Abstract
One of the biggest challenges facing behavioral economics is the lack of a single theoretical framework that is capable of directly utilizing all types of behavioral data. One of the biggest challenges of game theory is the lack of a framework for making predictions and designing markets in a manner that is consistent with the axioms of decision theory. An approach in which solution concepts are distribution-valued rather than set-valued (i.e. equilibrium theory) has both capabilities. We call this approach Predictive Game Theory (or PGT). This paper outlines a general Bayesian approach to PGT. It also presents one simple example to illustrate the way in which this approach differs from equilibrium approaches in both prediction and mechanism design settings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Myerson, R.B.: Game theory: Analysis of Conflict. Harvard University Press, Cambridge (1991)
McKelvey, R.D., Palfrey, T.R.: Quantal response equilibria for normal form games. Games and Economic Behavior 10, 6–38 (1995)
Crawford, V., Iriberri, N.: Level-k auctions: Can a nonequilibrium model of strategic thinking explain the winner’s curse and overbidding in private-value auctions? Econometrica 75, 1721–1770 (2007)
Jaynes, E.T., Bretthorst, G.L.: Probability Theory: The Logic of Science. Cambridge University Press, Cambridge (2003)
Berger, J.M.: Statistical Decision theory and Bayesian Analysis. Springer, Heidelberg (1985)
Zellner, A.: Some aspects of the history of bayesian information processing. Journal of Econometrics (2004)
Paris, J.B.: The Uncertain Reasoner’s Companion: A Mathematical Perspective. Cambridge University Press, Cambridge (1994)
Horn, K.S.V.: Constructing a logic of plausible inference: a guide to cox’s theorem. International Journal of Approximate Reasoning 34, 3–24 (2003)
Fudenberg, D., Tirole, J.: Game Theory. MIT Press, Cambridge (1991)
Nisan, N., Ronen, A.: Algorithmic mechanism design. Games and Economic Behavior 35, 166–196 (2001)
Camerer, C.: Behavioral Game theory: experiments in strategic interaction. Princeton University Press, Princeton (2003)
Starmer, C.: Developments in non-expected utility theory: the hunt for a descriptive theory of choice under risk. Journal of Economic Literature 38, 332–382 (2000)
Allais, M.: Econometrica 21, 503–546 (1953)
List, J.A., Haigh, M.S.: A simple test of expected utility theory using professional traders. Proceedings of the National Academy of Sciences 102, 945–948 (2005)
Kurzban, R., Houser, D.: Experiments investigating cooperative types in humans. Proceedings of the National Academy of Sciences 102, 1803–1807 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wolpert, D.H., Bono, J.W. (2010). PGT: A Statistical Approach to Prediction and Mechanism Design. In: Chai, SK., Salerno, J.J., Mabry, P.L. (eds) Advances in Social Computing. SBP 2010. Lecture Notes in Computer Science, vol 6007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12079-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-12079-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12078-7
Online ISBN: 978-3-642-12079-4
eBook Packages: Computer ScienceComputer Science (R0)