Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence

This paper presents an empirical investigation of the relation between decision speed and decision quality for a real-world setting of cognitively-demanding decisions in which the timing of decisions is endogenous: professional chess. Move-by-move data provide exceptionally detailed and precise information about decision times and decision quality, based on a comparison of actual decisions to a computational benchmark of best moves constructed using the artificial intelligence of a chess engine. The results reveal that faster decisions are associated with better performance. The findings are consistent with the predictions of procedural decision models like drift-diffusion-models in which decision makers sequentially acquire information about decision alternatives with uncertain valuations.

Optimal Targeting in Fundraising: A Machine Learning Approach

This paper studies optimal targeting as a means to increase fundraising efficacy. We randomly provide potential donors with an unconditional gift and use causal-machine learning techniques to "optimally" target this fundraising tool to the predicted net donors: individuals who, in expectation, give more than their solicitation costs. With this strategy, our fundraiser avoids lossy solicitations, significantly boosts available funds, and, consequently, can increase service and goods provision. Further, to realize these gains, the charity can merely rely on readily available data. We conclude that charities that refrain from fundraising targeting waste significant resources.

On the Causes and Consequences of Deviations from Rational Behavior

This paper presents novel evidence for the prevalence of deviations from rational behavior in human decision making - and for the corresponding causes and consequences. The analysis is based on move-by-move data from chess tournaments and an identification strategy that compares behavior of professional chess players to a rational behavioral benchmark that is constructed using modern chess engines. The evidence documents the existence of several distinct dimensions in which human players deviate from a rational benchmark. In particular, the results show deviations related to loss aversion, time pressure, fatigue, and cognitive limitations. The results also demonstrate that deviations do not necessarily lead to worse performance. Consistent with an important influence of intuition and experience, faster decisions are associated with more frequent deviations from the rational benchmark, yet they are also associated with better performance.