Ryan Oprea wins the Exeter Prize for the paper "What Makes a Rule Complex?"

 The Exeter Prize Committee has circulated the cheerful announcement below, about the latest winner of that prize (which has a very distinguished history):

"We are happy to announce the winner of the 2021 Exeter Prize for the best paper published in the previous calendar year in a peer-reviewed journal in the fields of Experimental Economics, Behavioural Economics and Decision Theory.

"The winner is Ryan Oprea (University of California at Santa Barbara) for his paper “What Makes a Rule Complex”, published in The American Economic Review. 

"This paper offers a crisp experimental measurement of complexity. It offers a rich description of how complexity affects actual humans, which has tremendous potential for informing policy making as well as theoretical research across disciplines (from psychology, computer science, and cognitive science to economics). In the experiment the subjects are asked to implement various decision rules. Five dimensions of complexity are studied: the number of states and transitions, existence of absorbing states and redundant states, and a measure of working memory. The paper looks at three different measures of behaviour: the error rate, the implementation time, and the subjects' own willingness to pay to get the decision rule implemented by a computer. The experiment also measures how fast people learn various decision rules and how transferable this knowledge is. This paper offers a new impetus for research, getting us outside of our comfortable box of constrained optimization. This is a risky and challenging attempt, with a high upside potential.

"The winning paper was selected by the panel of Nina Mazar (Boston University), Rosemarie Nagel (ICREA-UPF-Universitat Pompeu Fabra), and Tomasz Strzalecki (Harvard University). "

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Here's the paper:

What Makes a Rule Complex?  AMERICAN ECONOMIC REVIEW, VOL. 110, NO. 12, DECEMBER 2020  (pp. 3913-51  "By Ryan Oprea*

"We study the complexity of rules by paying experimental subjects to implement a series of algorithms and then eliciting their willingness-to-pay to avoid implementing them again in the future. The design allows us to examine hypotheses from the theoretical “automata” literature about the characteristics of rules that generate complexity costs. We find substantial aversion to complexity and a number of regularities in the characteristics of rules that make them complex and costly for subjects. Experience with a rule, the way a rule is represented, and the context in which a rule is implemented (mentally versus physically) also influence complexity"