Investment behavior under Knightian uncertainty - An evolutionary approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Lensberg:1999:JEDC,
  author =       "Terje Lensberg",
  title =        "Investment behavior under Knightian uncertainty - An
                 evolutionary approach",
  journal =      "Journal of Economic Dynamics and Control",
  year =         "1999",
  volume =       "23",
  pages =        "1587--1604",
  number =       "9-10",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6V85-3Y9RKX5-G/2/6c6369b7934fdea4d1937c49a35ada38",
  keywords =     "genetic algorithms, genetic programming, Knightian
                 uncertainty, Bayesian rationality",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.7821",
  URL =          "http://sci2s.ugr.es/keel/pdf/specific/articulo/science2_31.pdf",
  DOI =          "doi:10.1016/S0165-1889(98)00085-2",
  abstract =     "The as if view of economic rationality defends the
                 profit maximisation hypothesis by pointing out that
                 only those firms who act as if they maximise profits
                 can survive in the long run. Recently, the problem of
                 arriving at a logically consistent definition of
                 rational behaviour in games has shown that one must
                 sometimes study explicitly the evolutionary processes
                 that form the basis of this view. The purpose of this
                 paper is to investigate the usefulness of genetic
                 programming as a tool for generating hypotheses about
                 rational behavior in situations where explicit
                 maximization is not well defined. We use an investment
                 decision problem with Knightian uncertainty as a
                 borderline test case, and show that when the artificial
                 agents receive the same information about the unknown
                 probability distributions, they develop behaviour rules
                 as if they were expected utility maximisers with
                 Bayesian learning rules.",
  notes =        "JEL classification codes: B41; C63; D83",
}

Genetic Programming entries for Terje Lensberg

Citations