Skip to main content
SpringerLink
Log in

Evolutionary models in economics: a survey of methods and building blocks

  • Regular Article
  • Published:
Journal of Evolutionary Economics Aims and scope Submit manuscript

Abstract

This paper assesses methods and components of formal evolutionary-economic modelling. Methods are broadly classified into evolutionary game theory and selection dynamics, evolutionary computation and multi-agent models, each with relevant subcategories. The components or building blocks are organized into diversity, innovation, selection, bounded rationality, diffusion, path dependency and lock-in, coevolution, multilevel and group selection, and mechanisms of growth. The number of alternatives that has been proposed for each category is vast, making it difficult to comprehend the variety of assumptions and formalizations underlying existing evolutionary-economic models. Our survey aims to clarify for each model component the choice range, formal expressions, associated assumptions, and possible techniques for formalization. Our study is unique in that it provides more information about the formal details of specific model components and is considerably more inclusive than earlier reviews.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. Since economic applications of evolutionary programming and evolutionary strategies are rare, we do not discuss them further here.

  2. For examples of formalization of endogenous preference change, see Aversi et al. (1997) and the coevolutionary models described in Section 3.7.

References

  • Aghion P, Howitt P (1992) A model of growth through creative destruction. Econometrica 60:323–351

    Article  Google Scholar 

  • Alchian A (1950) Uncertainty evolution and economic theory. J Polit Econ 58:211–221

    Article  Google Scholar 

  • Alkemade F, Castaldi C (2005) Strategies for the diffusion of innovations on social networks. Comput Econ 25:3–23

    Article  Google Scholar 

  • Allen F, Karjalainen R (1999) Using genetic algorithms to find technical trading rules. J Financ Econ 5:245–271

    Article  Google Scholar 

  • Altenberg L (1997) NK fitness landscape. In: Back T, Fogel D, Michalewicz Z (eds) The handbook of evolutionary computation. Oxford University Press, Oxford, pp B27:2–B2:7:25

    Google Scholar 

  • Andersen ES (1994) Evolutionary economics: post-Schumpeterian contributions. Pinter, London

    Google Scholar 

  • Andersen ES (2004) Population thinking prices equation and the analysis of economic evolution. Aalborg University Denmark. www.businessaau.dk. Accessed 14 Jan 2009

  • Andersen PW, Arrow KJ, Pines D (eds.) (1988) The economy as an evolving complex system. Santa Fe Institute Studies in The Science Complexity. Addison-Wesley, Reading, MA

    Google Scholar 

  • Antonelli C (1996) Localized knowledge percolation processes and information networks. J Evol Econ 6:281–295

    Article  Google Scholar 

  • Aoki M (1996) New approaches to macroeconomic modelling. Cambridge University Press, Cambridge

    Google Scholar 

  • Arifovic J (1994) Genetic algorithm learning and the cobweb model. J Econ Dyn Control 18:3–28

    Article  Google Scholar 

  • Arifovic J (1995) Genetic algorithm learning and inflationary economics. J Monet Econ 36:219–243

    Article  Google Scholar 

  • Arifovic J (2000) Evolutionary algorithms in macroeconomic models. Macroecon Dyn 4:373–414

    Article  Google Scholar 

  • Arrow KJ (1962) The economic implications of learning by doing. Rev Econ Stud 29:155–173

    Article  Google Scholar 

  • Arthur WB (1988) Competing technologies: an overview. In: Dosi G, Freeman C, Nelson R, Silverberg G, Soete L (eds) Technical change and economic theory. Pinter, London, pp 590–607

    Google Scholar 

  • Arthur WB (1989) Competing technologies increasing returns and lock-in by historical events. Econ J 99:116–131

    Article  Google Scholar 

  • Arthur WB (1991) Designing economic agents that act like human agents: a behavioural approach to bounded rationality. Am Econ Rev 81:353–359

    Google Scholar 

  • Arthur WB (1994) Inductive reasoning and bounded rationality. Am Econ Assoc Proc 84:406–411

    Google Scholar 

  • Arthur WB, Lane DA (1993) Information contagion. Struct Chang Econ Dyn 4:81–104

    Article  Google Scholar 

  • Arthur WB, Ermoliev YM, Kaniovsky YM (1987) Path dependent processes and the emergence of macro-structure. Eur J Oper Res 30:294–303

    Article  Google Scholar 

  • Arthur WB, Holland JH, LeBaron B, Palmer R, Tayler P (1996) Asset pricing under endogenous expectations in an artificial stock market. www.santafe.edu

  • Auerswald P, Kauffman S, Lobo J, Shell K (2000) The production recipes approach to modelling technological innovation: an application to learning by doing. J Econ Dyn Control 24:389–450

    Article  Google Scholar 

  • Aversi RG, Dosi G, Fagiolo G, Meacci M, Olivetti C (1997) Demand dynamic with socially evolving preferences. IIASA working paper Laxenburg, Austria. www.iiasa.ac.at. Accessed 14 Jan 2009

  • Axelrod R (1984) The evolution of cooperation. Basic Books, New York

    Google Scholar 

  • Axelrod R (1987) The evolution of strategies in the iterated prisoners dilemma. In: David L (ed) Genetic algorithms and simulated annealing. Pitman, Boston, pp 32–41

    Google Scholar 

  • Axelrod R (1997) The complexity of cooperation. Basic Books, New York

    Google Scholar 

  • Axelrod R (2003) Advancing the art of simulation in the social sciences. Japan J Manage Inf Syst 12:3–16

    Google Scholar 

  • Back T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, Oxford

    Google Scholar 

  • Banzhalf W, Nordin P, Keller RE, Francone FD (1989) Genetic programming: an introduction. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Bass FM (1969) A new product for consumer durables. Manag Sci 15:215–227

    Article  Google Scholar 

  • Bell D (1985) Regret in decision making under uncertainty. Oper Res Sci 33:1367–1382

    Google Scholar 

  • Bergstrom TC (2002) Evolution of social behaviour: individual and group selection. J Econ Perspect 16:67–88

    Google Scholar 

  • Bergstrom TC (2003) The algebra of assortative encounters and the evolution of cooperation. Int Game Theory Rev 5:211–228

    Article  Google Scholar 

  • Bergstrom TC, Godfrey-Smith P (1998) On the evolution of behavioural heterogeneity in individuals and populations. Biol Philos 13:205–231

    Article  Google Scholar 

  • Beyer H-G (1998) The evolution of strategies. Spinger-Verlag, Leiden

    Google Scholar 

  • Birchenhall CR (1995) Review: genetic algorithms classifier systems and genetic programming and their use in the models of adaptive behaviour and learning. Econ J 105:788–795

    Article  Google Scholar 

  • Birchenhall CR, Kastrinos N, Metcalfe JS (1997) Genetic algorithms in evolutionary modelling. J Evol Econ 7:375–393

    Article  Google Scholar 

  • Bourgeois B, Nguyen P, Saviotti PP, Tommetter M (2005) Variety and the evolution of refinery processing. Ind Corp Change 14:469–500

    Article  Google Scholar 

  • Bower J, Bunn D (2001) Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market. J Econ Dyn Control 25:561–592

    Article  Google Scholar 

  • Bowles S, Choi J-K, Hopfensitz A (2004) The co-evolution of individual behaviors and social institutions. J Theor Biol 223:153–147

    Google Scholar 

  • Boyd R, Richardson P (1985) Culture and the evolutionary process. University of Chicago Press, Chicago

    Google Scholar 

  • Brannon EL, Anderson LJ, Ulrich PV, Marshall T, Donaldson DA (1997) Artificial life simulation of the textile/ apparel marketplace: an innovative approach to strategizing about evolving markets. National Textile Centre Annual Report. www.p2pays.org. Accessed 14 Jan 2009

  • Brenner T (1998) Can evolutionary algorithms describe learning processes. J Evol Econ 8:271–283

    Article  Google Scholar 

  • Bull L (ed) (2004) Applications of learning classifier systems. Springer, Berlin

    Google Scholar 

  • Bürger R (1998) Mathematical principles of mutation–selection. Genetica 102–103:279–298

    Article  Google Scholar 

  • Caldarelli SB, Marsili M, Zhang YC (1998) A prototype model of stock exchange. Europhys Lett 40:479–484

    Article  Google Scholar 

  • Canning D (1992) Average behaviour in learning models. J Econ Theory 57:442–472

    Article  Google Scholar 

  • Cantner U, Pyka A (1998a) Absorbing technological spillovers—simulations in an evolutionary framework. Ind Corp Change 7:369–397

    Google Scholar 

  • Cantner U, Pyka A (1998b) Technological evolution—an analysis within the knowledge-based approach. Struct Chang Econ Dyn 9:85–108

    Article  Google Scholar 

  • Cantner U, Hanusch H, Pyka A (1998) Pushing technological progress forward: a comparison of firm strategies. In: Lesourne J, Orlean A (eds) Advances in self-organization and evolutionary economics. Economica, London, pp 114–145

    Google Scholar 

  • Cantner U, Hanusch H, Pyka A (2000) Horizontal heterogeneity, technological progress and sectoral development. In: Cantner U, Hanusch H, Klepper S (eds) Economic evolution, learning, and complexity. Physica, Heidelberg, pp 73–96

    Google Scholar 

  • Cantono S, Silverberg G (2008) A percolation model of eco-innovation diffusion: the relationship between diffusion learning economies and subsidies. The UNU-MERIT Working Paper Series 2009-025

  • Carrillo-Hermosilla J (2006) A policy approach to the environmental impacts of technological lock-in. Ecol Econ 58:717–742

    Article  Google Scholar 

  • Chen S-H, Yeh C-H (2000) Simulating economic transition process by genetic programming. Ann Oper Res 97:265–286

    Article  Google Scholar 

  • Chiaromonte F, Dosi G (1993) Heterogeneity competition and macroeconomic dynamics. Struct Chang Econ Dyn 4:36–63

    Article  Google Scholar 

  • Conlisk J (1989) An aggregate model of technical change. Q J Econ 104:787–821

    Article  Google Scholar 

  • Conlisk J (1996) Why bounded rationality? J Econ Lit 34:669–700

    Google Scholar 

  • Conlisk J, Gong JC, Tong CH (2001) Actions influenced by a social network. J Evol Econ 11:277–305

    Article  Google Scholar 

  • Cowan R (2004) Network models of innovation and knowledge diffusion. University of Maastricht, The Netherlands

    Google Scholar 

  • Cowan R, Jonard N (2000) The dynamics of collective invention. University of Maastricht, The Netherlands

    Google Scholar 

  • Cowan R, Jonard N (2004) Network structure and the diffusion of knowledge. J Econ Dyn Control 28:1557–1575

    Article  Google Scholar 

  • Cowan R, Jonard N, Zimmermann JB (2006) Evolving networks of inventors. J Evol Econ 1:55–174

    Google Scholar 

  • Curzon Price T (1997) Using co-evolutionary programming to stimulate strategic behaviors in markets. J Evol Econ 7:219–254

    Article  Google Scholar 

  • David P (1985) Clio and the economics of QWERY. Am Econ Rev 75:332–337

    Google Scholar 

  • David G, Fagiolo G (2008) Agent-based models for economic policy design: an introduction to the special issue. J Econ Behav Organ 67:351–354

    Article  Google Scholar 

  • Dawid H (1999) Adaptive learning by genetic algorithms analytical results and application to economic models, 2nd version. Springer, Berlin

    Google Scholar 

  • Dawid H (2006) Agent-based models of innovation and technological change. In: Tesfatsion L, Judd K (eds) Handbook of computational economics II: agent-based computational economics. North-Holland, Amsterdam, pp 1235–1272

    Google Scholar 

  • Dawid H, Kopel M (1998) On economic applications of the genetic algorithm: a model of the cobweb type. J Evol Econ 8:297–315

    Article  Google Scholar 

  • Delre SA, Jager W, Janssen MA (2007) Diffusion dynamics in small-world networks with heterogeneous consumers. Comput Math Organ Theory 13:185–202

    Article  Google Scholar 

  • Dercole F, Rinaldi S (2008) Analysis of evolutionary processes. Princeton series in theoretical and computational biology. Princeton University Press, Princeton

    Google Scholar 

  • Dopfer K (ed) (2005) The evolutionary foundations of economics. Cambridge University Press, Cambridge

    Google Scholar 

  • Dosi G (1982) Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technological change. Res Policy 6:147–162

    Article  Google Scholar 

  • Dosi G, Ermoliev YM, Kaniovski YM (1994a) Generalized urn schemes and technological dynamics. J Math Econ 23:1–19

    Article  Google Scholar 

  • Dosi G, Fabiani S, Aversi R, Meacci M (1994b) The dynamics of international differentiation: a multi-country evolutionary model. Ind Corp Change 3:225–242

    Article  Google Scholar 

  • Dosi G, Fagiolo G, Roventini A (2006) An evolutionary model of endogenous business cycles. Comput Econ 27:3–34

    Article  Google Scholar 

  • Dosi G, Marengo L, Bassanini A, Valente M (1999) Norms as emergent properties of adaptive learning. J Evol Econ 9:5–26

    Article  Google Scholar 

  • Eiben AE (2000) Multiparent recombination. In: Bäck T, Fogel DB, Michalewicz Z (eds) Evolutionary computation 1: basic algorithms and operators. Institute of Physics, London, pp 289–307

    Google Scholar 

  • Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin

    Google Scholar 

  • Epstein C (2007) Generative social science: studies in agent-based computational modeling. Princeton University Press, Princeton

    Google Scholar 

  • Epstein C, Axtell R (1996) Growing artificial societies: social science from the bottom up. MIT Press, Cambridge

    Google Scholar 

  • Fagiolo G (2005) A note on equilibrium selection in Polya-urn coordination games. LEM Working Paper 2005/04 SantAnna School for Advanced Studies Pisa. www.lemsssup.it. Accessed 14 Jan 2009

  • Fagiolo G, Dosi G (2003) Exploitation exploration and innovation in a model of endogenous growth with locally interacting agents. Struct Chang Econ Dyn 14:237–273

    Article  Google Scholar 

  • Fagiolo G, Dosi G, Gabriele R (2004) Matching bargaining and wage setting in an evolutionary model of labor market and output dynamics. Adv Complex Systems 7:157–186

    Article  Google Scholar 

  • Fagiolo G, Windrum P, Moneta A (2007) A critical guide to empirical validation of agent-based economics models: methodologies procedures and open problems. Comput Econ 30:195–226

    Article  Google Scholar 

  • Fisher RA (1930) The genetic theory of natural selection. Dover Brooks, New York

    Google Scholar 

  • Fogel DB (2000) Evolutionary computation towards a new philosophy of machine intelligence. IEEE Press, New York

    Google Scholar 

  • Foray D (1997) The dynamic implications of increasing returns: technological change and path dependent inefficiency. Int J Ind Organ 15:733–752

    Article  Google Scholar 

  • Foster D, Young P (1990) Stochastic evolutionary games. Theor Popul Biol 38:219–232

    Article  Google Scholar 

  • Frank SA (1995) George Prices contributions to evolutionary genetics. J Theor Biol 175:375–388

    Article  Google Scholar 

  • Frederick S, Loewenstein G, O’Donoghue T (2002) Intertemporal choice: a critical view. J Econ Lit 40:351–402

    Article  Google Scholar 

  • Frenke R (1998) Coevolution and stable adjustment in the cobweb model. J Evol Econ 8:383–406

    Article  Google Scholar 

  • Frenken K (2006) Technological innovation and complexity theory. Econ Innov New Technol 15:137–155

    Article  Google Scholar 

  • Frenken K, Nuvolari A (2004) The early development of the steam engine: an evolutionary interpretation using complexity theory. Ind Corp Change 13:419–450

    Google Scholar 

  • Frenken K, Windrum P (2005) Product differentiation and product complexity. A conceptual model and an empirical application to microcomputers. University of Maastricht, The Netherlands

    Google Scholar 

  • Frenken K, Saviotti PP, Trommetter M (1999) Variety and niche creation in aircraft helicopters motorcycles and microcomputers. Res Policy 28:469–488

    Article  Google Scholar 

  • Friedman M (1953) On the methodology of positive economics. In: Friedman M (ed) Essays in positive economics. University of Chicago Press, Chicago

    Google Scholar 

  • Friedman D (1991) Evolutionary theory of games. Econometrica 59:637–666

    Article  Google Scholar 

  • Fudenberg D (2006) Advancing beyond advances in behavioural economics. J Econ Lit 54:649–711

    Google Scholar 

  • Fudenberg D, Levine DK (1997) The theory of learning in games. MIT Press, Cambridge

    Google Scholar 

  • Gabriele R (2002) Labour market dynamics and institutions: an evolutionary approach. LEM working paper 2002/07 SantAnna School for Advanced Studies Pisa. www.lemsssup.it. Accessed 14 Jan 2009

  • Garcia J, van den Bergh JCJM (2007) Models of genetic and cultural group selection: a critical survey. Free University Amsterdam, The Netherlands

    Google Scholar 

  • Geels FW (2002) Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Res Policy 31:1257–1274

    Article  Google Scholar 

  • Geels FW (2005) Technological transitions and system innovations: a co-evolutionary and socio-technical analysis. Edward Eldgar, Cheltenham

    Google Scholar 

  • Geroski PA (2000) Models of technology diffusion. Res Policy 29:603–625

    Article  Google Scholar 

  • Gilbert N, Pyka A, Ahrweiler P (2001) Innovation networks—a simulation approach. JASSS 4

  • Gilboa I, Schmeidler D (1995) Case-based decision theory. Q J Econ 110:605–639

    Article  Google Scholar 

  • Gintis H (2000) Game theory evolving. Princeton University Press, Princeton

    Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, Reading

    Google Scholar 

  • Grafen A (2000) Developments of the price equation and natural selection under uncertainty. Proc Biol Sci 267:1223–1227

    Article  Google Scholar 

  • Grossman G, Helpman E (1991) Innovation and growth. Duckworth, MA

    Google Scholar 

  • Gunderson LH, Holling CS (2001) Panarchy: understanding transformations in human and natural systems. Island Press, Washington, DC

    Google Scholar 

  • Guth W, Schmittberger R, Schwarze B (1982) An experimental analysis of ultimatum bargaining. J Econ Behav Organ 3:367–388

    Article  Google Scholar 

  • Helbing D (1995) Quantitative sociodynamics stochastic methods and models of social interaction processes. Kluwer Academic, Boston

    Google Scholar 

  • Henrich J (2004) Cultural group selection. Co-evolutionary process and large-scale cooperation. J Econ Behav Organ 53:85–88

    Google Scholar 

  • Henrich J, Boyd R, Young P, McCabe K, Alberts W, Ockenfelds A, Gigerenzer G (1999) What is the role of culture in bounded rationality? www.psych.ubc.ca/~henrich. Accessed 14 Jan 2009

  • Hill S, ORiordan C (2001) Genetic algorithms their operators and the NK Model. Technical report NUID-IT-150601 Department of Information Technology National, University of Ireland, Galway

  • Hodgson GM (1993) Economics and evolution: bringing life back into economics. Polity Press, Cambridge

    Google Scholar 

  • Hodgson GM (1997) The ubiquity of habits and rules. Camb J Econ 21:663–684

    Google Scholar 

  • Hodgson GM, Knudsen T (2004) The complex evolution of a simple traffic convention: the functions and implications of habit. J Econ Behav Organ 54:19–47

    Article  Google Scholar 

  • Hofbauer J, Sigmund K (1998) Evolution games and population dynamics. Cambridge University Press, Cambridge

    Google Scholar 

  • Hofbauer J, Sigmund K (1990) Adaptive dynamics and evolutionary stability. Appl Math Letters 3:75–79

    Article  Google Scholar 

  • Hofbauer J, Sigmund K (2003) Evolutionary game dynamics. Bull Am Math Soc 4:479–519

    Article  Google Scholar 

  • Hofbauer J, Oechssler J, Riedel F (2009) Brown–von Neumann–Nash dynamics: the continuous strategy case. Games Econ Behav 65:406–429

    Article  Google Scholar 

  • Hohnisch M, Pittnauer S, Stauffer D (2008) A percolation-based model explaining delayed takeoff in new product diffusion. Ind Corp Change 17:1001–1017

    Article  Google Scholar 

  • Holland JH (1980) Adaptive algorithms for discovering and using general patterns in growing knowledge-based. Int J Policy Anal Inf Syst 4:245–268

    Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems: an introduction analysis with applications to biology control and artificial intelligence, 2nd edn. MIT Press Cambridge, Cambridge

    Google Scholar 

  • Holland JH, Miller JH (1991) Artificial adaptive agents in economic theory. Am Econ Rev 81:365–370

    Google Scholar 

  • Ishibuchi H, Sakamota R, Nakashima T (2001) Evolution of unplanned coordination in a market selection game. IEEE Trans Evol Comput 5:524–534

    Article  Google Scholar 

  • Iwai K (1984a) Schumpeterian dynamics part I: evolutionary model of innovation and imitation. J Econ Behav Organ 5:159–190

    Article  Google Scholar 

  • Iwai K (1984b) Schumpeterian dynamics part II: technological progress firm growth and economic selection. J Econ Behav Organ 5:321–351

    Article  Google Scholar 

  • Iwai K (2000) A contribution to the evolutionary theory of innovation imitation and growth. J Econ Behav Organ 42:167–198

    Article  Google Scholar 

  • Janssen MA, Carpenter SR (1999) Managing the resilience of lakes: a multi-agent modelling approach. Ecol Soc 3:15. www.consecolorg/vol3/iss2/art15. Accessed 14 Jan 2009

  • Janssen MA, Jager W (2002) Simulating diffusion of green products. Co-evolution of firms and consumers. J Evol Econ 12:283–306

    Google Scholar 

  • Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47:263–291

    Article  Google Scholar 

  • Kandori SA, Mailath GJ, Rob R (1993) Learning mutations and long run equilibrium in games. Econometrica 61:29–56

    Article  Google Scholar 

  • Katz M, Shapiro C (1986) Technology adoption in the presence of network externalities. J Polit Econ 94:822–841

    Article  Google Scholar 

  • Kauffman SA (1993) The origins of order: self-organization and selection in evolution. Oxford University Press, New York

    Google Scholar 

  • Kirman A (1993) Ants rationality and recruitment. Q J Econ 108:137–156

    Article  Google Scholar 

  • Kirman A (1997) The economy as an evolving network. J Evol Econ 7:339–353

    Article  Google Scholar 

  • Kirman A, Vriend NJ (2001) Evolving market structure: an ACE model of price dispersion and loyalty. J Econ Dyn Control 25:459–502

    Article  Google Scholar 

  • Klos TB, Nooteboom B (2001) Agent based computational transaction cost economics. J Econ Dyn Control 25:503–526

    Article  Google Scholar 

  • Knudsen T (2002) Economic selection theory. J Evol Econ 12:434–470

    Article  Google Scholar 

  • Komarowa NL (2004) Replicator–mutator equation universality property and population dynamics of learning. J Theor Biol 230:227–239

    Article  Google Scholar 

  • Koza JR (1992) Genetic programming. MIT, Cambridge

    Google Scholar 

  • Koza JR (1994) Genetic programming II: automatic discovery of reusable programs. MIT, Cambridge

    Google Scholar 

  • Kwasnicki W (2001) Comparative analysis of selected neo-Schumpeterian models of industrial dynamics. www.prawouniwroc.pl/~kwasnicki. Accessed 14 Jan 2009

  • Kwasnicki W (2007) Schumpeterian modelling. In: Hanusch H, Pyka A (eds) The Elgar companion to neo-Schumpeterian economics. Edward Elgar, Cheltenham, pp 389–405

    Google Scholar 

  • Kwasnicki W, Kwasnicka H (1992) Market innovation competition: an evolutionary model of industrial dynamics. J Econ Behav Organ 19:343–368

    Article  Google Scholar 

  • Lande R, Arnold SJ (1983) The measurement of selection on correlated characters. Evol 37:1210–1226

    Article  Google Scholar 

  • Lansing JS, Miller JH (2004) Cooperation games and ecological feedback: some insights from Bali. http://zia.hss.cmu.edu/miller . Accessed 14 Jan 2009

  • Lazaric N, Raybaut A (2005) Knowledge hierarchy and the selection of routines: an interpretative model with group interactions. J Evol Econ 15:393–421

    Article  Google Scholar 

  • Lazi PL, Stolzmann W, Wilson SW (eds) (1998) Learning classifier systems from foundations to applications. Springer, Berlin

    Google Scholar 

  • LeBaron B (2001) Empirical regularities from interacting long and short horizon investors in an agent based stock market. IEEE Trans Evol Comput 5:442–455

    Article  Google Scholar 

  • Levy M, Levy H, Solomon S (2000) Microscopic simulation of financial markets. Academic, New York

    Google Scholar 

  • Leydesdorff L, van den Besselaar P (1998) Competing technologies: lock-ins and lock-outs. http://users.fmg.uva.nl/lleydesdorff . Accessed 14 Jan 2009

  • Lieberman JD, Hauert C, Nowak MA (2005) Evolutionary dynamics on graphs. Nat 433:312–316

    Article  Google Scholar 

  • Loomes G, Sugden R (1986) Regret theory: an alternative theory of rational choice under uncertainty. Econ J 92:805–824

    Article  Google Scholar 

  • Lucas R Jr (1988) On the mechanics of economic development. J Monet Econ 22:3–42

    Article  Google Scholar 

  • Lundvall BA (1988) Innovation as an interactive process: from user–producer interaction to the national system of innovation. In: Dosi G, Freeman C, Nelson R, Silverberg G, Soete L (eds) Technical change and economic theory. Pinter, London, pp 349–369

    Google Scholar 

  • Malerba F (2006) Innovation and the evolution of industries. J Evol Econ 16:3–23

    Article  Google Scholar 

  • Malerba F, Nelson R, Orsenigo L, Winter S (2005) The dynamics of the vertical scope of firms in related industries the coevolution of competences technical change and the size and structure of markets. CESPR Bocconi University, Milan

    Google Scholar 

  • Manfredi P, Bonaccorsi A, Secchi A (2004) Social heterogeneities in classical new product diffusion models. LEM working paper 1991/21 SantAnna School for Advanced Studies Pisa. www.lemsssup.it. Accessed 14 Jan 2009

  • Mansfield E (1961) Technical change and the rate of imitation. Econometrica 29:741–765

    Article  Google Scholar 

  • Marimon R, McGrattan E, Sargent TJ (1990) Money as a medium of exchange in an economy with artificially intelligent agents. J Econ Dyn Control 14:329–373

    Article  Google Scholar 

  • Matteo R, Leombruni NS, Sonnessa, M (2006) A common protocol for agent-based social simulation. JASSS 9

  • Maynard Smith J, Price GR (1973) The logic of animal conflict. Nat 246:15–18

    Article  Google Scholar 

  • McGill B, Brown JS (2007) Evolutionary game theory and adaptive dynamics of continuous traits. Ann Rev Ecolog Evol Syst 38:403–435

    Article  Google Scholar 

  • Metcalfe JS (1988) The diffusion of innovations: an interpretative survey. In: Dosi G, Freeman C, Nelson R, Silverberg G, Soete L (eds) Technical change and economic theory. Pinter, London, pp 560–589

    Google Scholar 

  • Metcalfe JS (1994) Competition Fisher’s principle and increasing returns in the selection process. J Evol Econ 4:327–346

    Article  Google Scholar 

  • Metcalfe JS, Foster J, Ramlogan R (2006) Adaptive economic growth. Camb J Econ 30:7–32

    Article  Google Scholar 

  • Miller JH (1996) The coevolution of automata in the repeated prisoners dilemma. J Econ Behav Organ 29:87–112

    Article  Google Scholar 

  • Mitchell M (1996) An introduction to genetic algorithms. MIT, Cambridge

    Google Scholar 

  • Mokyr J (1990) The lever of riches. Oxford University Press, New York

    Google Scholar 

  • Morone P, Taylor R (2004) Knowledge diffusion dynamics and network properties of face to face interactions. J Evol Econ 14:327–351

    Article  Google Scholar 

  • Neely CJ, Weller P, Dittmar R (1997) Is technical analysis in the foreign exchange market profitable? A genetic programming approach. J Financ Quant Anal 32:405–426

    Article  Google Scholar 

  • Nelson R (1995) Recent evolutionary theorizing about economic change. J Econ Lit 33:48–90

    Google Scholar 

  • Nelson R, Winter S (1977) In search of useful theory of innovation. Res Policy 6:36–76

    Article  Google Scholar 

  • Nelson R, Winter S (1982) An evolutionary theory of economic change. Harvard University Press, Cambridge

    Google Scholar 

  • Noailly J (2008) Coevolution of economic and ecological systems. J Evol Econ 18:1–29

    Article  Google Scholar 

  • Noailly J, van den Bergh JCJM, Withagen C (2009) Local and global interactions in an evolutionary resource game. Comput Econ 33:155–173

    Article  Google Scholar 

  • Noailly J, Withagen C, van den Bergh JCJM (2007) Evolution of social norms in a common-pool resource game. Environ Resour Econ 36:113–141

    Article  Google Scholar 

  • Norgaard RB (1984) Coevolutionary development potential. Land Econ 60:160–173

    Article  Google Scholar 

  • Nowak MA (2006) Evolutionary dynamics exploring the equations of life. Harvard University Press, Cambridge

    Google Scholar 

  • Nowak M, Sigmund K (1990) The evolution of stochastic strategies in the prisoner’s dilemma. Acta Appl Math 20:247–265

    Article  Google Scholar 

  • Nowak MA, Sigmund K (2004) Evolutionary dynamics of biological games. Sci 3030:796–798

    Google Scholar 

  • Olsson O, Frey BS (2002) Entrepreneurship as recombinant growth. Small Bus Econ 19:69–80

    Article  Google Scholar 

  • Önal H (1997) A computationally convenient diversity measure: theory and application. Environ Resour Econ 9:409–427

    Article  Google Scholar 

  • Paker DC, Manson SM, Janssen MA, Hoffman MJ, Deadman P (2003) Multi-agent systems for the simulation of land-use and land cover change: a review. Ann Assoc Am Geogr 93:314–337

    Article  Google Scholar 

  • Pesendorfer W (2006) Behavioral economic comes of age: a review essay on advance in behavioral economics. J Econ Lit 54:712–721

    Article  Google Scholar 

  • Potts J (2000) The new evolutionary microeconomics: complexity competence and adaptive behavior. Edward Elgar, Cheltenham

    Google Scholar 

  • Prelec D, Loewenstein G (1991) Decision making over time and under uncertainty: a common approach. Manag Sci 37:770–786

    Article  Google Scholar 

  • Price G (1970) Selection and covariance. Nat 227:520–521

    Article  Google Scholar 

  • Price G (1995) The nature of selection. J Theor Biol 175:389–396

    Article  Google Scholar 

  • Pyka A, Fagiolo G (2007) Agent-based modelling: A methodology for Neo-Schumpeterian economics. In: Hanusch H, Pyka A (eds) The Elgar Companion to Neo-Schumpeterian economics. Edward Elgar, Cheltenham, pp 467–492

    Google Scholar 

  • Riechmann T (1999) Learning and behavioural stability—an economic interpretation of genetic algorithms. J Evol Econ 9:225–242

    Article  Google Scholar 

  • Romer PM (1986) Increasing returns and long run growth. J Polit Econ 94:1002–1037

    Article  Google Scholar 

  • Romer PM (1990) Endogenous technological change. J Polit Econ 98:71–102

    Article  Google Scholar 

  • Safarzynska K, van den Bergh JCJM (2007) Policy for system innovation: demand–supply coevolution with multiple increasing returns. IVM, Vrije Universiteit, Amsterdam

    Google Scholar 

  • Safarzynska K, van den Bergh JCJM (2008) Beyond replicator dynamics. A model of selection mutation and recombinant innovation returns. IVM, Vrije Universiteit, Amsterdam

    Google Scholar 

  • Sahal D (1985) Technological guideposts and innovation avenues. Res Policy 14:61–82

    Article  Google Scholar 

  • Saint-Jean M (2006) Environmental innovation and policy: lessons from an evolutionary model of industrial dynamics. www.mnp.nl. Accessed 14 Jan 2009

  • Samuelson L (1997) Evolutionary games and equilibrium selection. MIT, Cambridge

    Google Scholar 

  • Sandholm WH (2007) Evolution and learning in games: overview. Working paper University of Wisconsin. http://www.sscwisc.edu/~whs. Accessed 14 Jan 2009

  • Sargent TJ (1993) An evolutionary theory of economic change. Harvard University Press, Harvard

    Google Scholar 

  • Saviotti PP (2001) Variety growth and demand. J Evol Econ 11:119–142

    Article  Google Scholar 

  • Saviotti PP, Metcalfe JS (eds) (1991) Evolutionary theories of economic and technological change. Harwood Academic, Chur

    Google Scholar 

  • Saviotti PP, Pyka A (2004) Economic development by the creation of new sectors. J Evol Econ 14:1–35

    Article  Google Scholar 

  • Saviotti PP, Pyka A (2008) Micro and macro dynamics: industry life cycles inter-sector coordination and aggregate growth. J Evol Econ 18:167–182

    Article  Google Scholar 

  • Saviotti PP, Trickett A (1992) The evolution of helicopter technology 1940–1986. Econ Innov New Technol 2:111–130

    Article  Google Scholar 

  • Schelling TC (1978) Micromotives and macrobehaviour. Norton, New York

    Google Scholar 

  • Schumpeter JA (1939) Business cycles: a theoretical historical and statistical analysis of the capitalistic process. McGraw-Hill, Cambridge

    Google Scholar 

  • Schwoon M (2006) Simulating the adoption of fuel cell vehicles. J Evol Econ 16:435–472

    Article  Google Scholar 

  • Silva ST (2009) On evolutionary technological change and economic growth: Lakatos as a staring point for appraisal. J Evol Econ 19:111–135

    Article  Google Scholar 

  • Silverberg G (1988) Modelling economic dynamics and technical change: mathematical approaches to self-organization and evolution. In: Dosi G, Freeman C, Nelson R, Silverberg G, Soete L (eds) Technical change and economic theory. Pinter, London, pp 531–559

    Google Scholar 

  • Silverberg G (1997) Evolutionary modelling in economics: recent history and immediate prospects. University of Maastricht, Maastricht

    Google Scholar 

  • Silverberg G, Lehnert D (1993) Long waves and evolutionary chaos in a simple Schumpeterian model of embodied technical change. Struct Chang Econ Dyn 4:9–37

    Article  Google Scholar 

  • Silverberg G, Verspagen B (1994a) Learning innovation and economic growth: a long-run model of industrial dynamic. Ind Corp Change 3:199–223

    Article  Google Scholar 

  • Silverberg G, Verspagen B (1994b) Collective learning innovation and growth in a boundedly rational evolutionary world. J Evol Econ 4:207–226

    Article  Google Scholar 

  • Silverberg G, Verspagen B (1995) An evolutionary model of long-term cyclical variations of catching up and falling behind. J Evol Econ 5:209–227

    Article  Google Scholar 

  • Silverberg G, Verspagen B (2003) Brewing the future: stylized facts about innovation and their confrontation with a percolation model. ECIS working paper 0306

  • Silverberg G, Verspagen B (2005a) Evolutionary theorising on economic growth. In: Dopfer K (ed) The evolutionary foundations of economics. Cambridge University Press, Cambridge, pp 506–539

    Chapter  Google Scholar 

  • Silverberg G, Verspagen B (2005b) A percolation model of innovation in complex technology space. J Econ Dyn Control 29:225–244

    Article  Google Scholar 

  • Silverberg G, Yildizoglu M (2002) An evolutionary interpretation of the Aghion & Howitt (1992) model. Working paper E3i2002-3 Equipe Industries Innovation Institutions Université Bordeaux IV France

  • Silverberg G, Dosi G, Orsenigo L (1988) Innovation diversity and diffusion: a self-organization model. Econ J 98:1032–1054

    Article  Google Scholar 

  • Simon H (1955) A behavioural model of rational choice. Q J Econ 69:99–118

    Article  Google Scholar 

  • Simon H (1956) Rational choice and the structure of the environment. Psychol Rev 63:129– 138

    Article  Google Scholar 

  • Soete L, Turner R (1984) Technology diffusion and the rate of technical change. Econ J 94:612–623

    Article  Google Scholar 

  • Solomon S, Wiebuch GL, de Arcangelis N, Stauffer JD (2000) Social percolation models. Physica A 277:239–247

    Article  Google Scholar 

  • Stirling A (2004) Diverse designs fostering technological diversity in innovation for sustainability. Paper presented at conference on Innovation, Sustainability and Policy. Seeon, Germany

    Google Scholar 

  • Stirling A (2007) A general framework for analyzing diversity in science technology and society. J R Soc Interface 4:707–719

    Article  Google Scholar 

  • Tassier T, Menczer F (2001) Emerging small-word referral networks in evolutionary labor markets. IEEE Trans Evol Comput 5:482–492

    Article  Google Scholar 

  • Taylor PD, Jonker L (1978) Evolutionary stable strategies and game dynamics. Math Biosci 40:145–156

    Article  Google Scholar 

  • Tesfatsion L (2001a) Introduction to the special issue on agent based computational economics. J Econ Dyn Control 25:281–293

    Article  Google Scholar 

  • Tesfatsion L (2001b) Guest editorial: agent-based modelling of evolutionary economic systems. IEEE Trans Evol Comput 5:437–441

    Article  Google Scholar 

  • Tesfatsion L, Judd K (eds) (2006) Handbook of computational economics II: agent-based computational economics. North-Holland, Amsterdam

    Google Scholar 

  • Thaler R (1981) Some empirical evidence on dynamic inconsistency. Econ Lett 8:201–207

    Article  Google Scholar 

  • Thebaud O, Locatelli B (2001) Modelling the emergence of recourse-sharing conventions: an agent-based approach. JASSS 4

  • Theil H (1967) Economics and information theory. North Holland, Amsterdam

    Google Scholar 

  • Trauslen A, Nowak MA (2006) Evolution of cooperation by multilevel selection. Proc Natl Acad Sci 103:10952–10955

    Article  Google Scholar 

  • Tsur Y, Zemel A (2007) Towards endogenous recombinant growth. J Econ Dyn Control 31:3459–3477

    Article  Google Scholar 

  • Unruh GC (2000) Understanding carbon lock-in. Energy Pol 28:817–830

    Article  Google Scholar 

  • van den Bergh JCJM (2004) Evolutionary modelling in ecological economics. In: Proops J, Safonov P (eds) Modelling in ecological economics. Edward Elgar, Cheltenham, pp 9–35

  • van den Bergh JCJM (2007) Evolutionary thinking in environmental economics. J Evol Econ 17:521–549

    Article  Google Scholar 

  • van den Bergh JCJM (2008) Optimal diversity: increasing returns versus recombinant innovation. J Econ Behav Organ 68:565–580

    Article  Google Scholar 

  • van den Bergh JCJM, Gowdy JM (2009) A group selection perspective on economic behavior institutions and organizations. J Econ Behav Organ. doi:10.1016/j.jebo.2009.04.017

    Google Scholar 

  • van den Bergh JCJM, Stagl S (2004) Coevolution of economic behavior and institutions: towards a positive theory of institutional change. J Evol Econ 13:289–317

    Article  Google Scholar 

  • van den Bergh JCJM, Faber A, Idenburg AM, Oosterhuis FH (2006) Survival of the greenest: evolutionary economics and policies for energy innovation. Environ Sci 3:57–71

    Article  Google Scholar 

  • van den Heuvel STA, van den Bergh JCJM (2009) Multilevel assessment of diversity innovation and selection in the solar photovoltaic industry. Struct Chang Econ Dyn 20:50–60

    Google Scholar 

  • van Veelen M (2005) On the use of the Price equation. J Theor Biol 237:412–426

    Article  Google Scholar 

  • Vriend N (1995) Self-organization of markets: an example of a computational approach. Comput Econ 8:205–231

    Article  Google Scholar 

  • Vriend N (2006) ACE models of endogenous interactions. In: Tesfatsion L, Judd K (eds) Handbook of computational economics II: agent-based computational economics. North-Holland, Amsterdam, pp 1048–1078

    Google Scholar 

  • Watson RA (2006) Compositional evolution: the impact of sex symbiosis and modularity on the gradualist framework of evolution. MIT, Cambridge

    Google Scholar 

  • Watts D, Strogatz S (1998) Collective dynamics of small-world networks. Lett Nation 393:440–442

    Article  Google Scholar 

  • Waxman D, Gavrilets S (2004) 20 questions on adaptive dynamics: a target review. J Evol Biol 18:1139–1154

    Article  Google Scholar 

  • Weibull JW (1995) Evolutionary game theory. MIT, Cambridge

    Google Scholar 

  • Weibull JW (1998) What have we learned from evolutionary game theory so far. Working paper Research Institute of Industrial Economics Sweden. http://swopechhsse/iuiwop . Accessed 14 Jan 2009

  • Weidlich W, Braun M (1992) The master equation approach to nonlinear economics. J Evol Econ 2:233–265

    Article  Google Scholar 

  • Weiss G (1999) Multiagent systems. A modern approach to distributed artificial intelligence. MIT Press, Cambridge

    Google Scholar 

  • Weitzman ML (1992) On diversity. Q J Econ 107:363–405

    Article  Google Scholar 

  • Weitzman ML (1998a) The Noah’s ark problem. Econometrica 66:1279–1298

    Article  Google Scholar 

  • Weitzman ML (1998b) Recombinant growth. Q J Econ 113:331–360

    Article  Google Scholar 

  • Wheeler SN, Bean N, Gaffney J, Taylor P (2006) A Markov analysis of social learning and adaptation. J Evol Econ 16:299–319

    Article  Google Scholar 

  • Wilson DS (2002) Darwin’s cathedral: evolution religion and the nature of society. University of Chicago Press, Chicago

    Google Scholar 

  • Wilson DS (2006) Human groups as adaptive units: toward a permanent consensus. In: Carruther P, Laurence S, Stich S (eds) The innate mind: culture and cognition. Oxford University Press, Oxford, pp 78–90

    Google Scholar 

  • Wilson DS, Sober E (1994) Reintroducing group selection to the human behavioural sciences. Behav Brain Sci 17:585–654

    Article  Google Scholar 

  • Winder N, McIntosh BS, Jeffrey P (2005) The origin diagnostic and practical application of co-evolutionary theory. Ecol Econ 54:347–361

    Article  Google Scholar 

  • Windrum P (2004) Neo-Schumpeterian simulation models. University of Maastricht, Maastricht

    Google Scholar 

  • Windrum P, Birchenhall CT (1998) Is life cycle theory a special case?: dominant designs and emergence of market niches through co-evolutionary learning. Struct Chang Econ Dyn 9:109–134

    Article  Google Scholar 

  • Windrum P, Birchenhall CT (2005) Structural change in the presence of network externalities: a co-evolutionary model of technological successions. J Evol Econ 15:123–148

    Article  Google Scholar 

  • Windrum P, Birchenhall CT (2009a) Consumer heterogeneity and the development of environmentally friendly products. Technol Forecast Soc Change 76:533–551

    Article  Google Scholar 

  • Windrum P, Birchenhall CT (2009b) Environmental impact quality and price: consumer trade-offs and the development of environmentally friendly technologies. Technol Forecast Soc Change 76:552–556

    Article  Google Scholar 

  • Winter SG (1964) Economic natural selection and the theory of the firm. Yale Econ Essays 4:225–272

    Google Scholar 

  • Witt U (1997) “Lock-in” vs “critical masses”—industrial change under network externalities. Int J Ind Organ 15:753–773

    Article  Google Scholar 

  • Witt U (2008) What is specific about evolutionary economics? J Evol Econ 18:547–575

    Article  Google Scholar 

  • Wooldridge M (1999) Intelligent agents. In: Weiss E (ed) Multi-agent systems: a modern approach to distributed artificial intelligence. MIT, Cambridge, pp 27–78

    Google Scholar 

  • Wooldridge M (2002) An introduction to multiagent systems. Wiley, Chichester

    Google Scholar 

  • Young HP (1993) The evolution of conventions. Econometrica 61:57–84

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank Guszti Eiben, Julian Garcia, Koen Frenken, Witold Kwasnicki, Ramzi Mabsout, Ulrich Witt and two anonymous referees for comments and suggestions.

Author information

Authors and Affiliations

  1. Institute for Environmental Studies, Vrije Universiteit, De Boelelaan 1087, 1081 HV, Amsterdam, The Netherlands

    Karolina Safarzyńska

  2. ICREA, Barcelona, Spain

    Jeroen C. J. M. van den Bergh

  3. Institute for Environmental Science and Technology, Department of Economics and Economic History, Universitat Autonòma de Barcelona, Edifici Cn—Campus UAB, 08193, Bellaterra, Spain

    Jeroen C. J. M. van den Bergh

  4. Faculty of Economics and Business Administration, and Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands

    Jeroen C. J. M. van den Bergh

  5. Tinbergen Institute, Rotterdam, The Netherlands

    Jeroen C. J. M. van den Bergh

  6. NAKE, Tilburg, The Netherlands

    Jeroen C. J. M. van den Bergh

Authors
  1. Karolina Safarzyńska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeroen C. J. M. van den Bergh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karolina Safarzyńska.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Safarzyńska, K., van den Bergh, J.C.J.M. Evolutionary models in economics: a survey of methods and building blocks. J Evol Econ 20, 329–373 (2010). https://doi.org/10.1007/s00191-009-0153-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00191-009-0153-9

Keywords

JEL Classification

Search

Navigation