Abstract
Financial theory and financial markets are complex constructs that are not always easy to understand. It is even not possible to explain the function of financial markets on basis of theoretical analysis, since theory normally assumes fully rational agents. [37] have presented the so called “No trade theorem” stating that in a state of efficient equilibrium there will be no trade on markets as long as all participants are fully rational (no noise traders or other uninformed traders) and the structure of information acqusition is common knowledge. It is obvious that the assumptions do not hold in real markets, since we observe asymmetric informed participants or technical traders who base their decisions on the analysis of market movements (and not on the assessment of the underlying value of a stock).
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References
Andreoni JA, Miller JH (1990) Auctions with adaptive arti?cialy intelligent agents. Santa Fe Institute Working Paper, No. 90-01-004
Arifovic J (1994) Genetic algorithm learning and the cobweb model. Journal of Economic Dynamics and Control, 18:3–28
Arthur WB, Holland J, LeBaron B, Palmer R, Tayler P (1996) Asset pricing under endogenous expectations in an arti?cial stock market. Technical report, Santa Fe Institute
Axelrod R (1987) Evolving new strategies – the evolution of strategies in the iterated prisoner’s dilemma. In Davis L, editor, Genetic Algorithms and Simulated Annealing, Pitman and Morgan Kaufman, pp. 32–41
Axelrod R (2003) Advancing the art of simulation in the social science. Japanese Journal for Management Information System, 12(3), Special Issue on Agent-Based Modeling
Axtell R (2000) Why agents? on the varied motivations for agent computing in the social sciences. In Proceedings of the Workshop on Agent Simulation: Applications, Models and Tools, IL, Argonne National Laboratory
Banks J (1998) Principles of simulation. In Banks J, editor, Handbook of Simulation, New York, John Wiley & Sons, Inc., pp. 3–31
Bellman RE (1957) Dynamic Programming, Princeton University Press, Princeton
Bellman RE (1957) A markov decision process, Journal of Mathematical Mechanics, 6:679–684
Blackburn JM (1936) Acquisition of skill: An analysis of learning curves, IHRB Report, No. 73
Bonabeau E (2002) Agent-based modeling: Methods and techniques for simulationg human systems, in Adaptive Agents, Intelligence, and Emergent Human Organization: Capturing Complexity Through Agent-Based Modeling, Proceedings of the National Academy of Sciences of the USA (PNAS), 99 (3), Washington, DC, National Academy of Sciences of the USA, pp. 7280–7287
Bowling M, Veloso M (2000) An analysis of stochastic game theory for multiagent reinforcement learning, Technical Report CMU-CS00-165, Computer Science Department, Carnegie Mellon University, http://www.cs.ualberta.ca/ bowling/papers/00tr.pdf
Cai G, Wurman PR (2005) Monte carlo approximation in incomplete information, sequential auction games, Decision Support Systems, Special Issue: Decision Theory and Game Theory 39(2):153–168
Catania C (1999) Thorndike’s legacy: Learning, selection, and the law of effect, Journal of The Experimental Analysis of Behavior, 72(3):425–428
Epstein J, Axtell R (1996) Growing Arti?cial Societies – Social Science From the BottomUp, MIT Press, Boston
Erev I, Roth AE (1998) Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria, The American Economic Review, 88(4):848–881
Filar J, Vrieze K (1997) Competitive Markov Decision Processes, Springer Verlag, New York
Friedman D (1998) On economic applications of evolutionary game theory. Journal of Evolutionary Economics, 8(1):15–43
Fudenberg D, Tirole J (1991) Game Theory MIT Press, Cambridge
Gilbert N, Troitzsch K (2005) Simulation for the Social Scientist, Open University Press, Graw-Hill Education, Berkshire, England, second edition
Gode D, Sunder, S (1993) Allocative e?ciency of markes with zero-intelligence traders: Market as a partial substitute for individual rationality, Journal of Political Economy, 101:119–137
Hall. On an experimental determination of pi, Messeng. Math., 2:113–114, 1873
Hammersley JM, Handscomb DC (1964) Monte Carlo Spottiswoode, Ballantyne & Co Ltd, London and Colchester
Holland JH (1992) Adaptation in Natural and Arti?cial Systems, MIT Press, Boston, 1st MIT-Press edition (book was originally published 1975)
Holland JH, Miller JH (1991) Arti?cial adaptive agents in economic theory, American Economic Review, Papers and Proceedings, 81:365–370
Hommes CH (2006) Heterogeneous agent models in economics and ?nance, in: Tesfatsion L, Judd KL, editors, Handbook of Computational Economics
Howard RA (1960) Dynamic Programming and Markov Processes, The MIT Press, Cambridge, MA
Hu J, Wellman, MP (1998) Multiagent reinforcement learning: Theoretical framework and an algorithm, in: Proceedings of the 15th International Conference on Machine Learning
Judd KL (1997) Computational economics and economic theory: Substitutes or complements? Journal of Economic Dynamics and Control, 21(6):907–942
Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: A survey, Journal of Arti?cial Intelligence Research, 4:237–285
Kirman A, The structure of economic interaction: Individual and collective rationality, in: Bourgine P, Nadal JP, editors, Cognitive Economics: An Interdisciplinary Approach, Chapter 18, Springer, Heidelberg, pp. 293–312
Krugman P (1996) What economists can learn from evolutionary theorists, talk given to the European Association for Evolutionary Political Economy, http://www.mit.edu/krugman/evolute.html
Kunzelmann M, Mäkiö J (2005) Pegged and bracket order as a success factor in stock exchange competition, in: Proceedings of the 2nd Conference FinanceCom05, IEEE
Littman, ML (1994) Markov games as a framework for multi-agent reinforcement learning, in: Proceedings of the 11th International Conference on Machine Learning, San Francisco, CA, pp. 157–163
Marimon R (1993) Adaptive learning, evolutionary dynamics and equilibrium selection in games, European Economic Review, 37:603–611
Metropolis N, Ulam, S (1949) The monte carlo method, Journal of the American Statistical Association, 44(247):335–341
Milgrom P, Stokey NL (1982) Information, trade, and common knowledge, Journal of Economic Theory, 26(1):17–27
Miller JH (1986) A genetic model of adaptive economic behaviour, Working paper, University of Michigan, Ann Arbor
Neumann D (2004) Market Engineering – A Structured Design Process for Electronic Markets, PhD thesis, Universität Karlsruhe (TH), Karlsruhe, Germany
Nicolaisen J, Petrov V, Tesfatsion, L (2001) Market power and efficiency in a computational electricity market with discriminatory double-auction pricing. IEEE Transactions on Evolutionary Computation, 5(5):504–523, http://www. econ.iastate.edu/tesfatsi/mpeieee.pdf
Optimatics (2004) Genetic Algorithm Technology for the Water Industry, information on http://www.optimatics.com/GAhistory.htm
Ostrom TM (1988) Computer simulation: The third symbol system, Journal of Experimental Social Psychology, 24(5):381–392
Owen G (1982) Game Theory. The MIT Press, Cambridge, MA, second edition
Puterman ML (1994) Markov Decision Processes – Discrete Stochastic Dynmaic Programming, Wiley Series in Probability and Statistics, John Wiley & Sons Inc., New York
Puterman ML, Shin MC (1978) Modi?ed policy iteration algorithms for discounted markov decision processes, Management Science, 24:1127–1137
Riechmann T (2002) Genetic algorithm learning and economic evolution, in: Chen SH, editor, Evolutionary Computation in Economics and Finance, Physica-Verlag, Heidelberg, pp. 45–60
Schelling TC, Models of segregation, The American Economic Review, 59(2):488–493
Sen S, Weiss G (1999) Learning in multiagent systems, In Weiss G, editor, Multiagent Systems, The MIT Press, Cambridge (Massachusetts) pp. 259–298
Shapley LS (1953) Stochastic games, Proceedings of the National Academy of Sciences of the United States of America, 39(10):1095–1100
Steiglitz K, Honig ML, Cohen LM (1996) A computational market model based on individual action, in Clearwater S, editor, Market-Based Control: A Paradigm for Distributed Resource Allocation, Hong Kong, 1996, World Scienti?c, see http://www.cs.princeton.edu/ ken/scott.ps
Sutton RS, Barto AG (2002), Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 2002
Tesfatsion L (2002) Agent-based computational economics: Growing economies from the bottom-up, ISU Economics Working Paper No. 1, February 2002, http://www.econ.iastate.edu/tesfatsi/
Thorndike EL (1898) Animal intelligence: An experimental study of the associative processes in animals, Psychological Review Monograph Supplement, II(4, Whole No. 8)
Thorndike EL (1901) The Human Nature Club; an Introduction to the Study of Mental Life, Longman, Green and Co., New York, 2nd, enl. edition
Thorndike EL (1907) The Elements of Psychology, A.G. Seiler, New York, 2nd edition
Thorndike EL (1909) Darwin’s contribution to psychology, University of California Chronicle, 12:65–80, 1909
Thorndike EL (1911) Animal Intelligence: Experimental Studies, The Animal Behavior Series. Macmillan, New York
von Neumann J (1966) Theory of Self-Reproducing Automata, University of Illinois Press, Urbana and London
Watkins C. (1989) Learning from Delayed Rewards, PhD thesis, University of Cambridge, England
Watkins C, Dayan P (1992) Qlearning, Machine Learning, 8(3–4):279–292
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van Dinther, C. (2008). Agent-based Simulation for Research in Economics. In: Seese, D., Weinhardt, C., Schlottmann, F. (eds) Handbook on Information Technology in Finance. International Handbooks Information System. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49487-4_18
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