DAVID LEVY. Department of Management, lJniversity of Massachusetfs - Boston ... application of chaos theory to a business ituation is illustrated ... Although we all recognize the .... endogenous in a more complex and comprehensive one.
StrategicManagementJournal, Vol. 15, 167-178(1994)
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THEORY, CHAOSTHEORYAND STRATEGY: APPLICATION. AND MANAGERIAL IMPLICATIONS DAVIDLEVY
Department of Management, lJniversity of Massachusetfs- Boston Boston, Massachusetts, U.S.A.
This paper argues that chaos theory provides a useful theorectical framework for understanding the dynamic evolution of industries and the complex interactions among industry actors. It is argued that industries can be conceptualized and modeled as complex, dynamic systems, which exhibit both unpredictability and underlying order. The relevance of chaos theory for strategy is discussed, and a number of managerial implications are suggested. To illustrate the application of chaos theory, a simulation model is presented that depicts the interactions between a manufacturer of computers, its suppliers, and its market. The resuhs of the simulation demonstrate how managers might underestimate the costs of international production. The paper concludes that, by understanding industries as complex systems, managers con improve decision making and search for innovative solutions.
INTRODUCTION One of the enduring problems facing the field of strategicmanagmentis the lack of theoretical tools available to describe and predict the behaviorof firms and industries.For example, even if we know that oligopolisticindustriesare likely to experienceperiodsof stabilityalternating with periods of intense competition, we do not know when they will occur or what will be the outcome. Similarly, it is almost impossibleto predict the impact of the advent of a new competitor or technology in an industry. The fundamentalproblem is that industriesevolvein a dynamicway over time as a result of complex interactionsamong firms, government, labor, consumers, financial institutions, and other elementsof the environment. Not only does industry structure influence firm behavior, but
Key words: Chaostheory, simulation,international, supply chain
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firm behavior in turn can alter the structure of an industry and the contours of competition. Existing theoretical models, however, tend to assume relatively simple linear relationships without feedback.Indeed,manystrategictheories attempt to classify firms and industries and to describe appropriate strategiesfor each class; examplesinclude the Boston ConsultingGroup matrix for resource allocation and Bartlett's classificationof international strategies(Bartlett and Ghoshal,1989).Although thesemodelsare basedon recurrent patternsthat we recognizein the real world, there are usually far too many exceptionsfor the modelsto havemuchpredictive value. Chaostheory, which is the study of nonlinear dynamic systems, promises to be a useful conceptualframework that reconcilesthe essential unpredictabilityof industrieswith the emergenceof distinctivepatterns(Cartwright,1991). Although chaostheory was originally developed in the contextof the physicalsciences,Radzicki (1990) and Butler (1990) amongstothers have
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noted that social, ecological, and economic have confoundedmathematiciansfor years. A systemsalsotend to be characterized by nonlinear similar problem afflicts someonewho is trying relationships andcomplexinteractionsthat evolve to calculate the path of an object in the dynamicallyover time. This recognitionhas led gravitationalpull of two or more bodies.While to a surgeof interestin applyingchaostheory to we canusesimpleNewtonianequationsto predict a numberof fields,includingecology(Kauffman, the orbits of planetsaroundthe sun with a high l99l), medicine(Goldberger,Rigney and West, degreeof accuracy,the mathematicsinvolvedin 1990) internationalrelations (Mayer-Kressand the caseof two or more 'suns'becomeintractable. Grossman,1989),and economics(Baumol and The problem can be illustratedon a terrestrial Benhabib, 1989; Kelsey, 1988).t Despite the level by observingthe motion of a simpletoy, a apparentapplicabilityof chaostheoryto the field metal ball suspended over two or more magnets. of businessstrategy',there has been surprisingly The ball will tracea seriesof patternsthat never little work in this area. exactlyrepeatthemselves, and yet aie not totally This paperintroducesreadersto chaostheory, random. and discusses its relevanceto the socialsciences The paradox here is that the motion of the in generaland to aspectsof strategyin particular, metal ball is driven by the same Newtonian including planning and forecasting, and the equationsas the well understoodcaseof a single impact of changeon firms and industries.The gravitationalattractor.If we knew preciselythe applicationof chaostheoryto a business situation original location, speed, and direction of the is illustrated using a simulation model of an ball, we ought to be able to predictits path with internationalsupply chain. The model, which is a reasonabledegree of accuracy. How is it based on the author's researchinto the supply that deterministic systems can give rise to chain of a California-based computercompany, unpredictability?The explanation is that tiny depicts the complex interactionsbetween the variationsin the motion of the ball are magnified firm, its suppliers,andits markets.The simulation every time it swingsby one of the magnets.It resultsillustrate the managerialimplicationsof is a combination of this divergenceand the applyingchaostheory to strategicmanagement. repeatedinteractionsthat give rise to 'chaotic' The model demonstrateshow small disruptions behavior. Mathematically,chaotic systemsare to the supply chain interact to make the chain representedby differentialequationsthat cannot highly volatile, imposingsignificantcostson the be solved,so that we are unableto calculatethe organization.Although forecastingis very difficult stateof the systemat a specificfuture time 't'. in the supply chain, distinct patterns emerge At the limit, chaoticsystemscan becometruly which are useful for managers.The simulation random. A toss of a coin or the roll of a die also shows that by understandingthe supply are, in theory, deterministicsystems,but yield chainas a complexdynamicsystem,it is possible more or less random outcomes.Not only is it to identify managerialapproachesthat lower the impossibleto toss a coin twice in exactly the cost of operatingthe supplychain. sameway, but on each toss the coin is subject to slightly different air currents, themselvesa result of turbulent air flow (Ford, 1983). AN INTRODUCTION TO CHAOS To overcomethe problem of intractabledifferTHEORY ential equations,researchers usuallymodel systems as discrete difference equations, which Chaostheory is the studyof complex,nonlinear, specifywhat the state of the systemwill be at dynamic systems.The field was pioneered by time 't* 1' given the stateof the systemat time Lorenz (1963), who was studying the dynamics 't.' Computer simulationscan then be used to of turbulent flow in fluids. Although we all seehow the systemevolvesover time. recognizethe swirlsand vorticesthat characterize One of the major achievements of chaostheory turbulentflow, the complexitiesof turbulentflow is its ability to demonstratehow a simpleset of deterministicrelationships canproducepatterned yet unpredictableoutcomes. Chaotic systems t See also special issues of. Journal of Economic Theory, never return to the same exact state, yet the 40(1), 1986, and Journal of Economic Behavior and Organization,8(3), 1987. outcomesare boundedand createpatternsthat
Chaos Theory and Strategy embody mathematicalconstants(Feigenbaum, 1983).It is the promiseof findinga fundamental order and structurebehind complexeventsthat probablyexplainsthe greatinterestchaostheory has generatedin so many fields.
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that reflectvery complexunderlyingrelationships include the interaction of several potentially chaotic systems;crop prices, for example, are influenced by the interaction of economic and weathersystems.The searchfor a simpleset of equationsto explain complex phenomenamay be a futile attempt to construct grand 'metaChaostheory and the social sciences theory,' a projectthat is rejectedin the postmodsee ern paradigm. The applicationpresentedhere Proponentsof chaostheory enthusiastically signsof it everywhere,pointing to the ubiquity usesa different approach;field study researchis of complex,dynamicsystemsin the socialworld used to derive a set of relationships among and the resemblance betweenpatternsgenerated variables and the influence of external systems by simulatednonlinear systemsand real time is modeledprobabilistically,a methodsuggested series of stock exchangeor commodity prices. by Kelsey(1988). Social and physicalsystemsalso differ in the From a theoreticalperspective,chaostheory is congruouswith the postmodernparadigm,which source of unpredictability. In the physical questionsdeterministicpositivismas it acknowl- world, unpredictability arises due to many edgesthe complexityand diversityof experience. iterations, nonlinearity, and our inability to While postmodernismhas had a profound influ- definestartingconditionswith infinite precision. ence on many areas of social scienceand the In the social world, far less accuracy is humanities,it hasbeenneglectedby organization possiblein definingstartingconditions,and the theoristsuntil very recently(Hassardand Parker, specificationof the system structure itself is much lessprecise. 1ee3). A final difference is that physical systems Despiteits attractions,the applicationof chaos naturallaws,whereas is still in its infancy, are shapedby unchanging theory to the socialsciences and there are thosewho think that expectations social systemsare subject to intervention by are too high (Baumol and Benhabib, 1989). individualsand organizations.Investigationsof Although real life phenomenamay resemblethe economictime seriesby chaos theoristshave patternsgeneratedby simplenonlinearsystems, usually assumedthat relationshipsamong ecothat does not mean that we can easily model nomic actors are fixed over time. In reality, and forecast these phenomena; it is almost methods of stabilizing the economy have impossibleto take a set of data and determine changed from the use of the gold standard the system of relationshipsthat generatesit and balanced budgets to Keynesian demand (Butler, 1990). In fact, there is considerable managementand, later, to monetaristcontols. debate in the economicsand finance literature Human agency can alter the parametersand about how one tests a data seriesto determine very structures of social systems, and it is if it is chaotic or simply subject to random perhapsunrealisticallyambitiousto think that influences(Brock and Malliaris, 1989; Hsieh, the effects of such intervention can be endo1991). Moreover, it is important to recognize genized in chaotic models.2 Nevertheless, that many systemsare not chaotic, and that chaotic models can be used to suggestways systems can transition between chaotic and that people might interveneto achievecertain nonchaoticstates.Chaostheory is perhapsbetter goals. The application presented here, for seen as an extension of systemstheory (Katz example,showshow managementcan reduce and Kahn, 1966;Thompson,1967)into the realm the volatility of the supply chain to improve of nonlinear dynamics rather than as a total performance. paradigmshift. It is possible that the applicationof chaos theory to socialsciencehas been constrainedby 2 To some extent, the distinction between endogenous and the fact that it has developed in relation to exogenous variables in a model is one of convenience; a physical systems,without taking into account factor that is exogenous in a simple model might become endogenous in a more complex and comprehensive one. fundamentaldifferencesbetween physical and Exogenous factors can be included as random variables in socialscience.In the socialworld, outcomesoften chaotic systems for modeling purposes (Kelsey. 1988).
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RELEVANCE OF CHAOS THBORY TO STRATEGY To understandthe relevanceof chaostheory to strategy,we need to conceptualizeindustriesas complex, dynamic, nonlinear systems. Firms interact with each other and with other actorsin their environment,suchas consumers,labor, the government, and financial institutions. These interactions are strategic in the sense that decisionsby one actor take into accountanticipated reactions by others, and thus reflect a recognition of interdependence.Although interfirm behavior has been modeledformally in economicsand businessstrategy using game theory (Camerer, I99L), these models tend to presumethe emergenceof equilibrium and do not adequately reflect industry dynamics. As Porter (1990) emphasizes,the evolution of industriesis dynamicand path dependent:corporate (and country-level) capabilities acquired during previous competitive episodesshape the contextfor future competitivebattles.Moreover, the accumulationof competitive advantagecan be self-reinforcing,suggestingat least one way in which industriesare nonlinear. If industries do behave as chaotic systems,a number of implicationsfor strategycan be drawn. Long-term planning is very difficult In chaotic systems,small disturbancesmultiply over time becauseof nonlinearrelationshipsand the dynamic,repetitivenatureof chaoticsystems. As a result,suchsystemsare extremelysensitive to initial conditions, which makes forecasting very difficult. This is a problem that has confronted meteorologiststrying to model the weather: the fundamentalproblem is trying to use finite measurementsin an infinite world. A related problem is that as systemsevolve dynamically,they are subject to myriad small random(or perhaschaotic)influencesthat cannot be incorporatedinto the model. Formulatinga long-termplan is clearly a key strategictask facing any organization.People involved in planning,whether in business,economics,or someother area,have alwaysknown that modelsare alwaysjust models,that forecasts are uncertain,and that uncertaintygrows over time. Nevertheless. our conventionalunderstanding of linearmodelsand the influenceof random
errors would lead us to think that better models and a more accurate specificationof starting conditionswould yield better forecasts,useful for perhapsmonths if not years into the future. Chaos theory suggestsotherwise; the payoff in terms of better forecasts of building more complexand more accuratemodelsmay be small. Similarly, we cannot learn too much about the future by studyingthe past: if history is the sum of complex and nonlinear interactionsamong peopleand nations,then history doesnot repeat itself. Concerningurban planning, Cartwright (1991)has noted that we have to acknowledge that 'a completeunderstandingof some of the thingswe plan may be beyondall possibility.' The notion that long-termplanningfor chaotic systems is not only difficult but essentially impossiblehas profound implicationsfor organizations trying to set strategy based on their anticipationof the future. Rather than expend large amountsof resourceson forecasting,strategic planning needs to take into account a number of possible scenarios.Moreover, too narrow a focus on a firm's core products and markets might reduce the ability of the organizationto adapt and be flexible in the face of change. The proliferation of joint-ventures and the acquisition by large firms of stakes in entrepreneurialenterprisescan perhaps be understoodas attempts to keep a foothold in a number of potential scenariosin the face of uncertaintyand acceleratingchange. Industries do not reach a stable equilibrium The traditional approachto understandingthe influenceof industry structure on firm behavior and competitive outcomes has been derived from microeconomics,with its emphasis on comparativestaticsand equilibrium.More recent applicationsof game theory have attempted to accountfor interactionsamongsmall numbersof firms (usuallytwo), yielding predictionsabout, for example, investmentsin R&D or plant capacity to seize first-mover advantages.Even the most complexgame theoreticmodels,however, are only considereduseful if they predict an equilibrium outcome. By contrast, chaotic systemsdo not reacha stableequilibrium;indeed, they can never passthrough the sameexactstate more than once. If they did, they would cycle endlesslythrough the same path becausethey
Chaos Theory and Strategy are driven by deterministic relationships.The implicationis that industriesdo not 'settle down' and any apparentstability, for examplein pricing or investmentpatterns,is likely to be shortlived. Chaos theory also suggeststhat changesin industrystructurescanbe endogenous.Corporate decisionsto enter or exit the market, or to developnew technologies,alter the very structure of the industry, which in turn influencesfuture firm behavior. One of the most provocativeand controversialelements of chaos theory is that chaotic systemscan spontaneouslyself-organize into more complexstructures(Allen, 1988).The notion has been applied to biological evolution (Laszlo, 1987) as well as to economicsystems (Mosekildeand Rasmussen, 1986).In the context of businessstrategy,the conceptcould potentially be applied to the evolution of complexorganizational relationshipssuch as long-term contracts and technical cooperation with suppliers, and hybrid forms of organizationalcontrol such as joint ventures.Chaostheory suggeststhat new, more complex organizationalforms will appear more frequently than if they were simply the result of random mutations. Dramatic changecan occur unexpectedly Traditional paradigmsof economicsand strategy, which are generally based upon assumptionsof linear relationshipsand the use of comparative static analysis, lead to the conclusion that small changesin parametersshould lead to correspondinglysmall changesin the equilibrium outcome.Chaostheoryforcesusto reconsiderthis conclusion.Large fluctuationscan be generated internally by deterministicchaoticsystems.Models of population growth based on the logistic differenceequation illustrate how sudden,large changesin population levels can arise from the dynamics of the system rather than from the influenceof external shocks(Radzicki, 1990).3 Similarly, if economic systemsare chaotic then we do not need to search for wars or natural
3 The logistic difference equation has the form: P,*t : P, * R* (1-P,) P, a fraction between 0 and 1, representsthe population level as a proportion of the maximum carrying capacityof the environment.R is the growth rate from one cycle to the next. Populationgrowth is constrainedby the factor 1-P,, which can be understoodas a resourceconstraint.
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disastersto accountfor economicdepressionsor a crashin the stock market. The size of fluctuations from one period to the next in chaotic systemshave a characteristic probability distribution (Bak and Chen, 1991). Under this distribution, large fluctuationsoccur more frequently than under the normal distribution, suggesting that managersmight underestimate the potential for large changesin industry conditionsor competitors'behavior. Small exogenousdisturbancesto chaotic systemscan alsocauseunexpectedlylargechanges. The implication for businessstrategyis that the entry of one new competitoror the development of a seeminglyminor technologycan have a substantialimpacton competitionin an industry. An examplethat comesto mind is the way Dell's mail order strategy in the personal computer industry forced other companiesto reduce their prices and reexaminetheir traditional high-cost salesand servicechannels. Short-term forecastsand predictions of patterns can be made Although the unpredictabilityand instabilityof chaotic systemshas been emphasized,there is also a surprising degree of order in chaotic systems. Short-term forecasting is possible becausein a deterministicsystem, given the conditions at time 't,' we can calculate the conditionsat time 't+1.' A carefullyconstructed simulation model of a complex system with accuratelyspecifiedstarting conditionscan yield useful forecastsat least for severaltime periods. Weather forecastsbased on sophisticatedcomputer modelsusingmeasurements from thousands of points around the globe do provide useful forecastsfor a few days,which is usuallysufficient for purposessuch as hurricane warnings. If we imaginethat strategicdecisionsin companiesare made on a monthly or even annual cycle, then industry simulation models might be able to make useful predictionsover a time horizon of severalmonths or possiblyyears. Another feature of chaotic systemsthat lends them a degreeof order is that they are bounded; outcomevariablessuchpricing or investmentsin new capacityfluctuatewithin certain boundsthat are determined by the structure of the system and its parametersbut not its initial conditions. In the context of businesstrategythese bounds
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might be set by feedback loops such as the betweencountries,betweenfirms in an industry, entrance of new firms or antitrust action by or even betweendepartmentsin a firm. the government in response to monopolistic conditions. Although we cannot forecastthe precisestate Guidelinesare neededto cope with complexity of a chaotic systemin the longer term, chaotic and uncertainty systems trace repetitive patterns which often 'Strategy'can refer to a set of guidelinesthat provide useful information. According to Rad- influence decisions and behavior. It is the zicki (1990),deterministicchaos'is characterized complexityof strategicinteractions,whether in by self-sustainedoscillationswhose period and chess, soccer, or in business,that makes it amplitude are nonrepetitive and unpredictable, essentialto adopt simplifying strategiesto guide yet generatedby a systemdevoidof randomness.' decisions;even the most powerful computersare For example, while we do not know exactly unable to track all possiblemovesand counterwhere or when tornadoes and hurricanes will movesin a chessgame. General Electric'swell strike, we do know what conditionslead to their known strategyof being numberone or number occurrence, when and where they are most two in every industry in which it participatesis frequent,and their likely paths.In a similarway, a simple example of a guideline which may be we know that oligopolistic industriestend to generally useful but is not always optimal in alternatebetweenperiodsof intensecompetition every situation. We need general guidelines andperiodsof morecooperativebehavior,though becauseit is impossibleto specify the optimal we do not know when an industry will make the courseof action for every possiblescenario. transitionfrom one state to another.To give a It is important to distinguish the guidelines third example,we know that the economycycles and patternsof behavior that constitutestrategy through recessionsand booms,though we cannot from the underlying rules of the game. In a predict very well the depth or duration of a game of chess,for example, knowing the rules particular recession (Butler, 199). Observing for playing the game does not necessarilygive patterns is especiallyuseful if we can associate one insights into strategiesfor successfulplay. different phasesof the systemwith other charac- One can only learn thesestrategiesafter experiteristics;for example,thereis a strongrelationship encing the complexitiesof interactions on the betweenbusinesscyclesand other variablessuch chessboard. Indeed, becauseof the complexity as demand, interest rates, the availability of of strategicinteractions,one does not always credit, vendor lead times, and the tightnessof know why a particular strategyis successful. the labor market. While the complexity of industry systems An intriguing aspectof the patternstraced by dictatesthe needfor broadstrategies,the dynamic chaotic systemsis that they are independentof natureof chaoticsystemsmandatesthat strategies scale;in other words, similarpatternsare traced adapt.As industrystructuresevolveand competiby a systemwhateverhorizon is usedto view it. tors changetheir strategies,a firm clearly needs Economic time series often appear to display to changeits own guidelinesand decisionrules. this property.Stockprices,for example,displaya The problem here is that there is no simple way remarkablysimilarpatternwhetherone observes of deriving optimal strategiesfor a given system. daily changesover 1 year or minute-by-minute Indeed, in a complexsystemthe best strategies changesover a day. These imagesof patterns might achievegoals indirectly and even appear within patterns are termed fractals when they counter-intuitive. The bestwayto improvequality are generatedby chaotic systems.In the natural is not necessarilyto check every product several world, fractalscanbe found in manyphenomena, times: it may be to improve labor relationsand from the shapeof coastlinesto ice crystals.The thus gain labor's cooperationin finding waysto implicationsfor businessstrategyare not entirely reduce defects. IBM's decision in 1981 to clear. One interpretationis that previousexperi- let other 'clone' manufacturersuse the DOS encesin an industry are likely to recur on a operatingsystemfor personalcomputershelped much larger scale. A second interpretationis competitors but also indirectly helped IBM to that similar patterns of behavior might be build market share by creating the industry expected whether one examines competition standard.
Chaos Theory and Strategy In order to understandindirect or counterintuitive meansto an end, a systemneedsto be I understood as a whole. If systems are very complex, then simulation models might pronl helpful in finding the most effective way to achieve a goal. The example discussed later in
this paper illustrateshow the best way to cut inventoryin a supplychainmight be to reduce disruptions to the chain rather than shorten lead
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