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Building Artificial Economies: From Aggregate Data to Experimental Microstructure. A Methodological Survey

  • Gianfranco GiulioniEmail author
  • Paola D’Orazio
  • Edgardo Bucciarelli
  • Marcello Silvestri
Conference paper
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 676)

Abstract

This paper suggests a methodological appraisal of the main improvements witnessed by the methodology based on the interplay between Experimental Economics (EE) and Agent-based Computational Economics (ACE) in the last 5–6 years. EE and ACE proved to be “natural allies” in that they complement each other: EE helps ACE in dealing with its “degree of freedom” problem and ACE helps EE in controlling and providing benchmarks for experimental subjects’ behavior. The paper discusses the role Evolutionary Computation plays in this bidirectional relationship.

Keywords

Genetic Algorithm Artificial Agent Experimental Economic Macroeconomic Model Heterogeneous Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Anufriev M, Hommes C, Makarewicz T (2013) Learning to forecast with genetic algorithm. Tech. rep.Google Scholar
  2. Arifovic J (1996) The behavior of the exchange rate in the genetic algorithm and experimental economies. J Polit Econ 104(3):510–541CrossRefGoogle Scholar
  3. Arifovic J (2000) Evolutionary algorithms in macroeconomic models. Macroecon Dyn 4:373–414CrossRefGoogle Scholar
  4. Arthur WB (1991) Designing economic agents that act like human agents: a behavioral approach to bounded rationality. Am Econ Rev Pap Proc 81(2):353–359Google Scholar
  5. Assenza T, Heemeijer P, Hommes C, Massaro D (2013) Individual Expectations and Aggregate Macro Behavior. Tinbergen Institute Discussion Papers 13-016/II, Tinbergen InstituteGoogle Scholar
  6. Bao T, Duffy J, Hommes C (2013) Learning, forecasting and optimizing: an experimental study. Eur Econ Rev 61(C):186–204Google Scholar
  7. Casari M (2004) Can genetic algorithms explain experimental anomalies? Comput Econ 24(3):257–275CrossRefGoogle Scholar
  8. Casari M (2008) Markets in equilibrium with firms out of equilibrium: a simulation study. J Econ Behav Organ 65(2):261–276CrossRefGoogle Scholar
  9. Chen SH (2012) Varieties of agents in agent-based computational economics: a historical and an interdisciplinary perspective. J Econ Dyn Control 36(1):1–25CrossRefGoogle Scholar
  10. Chen SH, Wang SG (2011) Emergent complexity in agent-based computational economics. J Econ Surv 25(3):527–546CrossRefGoogle Scholar
  11. Chen SH, Yu T (2011) Agents learned, but do we? Knowledge discovery using the agent-based double auction markets. Front Electric Electron Eng China 6:159–170CrossRefGoogle Scholar
  12. Chen SH, Zeng RJ, Yu T (2008) Co-evolving trading strategies to analyze bounded rationality in double auction markets. In: Genetic programming, theory and practice VI. Springer, Heidelberg, pp 195–213Google Scholar
  13. Dawid H (1996) Adaptive learning by genetic algorithms: analytical results and applications to economic models. Springer, New YorkCrossRefGoogle Scholar
  14. Dawid H, Dermietzel J (2006) How robust is the equal split norm? Responsive strategies, selection mechanisms and the need for economic interpretation of simulation parameters. Comput. Econ. 28(4):371–397CrossRefGoogle Scholar
  15. D’Orazio P, Silvestri M (2014) The empirical microstructure of agent-based models: recent trends in the interplay between ACE and Experimental Economics. In: Proc. 11th Int. Symp. Distrib. Comput. Artif. Intell., pp 1–6Google Scholar
  16. Duffy J (2006) Agent-based models and human subject experiments. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics, chap 19, vol 2. Elsevier, Amsterdam, pp 949–1011Google Scholar
  17. Duffy J (2008) Macroeconomics: a survey of laboratory research. Working Papers 334, University of Pittsburgh, Department of EconomicsGoogle Scholar
  18. Farmer JD, Foley D (2009) The economy needs agent-based modeling. Nature 460:685–686CrossRefGoogle Scholar
  19. Giulioni G, Bucciarelli E, Silvestri M, D’Orazio P (2014) Avatar-based macroeconomics - experimental insights into artificial agents behavior. In: Proc. 6th Int. Conf. Agents Artif. Intell. SCITEPRESS - Science and Technology Publications, pp 272–277. doi:10.5220/0004917902720277. http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.522%0/0004917902720277Google Scholar
  20. Gode D, Sunder S (1991) Allocative efficiency of markets with zero intelligence (z1) traders: market as a partial substitute for individual rationality. Gsia working papers, Carnegie Mellon University, Tepper School of BusinessGoogle Scholar
  21. Herrera F, Lozano M, Verdegay JL (1998) Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif Intell Rev 12(4):265–319. doi:10.1023/A:1006504901164CrossRefGoogle Scholar
  22. Holland JH, Miller JH (1991) Artificial adaptive agents in economic theory. Am Econ Rev 81:365–370Google Scholar
  23. Hommes C (2006) Heterogeneous agent models in economics and finance. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics, chap 23, vol 2. Elsevier, Amsterdam, pp 1109–1186Google Scholar
  24. Hommes C (2007) Bounded rationality and learning in complex markets. CeNDEF Working Papers 07-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and FinanceGoogle Scholar
  25. Hommes C (2011) The heterogeneous expectations hypothesis: some evidence from the lab. J Econ Dyn Control 35(1):1–24CrossRefGoogle Scholar
  26. Hommes C, Lux T (2013) Individual expectations and aggregate behavior in learning-to-forecast experiments. Macroecon Dyn 17:373–401CrossRefGoogle Scholar
  27. Kirman AP (1992) Whom or what does the representative individual represent. J Econ Perspect 6:117–136CrossRefGoogle Scholar
  28. Kirman A (2006) Heterogeneity in economics. J Econ Interact Coord 1(1):89–117. doi:10.1007/s11403-006-0005-8CrossRefGoogle Scholar
  29. LeBaron B (2000) Agent-based computational finance: suggested readings and early research. J Econ Dyn Control 24(5–7):679–702CrossRefGoogle Scholar
  30. Lebaron B (2001) A builder’s guide to agent based financial markets. Quant Financ 1:1–2Google Scholar
  31. Sargent TJ (2000) Evolution and intelligent design. Am Econ Rev 98(1):5–37CrossRefGoogle Scholar
  32. Smith VL (1982) Microeconomic systems as an experimental science. Am Econ Rev 72(5):923–55Google Scholar
  33. Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185:1124–1131CrossRefGoogle Scholar
  34. Velupillai KV (2011) Towards an algorithmic revolution in economic theory. J Econ Surv 25(3):401–430CrossRefGoogle Scholar
  35. Waltman L, Eck N, Dekker R, Kaymak U (2011) Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies. J Evol Econ 21(5):737–756CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gianfranco Giulioni
    • 1
    • 2
    Email author
  • Paola D’Orazio
    • 1
    • 2
  • Edgardo Bucciarelli
    • 1
    • 2
  • Marcello Silvestri
    • 1
    • 2
  1. 1.Department of PhilosophicalPedagogical and Economic-Quantitative Sciences, “G. D’Annunzio” UniversityPescaraItaly
  2. 2.Research Group for Experimental Microfoundations of Macroeconomics (GEMM)PescaraItaly

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