Learning in a Black Box

University of Oxford, Department of Economics Discussion Paper Series, 653

32 Pages Posted: 24 Sep 2015

See all articles by Heinrich H. Nax

Heinrich H. Nax

ETH Zürich; University of Zurich

Maxwell N. Burton-Chellew

University of Oxford

Stuart West

University of Oxford

H. Young

University of Oxford

Date Written: April 23, 2013

Abstract

We study behavior in repeated interactions when agents have no information about the structure of the underlying game and they cannot observe other agents' actions or payoffs. Theory shows that even when players have no such information, simple payoff-based learning rules eventually lead to equilibrium. Such rules have previously been documented for some forms of animal behavior, but to date they have not been tested empirically for humans. This paper analyzes human behavior in a laboratory setting and finds strong confirmation for key features of payoff-based learning that distinguish it from classical reinforcement models, and that are crucial for equilibrium convergence. By varying the amount of information we show that these features are also present even when subjects have full information about the game.

Keywords: black box, learning, information, public goods game

JEL Classification: C70, C73, C91, D83, H41

Suggested Citation

Nax, Heinrich H. and Burton-Chellew, Maxwell N. and West, Stuart and Young, H., Learning in a Black Box (April 23, 2013). University of Oxford, Department of Economics Discussion Paper Series, 653, Available at SSRN: https://ssrn.com/abstract=2664646 or http://dx.doi.org/10.2139/ssrn.2664646

Heinrich H. Nax (Contact Author)

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

University of Zurich ( email )

Rämistrasse 71
Zürich, CH-8006
Switzerland

Maxwell N. Burton-Chellew

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

Stuart West

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

H. Young

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

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