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Implications of Complex Behavioral Economics for Post Keynesian Economics

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Future of Post Keynesian Economics session at 12th International Conference

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Implications of Complex Behavioral Economics for Post Keynesian Economics

  1. 1. Implications of Complex Behavioral Economics for Post Keynesian Economics J. Barkley Rosser, Jr. Professor of Economics James Madison University rosserjb@jmu.edu http://cob.jmu.edu/rosserjb
  2. 2. Why Might Post Keynesian Economics not be Complex or Behavioral? • Terzi and Davidson (separate papers) argue (JPKE, 2010) that Talebian black swans, Knightian uncertainty, and behavioral economics based on post-Walrasian (i.e. “complexity”) economics all assume ergodic system and thus this uncertainty is merely epistemological. • Risk versus uncertainty • However, many models based on these make no such assumption. Non-ergodic complexity can be ontological foundation of uncertainty, which leads to behavioral economics as response.
  3. 3. Keynes and Behavioral Economics • Keynes clearly described economic agents using behavioral heuristics and rules, not always rationally, in the face of uncertainty. • “Better to fail conventionally than to succeed unconventionally.” (GT, p. 158) • Beauty contest and herd dynamics • Loss aversion • Hyperbolic discounting • Conventions (rules of thumb) and “conventual valuation” • Animal spirits • Bounded rationality
  4. 4. What is Complexity? • Meta-Complexity • Dynamic, Computational, Structural, Others • The 4 C’s of Dynamic Complexity • 1) Cybernetics • 2) Catastrophe Theory • 3)Chaos Theory • 4) Agent-Based Dynamic Complexity
  5. 5. Figure 5: Fractal basin boundaries for three magnets
  6. 6. Herbert Simon and Bounded Rationality • Administrative Behavior, 1947 (influence of Commons) • “A Behavioral Model of Rational Choice,” 1955, QJE • Models of Man, 1957 • Limits due to information, problem of infinite regress (Conlisk, “Why Bounded Rationality?” 1956, JEL; problem of “planification” versus “planning”) • Limits due to computation (Simon as Father of Artificial Intelligence, Sciences of the Artificial, 1969) • Satifisficing (not just minimizing information cost, Stigler, 1961, evolutionary adaptation) • ID problem, search for solutions, evaluate solutions in light of satisficing, from substantive to procedural rationality (1976)
  7. 7. Heuristics for Procedural Rationality • Trial and error • Imitation • Follow authority • Unmotivated search • Hunch • (Pingle and Day, 1996, “Modes of Economizing Behavior: Experimental Evidence,” JEBO.
  8. 8. Imitation and Bubbles • Bounded rationality and foundations of behavioral finance • Imitation heuristic central to formation of speculative bubbles • Herding dynamics and heterogeneous agents • Bubbles and skew functions (Simon, 1955, Biometrika), or fat tails and kurtosis • Chaotic bubbles (Day and Huang, 1990) • Minsky-Kindleberger-Shiller view
  9. 9. Figure 2: stylized representation of a bubble produced by rational bubbles Figure 3: a stylized representation of a bubble produced by interacting heterogeneous agents. It can be asymmetric but it falls much slower than the rational bubble. Figure 4: a stylized representation of a bubble with a crash preceded by a period of financial distress.
  10. 10. Housing Prices in US, Case-Shiller Index, 1987- 2011
  11. 11. : Dow-Jones Industrial Average, 200-2011, Daily Data
  12. 12. Increase in J. The two time series share the same random numbers and same parameters but J. In the grey time series J = 0.5; in the black time series J = 3.
  13. 13. The Meaning of Hierarchical Complexity • Simon on hierarchical complexity (“The Architecture of Complexity,” Proceedings of the American Philosophical Society, 1962) • Question of Decomposibility • Link to evolution (Crutchfield, “When Evolution is Revolution --- The Origins of Innovation,” 2003) • Emergence Question: From Mill to Lewes to Morgan to Kauffman
  14. 14. Adaptation and Bounded Rationality • Alchian, 1950 • Nelson and Winter, 1982, An Evolutionary Theory of Economic Change • Selten, “Aspiration Adaptation Theory,” Journal of Mathematical Psychology, 1998; later work with Gigerenzer • Quantal response equilibria: McKelvey and Palfrey (1995) • Consistent expectations equilibria: Hommes- Sorger, Hommes-Rosser (1998, 2001)
  15. 15. Figure 3 here: Gordon-Schaefer-Clark Fishery Model
  16. 16. Figure 4: Learning to believe in chaos
  17. 17. Conclusions • Complexity as foundation of bounded rationality • Behavioral response is emphasis on satisficing. • Use of heuristics for procedural rationality • Evolutionary adaptation and complexity in a world of fundamental Keynesian uncertainty

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