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Sequential search code9/25/2023 ![]() Ultimately, moving away from a binary antagonistic stance regarding heuristic performance, research should be directed at demarcating the boundary between environments where heuristics perform well and environments where they may be inappropriate. ( 2019) for examples of this across a wide range of different decision-making tasks. This latter strand of the literature emphasizes that heuristics perform best when their processes match relevant characteristics of the environment, thereby exploiting them efficiently in the spirit of Simon’s ‘scissors’ metaphor, particularly in the face of uncertainty-see Hertwig et al. An alternative viewpoint contends that heuristics can be fast and frugal, exhibiting excellent performance and even outperforming normative models in environments of irreducible uncertainty arising from nature (Gigerenzer et al., 1999, 2011 Hertwig et al., 2013 Todd et al., 2012) or imperfect knowledge of opponents’ strategic behavior and payoffs in games (Spiliopoulos & Hertwig, 2020). ![]() One viewpoint emphasizes the performance shortcomings of descriptive heuristics, arguing that they often lead to biases and sub-optimal behavior (e.g., Kahneman, 2003a, b, 2011 Tversky & Kahneman, 1974). In contrast, our manuscript focuses on the second question, namely, the relative performance of heuristics (simple rule-of-thumb models) vis-à-vis an optimal benchmark. ![]() First, are heuristics predictive of human behavior? Second, how does heuristic performance compare to the optimal solutions of the decision-making tasks? When testing models in the controlled environment of the laboratory, the focus of the literature typically is on the former question, their predictiveness, namely, how well the predictions of the model match systematic and replicable patterns of behavior that are observed in the raw data. The heuristics literature in cognitive psychology is concerned with two different but related issues. We present the subgame-perfect Nash equilibrium for this competitive variant and an algorithm for its computation. Both heuristics share a simple computational component: the ratio of the number of interviewed applicants to the number of those remaining to be searched. Finally, we propose a new heuristic with near optimal performance in a competitive or strategic variant of the secretary problem with multiple employers competing with one another to hire job applicants. ![]() We show that a computational heuristic originally proposed as an approximate solution to a single variant of the secretary problem performs equally well in many other variants where the optimal solution prescribes multiple threshold values that gradually relax the criterion for stopping the search. Considering multiple variants of the secretary problem, that vary from one another in their formulation and method of solution, we find that descriptive heuristics perform well only when the optimal solution prescribes a single threshold value. We study the performance of heuristics relative to the performance of optimal solutions in the rich domain of sequential search, where the decision to stop the search depends only on the applicant’s relative rank.
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