Skip to main content
SpringerLink
Log in

The New Evolutionary Computational Paradigm of Complex Adaptive Systems

Challenges and Prospects for Economics and Finance

  • Chapter
Genetic Algorithms and Genetic Programming in Computational Finance

Abstract

The new evolutionary computational paradigm of market systems views these as complex adaptive systems. The major premise of 18th century classical political economy was that order in market systems is spontaneous or emergent, in that it is the result of “human action but not of human design.” This early observation on the disjunction between system wide outcomes and capabilities of micro level rational calculation marks the provenance of modern evolutionary thought. However, it will take a powerful confluence of two 20th century epochal developments for the new evolutionary computational paradigm to rise to the challenge of providing long awaited explanations of what has remained anomalies or outside the ambit of traditional economic analysis. The first of these is the Gödel-Turing-Post results on incompleteness and algorithmically unsolvable problems that delimit formalist calculation or deductive methods. The second is the Anderson-Holland-Arthur heterogeneous adaptive agent theory and models for inductive search, emergence and self-organized criticality which can crucially show and explicitly study the processes underpinning the emergence of ordered complexity. Multi agent model simulation of asset price formation and the innovation based structure changing dynamics of capitalist growth are singled out for analysis of this disjunction between non-anticipating global outcomes and computational micro rationality.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Ackley, D. H. and M. Litterman (1992). “Interactions Between Learning and Evolution,” in C.G. Langton et al. (eds.), Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity, 10, MA: Addison Wesley.

    Google Scholar 

  • Albin, P. (1998). Barriers and Bounds to Rationality, Essays on Economic Complexity and Dynamics in Interactive Systems, Edited and with an Introduction by D. Foley. Princeton University Press.

    Google Scholar 

  • Anderlini, L. and H. Sabourian (1995). “Cooperation and Effective Computability,” Econometrica, 63, 1337–1369.

    Article  Google Scholar 

  • Arifovic, J. (1994). “Genetic Algorithm Learning and the Cobweb Model,” Journal of Economic Dynamics and Control, 18(1), 3–28.

    Article  Google Scholar 

  • Arrow, K. J. and F. H. Hahn (1971). General Competitive Analysis, Amsterdam: North Holland.

    Google Scholar 

  • Arthur, W. B. (1991). “Designing Economic Agents that Act Like Human Agents: A Behavioral Approach To Bounded Rationality,” American Economic Review, 81, 353–359.

    Google Scholar 

  • Arthur, W. B. (1993a). “On Learning and Adaptation in the Economy,” Working Paper, 92-07-038, Santa Fe, NM: Santa Fe Institute.

    Google Scholar 

  • Arthur, W. B.(1993b). “On The Evolution of Complexity,” Working Paper, 93-11-070, Santa Fe, NM: Santa Fe Institute.

    Google Scholar 

  • Arthur, W. B. (1994). “Inductive Behaviour and Bounded Rationality,” American Economic Review, 84, 406–411.

    Google Scholar 

  • Arthur, W. B., J. Holland, B. LeBaron, R. Palmer, and P. Taylor (1997). “Asset Pricing Under Endogenous Expectations in an Artificial Stock Market,” in Arthur W.B., Durlauf S., Lane, D. (eds.), The Economy as an Evolving Complex System II, Reading MA: Addison Wesley.

    Google Scholar 

  • Axtell,R. (2000). “Why Agents? On the Varied Motivations For Agent Computing in the Social Sciences,” Center on Social and Economic Dynamics, Working Paper No.17.

    Google Scholar 

  • Bak, P. (1996). How Nature Works:The Science of Self-Organized Criticality, New York: Springer-Verlag(Copernicus).

    Google Scholar 

  • Barry, N. (1982). “The Tradition of Spontaneous Order,” Literature of Liberty, 7–58.

    Google Scholar 

  • Binmore, K. (1987). “Modelling Rational Players: Part 1,” Journal of Economics and Philosophy, 3, 179–214.

    Article  Google Scholar 

  • Brock, W. and C. Holmes (1997a). “A Rational Route To Randomness,” Econometrica, 65, 1059–1095.

    Article  Google Scholar 

  • Burks, A. W. (1970). Essays on Cellular Automata. Urbana: University of Ilinois Press.

    Google Scholar 

  • Casti, J. (1994). Complexification: Explaining A Paradoxical World Through the Science of Surprises. London: Harper Collins.

    Google Scholar 

  • Challet, D. and Zhang, Y.C. (1998). “On The Minority Game: Analytical And Numerical Studies,” Physica, 256–514.

    Google Scholar 

  • Chen, S.-H. and C.-H. Yeh (2000). “On Emergent Properties of Artificial Stock Markets: The Efficient market Hypothesis and the Rational Expectations Hypothesis”, Mimeo Presented at the Fourth Conference on Economics With Heterogeneous Interacting Agents (WEHIA, 1999).

    Google Scholar 

  • Cutland, N. J. (1980). Computability: An Introduction to Recursive Function Theory. Cambridge University Press.

    Google Scholar 

  • Dawid, H. (1999). Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models, 2nd ed. Springer.

    Google Scholar 

  • Dosi, G. and R. Nelson (1994). “An Introduction to Evolutionary Theories in Economics,” Journal of Evolutionary Economics, 4, 153–172.

    Article  Google Scholar 

  • Easley, D. and J. Ledyard (1993). “Theories of Price Formation and Exchange in Double Oral Auctions,” In D. Friedman and J. Rust (eds.) The Double Auction Market Institutions, Theories and Evidence, Proceeding Volume XIV, Santa Fe Institute. Addison Wesley Publishing Company.

    Google Scholar 

  • Easley, D. and A. Rustichini (1999). “Choice Without Beliefs,” Econometrica, 69(5), 1157–1185.

    Article  Google Scholar 

  • Friedman D. and J. Rust (1993). The Double Auction Market Institutions, Theories and Evidence, Proceedings Volume XIV, Santa Fe Institute. Addison Wesley Publishing Company.

    Google Scholar 

  • Epstein, J. and R. Axtell (1996). Growing Artificial Societies Social Science From Bottom Up. MIT Press.

    Google Scholar 

  • Gode, D. K. and S. Sunder (1993). “Allocative Efficiency of Markets With Zero Intelligence Traders: Markets as a Partial Substitute for Individual Rationality,” Journal of Political Economy, 101, 119–137.

    Article  Google Scholar 

  • Gödel, K. (1931). “On Formally Undecidable Propositions of Principia Mathematica and Related Systems,” Translation in English in Gödel’s Theorem in Focus, ed. S. G. Shanker, 1988, Croom Helm.

    Google Scholar 

  • Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Reading, M.A: Addison Wesley.

    Google Scholar 

  • Goldberg, D. E. (1995). “The Existential Pleasures of Genetic Algorithms,” in G. Winter, J. Perioux, M. Galan and P. Cuesta (eds.), Genetic Algorithms in Engineering and Computer Science, 23–31, Wiley and Sons.

    Google Scholar 

  • Hayek, F. A. (1945). “The Use of Knowledge in Society,” American Economic Review, 35, 519–530.

    Google Scholar 

  • Hayek, F. A. (1953). The Sensory Order, University of Chicago Press, Chicago, IL.

    Google Scholar 

  • Hayek, F. A. (1967). Studies in Philosophy, Politics and Economics, New York: Simon and Schuster.

    Google Scholar 

  • Hayek, F. A. (1982). “The Sensory Order After 25 Years,” in W. B. Weimar and D. S. Palermo, (eds.), Cognition and Symbolic Processes, 2, NJ: Lea Publishers.

    Google Scholar 

  • Hertz, J., A. Krogh, and R. Palmer (1991). Introduction to the Theory of Neural Computation, Redwood City, CA: Addison Wesley.

    Google Scholar 

  • Hillis, W. D. (1992). “Co-Evolving Parasites Improve Simulated Evolution as an Optimization Procedure,” in C.G. Langton, et al. (eds.), Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity, 10. Addison-Wesley.

    Google Scholar 

  • Hodgson, G. (1993). Economics and Evolution: Bringing Life Back Into Economics, Oxford: Basil Blackwell.

    Google Scholar 

  • Holland, J. H. (1975). Adaptation in Natural and Artificial Systems, Ann Arbor, MI: The University of Michigan Press.

    Google Scholar 

  • Holland, J. H.(1992). Adaptation in Natural and Artificial Systems, An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence, 2nd ed. Cambridge, MA: MIT Press.

    Google Scholar 

  • Hurwicz, L. (1960). “Optimality and Informational Efficiency in Resource Allocation processes,” Proceedings of the first Stanford Symposium on Mathematical Methods In Social Sciences, Stanford, CA: Stanford University Press.

    Google Scholar 

  • Hume, D. (1888). A Treatise of Human Nature, in L. A. Selby-Bigge (ed.). Oxford University Press.

    Google Scholar 

  • Kant, I. (1965). The Metaphysical Elements of Justice, Translated, with an Introduction by John Ladd, Indianapolis, IN: The Bobbs-Merriel Company.

    Google Scholar 

  • Kauffman, S. A. (1993). The Origins of Order: Self Organization and Selection in Evolution. Oxford University Press.

    Google Scholar 

  • Kirman, A. and G. Teyssiere (2001). “Microeconomic Models for Long-Memory in the Volatility of Financial Time Series,” Studies in Nonlinear Dynamics and Econometrics, forthcoming.

    Google Scholar 

  • Koons, R. (1992). Paradoxes of Belief and Strategic Rationality. Cambridge University Press.

    Google Scholar 

  • Koza, J. (1992). Genetic Programming, Cambridge, MA: MIT Press.

    Google Scholar 

  • Krugman, P. (1996). The Self-Organizing Economy. Blackwell Publishers.

    Google Scholar 

  • Langton, C. (1992). “Life at the Edge of Chaos,” In, C. Langton et al. (eds.), Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity, 10. Addison-Wesley.

    Google Scholar 

  • LeBaron, B. (2001). “Empirical Regularities from Interacting Long and Short Memory Investors in an Agent Based Stock Market,” IEEE Transactions on Evolutionary Computation.

    Google Scholar 

  • Lewis, A. A. (1985). “On Effectively Computable Choice Functions,” Mathematical Social Sciences, 10.

    Google Scholar 

  • Lewis, A. A. (1987), “On Turing Degrees of Walrasian Models and a General Impossibility Result in the Theory of Decision Making,” Technical Report no. 512 (Centre for Research for Organization Efficiency, Stanford University, CA).

    Google Scholar 

  • Lucas, R. (1972). “Expectations and the Neutrality of Money,” Journal of Economic Theory, 4, 103–124.

    Article  Google Scholar 

  • Lucas, R. (1976). “Econometric Policy Evaluation: A Critique,” Carnegie-Rochester Conference Series on Public Policy, 1, 19–46.

    Article  Google Scholar 

  • Lux, T. and M. Marchesi (1999). “Scaling and Criticality in Stochastic Multi-Agent Model of a Financial Market,” Nature, 397, 498–500.

    Article  Google Scholar 

  • Mandelbroit, B. (1966). “When Can Price Be Arbitraged Efficiently? A Limit to The Validity of the Random Walk and Martingale Models,” The Review of Economics and Statistics, 225–236.

    Google Scholar 

  • Markose S. (1991). “End Independent Rules and the Political Economy of Expanding Market Societies of Europe,” European Journal of Political Economy, 7, 579–601.

    Article  Google Scholar 

  • Markose, S. (2001a). Book Review, Computable Economics by K. Velupillai, In, The Economic Journal, June, 111, 468–470.

    Google Scholar 

  • Markose, S. (2001b), “The Liar and Surprises: So What Is The Lucas Critique — A New Perspective From Computability Theory,” Mimeo University of Essex, <http://www.essex.ac.uk/economics/staff/scher.html>.

  • Marschak, T. (1959). “Centralization and Decentralization in Economic Organizations,” Econometrica, 27.

    Google Scholar 

  • Miller, M. (1986). “Financial Innovation: The Last Twenty Years and the Next,” Journal of Financial and Quantitative Analysis, 21, 459–471.

    Article  Google Scholar 

  • Nicolis, G. and I. Prigogine (1989). Exploring Complexity: An Introduction, New York: Freeman.

    Google Scholar 

  • Nozick, R. (1993). The Nature of Rationality. Princeton University Press.

    Google Scholar 

  • Penrose, R. (1988). “On Physics and Mathematics of Thought,” in Rolf Herken (ed.), The Universal Thring Machine: A Half Century Survey. Oxford University Press.

    Google Scholar 

  • Penrose, R. (1989). The Emperor’s New Mind:Concerning Computers, Minds and The Laws of Physics. Oxford University Press.

    Google Scholar 

  • Post, E.(1944). “Recursively Enumerable Sets of Positive Integers and Their Decision Problems,” Bulletin of American Mathematical Society, 50, 284–316.

    Google Scholar 

  • Pour-El, M. B. and J. I. Richards (1989). Computability In Analysis and Physics, Berlin: Springer-Verlag.

    Google Scholar 

  • Ray, T. S. (1992). “An Approach to The Synthesis of Life,” in C. Langton et. al (eds.), Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity, 10. Addison-Wesley.

    Google Scholar 

  • Rust, J. (1997). “Dealing With the Complexity of Economic Calculations,” Mimeo, Yale University.

    Google Scholar 

  • Samuelson, P. A. (1965). “Proof That Properly Anticipated Prices Fluctuate Randomly,” Industrial Management Review, VI, 41–49.

    Google Scholar 

  • Savit, R., R. Manuca, and R. Riolo (1998). “Adaptive Competition, Market Efficiency and Phase Transition,” Physics Review Letters, 82.

    Google Scholar 

  • Schanze, E.(1995). “Hare and Hedgehog Revisited: The Regualtion of Markets That Have Escaped Regulated Markets,” Journal of Institutional and Theoretical Economics, 15(1), 162–176.

    Google Scholar 

  • Schelling, T. (1978). Micromotives and Macrobehaviour, New York, NY: Norton.

    Google Scholar 

  • Shiller, R. (1981). “Do Stock Market Prices Move Too Much to be Justified by Subsequent Changes in dividends?” American Economic Review, 71, 421–436.

    Google Scholar 

  • Simon, H. A. (1981). The Sciences of the Artificial, Cambridge, MA: MIT Press.

    Google Scholar 

  • Solomon, S. (2000). “Generalized Lotka-Volterra (GLV) Models and Generic Emergence of Scaling Laws in Stock Markets,” in G. Ballot and G. Weisbuch (eds.), Proceedings of the International Conference on Computer Simulations and the Social Sciences, Hermes Science Publications.

    Google Scholar 

  • Solomon, S and M. Levy (1996). “Spontaneous Scaling Emergence in Generic Stochastic Systems,” International Journal of Modern Physics C, 7(5).

    Google Scholar 

  • Spear, S. (1989). “Learning Rational Expectations Under Computability Constraints,” Econometrica, 57, 889–910.

    Article  Google Scholar 

  • Sugden, R. (1989). “Spontaneous Order,” Journal of Economic Perspectives, 4, 85–87.

    Article  Google Scholar 

  • Suzumura, K. (1990). “Alternative Approaches to Libertarian Rights in The Theory of Social Choice,” in K. J. Arrow (ed.), Issues In Contemporary Economics, Vol.1, Markets and Welfare, Macmillan. Testfasion, L. 1998. ACE Website. <http://www.econ.iastate.edu/tesfatsi/ace.htm>.

    Google Scholar 

  • Turing, A. (1936). “On Computable Numbers With an Application To the Entscheidungsproblem,” in M. Davis (ed.), The Undecidable, New York: Raven.

    Google Scholar 

  • Ullmann-Margalit, E. (1978). “Invisible Hand Explanations,” Synthese, 39, 263–290.

    Article  Google Scholar 

  • Velupillai, K. (2000). Computable Economics, Arne Ryde Lectures, Oxford University Press.

    Book  Google Scholar 

  • Vriend, N.(2000). “Was Hayek an ACE?” Mimeo, <http://www.qmw.qc.uk/ ugtel73/.

  • Weibull, J.W.(1996). Evolutionary Game Theory, The MIT Press.

    Google Scholar 

  • Weimar, W. B. (1982). “Hayek’s Approach to the Problems of Complex Phenomenon: An Introduction to the Theoretical Psychology of the Sensory Order,” in W.B. Weimar and D.S. Palermo (eds.), Cognition and Symbolic Processes, 2, NJ: Lea Publishers.

    Google Scholar 

  • Wolfram, S. (1984). “Cellular Automata As Models of Complexity,” Physica, 10D, 1–35.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Economics Department and Institute of Studies in Finance (ISF), University of Essex, UK

    Sheri M. Markose

Authors
  1. Sheri M. Markose
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Economics, National Chengchi University, Taiwan

    Shu-Heng Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer Science+Business Media New York

About this chapter

Cite this chapter

Markose, S.M. (2002). The New Evolutionary Computational Paradigm of Complex Adaptive Systems. In: Chen, SH. (eds) Genetic Algorithms and Genetic Programming in Computational Finance. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0835-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-0835-9_21

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-5262-4

  • Online ISBN: 978-1-4615-0835-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation