Articlehybrid2009

Rage against the machines: how subjects play against learning algorithms

Peter Duersch · Albert Kolb · Jörg Oechssler · Burkhard C. Schipper

43 citations0/0 files storedEconomic Theory

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Experimental Behavioral Economics Studies Game Theory and Applications Innovative Teaching Methodologies in Social Sciences

Abstract

We use a large-scale internet experiment to explore how subjects learn to play against computers that are programmed to follow one of a number of standard learning algorithms. The learning theories are (unbeknown to subjects) a best response process, fictitious play, imitation, reinforcement learning, and a trial & error process. We explore how subjects’ performances depend on their opponents’ learning algorithm. Furthermore, we test whether subjects try to influence those algorithms to their advantage in a forward-looking way (strategic teaching). We find that strategic teaching occurs frequently and that all learning algorithms are subject to exploitation with the notable exception of imitation.

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References

1995The Handbook of experimental economics 1996Potential Games 1998The Theory of Learning in Games 1995Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term 1952Activity Analysis of Production and Allocation 2020Researches into the Mathematical Principles of the Theory of Wealth 1951An Iterative Method of Solving a Game 1997The Evolution of Walrasian Behavior

Cited by

2012Unbeatable imitation 2009Imitators and optimizers in Cournot oligopoly 2010“A profit table or a profit calculator?” A note on the design of Cournot oligopoly experiments 2011Learning about learning in games through experimental control of strategic interdependence

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