Abstract
This paper presents several ideas for combining experimental economics (EXP) with multi-agent simulation (MAS) more effectively. It argues that from an epistemological perspective a closer integration of both methods allows for a better use of their complementary advantages and can accelerate scientific progress. To realize this potential, we suggest an iterative, incremental procedural model as a framework for the collaboration between researchers. To further foster the integration, we recommend a higher level of documentation and standardization with respect to model and result description. An example from prediction markets research illustrates our methodological considerations. It can be shown how the suggested model and result documentations align research efforts and facilitate the transfer of results between EXP and MAS and how the procedural research model augments the scientific contributions of both methods.
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References
Antony J (2003) Design of experiments for engineers and scientists. Butterworth–Heinemann, Amsterdam
Axelrod RM (1984) The evolution of cooperation. Basic Books, New York
Axelrod RM (1997a) Advancing the art of simulation in the social sciences. In: Conte R, Hegselmann R, Terna P (eds) Simulating social phenomena. Springer, Berlin, pp 21–40
Axelrod RM (1997b) The complexity of cooperation: agent-based models of competition and collaboration. Princeton University Press, Princeton
Axelrod RM, Tesfatsion L (2006) A guide for newcomers to agent-based modeling in the social sciences. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics: agent-based computational economics. Amsterdam, Elsevier, pp 1647–1659
Boehm BW (1988) A spiral model of software development and enhancement. IEEE Comput 21:61–72
Boero R, Bravo G, Castellani M, Squazzoni F (2010) Why bother with what others tell you? An experimental data-driven agent-based model. J Artif Soc Soc Simul 13
Dal Forno A, Merlone U (2004) From classroom experiments to computer code. J Artif Soc Soc Simul 7
Davis DD, Holt CA (1993) Experimental economics. Princeton University Press, Princeton
DiNardo J (2008) Natural experiments and quasi-natural experiments. In: Durlauf SN, Blume LE (eds) The new Palgrave dictionary of economics, 2nd edn. Palgrave Macmillan, Basingstoke, pp 856–864
Duffy J (2001) Learning to speculate: experiments with artificial and real agents. J Econ Dyn Control 25:295–319
Duffy J (2006) Agent-based models and human subject experiments. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics: agent-based computational economics. Amsterdam, Elsevier, pp 949–1011
Friedman D, Cassar A (2009) Economics lab: an intensive course in experimental economics. Routledge, London
Gaechter S (2009) Improvements and future challenges for the research infrastructure in the field ‘experimental economics’. RatSWD Working Paper No. 56, Berlin
Gilbert N (2008) Agent-based models. Sage, Los Angeles
Gilbert N, Troitzsch KG (2005) Simulation for the social scientist. 2nd edn. Open University Press, Maidenhead
Gode DK, Sunder S (1993) Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. J Polit Econ 101:119–137
Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J, Grand T, Heinz SK, Huse G, Huth A, Jepsen JU, Jørgensen C, Mooij WM, Müller B, Pe’er G, Piou C, Railsback SF, Robbins AM, Robbins MM, Rossmanith E, Rüger N, Strand E, Souissi S, Stillman RA, Vabø R, Visser U, DeAngelis DL (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198:115–126
Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF (2010) The ODD protocol for describing individual-based and agent-based models: a first update. Ecol Model 221:2760–2768
Hanson R (2007) Logarithmic market scoring rules for modular combinatorial information aggregation. J Predict Mark 1:3–15
Hanson R, Oprea R, Porter D (2006) Information aggregation and manipulation in an experimental market. J Econ Behav Organ 60:449–459
Hayek FA (1945) The use of knowledge in society. Am Econ Rev 35:519–530
Heath B, Hill R, Ciarallo F (2009) A survey of agent-based modeling practices (january 1998 to july 2008). J Artif Soc Soc Simul 12:9
Helbing D, Yu W (2010) The future of social experimenting. Proc Natl Acad Sci USA 107:5265–5266
Houser D, Keane M, McCabe K (2004) Behavior in a dynamic decision problem: an analysis of experimental evidence using a bayesian type classification algorithm. Econometrica 72:781–822
Kleijnen J, Sanchez S, Lucas T, Cioppa T (2005) A user’s guide to the brave new world of designing simulation experiments. INFORMS J Comput 17:263–289
Kruchten P (2000) The rational unified process: an introduction. 2nd edn. Addison-Wesley, Boston
Landau D, Binder K (2009) A guide to Monte Carlo simulations in statistical physics, 3rd edn. Cambridge University Press, Cambridge
Law A (2006) Simulation modeling and analysis. McGraw-Hill, Boston
Lorscheid I, Meyer M, Heine B-O (2011) Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments. Comput Math Organ Theory (this issue)
Meyer M, Lorscheid I, Troitzsch KG (2009) The development of social simulation as reflected in the first ten years of JASSS: a citation and co-citation analysis. J Artif Soc Soc Simul 12
Meyer M, Zaggl MA, Carley KM (2011) Measuring CMOT’s intellectual structure and its development. Comput Math Organ Theory 17:1–34
Montgomery D (2009) Design and analysis of experiments. Wiley, Hoboken
Oprea R, Porter D, Hibbert C, Hanson R, Tila D (2007) Can manipulators mislead market observers? Working Paper
PMI (2004) A guide to the project management body of knowledge: PMBOK guide, 3rd edn. Project Management Institute, Newtown Square
Popper KR (2002) Science: conjectures and refutations. In: Popper KR (ed) Conjectures and refutations: the growth of scientific knowledge. Routledge, London, pp 43–77
Rauhut H, Junker M (2009) Punishment deters crime because humans are bounded in their strategic decision-making. J Artif Soc Soc Simul 12
Reiss J (2011) A plea for (good) simulations: nudging economics toward an experimental science. Simul Gaming 42:243–264
Rouchier J, Robin S (2006) Information perception and price dynamics in a continuous double auction. Simul Gaming 37:195–208
Smith VL (1982) Microeconomic systems as an experimental science. Am Econ Rev 72:923–955
Smith VL (1994) Economics in the laboratory. J Econ Perspect 8:113–131
Smith VL (2008) Experimental methods in economics. In: Durlauf SN, Blume LE (eds). The new Palgrave dictionary of economics, 2nd edn. Palgrave Macmillan, Basingstoke, pp 163–173
Sunder S (1995) Experimental asset markets: a survey. In: Kagel JH, Roth AE (eds) The handbook of experimental economics. Princeton University Press, Princeton, pp 445–498
Wilde LL (1981) On the use of laboratory experiments in economics. In: Pitt JC (ed) Philosophy in economics. Reidel, Dordrecht, pp 137–148
Wolfers J, Zitzewitz E (2006) Five open questions about prediction markets. NBER Working Paper
Wooldridge M (2002) An introduction to multiagent systems. Wiley, Chichester
Wu C-FJ, Hamada M (2000) Experiments: planning, analysis, and parameter design optimization. Wiley, New York
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Klingert, F.M.A., Meyer, M. Effectively combining experimental economics and multi-agent simulation: suggestions for a procedural integration with an example from prediction markets research. Comput Math Organ Theory 18, 63–90 (2012). https://doi.org/10.1007/s10588-011-9098-2
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DOI: https://doi.org/10.1007/s10588-011-9098-2