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題名 論太陽黑子均衡的可能性--代理人基人工股票市場的應用
On the Plausibility of Sunspot Equilibria: An Analysis Based on Agent-Based Artifical Stock Markets
作者 周佩蓉
Chou, peijung
貢獻者 陳樹衡
Chen,Shu-Heng
周佩蓉
Chou,peijung
關鍵詞 太陽黑子
太陽黑子均衡
人工股票市場
agent-based計算經濟學
遺傳程式
sunspots
sunspot equilbira
artificial stock market
agent-based computational economics
Genetic Programming
日期 2006
上傳時間 6-May-2016 16:55:07 (UTC+8)
摘要 The existence of sunspots or sunspot equilibria has been debated for several decades on its influence in the field of Economics. While models of sunspots or sunspot equilibria have fitted well for some subsets of empirical features, it comes at a cost of moving further away from economic believability and robustness. Studies on the theoretical plausibility of sunspot equilibria have been addressed extensively in several different economic models, but exist almost entirely within the framework of the homogeneous rational expectations equilibrium devised of representative agents. This framework shapes later arising learning approaches to sunspot equilibria. These models have proposed various ways of learning, but they deal mainly with the learning of representative agents. Models of adaptive learning with heterogeneous agents, however, enable us to explicitly tackle coordination issues, such as the coordination mechanism of expectations. This is certainly desirable since sunspots are often used as a coordination device of expectations. In this dissertation, we continue this line of research, investigating the plausibility of sunspot equilibria in stock markets within the framework of heterogeneous agents and the dynamic relationship between sunspot variables and stock returns. We adopt an Agent-based Computational Approach, now known as Agent-based Computational Economics or ACE, to study the plausibility of sunspot equilibria. More specifically, we deal with this issue in the context of an Agent-based Artificial Stock Market (AASM). We contemplate AASMs to be highly suitable to the issue we examine here. Currently, none of the theoretical, empirical, experimental, or simulation models of sunspot equilibria directly capture sunspots within a stock market composed of heterogeneous agents. We conducted three series of experiments to examine this issue. From the results of these three series of simulations, we observed that sunspot variables generally do not have influence on market dynamics. This indicates that sunspot variables remain largely exogenous to the system. Furthermore, we traced the evolution of agents` beliefs and examined their consistency with the observed aggregate market behavior. Additionally, this dissertation takes the advantage of and investigates the micro-macro relationship within the market. We argue that a full understanding of the dynamic linkage between sunspot variables and stock returns cannot be accomplished unless the feedback relationship between individual behaviors, at the micro view, and aggregate phenomena, at the macro view, is well understood
The existence of sunspots or sunspot equilibria has been debated for several decades on its influence in the field of Economics. While models of sunspots or sunspot equilibria have fitted well for some subsets of empirical features, it comes at a cost of moving further away from economic believability and robustness. Studies on the theoretical plausibility of sunspot equilibria have been addressed extensively in several different economic models, but exist almost entirely within the framework of the homogeneous rational expectations equilibrium devised of representative agents. This framework shapes later arising learning approaches to sunspot equilibria. These models have proposed various ways of learning, but they deal mainly with the learning of representative agents. Models of adaptive learning with heterogeneous agents, however, enable us to explicitly tackle coordination issues, such as the coordination mechanism of expectations. This is certainly desirable since sunspots are often used as a coordination device of expectations. In this dissertation, we continue this line of research, investigating the plausibility of sunspot equilibria in stock markets within the framework of heterogeneous agents and the dynamic relationship between sunspot variables and stock returns. We adopt an Agent-based Computational Approach, now known as Agent-based Computational Economics or ACE, to study the plausibility of sunspot equilibria. More specifically, we deal with this issue in the context of an Agent-based Artificial Stock Market (AASM). We contemplate AASMs to be highly suitable to the issue we examine here. Currently, none of the theoretical, empirical, experimental, or simulation models of sunspot equilibria directly capture sunspots within a stock market composed of heterogeneous agents. We conducted three series of experiments to examine this issue. From the results of these three series of simulations, we observed that sunspot variables generally do not have influence on market dynamics. This indicates that sunspot variables remain largely exogenous to the system. Furthermore, we traced the evolution of agents` beliefs and examined their consistency with the observed aggregate market behavior. Additionally, this dissertation takes the advantage of and investigates the micro-macro relationship within the market. We argue that a full understanding of the dynamic linkage between sunspot variables and stock returns cannot be accomplished unless the feedback relationship between individual behaviors, at the micro view, and aggregate phenomena, at the macro view, is well understood.
參考文獻 1. Albin, P.S. (1998), "Barriers and bounds to rationality: essays on economic complexity and dynamics in interactive system," Princeton University Press, Princeton (NJ).
     2. Arifovic, J. (1994), "Genetic Algorithm Learning and the Cobweb Model," Journal of Economic Dynamics and Control, 18, pp. 3-28.
     3. Arifovic, J. (1995), "Genetic Algorithms and Inflationary Economics," Journal of Monetary Economics, 36(1), pp. 219-243.
     4. Arthur, W.B., J.H. Holland, B. LeBaron, R. Palmer, and P. Tayler (1997), "Asset pricing under endogenous expectations in an artificial stock market," in: W. B. Arthur, S. Durlauf, and D. Lane (eds.), The Economy as an Evolving Complex System II, Addison-Wesley, pp. 15--44.
     5. Arthur, W.B. (1994) Inductive behavior and bounded rationality. American Economic Review, 84, pp. 406-411.
     6. Axelrod, R. (1997), The Complexity of Cooperation, Princeton University Press.
     7. Azariadis, C. (1981), "Self-fulfilling prophecies," Review of Economic Studies LIII, pp. 725-737.
     8. Baek, E., and W.A. Brock, (1991), "A general test for nonlinear Granger causality: bivariate model," unpublished manuscrip, University of Wisconsin, Madison, WI.
     9. Banhabib, J., and R.A. Farmer (1994), "Indeterminacy and increasing returns," Journal of Economic Theory, 63, pp. 19-41.
     10. Board, R. (1994), "Polynomial bounded rationality," Journal of Economic Theory, 63, pp. 246-270.
     11. Bray, M.M., and D.M.Kreps (1987), "Rational Learning and Rational Expectations," in G.R.Feiwl (ed.), Arrow and the Ascent of Modern Economic Theory, NYU Press.
     12. Bullard, J. and J. Duffy (1998a), "A Model of Learning and Emulation with Artificial Adaptive Agents," Journal of Economic Dynamics and Control, 22, pp. 179-207.
     13. Bullard, J. and J. Duffy (1998b), "Learning and the Stability of Cycles," Macroeconomic Dynamic, 2, pp. 22-48.
     14. Bullard, J. and J. Duffy (1999), "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, 13, pp. 41-60.
     15. Cass, D., and K. Shell (1983), "Do Sunspots Matter?" Journal of Economic Theory, 25, pp. 380-396.
     16. Casti, J.L. (1997), Would-Be Worlds--How Simulation Is Changing the Frontiers of Science, John Wiley & Sons, Inc., New York.
     17. Chen, S.-H. (2002), Evolutionary computational in economics and finance, New York: Physica-Verlag Press.
     18. Chen, S.-H., and C.-C. Liao (2002), "Price discovery in agent-based computational modeling of artificial stock markets," in: S.-H. Chen (Ed.), Genetic Algorithm and Genetic Programming in Computational Finance, Kluwer Academic Publisher, Dordrecht, 2002, pp. 335-356.
     19. Chen, S.-H., and C.-C. Liao (2005), "Agent-Based Computational Modeling of the Stock Price-Volume Relation," Information Sciences 170, pp. 75-100.
     20. Chen,S.-H, C.-C. Liao, P.-J. Chou (2006), "On the Plausibility of Sunspot Equilibria: Simulations Based on Agent-Based Artificial Stock Markets," AI-ECON Research Center Working Paper, National Chengchi University, Taipei, Taiwan.
     21. Chen, S.-H., and C.-H. Yeh (2001), "On the Role of Intensive Search in Stock Markets: Simulations Based on Agent-Based Computational Modeling of Artificial Stock Markets. In Proceedings of the Second Asia-Pasific Conference on Genetic Algorithms and Applications. Global Link Publishing Company, Hong Kong, pp. 397-402.
     22. Chen, S.-H., and C.-H. Yeh (2001), "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, 25, pp. 363--393.
     23. Chen, S.-H., and C.-H. Yeh (2002), "On the emergent properties of artificial stock markets," Journal of Economic Behavior and Organization, 49, pp. 217-239.
     24. Chen, S.-H., C.-H. Yeh, and C.-C. Liao (2002), "On AIE-ASM: Software to Simulate Artificial Stock Markets with Genetic Programming," in S.-H. Chen (ed.), Evolutionary Computation in Economics and Finance, Physica-Verlag, pp. 107-122.
     25. Denker, M., and G. Keller (1983), "Rigorous Statistical Procedures for Data from Dynamical System," Journal of Statistical Physics, 44, pp. 67-93.
     26. Gode, D.K. and S.Sunder (1993), "Allocative efficiency of markets with zero intelligence traders: Market as a partial substitue for individual rationality, Journal of Political Eoconomics, 101(1), pp. 119-137.
     27. Duffy, J. and E. Fisher (2005), "Sunspots in the Laboratory," The American Economic Review, 95(3), pp. 510-529.
     28. Dawid, H. (1996), "Learning of cycles and sunspot equilibria by Genetic Algorithms," Evolutionary Economics, 6, pp. 361-373.
     29. Egenter, E., T. Lux and D. Stauffer (1999), "Finite-Size Effects in Monte Carlo Simulations of Two Stock Market Models, Physica A, pp. 250-256.
     30. Evans, G., and S. Honkapohja (1994), "On the Local Stability of Sunspot Equilibria under Adaptive Learning Rules," Journal of Economic Theory, 64, pp. 142V161.
     31. Farmer, R. E.A., and J.-H. Guo (1994), "Real business cycles and the animal spirits hypothesis," Journal of Economic Theory, 63, pp. 42-72.
     32. Granger, C.W.J. (1969), "Investigating causal relations by econometric models and cross-spectral methods," Econometrica, 37, pp. 424--438.
     33. Grossman, S.J., and J.E. Stiglitz (1980), "On the impossibility of informationally efficient markets," American Economic Review, 70, pp. 393--408.
     34. Guesnerie, R. (1986), "Stationary sunspot equilibria in an N-commodity world," Journal of Economic Theory, 40, pp. 103-128.
     35. Hamilton, J.D. (1994), Time Series Analysis, Princeton University Press.
     36. Hiemstra, C., and J.D. Jones (1994), "Testing for linear and nonlinear Granger causality in the stock price-volume relation," Journal of Finance, 49, pp. 1639--1664.
     37. Holland, J.H., and J.H. Miller (1991), "Artificial adaptive agents in economic theory," American Economic Review 81, pp. 365--370.
     38. Hsiao, C. (1981), "Autoregressive modelling and money-income causality detection," Journal of Monetary Economics, 7, pp. 85--106.
     39. Kennedy, J., Eberhart, R. and Shi, Y. (2001), Swarm Intelligence, San Mateo, California: Morgan Kauffaman.
     40. LeBaron, L.B., B.W. Arthur, and R. Palmer (1999), "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control 25, pp. 363-393.
     41. LeBaron, L.B. (2000), "Agent-based computational finance: Suggested reading and early research," Journal of Economic Dynamics and Control, 24, pp. 679-702.
     42. LeBaron, B. (2001), "Evolution and time horizons in an agent-based stock market," Macroeconomic Dynamics, 5, pp. 225-254.
     43. LeBaron, B. (2006), "Agent-based Computational Finance," forthcoming in L. Tesfatsion and K.L. Judd (eds.), Handbook of Computational Economics, Vol. 2, North Holland.
     44. Leombruni, R., and M. Richiardi (2005), "Why are economists sceptical about agent-based simulations?" Physica A 355, pp. 103-109.
     45. Levy, H., H. Levy, and S. Solomon, Microscopic Simulation of Financial Markets, Academic Press, 2000.
     46. Kajii, A. (1997), "On the Role of Options in Sunspot Equilibria," Econometrica, 65 (4), pp. 977-986.
     47. Koza, J.R. (1992), "A Genetic Approach to Econometric Modeling," in P. Bourgine, and B. Williser (eds.), Economics and Cognitive Science, pp. 57-75. Pergamon Press.
     48. Hens, T. (2000), "Do Sunspots Matter when Spot Market Equilibrium Are Unique," Econometrica, 68(2), pp.435-441.
     49. NachBar, J.H. (1997), "Prediction, optimization, and learning in repeated games," Econometrica, 65(2), pp. 275-309.
     50. Negroni, G. (2005), "Educative Expectations Coordination on Deterministic Cycles in an Economy with Heterogeneous Agents," Journal of Economic Dynamics and Control, 29, pp. 931-952.
     51. Marimon, R., E.McGrattan, and T. Sargent (1990), "Money as Medium of Exchange in an Economy with Artificially Intelligent Agents," Journal of Economic Dynamics and Control, 14, pp. 329-374.
     52. Marimon, R., S. Spear, and S. Sunder (1993), "Exceptionally Driven Market Volatility: An Experimental Study," Journal of Economic Theory, 61(1), pp. 74-103.
     53. Palmer, R.G., W.B. Arthur, J.H. Holland, B. LeBaron, and P. Tayler (1994), "Artificial economic life: A simple model of a stock market," Physica D, 75, pp. 264--274.
     54. Sargent, T.J. (1993), "Bounded Rationality in Macroeconomics." Oxford University Press, New York (NY).
     55. Simon, H.A. (1984), "Models of Bounded Rationality," The MIT Press, Cambridge (MA).
     56. Tesfatsion, L. (2001), "Introduction to the special issue on agent-based computational economics," Journal of Economic Dynamics and Control 25, pp. 281-293.
     57. Tesfatsion, L. (2006), "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Retrieved April 14, 2006, from Iowa State University, Department of Economics, Web site: http://www.econ.iastate.edu/tesfatsi/ace.htm
     58. Townsend, R.M. (1983), "Forecasting the Forecasts of Others," Journal of Political Economy, 91, pp.546-588.
     59. Wiener, N. (1956), "The theory of prediction," in: E.F. Beckenbach (eds.), Modern Mathematics for Engineers, Series 1, McGraw-Hill, New York (Chapter8).
     60. Woodford, M. (1990), "Learning to Believe in Sunspots," Econometrica, 58, pp. 277-307.
描述 碩士
國立政治大學
經濟學系
91258038
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0912580381
資料類型 thesis
dc.contributor.advisor 陳樹衡zh_TW
dc.contributor.advisor Chen,Shu-Hengen_US
dc.contributor.author (Authors) 周佩蓉zh_TW
dc.contributor.author (Authors) Chou,peijungen_US
dc.creator (作者) 周佩蓉zh_TW
dc.creator (作者) Chou, peijungen_US
dc.date (日期) 2006en_US
dc.date.accessioned 6-May-2016 16:55:07 (UTC+8)-
dc.date.available 6-May-2016 16:55:07 (UTC+8)-
dc.date.issued (上傳時間) 6-May-2016 16:55:07 (UTC+8)-
dc.identifier (Other Identifiers) G0912580381en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/94548-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經濟學系zh_TW
dc.description (描述) 91258038zh_TW
dc.description.abstract (摘要) The existence of sunspots or sunspot equilibria has been debated for several decades on its influence in the field of Economics. While models of sunspots or sunspot equilibria have fitted well for some subsets of empirical features, it comes at a cost of moving further away from economic believability and robustness. Studies on the theoretical plausibility of sunspot equilibria have been addressed extensively in several different economic models, but exist almost entirely within the framework of the homogeneous rational expectations equilibrium devised of representative agents. This framework shapes later arising learning approaches to sunspot equilibria. These models have proposed various ways of learning, but they deal mainly with the learning of representative agents. Models of adaptive learning with heterogeneous agents, however, enable us to explicitly tackle coordination issues, such as the coordination mechanism of expectations. This is certainly desirable since sunspots are often used as a coordination device of expectations. In this dissertation, we continue this line of research, investigating the plausibility of sunspot equilibria in stock markets within the framework of heterogeneous agents and the dynamic relationship between sunspot variables and stock returns. We adopt an Agent-based Computational Approach, now known as Agent-based Computational Economics or ACE, to study the plausibility of sunspot equilibria. More specifically, we deal with this issue in the context of an Agent-based Artificial Stock Market (AASM). We contemplate AASMs to be highly suitable to the issue we examine here. Currently, none of the theoretical, empirical, experimental, or simulation models of sunspot equilibria directly capture sunspots within a stock market composed of heterogeneous agents. We conducted three series of experiments to examine this issue. From the results of these three series of simulations, we observed that sunspot variables generally do not have influence on market dynamics. This indicates that sunspot variables remain largely exogenous to the system. Furthermore, we traced the evolution of agents` beliefs and examined their consistency with the observed aggregate market behavior. Additionally, this dissertation takes the advantage of and investigates the micro-macro relationship within the market. We argue that a full understanding of the dynamic linkage between sunspot variables and stock returns cannot be accomplished unless the feedback relationship between individual behaviors, at the micro view, and aggregate phenomena, at the macro view, is well understoodzh_TW
dc.description.abstract (摘要) The existence of sunspots or sunspot equilibria has been debated for several decades on its influence in the field of Economics. While models of sunspots or sunspot equilibria have fitted well for some subsets of empirical features, it comes at a cost of moving further away from economic believability and robustness. Studies on the theoretical plausibility of sunspot equilibria have been addressed extensively in several different economic models, but exist almost entirely within the framework of the homogeneous rational expectations equilibrium devised of representative agents. This framework shapes later arising learning approaches to sunspot equilibria. These models have proposed various ways of learning, but they deal mainly with the learning of representative agents. Models of adaptive learning with heterogeneous agents, however, enable us to explicitly tackle coordination issues, such as the coordination mechanism of expectations. This is certainly desirable since sunspots are often used as a coordination device of expectations. In this dissertation, we continue this line of research, investigating the plausibility of sunspot equilibria in stock markets within the framework of heterogeneous agents and the dynamic relationship between sunspot variables and stock returns. We adopt an Agent-based Computational Approach, now known as Agent-based Computational Economics or ACE, to study the plausibility of sunspot equilibria. More specifically, we deal with this issue in the context of an Agent-based Artificial Stock Market (AASM). We contemplate AASMs to be highly suitable to the issue we examine here. Currently, none of the theoretical, empirical, experimental, or simulation models of sunspot equilibria directly capture sunspots within a stock market composed of heterogeneous agents. We conducted three series of experiments to examine this issue. From the results of these three series of simulations, we observed that sunspot variables generally do not have influence on market dynamics. This indicates that sunspot variables remain largely exogenous to the system. Furthermore, we traced the evolution of agents` beliefs and examined their consistency with the observed aggregate market behavior. Additionally, this dissertation takes the advantage of and investigates the micro-macro relationship within the market. We argue that a full understanding of the dynamic linkage between sunspot variables and stock returns cannot be accomplished unless the feedback relationship between individual behaviors, at the micro view, and aggregate phenomena, at the macro view, is well understood.en_US
dc.description.tableofcontents ACKNOWLEDGEMENTS i
     ABSTRACT ii
     1.Introduction 1
      1.1 Background 3
      1.1.1 Agent-based Artificial Stock Markets 4
      1.1.2 Sunspot equilibria 5
      1.1.3 Forecasting Mechanism with Sunspots 5
      1.1.4 Sunspots as a Coordinating Mechanism 6
      1.2 Objective and Motivations 7
      1.3 Overview 8
     2 Exploring Agent-Based Artificial Stock Markets 9
      2.1 The Santa Fe Artificial Stock Market 10
      2.2 The AI-ECON Artificial Stock Market 11
     3 Experiments with the Agent-Based Artificial Stock Market Model 14
      3.1 The Mathematical Model of the AI-ECON Artificial Stock Market 14
      3.1.1 A Static View of the Trader 15
      3.1.2 Model of Price Determination 16
      3.1.3 A Dynamic View of the Trader 17
      3.2 Sunspot Equilibria and Granger Causality 22
      3.2.1 Definition of Sunspot Equilibria 22
      3.2.2 Wiener-Granger Causality Test 24
      3.2.3 Definition 24
      3.2.4 Testing for linear Granger causality 25
      3.2.5 Testing for non-linear Granger causality 26
      3.3 Experimental Designs 28
      3.3.1 Series I 31
      3.3.2 Series II 31
      3.3.3 Series III 32
     4 Results of Experiments 35
      4.1 Experiments with Identical Sunspot Densities 35
      4.1.1 Perspective from the Top 35
      4.1.2 Perspective from the Bottom 38
      4.2 Experiments with Different Sunspot Densities 39
      4.2.1 Perspective from the Top 39
      4.2.2 Perspective from the Bottom 41
      4.3 Experiments with the Time Horizon 43
      4.3.1 Discussion 44
     5 Conclusions and Future Work 46
      5.1 Future Directions 48
     A The Methodology of Agent-Based Computational Economics 50
      A.1 The Problem Facing Agent-Based Computational Economics 51
     B The Additional Analysis into the Plausibility of Sunspot Equilibria 53
      B.1 Statistical Analysis 53
     C Homogeneous Rational Expectations Equilibrium (HREEP) 56		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0912580381en_US
dc.subject (關鍵詞) 太陽黑子zh_TW
dc.subject (關鍵詞) 太陽黑子均衡zh_TW
dc.subject (關鍵詞) 人工股票市場zh_TW
dc.subject (關鍵詞) agent-based計算經濟學zh_TW
dc.subject (關鍵詞) 遺傳程式zh_TW
dc.subject (關鍵詞) sunspotsen_US
dc.subject (關鍵詞) sunspot equilbiraen_US
dc.subject (關鍵詞) artificial stock marketen_US
dc.subject (關鍵詞) agent-based computational economicsen_US
dc.subject (關鍵詞) Genetic Programmingen_US
dc.title (題名) 論太陽黑子均衡的可能性--代理人基人工股票市場的應用zh_TW
dc.title (題名) On the Plausibility of Sunspot Equilibria: An Analysis Based on Agent-Based Artifical Stock Marketsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. Albin, P.S. (1998), "Barriers and bounds to rationality: essays on economic complexity and dynamics in interactive system," Princeton University Press, Princeton (NJ).
     2. Arifovic, J. (1994), "Genetic Algorithm Learning and the Cobweb Model," Journal of Economic Dynamics and Control, 18, pp. 3-28.
     3. Arifovic, J. (1995), "Genetic Algorithms and Inflationary Economics," Journal of Monetary Economics, 36(1), pp. 219-243.
     4. Arthur, W.B., J.H. Holland, B. LeBaron, R. Palmer, and P. Tayler (1997), "Asset pricing under endogenous expectations in an artificial stock market," in: W. B. Arthur, S. Durlauf, and D. Lane (eds.), The Economy as an Evolving Complex System II, Addison-Wesley, pp. 15--44.
     5. Arthur, W.B. (1994) Inductive behavior and bounded rationality. American Economic Review, 84, pp. 406-411.
     6. Axelrod, R. (1997), The Complexity of Cooperation, Princeton University Press.
     7. Azariadis, C. (1981), "Self-fulfilling prophecies," Review of Economic Studies LIII, pp. 725-737.
     8. Baek, E., and W.A. Brock, (1991), "A general test for nonlinear Granger causality: bivariate model," unpublished manuscrip, University of Wisconsin, Madison, WI.
     9. Banhabib, J., and R.A. Farmer (1994), "Indeterminacy and increasing returns," Journal of Economic Theory, 63, pp. 19-41.
     10. Board, R. (1994), "Polynomial bounded rationality," Journal of Economic Theory, 63, pp. 246-270.
     11. Bray, M.M., and D.M.Kreps (1987), "Rational Learning and Rational Expectations," in G.R.Feiwl (ed.), Arrow and the Ascent of Modern Economic Theory, NYU Press.
     12. Bullard, J. and J. Duffy (1998a), "A Model of Learning and Emulation with Artificial Adaptive Agents," Journal of Economic Dynamics and Control, 22, pp. 179-207.
     13. Bullard, J. and J. Duffy (1998b), "Learning and the Stability of Cycles," Macroeconomic Dynamic, 2, pp. 22-48.
     14. Bullard, J. and J. Duffy (1999), "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, 13, pp. 41-60.
     15. Cass, D., and K. Shell (1983), "Do Sunspots Matter?" Journal of Economic Theory, 25, pp. 380-396.
     16. Casti, J.L. (1997), Would-Be Worlds--How Simulation Is Changing the Frontiers of Science, John Wiley & Sons, Inc., New York.
     17. Chen, S.-H. (2002), Evolutionary computational in economics and finance, New York: Physica-Verlag Press.
     18. Chen, S.-H., and C.-C. Liao (2002), "Price discovery in agent-based computational modeling of artificial stock markets," in: S.-H. Chen (Ed.), Genetic Algorithm and Genetic Programming in Computational Finance, Kluwer Academic Publisher, Dordrecht, 2002, pp. 335-356.
     19. Chen, S.-H., and C.-C. Liao (2005), "Agent-Based Computational Modeling of the Stock Price-Volume Relation," Information Sciences 170, pp. 75-100.
     20. Chen,S.-H, C.-C. Liao, P.-J. Chou (2006), "On the Plausibility of Sunspot Equilibria: Simulations Based on Agent-Based Artificial Stock Markets," AI-ECON Research Center Working Paper, National Chengchi University, Taipei, Taiwan.
     21. Chen, S.-H., and C.-H. Yeh (2001), "On the Role of Intensive Search in Stock Markets: Simulations Based on Agent-Based Computational Modeling of Artificial Stock Markets. In Proceedings of the Second Asia-Pasific Conference on Genetic Algorithms and Applications. Global Link Publishing Company, Hong Kong, pp. 397-402.
     22. Chen, S.-H., and C.-H. Yeh (2001), "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, 25, pp. 363--393.
     23. Chen, S.-H., and C.-H. Yeh (2002), "On the emergent properties of artificial stock markets," Journal of Economic Behavior and Organization, 49, pp. 217-239.
     24. Chen, S.-H., C.-H. Yeh, and C.-C. Liao (2002), "On AIE-ASM: Software to Simulate Artificial Stock Markets with Genetic Programming," in S.-H. Chen (ed.), Evolutionary Computation in Economics and Finance, Physica-Verlag, pp. 107-122.
     25. Denker, M., and G. Keller (1983), "Rigorous Statistical Procedures for Data from Dynamical System," Journal of Statistical Physics, 44, pp. 67-93.
     26. Gode, D.K. and S.Sunder (1993), "Allocative efficiency of markets with zero intelligence traders: Market as a partial substitue for individual rationality, Journal of Political Eoconomics, 101(1), pp. 119-137.
     27. Duffy, J. and E. Fisher (2005), "Sunspots in the Laboratory," The American Economic Review, 95(3), pp. 510-529.
     28. Dawid, H. (1996), "Learning of cycles and sunspot equilibria by Genetic Algorithms," Evolutionary Economics, 6, pp. 361-373.
     29. Egenter, E., T. Lux and D. Stauffer (1999), "Finite-Size Effects in Monte Carlo Simulations of Two Stock Market Models, Physica A, pp. 250-256.
     30. Evans, G., and S. Honkapohja (1994), "On the Local Stability of Sunspot Equilibria under Adaptive Learning Rules," Journal of Economic Theory, 64, pp. 142V161.
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