually implies an autonomous,intelligent entity that may interact or communi-cate with other autonomous, intelligent entities(Batten 2000). As with CAs, there are rulesgoverning interactive behavior, and the agents“operate” on one or several environments ofsome sort.

The agents emanating from the literatureon (distributed) artificial intelligence oftencorrespond to self-contained software or hard-ware units (e.g., robots) that control their ownactions on the basis of their perceptions of theiroperating environments. Multiple agents aredesigned to work together to achieve a desiredgoal. Typically, their goal is prespecified, andthey are engineered to achieve it with a very lowerror tolerance. Although purposive, systems inwhich human agents interact with ecologicalsystems, for example, often display open-endedoutcomes. Here the collective behavior isunknown in advance but emerges during thesimulation. Some of the emergent outcomescan be unexpected and undesirable (Perez andBatten 2006). The field of artificial life has beenthe source of inspiration for this open-endedkind of simulation (Langton 1995). Imbuingagents with beliefs, desires, and intentions (BDIagents) is one classical approach to human agentrepresentation within a computer.

In agent-based simulation, rules govern-ing agents’ behaviors can range from simple“if−then” statements to quite sophisticated ma-chine learning algorithms (e.g., genetic algo-rithms) that provide agents with a learning ca-pability to modify and improve their behaviorduring the simulation. Parameters of the modelare set to represent a real situation of interest,

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and the model is run for several hundreds of itera-tions, until a satisfactory solution is found. Whenpossible, simulation models make use of histor-ical data to ensure that they can replicate thequalitative behavior of the real system. Replica-tion of historical outcomes does not mean thatthe model is a reliable one for exploring futurescenarios, however. Data mining is also used toensure that agents behave in ways that realisti-cally depict how individual decisions are made inthat type of system.

It is important to note that agent-based sim-ulations can provide valuable information aboutthe dynamics of the real worlds that they em-ulate. Complex systems scientists see them asmore useful than equations-based methods in aworld of multiple possible futures, partly becausethey are built synthetically “from the bottom up”(Epstein and Axtell 1996). As a variety of agentsinteract within a social simulation, for example,the results show how their collective behaviorsgovern the performance of the entire system—forinstance, the emergence of a successful product,a congested area of traffic, or a polluted water sys-tem. This is of benefit to stakeholders, becausethey may see a role for themselves (as agents) inthe simulation as well as an opportunity to learnfrom the simulated outcomes.

Agent-based simulations are also powerfultools for “what if” scenario analysis. As cer-tain agents’ characteristics (i.e., behavioral rules)change, the impact of the change can be seen inthe simulation’s collective results. These adaptivelearning features give agent-based simulation anedge over more traditional mathematical mod-eling and optimization methods. The simula-tion sometimes generates outcomes or strategiesthat a scientist or stakeholder might never haveimagined.

Participatory approaches that make use ofagent-based simulation have developed rapidly inEurope during the last decade. Sometimes knownas companion modeling, they use RPGs and par-ticipatory agent-based simulation to build trustamong a group of stakeholders and improve thestakeholders’ mutual understanding of the alter-native evolutionary outcomes that are possiblewhen humans interact with one another and withour natural environment (Bousquet et al. 1998;Barreteau 2003; Perez and Batten 2006).

In a later section of this article, I illus-trate the use of participatory agent-based simu-lation and RPGs to facilitate water managementnegotiations in Bhutan. For more detailed ex-planations and discussion of different kinds ofagent-based simulations and their growing con-tribution to the social, economic, and ecologicalsciences, see work by Epstein and Axtell (1996),Axelrod (1997), Batten (2000), Tesfatsion andJudd (2006), Janssen and Ostrom (2006), andPerez and Batten (2006). For a look at the pro-gramming side of agent-based models, see workby Gilbert and Troitzsch (1999).

Industrial Symbiosis asEmergent Behavior of aComplex Industrial System

How could agent-based simulation promoteindustrial symbiosis? In the latter, the overallgoal is to maximize the reuse of by-productsthat are otherwise dumped as wastes, therebyhaving a positive effect on environmental, hu-man, and social capital (Frosch and Gallopoulos1989). As most readers of this journal know,the term symbiosis was borrowed from ecologyand means “living together.” Economic agentsthat can exchange resource streams profitably ow-ing to their close proximity and output-to-inputaffinity are known as symbionts. In the Danishtownship of Kalundborg, for example, four sym-biotic companies—a power station, an oil refin-ery, a pharmaceutical plant, and a gyproc plant—somehow managed to self-organize together overa period of 30 years to provide today’s mutuallyprofitable role model for industrial symbiosis.

Although the Kalundborg story has beenwidely discussed in the literature on eco-industrial parks, it is important to understandhow the four symbiotic Kalundborg agents cametogether in the first place. There was no granddesign, nor did cooperation emerge overnight.Industrial symbiosis coevolved gradually over 30years or more (see Ehrenfeld and Gertler 1997).The manager of Asnaes Power Station has beenquoted as saying that the existing economic in-centives were generally enough to generate mostof Kalundborg’s symbiosis. According to Loweand Evans (1995, 49), Jorgen Christensen, the


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vice president of Novo Nordisk in the 1990s, hasbeen quoted as follows:

I was asked to speak on how we designedKalundborg. We didn’t design the wholething. It wasn’t designed at all. It happenedover time. It’s not the kind of thing that youcan engineer in a moment and drop in place.It takes more time. . . . At the time, we werejust doing what was profitable and what madesense. . . . Until now, we have had no formalorganization, no common board or budget.We do what pairs of us think is a good idea.

Doing what pairs or small clusters of agentsthink is a good idea is an excellent example of self-organization. An important point to note is thatthere is no central controller involved. Many so-cial and technical systems elsewhere in the worldare shaped by a centralized mind-set. Most peopleseem to think that instructions invariably comefrom an external source (e.g., an architect’s build-ing plans). As Mitch Resnick (1999, 4) put it,

At some deep level, people seem to havestrong attachments to centralized ways ofthinking. When people see pattern in theworld, like a flock of birds, they often assumethat there is some type of centralized control.According to this way of thinking, a patterncan exist only if someone (or something) cre-ates and orchestrates that pattern. Everythingmust have a single cause, an ultimate control-ling factor.

By way of contrast, natural systems thatachieve their remarkable structure and formby their own internal processes are called self-organizing systems (Haken 1983; Nicolis andPrigogine 1977). In these open systems, patternand structure emerge by way of interactive pro-cesses within the system, without any overall con-trol from an external instructor or guidance froma master plan. The exciting aspect of this discov-ery is that we are just beginning to realize thatself-organization is responsible for a very broadrange of pattern-formation processes in both liv-ing and nonliving systems, including geographi-cal and industrial systems (Batten 1982).

Formally, self-organization is a dynamic pro-cess in which pattern at the global level of asystem emerges solely from interactions among

its lower level components (or agents). In turn,the global pattern may influence the future deci-sions of agents. The rules governing interactionsamong the system’s components are executed viaonly local information, however, without any ref-erence to the global pattern. In other words, pat-tern is an emergent property of the system, ratherthan a property imposed on the system from out-side by an external agent or controller.

Emergent properties of a system cannot be de-duced from one’s knowledge of the system’s lowerlevel components and the interactions betweenthem. They are higher level, global phenomena.In the terminology of dynamical systems, emer-gent properties are particular attractors of thesystem. A particular pattern is one emergentproperty out of many that self-organizing systemsdisplay, crafted collectively by a specific set ofinteractions among the agents involved.

Clustering, with eco-efficiencies derived frominterenterprise cooperation, can be viewed as anemergent property of a self-organizing industrialsystem that is complex, adaptive, and selective(Batten 1982; Wallner 1999). Firms arranged inclusters are able to achieve synergies and lever-age economic advantage from shared access toinformation and knowledge networks, supplierand distribution chains, markets and marketingintelligence, special competencies, resources, in-frastructure, and support institutions available ina specific locality (Batten and Cook 2008).

Although eco-clustering is a desirable prop-erty of an industrial system, it is merely one ofmany emergent properties that such a system candisplay as it coevolves over time. Several otheremergent properties are less desirable from an en-vironmental viewpoint. Agent-based simulationis a way of exploring the collective behavior thata self-organizing system might display by study-ing a range of emergent outcomes associated withthe many alternative pathways that such a systemmight follow under different conditions—nowand in the future. Then can we begin to identifyconditions under which some of the more desir-able outcomes might be more likely to emerge.

Tuning the rules is an important skill in agent-based simulation. For example, Jorgen Chris-tensen identified several conditions for a newKalundborg to develop (Lowe and Evans 1995):


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Figure 1 The national electricity market (NEM) as a complex adaptive system. OTC = over-the-countermarkets; RECs = renewable energy credits; DG = distributed generation of electricity; GHG = greenhousegas.

• Industries must be different and “fit” eachother.

• Arrangements must be commercially soundand profitable.

• Development must be voluntary, in closecollaboration with the authorities.

• A short physical distance between the part-ners is necessary for economy of transporta-tion.

• Managers of the different plants must allknow and trust each other.

The above conditions can be programmedinto an agent-based simulation model as part ofits set of internal rules. Sensitivity studies couldshow just how close partners might need to bein geographical space, how different yet comple-mentary they need to be in terms of their inputsand output, and so on.

Electricity Systems asPotentially Symbiotic ComplexAdaptive Systems

Nowadays, electricity markets are an evolvingsystem of complex interactions among nature,physical structures, market rules, and partici-pants. They exhibit a high degree of interdepen-dency with other markets—such as those for fuels,carbon dioxide (CO2), renewable energy credits(RECs), capital, and other financial markets (e.g.,

over-the-counter markets)—and with other sec-tors (e.g., industry, households). In brief, theypossess the intrinsic features of a complex adaptivesystem (see figure 1). Participants face risk andvolatility as they pursue their goals, making deci-sions based on limited information and their ownmental models of how they believe the wholesystem operates. A diverse collection of agentsparticipate in this market. They adopt differentstrategies, have different capacities, use differentgeneration technologies, have different forms ofownership, are often located at different sites, facedifferent grid constraints, and must operate in-creasingly in a carbon-constrained marketplace.In summary, their objectives, beliefs, and deci-sion processes vary markedly. Such a diversity ofinputs may be expected to lead to a rich varietyof collective outcomes.

One intrinsic feature of an electricity marketas a complex adaptive system is that it featuresa “largish” number of intelligent agents, reactingto changes in demand (due to weather and con-sumer needs) and supply potential and interact-ing on the basis of limited information. Becauseno one agent is in control, some agents performbetter than others. Some of the common collec-tive outcomes include price volatility, inadequatereserves, network congestion, power failures, de-mand uncertainty, and unacceptably high levelsof greenhouse gas (GHG) emissions. All of theabove are emergent properties that have been


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observed in electricity systems, and all can be gen-erated as endogenous, emergent phenomena inagent-based simulators of electricity systems (see,e.g., Bower and Bunn 2000; Bunn and Oliveira2001; Nicolaisen et al. 2001; Veselka et al. 2002;Batten and Grozev 2006, 2008).

The interactions within an electricity mar-ket constitute a repeated game, whereby a pro-cess of experimentation and learning changesthe behavior of agents in the market (Rothand Erev 1995). Market outcomes depend onhow each agent responds to market design—including rules, market observations, operatingprocedures, and information revelation. It is of-ten the case that particular agents (e.g., generatorfirms) gain more from these markets than oth-ers (e.g., retailers, local communities, or house-holds), partly because they have more opportuni-ties to learn about how to exploit the system andadapt their behavior to changing market condi-tions in profit-maximizing ways. Unfortunately,some of this profit-driven behavior exacerbatesGHG emissions.

Some forms of cooperative behavior by smallgroups of generator companies lead to the exer-cise of market power, causing dramatic price fluc-tuations from day to day (Nicolaisen et al. 2001;Bunn and Oliveira 2003; Hu et al. 2005). Tem-perature variations and network congestion canplay a similar role. Like most industries in whicha large number of agents interact, many of theevolving markets for electric power have becomemore complex.

Economic volatility is one aspect of the prob-lem. The need to mitigate ecological impacts—especially GHG emissions—has also highlightedthe need to develop methods capable of address-ing economic and ecological uncertainties con-sistently within an integrated framework. Wenow believe that GHGs contribute significantlyto global warming. In Australia, the stationaryenergy sector accounts for almost 50% of theseemissions. Electricity generation dominates thissector, with 66% of the emissions, and is thus themajor culprit in terms of total emissions. Yet ifthe key participants in this sector were to partnerin more eco-efficient ways, the amount of GHGemissions could be cut significantly.

Agent-based simulation provides a flexiblemeans of exploring the influence that the repet-

itive interaction of agents exerts on the evolu-tion of the system as a whole. Static models ne-glect the fact that interacting agents have goodmemories, learn from past experiences (and mis-takes) to improve their decision making, andadapt simultaneously to changes in several en-vironments (natural, economic, technical, andinstitutional). Thus, agent-based simulationtechniques can shed light on some of the com-plex, interactive features of electricity marketsthat static equilibrium models ignore.

NEMSIM: The National ElectricityMarket Simulator

NEMSIM is an agent-based simulation modelrepresenting Australia’s national electricity mar-ket as an evolving system of complex interac-tions among human behavior, technical infras-tructures, and the natural environment. Usersof NEMSIM can explore different evolutionarypathways under various assumptions about trad-ing and investment opportunities, institutionalchanges, ecological outcomes, and technologicalfutures—including alternative learning patternsas participants grow and change. In the spiritof industrial ecology, the simulated outcomescan help to identify futures that are more eco-efficient (e.g., that maximize profits in a carbon-constrained future). Questions about sustainabledevelopment, market stability, infrastructure se-curity, price volatility, and GHG emissions canbe explored with the help of this simulation sys-tem (see Batten and Grozev 2006, 2008).

Learning Agents in NEMSIM

The NEMSIM project is part of the AustralianCommonwealth Scientific and Research Orga-nization’s (CSIRO’s) Energy Transformed Flag-ship research program, which aims to developlow-emissions, eco-efficient energy systems. Anoverview of the simulation system is shown infigure 2. In NEMSIM, silicon agents representnatural resource companies, generator firms, net-work service providers, retail companies, the mar-ket operator, and others. In NEMSIM, mostagents simply buy and sell electricity in a sim-ulated environment. A few agents may engage inresource exchanges and recycling of by-products.


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Figure 2 An overview of the National Electricity Market Simulator (NEMSIM). GHG = greenhouse gas.

It is important to note that agents can (and usu-ally do) have different goals. Instead of just maxi-mizing their own profits, some pairs of agents mayrecognize the benefits of trying to maximize jointprofits and minimize CO2 emissions by workingtogether. Other agents may be willing to sharecommon infrastructure (e.g., distributed energyor recycled water) that is eco-efficient or use othernearby firms’ by-products as feedstock for theirenergy generation. It is among these more inno-vative agents that the potential for symbiosis lies.

NEMSIM treats agents as being uniquely in-telligent, making operational and strategic deci-sions using the individual information availableto them. It is important to note that they areadaptive, learning to modify their behavior to re-alize their goals more efficiently. Learning algo-rithms can allow agents to look back (learn fromhistorical performances), look sideways (learn toanticipate or cooperate with other participants),and look ahead (take future plans and forecastsinto account).

In an adaptive market, no single agent hascontrol over what all the other agents are doing.Because of the way the market is structured, somemay exert more influence on market outcomes.The overall outcome is not always obvious, be-cause it depends on many factors. NEMSIM pro-vides a platform on which simulated agents in-teract, constrained only by realistic rules and thephysical grid system. Agents’ individual and col-lective behaviors coevolve from the bottom up,sometimes producing unexpected emergent out-comes at the system level.

NEMSIM as a GHG Emissions Calculator

Simulated agent life in NEMSIM unfolds inthree “environments”: first, a trading environmentin which transactions can occur in interlinkedmarkets; second, a physical grid of sites, generationunits, lines, and interconnectors across whichelectricity flows; and third, a natural environment,which provides resources and accumulates GHG


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Figure 3 National Electricity Market Simulator (NEMSIM) greenhouse gas (GHG) emissions calculator :graphical plot for a generator plant.

emissions. Each environment is separate from theagents, which operate on and interact with it.The links and feedback effects between these en-vironments have an important influence on theeco-efficiency of any simulated scenario of thefuture.

NEMSIM estimates the GHG emissions as-sociated with electricity generation of a givensimulation scenario by way of bottom-up ag-gregation. This approach allows quite precisemodeling of the emissions up to the level ofeach generating unit, accommodating a vari-ety of changes in the operational, technologi-cal, company, market, and regulatory values andparameters. The advantage is that such an agent-based framework allows slow changes to accumu-late over long periods as well as sudden changesdue to the emerging events based on the deci-sion making and interactions of the participatingagents.

NEMSIM calculates the GHG emissions dueto electricity generation on a fossil fuel consump-tion basis using fuel-specific (generation tech-nology) emission factors. A set of generationtechnologies are modeled: conventional blackcoal-pulverized fuel, conventional brown coal-pulverized fuel, natural gas simple cycle, and soforth. The set of generation technologies is spec-

ified in an XML input file that allows changes togeneration technologies and their parameters tobe introduced easily.

The main attributes of each generation tech-nology are the emission GHG factor and the netenergy efficiency—how much of the embodied en-ergy of the fossil fuel is transformed into elec-tricity. One can also define the net energy ef-ficiency for a selected generating unit to allowflexibility over the longer term, when the effi-ciency may change. This approach allows easiercomparison between different plants, companies,and regions. Its accuracy is inferior, however, as itdoes not include indirect emissions that usuallyshow moderate variability by region, company,and technology.

A typical example of a NEMSIM output win-dow for GHG emissions is depicted in figure 3.This graphical plot displays company data, and(for commercial reasons) the values are illustra-tive only. A regional summary graph of GHGemissions is shown in figure 4. Although thenumbers are only illustrative, other options foroutput windows and reports pertaining to GHGemissions are available within NEMSIM. Foradditional details regarding NEMSIM’s capabil-ities, see work by Batten and Grozev (2006,2008).


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Figure 4 National Electricity Market Simulator (NEMSIM) regional summary table for greenhouse gas(GHG) emissions.

The Eco-efficiency of DistributedGeneration

Does the deregulation of electricity marketspromote eco-efficiency and the wider use ofdistributed generation (DG)? Clinton Andrews(2001) posed such a question recently with re-spect to the so-called introduction of competitioninto the U.S. electric power industry. Andrews’analysis of broad sectoral trends revealed littleevidence of eco-efficiency gains, and his statusreport on “soft-path” DG technologies suggeststhat there are technical and institutional barriersthat add inertia to the system, thereby hinderingsuch eco-efficient innovations.

DG is loosely defined as a set of small-scalepower generation technologies located close tocustomers, where the power is used. It is not anentirely new idea, given that Thomas Edison’svery first power station followed the concept ofDG—produce power near the user. Also, smallgenerators have been used as backup generatorsand on-site power systems for a long time. Todaythe most common means of achieving econom-ical provision of electricity to an entire coun-try is through the model of large power stationscoupled with extensive transmission and distri-bution networks so that power production anduse can be separated by hundreds of kilometers—economically.

Recently, considerable technological progresshas seen the development of much more compet-itive DG technologies (e.g., microturbines, fuelcells, photovoltaics, and wind systems). Poten-tial benefits from these newer DG technologiesare reduced cost, higher service reliability, higherpower quality, and increased energy efficiency(see Pepermans et al. 2005). DG is a promisingsolution for the security of electricity supply, pro-viding distributed and diverse energy source in-frastructure. Some types of renewable, distributedgeneration can also provide a significant environ-mental benefit in terms of reducing GHG emis-sions. Not all DG is environmentally beneficial,however, given that smaller generating plants areusually less efficient in terms of thermal efficiencyand fuel utilization.

Fortuitously, DG can be of help to electric-ity consumers and energy utilities. In combina-tion with demand-side management, it can offerelectric utilities alternatives to system capacityinvestment in large central generation, transmis-sion, and distribution (Hoff et al. 1996). Becauseit can help to manage peak load demands, it couldreduce price volatility and companies’ marketpower. Deregulation is an additional reason forthe growing level of interest in DG.

Network integration of DG is a very com-plex issue, significantly different from the con-ventional networking of generating units and


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transmission lines (Ackermann et al. 2001). Forexample, the traditional distribution network in-frastructure allows power to flow only in one di-rection. In the case of DG, the power should beable to flow in both directions—to and from thecustomer. This is clearly more eco-efficient. Onehas to take into account the additional cost ofthe network protection system, which has to bedesigned and implemented, when assessing eco-nomic impacts and conducting reliability analysisof DG.

Potentially, small clusters of low-emissionsDG can serve local communities and eco-industrial developments more reliably than large,centralized power stations, while also reducingGHG emissions and earning income by reexport-ing some power to the main electricity grid. Se-rious uptake of DG, however, will require newclusters of small generating units with recyclablefeedstocks, new grid systems, new markets, newparticipants (e.g., aggregators), and new rulesthat are not as inhibiting as those that Andrews(2001) identified.

One way to explore conditions under whichnew industrial clusters of DG could work aheadof their introduction is to use agent-based simu-lation to create “would-be worlds” of new agents,environments, and rules inside a computer andsee what happens as they evolve over time. Ifagents and their actions are allowed to evolveendogenously inside an agent-based, computa-tional simulation model, new industrial clustersof agents that could make more eco-efficient useand reuse of our solid, liquid, and gaseous re-sources could be discovered.

Agent-based models such as NEMSIM canhelp us to identify the advantages and disadvan-tages of DG (e.g., as an anchor tenant for aneco-industrial park [EIP], by providing a reliablelocal and renewable energy source that may beshared by all of the park’s tenants). NEMSIM ag-gregates a number of homogeneous DG units intoclusters of DG. Each cluster may aggregate unitsthat use the same generation technology and pos-sess similar operational characteristics, but theyare not necessarily identical in terms of capacityor manufacturer. Aggregation is based not onlyon similarity of generation parameters but alsoon location (e.g., for a load center). In summary,simulators such as NEMSIM could play an import

role in exploring potentially symbiotic outcomesthat involve the shared use of locally generatedelectric power.

Companion Modeling forIndustrial Symbiosis

Recently, several researchers have advocatedan iterative, participatory approach to multi-agent problems, using role-playing games (RPGs)and participatory agent-based simulation. Theaim is to build trust among a group of stake-holders and improve their understanding of thealternative evolutionary outcomes that are possi-ble when humans interact with our natural envi-ronment (Bousquet et al. 1998; Barreteau 2003;Bousquet et al. 2005; Perez and Batten 2006,chapters 3 and 12). Participatory approacheshave grown rapidly in parts of Europe and arenow commonly known as companion modeling.Typically, researchers join with stakeholders in arepeated, multistage process involving game play-ing and modeling. Specific stages in the processmay include

• field study and data analysis,• RPGs,• agent-based simulation development and

implementation, and• other computational experiments.

Researchers at French Agricultural ResearchCentre for International Development (CIRAD)in Montpellier have developed a companionmodeling platform known as Common-Pool Re-source Multi-Agent Systems (CORMAS), ded-icated to the study of common-pool resourceproblems with the aid of agent-based simulation(see Bousquet et al. 1998; http://cormas.cirad.fr/indexeng.htm). They have undertaken many ap-plications in Africa and Asia, working togetherwith the local stakeholders to develop RPGs andagent-based simulations for practical natural re-source management problems (Bousquet et al.2005). Participatory approaches recognize thatmanagement does not only consist of understand-ing the state of the ecosystem and its dynamicsbut also addresses the social process leading tothis ecological state and those that may lead tomore preferred states. In other words, what are


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Figure 5 The companion modeling process. MAS = multi-agent system.

important are the solutions emerging from theinteractions among the different stakeholders.

Could a companion modeling approach helpto achieve industrial symbiosis among potentialstakeholders in an EIP? Critics of the EIP con-cept often suggest that a primary obstacle to EIPs’formation is the reluctance of tenants to becomeinterdependent. They may not trust one another,may be unwilling to share common infrastructure(e.g., energy or water), or may disagree with EIPregulations. To be effective, an EIP managementsystem must display fundamental respect for theautonomy of each participating company. Thissuggests that, if possible, constraints on tenants’autonomy should be developed via participatoryapproaches, in which each tenant can understandhis or her role, express concerns, and see that hisor her views are taken into account. In this way,trust can be built up gradually and future conflictsavoided.

A fundamental assumption of EIP operation isthat companies can self-organize and self-regulatetheir behavior more effectively than any out-siders, provided information flows and feedbackloops are in place. Principles of self-organizationand self-regulation are central to establishingtrust and dissolving the fear that participationin a community of companies could undercut theautonomy of members. A well-designed environ-mental management system for the eco-park andits members will provide the structures for feed-back that support this self-regulation. An iter-

ative, companion modeling approach may helpto build trust and achieve the required degree ofself-organized regulation.

Building Trust in a New Institution

Gurung and colleagues (2006) used RPGs andagent-based simulation, following the compan-ion modeling method, to facilitate water manage-ment negotiations in Bhutan. Their methodologyis interesting for industrial ecologists because itdemonstrates a way of resolving conflicts such asthe sharing of common-pool resources by estab-lishing a concrete, self-regulated agreement andalso creating an institution for collective man-agement. Their conceptual model begins with anRPG. Stakeholders play the game, thus validat-ing the proposed environment, the behavioralrules, and the emergent properties of the game.It is then relatively easy to translate the RPGinto a computerized simulation model that al-lows different scenarios to be explored. Later, thestakeholders themselves can create a managerialinstitution.

The steps in the companion modeling processare depicted in figure 5 and consist of three stagesthat can be repeated as many times as needed:

1. Field investigations and a literature searchon the observed world supply informationare performed to help generate explicit hy-potheses for modeling.


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2. Modeling converts existing knowledgeinto a formal tool, to be encapsulated asa simulator.

3. Simulations are conducted according to anexperimental protocol either on a com-puter or through an RPG, to challenge theformer understanding of the system and toidentify new key questions for new focusedinvestigations in the field.

This process is called companion modelingbecause it is often used in the mediation pro-cess (the social dimension of the companion)and it coevolves with the social process (tempo-ral and adaptive dimensions). A crucial questionwas how to use these models in an interactive waywith stakeholders. Given that an RPG or simu-lation is a specific kind of representation amongother possible ones, albeit a simplified one, Gu-rung and colleagues (2006) decided to present itin an explicit and transparent way, so as to avoid,as much as possible, the “black box effect,” whichcan annoy stakeholders.

Comparison of the lessons learned from thetwo gaming sessions held for the Bhutan projectindicated that, over the period between the twoRPGs, community members informally discussedand even assessed the impact of their decisionsabout resource sharing. One player reported tak-ing part in discussions on water sharing beforeattending the second RPG session. In both cases,the importance of sharing water was the most im-portant lesson for all players. Compared with thelessons learned in the first RPG, 90% of the play-ers in the second game learned of the need for andbenefit of water management and sharing (70%water sharing, 10% canal management, and 10%on-farm water management). This shared learn-ing is an important output from an RPG andshould have a dramatic influence on the way theplayers involved will behave in the future.

The farmers of two conflicting villages will-ingly accepted the RPG as a means of express-ing their concern about water sharing. Resultsfrom the game sessions confirmed that the RPGhad been effective for collective learning, learn-ing about the problem, and building trust aboutthe process. The game outputs fostered better un-derstanding of the water-sharing problem and itsimpact, and the use of three particular scenar-

ios created a friendly environment for the activeparticipation of the players.

The information generated by the diagnosticstudy and the RPGs was then used to implementthe agent-based simulation model, the objectivebeing to simulate alternative scenarios of how thefuture could unfold. Suitable entities were identi-fied, and an initial class diagram was constructedto show all model entities, attributes, methods,and their structural relationships. Full details ofthis class diagram and the model can be found inthe article by Gurung and colleagues (2006).

Some critical findings were that the RPG fa-cilitated self-motivating and nonconfrontationalinteractions among the players, that the stake-holders’ (here farmers’) knowledge and under-standing of water sharing increased significantlybetween the two games, and that the collectivemode of communication facilitated better andmore frequent exchange of water. The role of thecomputerized simulation model was to explorescenarios that were collectively identified dur-ing the RPG sessions by the stakeholders. Gu-rung and colleagues (2006) concluded that theRPG may be looked upon as an open simulationmodel, in the sense that the environment is de-fined together with the agents, their roles, someof their actions and interactions, and the overallschedule of the agents’ interventions. A degreeof freedom was left to the players, as it would bein real-life situations of interest (e.g., potentialindustrial tenants for an EIP).

Some Ramifications for IndustrialSymbiosis

What could companion modeling bring toindustrial symbiosis? In participatory RPGs andcomputer simulations, an artificial environmentis conceived from observation of real-life situa-tions, several types of players are identified, anda schedule of activities or events is planned. Inthe context of an EIP, for example, the artificialenvironment might include a chessboard or gridrepresenting the park site (without any buildingsor tenants), key infrastructure networks (waterand energy), and the atmospheric system. Play-ers could include potential tenants (with sim-ple facility profiles of inputs, outputs, and by-products), a government agent or regulator, and


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a park supervisor. The game or model itself couldfocus on some basic exchange processes (e.g., ma-terial inputs, by-product exchanges, or energy andwater sharing) that could be measured in termsof a simple joint index of profitability and CO2

emissions.For the resulting model to be “playable,” it

cannot consider numerous time steps or dozensof players. Starting with the model implies thatit will be simple and will not include many de-tailed processes. Conversely, starting with theRPG provides an immediate test of the realismof the model of each individual’s behavior and ofthe emergent process. When the players play thegame, they can comment on the actions plannedfor them. They validate the behavioral rules, but,more generally, they validate the model. They ob-serve and comment on properties of the exchangesystem emerging from the interactions among theplayers (e.g., number of supply, by-product, orutility synergies), and they can comment on thelinks between various organizational levels.

The rules for decision making in the gameshould be the same as those in reality. For exam-ple, by-product exchanges should result primarilyfrom pairs of players thinking that an exchangeis a good idea. The park supervisor player canevaluate these ideas in terms of the joint indexof profitability and CO2 emissions. Refinementsto the basic game could include a system for auc-tioning by-products run by an auctioneer.

Transcription of the model from the RPGimplementation to computer simulation is of-ten very easy. The challenge is to implementthe decision-making process of pairs of players.The KISS (“keep it simple, stupid”) principle ap-plies for implementation of the decision-makingprocess. The objective is not to implement a de-tailed decision-making process involving a lot ofdata and complex calculation but rather to seehow simple behaviors lead to complex phenom-ena an a wide range of different outcomes. Theexample discussed in this article, along with oth-ers reported by the CORMAS group, show howsimple decisions, if combined with different in-teraction protocols and different networks, oftenlead to complex outcomes that are still meaning-ful for the stakeholders. A tentative conclusionis that very simple models with a low degree of

realism can be very useful and effective among acollection of motivated stakeholders.

RPGs or simulation models can be particularlyuseful in situations where researchers have at-tempted to promote discussions among key stake-holders without success, because such models canserve as mediator tools. For example, issues suchas water or energy sharing between prospectivetenants in an EIP can cause problems. In this sit-uation, the computerized model can be designed(or redesigned) in a way that explores scenarioswith and without water or energy sharing, withthe output table comparing the environmentaland economic costs and benefits of these.

In the work by Gurung and colleagues (2006),three model scenarios led to the conclusion thata managed watershed would increase benefits forall the stakeholders. Later, stakeholders (farm-ers, governmental organizations) participated indetailed discussions to reach an agreement onthe establishment of a watershed committee. Inother words, the first steps taken by stakeholdersresulted not in the sharing of water among vil-lages (which was the topic of the game) but inthe creation of a watershed body. This shows howthe game and the model are taken for what theyare: mediation tools. Stakeholders are in con-trol of the negotiation process and are able tosee the difference between the model and reality.Thus, the actual risk of manipulation, which isa potential danger of this open-ended method,is low.

According to the stakeholders themselves,some of the benefits they realized by participatingin RPGs were as follows:

• The game setting promotes dynamic discus-sions among players.

• Only after the game session ends are thereal implications of the game fully realized;thus, the spontaneous actions and reactionsgenerate new ideas that would not emergeotherwise.

• Players can use the RPG as means of com-municating with their counterparts.

• The RPG seems, at first, like child’s play,but it soon becomes a very strong tool tostudy complex interactions and collectivedynamics.


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• Alternating game sessions with plenary ses-sions allows people to relate the game totheir real-life situation.

• Some local experience and knowl-edge are necessary for facilitating theprocess.

• The RPG can be used as a platform for con-flict resolution.

• The RPG effectively provides equal oppor-tunities for all strata of players to participatein the game.

In summary, the main aim of the companionmodeling approach is to develop RPGs and sim-ulation models integrating various stakeholders’viewpoints and to use them within the context ofthe stakeholders’ platform for collective learning.This modeling approach ensures that stakehold-ers participate fully in the construction of mod-els to improve the models’ relevance, establishtrust, and increase model use for the collectiveassessment of scenarios. The general objectiveis to facilitate dialogue, shared learning, and col-lective decision making through interdisciplinaryand “implicated” research to strengthen the adap-tive management capacity of local communities.By adopting such an approach, the field of indus-trial ecology could be better equipped to deal withthe increased complexity of integrated EIP man-agement problems, their evolving and continuouscharacteristics, and the possible impacts of tech-nological progress and changes in the number ofpark tenants.

Conclusions

As stated earlier, the sciences of industrialecology, complex systems, and adaptive manage-ment are intimately related, as they deal withflows and dynamic interdependencies betweensystem elements of various kinds. As such, thetool kit of complex systems science could en-rich our understanding of how industrial ecosys-tems might evolve over time. In this article,I have shown how an important tool of com-plex systems science—agent-based simulation—can help to identify potential elements of an in-dustrial ecosystem that could work together toachieve more eco-efficient outcomes. For exam-ple, I showed how agent-based simulation can

generate cost-efficient energy futures in whichgroups of firms behave more eco-efficiently by in-troducing strategically located clusters of renew-able, low-emissions, distributed generation. ThenI explained how RPGs and participatory model-ing can build trust and reduce conflicts aboutthe sharing of common-pool resources such aswater and energy among small clusters of evolv-ing agents.

Eco-industrial development projects based onindustrial symbiosis are learning systems in whichagents (if motivated) strive to gain economic andecological benefits by clustering some of their ac-tivities together in geographical space and arrang-ing mutually beneficial exchanges. Such learningprocesses are not evolutionary but coevolution-ary and self-organizing (Batten 2000). They canencourage potential industrial partners to gradu-ally cooperate by exchanging by-products or shar-ing common infrastructure by dint of their closeproximity. This coevolutionary learning process,aided by participatory modeling, could help tobring about industrial symbiosis.

The purpose of agent-based simulation mod-els and RPGs (like those discussed in this arti-cle) is not to predict the future but to generateand explore alternative futures that might de-velop under different conditions, thus affectingkey stakeholders in different ways. Such modelscan explore various “what-if” scenarios under dif-ferent eco-efficiency goals. Also, they can showthe possible evolutionary trajectories of a givenscenario under given conditions.

For example, the introduction of more DG ofelectricity into the marketplace involves a tran-sition from the current paradigm of the centrallydispatched electricity grid to new, more decen-tralized ones. This may require new markets, newbrokers, new technology, and new grid structures.Some of these distributed strategies can reducethe level of GHG emissions considerably. NEM-SIM and similar simulators are generative toolsthat can identify the transition states needed toreach specific final states, including more eco-efficient ones. In an eco-industrial setting, twoimportant challenges for agent-based simulationtools are (1) to achieve realistic representation ofthe adaptive behaviors of pairs of company agentswith the help of a suitable learning algorithm and(2) to “validate” the model, mainly by collective


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stakeholder approval in a qualitative world wherequantitative validation is mostly inappropriate.

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About the Author

David F. Batten coordinates CSIRO’sAgent-Based Modelling Working Group (www.csiro.au/science/CABM.html) and Theme 5(Cellular Automata, Agent-Based Modelling andSimulation) of the Australian Research Coun-cil’s Complex Open Systems Research Network(COSNet; www.complexsystems.net.au/). Also,he manages Temaplan Australia, part of an in-ternational group of small, scientific consultancyfirms specializing in industrial ecology and infras-tructure analysis for industry and government.


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