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Comparing Theories: What are we Looking For?

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  • John D. Hey

Abstract

Two recent papers, Harless and Camerer(1994) and Hey and Orme(1994) were both addressed to the same question: which is the 'best' theory of decision making under risk? The two papers shared a common concern: the appropriate trade-off between the descriptive accuracy of a theory and the predictive parsimony of that theory. In other respects, however, the two papers differed markedly: first in their treatment of the stochastic specification underlying the data generating process; second, and more importantly, in their interpretation of the question posed. This current paper tackles these two issues; first, trying to resolve the issue of the correct stochastic specification; second, by clarifying what economists might mean by a `best' theory. The paper provides a general framework for answering such questions, and illustrates the application of this framework through two experiments aimed at answering the question: `which is the best theory of decision making under risk?'.

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Paper provided by Department of Economics, University of York in its series Discussion Papers with number 99/18.

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Handle: RePEc:yor:yorken:99/18

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  1. Hey, John D., 1995. "Experimental investigations of errors in decision making under risk," European Economic Review, Elsevier, vol. 39(3-4), pages 633-640, April.
  2. John Hey & Enrica Carbone, . "Which Error Theory is Best?," Discussion Papers 99/31, Department of Economics, University of York.
  3. Hey, John D. & Carbone, Enrica, 1995. "Stochastic choice with deterministic preferences: An experimental investigation," Economics Letters, Elsevier, vol. 47(2), pages 161-167, February.
  4. Selten,Reinhard, . "Properties of a measure of predictive succes," Discussion Paper Serie B 130, University of Bonn, Germany.
  5. Hey, John D., 1998. "An application of Selten's measure of predictive success," Mathematical Social Sciences, Elsevier, vol. 35(1), pages 1-15, January.
  6. Carbone, Enrica, 1997. "Discriminating between Preference Functionals: A Monte Carlo Study," Journal of Risk and Uncertainty, Springer, vol. 15(1), pages 29-54, October.
  7. Hey, John D & Orme, Chris, 1994. "Investigating Generalizations of Expected Utility Theory Using Experimental Data," Econometrica, Econometric Society, vol. 62(6), pages 1291-1326, November.
  8. Carbone, Enrica, 1997. "Investigation of stochastic preference theory using experimental data," Economics Letters, Elsevier, vol. 57(3), pages 305-311, December.
  9. Harless, David W & Camerer, Colin F, 1994. "The Predictive Utility of Generalized Expected Utility Theories," Econometrica, Econometric Society, vol. 62(6), pages 1251-89, November.
  10. Carbone, Enrica & Hey, John D, 1994. "Discriminating between Preference Functionals: A Preliminary Monte Carlo Study," Journal of Risk and Uncertainty, Springer, vol. 8(3), pages 223-42, May.
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