The Risk of Failure: Trial and Error Learning and Long-Run Performance

Steven Callander, Niko Matouschek

Research output: Working paper

Abstract

Learning by trial and error is ubiquitous. It is also risky. Success is far from guaranteed as breakthroughs are mixed with setbacks and the path of learning is typically far from smooth. How decision makers learn by trial and error and the efficacy of the process, therefore, are inextricably linked to the incentives of the decision makers themselves and, in particular, to their tolerance for risk. In this paper we develop a model of trial and error learning with risk averse agents. We identify a causal loop between risk aversion and experimentation. Lower risk aversion leads to bolder experimentation which, in turn, leads to better performance and lower risk aversion. This feedback loop leads to long-term divergence in performance. We show that initial performance differences across otherwise similar organizations persist and grow over time in expectation, a result that resonates with empirical experience.
Original languageEnglish (US)
Number of pages40
Publication statusPublished - Apr 11 2016

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