Evolutionarily Stable (Mis)specifications: Theory and Applications

He, Kevin; Libgober, Jonathan

doi:10.2139/ssrn.3914870

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…Thus, the present paper, for the first time in this literature, introduces statistical consistency to dynamic decisions under ambiguity. The objective approach also resonates with some recent papers that study misspecified learning according to objective criteria such as He and Libgober (2020), Frick, Iijima and Ishii (2021), and references therein.…”

Section: Related Literaturesupporting

confidence: 62%

Robust Data-Driven Decisions Under Model Uncertainty

Cheng¹

2022

Preprint

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When sample data are governed by an unknown sequence of independent but possibly non-identical distributions, the data-generating process (DGP) in general cannot be perfectly identified from the data. For making decisions facing such uncertainty, this paper presents a novel approach by studying how the data can best be used to robustly improve decisions.That is, no matter which DGP governs the uncertainty, one can make a better decision than without using the data. I show that common inference methods, e.g., maximum likelihood and Bayesian updating cannot achieve this goal. To address, I develop new updating rules that lead to robustly better decisions either asymptotically almost surely or in finite sample with a prespecified probability. Especially, they are easy to implement as are given by simple extensions of the standard statistical procedures in the case where the possible DGPs are all independent and identically distributed. Finally, I show that the new updating rules also lead to more intuitive conclusions in existing economic models such as asset pricing under ambiguity.

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Section: Related Literaturesupporting

confidence: 62%

Robust Data-Driven Decisions Under Model Uncertainty

Cheng¹

2022

Preprint

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“…Esponda and Pouzo (2016) propose an equilibrium concept for such settings: the Berk–Nash equilibrium. Subsequently, a number of papers have studied the properties of Berk–Nash equilibria in different applied contexts (Fudenberg, Romanyuk, and Strack (2017), Heidhues, Kőszegi, and Strack (2018), Frick, Iijima, and Ishii (2020)) and the persistence and stability of misspecifications (Frick, Iijima, and Ishii (2021b), Fudenberg and Lanzani (2021), He and Libgober (2021)). In addition to using this framework to explore the gambler's fallacy, I also highlight a new source of data endogeneity relative to the existing papers: the censoring effect in an optimal‐stopping problem.…”

Section: Related Theoretical Literaturementioning

confidence: 99%

Mislearning from censored data: The gambler's fallacy and other correlational mistakes in optimal‐stopping problems

2022

Self Cite

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I study endogenous learning dynamics for people who misperceive intertemporal correlations in random sequences. Biased agents face an optimal‐stopping problem. They are uncertain about the underlying distribution and learn its parameters from predecessors. Agents stop when early draws are “good enough,” so predecessors' experiences contain negative streaks but not positive streaks. When agents wrongly expect systematic reversals (the “gambler's fallacy”), they understate the likelihood of consecutive below‐average draws, converge to overpessimistic beliefs about the distribution's mean, and stop too early. Agents uncertain about the distribution's variance overestimate it to an extent that depends on predecessors' stopping thresholds. I also analyze how other misperceptions of intertemporal correlation interact with endogenous data censoring.

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“…It assumes actions do not influence the distribution over outcomes, so the issues that we address do not arise. He and Libgober (2021) study competition between two models in a game setting, where even correctly specified models can be outperformed by some mutants. The inference in their model does not depend on data that was generated before the mutation.…”

Section: Introductionmentioning

confidence: 99%

Which misspecifications persist?

Fudenberg¹,

Lanzani²

2023

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We use an evolutionary model to determine which misperceptions can persist. Every period, a new generation of agents use their subjective models and the data generated by the previous generation to update their beliefs, and models that induce better actions become more prevalent. An equilibrium can resist mutations that lead agents to use a model that better fits the equilibrium data but induce the mutated agents to take an action with lower payoffs. We characterize which steady states resist mutations to a nearby model, and which resist mutations that drop a qualitative restriction such as independence.

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Evolutionarily Stable (Mis)specifications: Theory and Applications

Cited by 5 publications

References 19 publications

Robust Data-Driven Decisions Under Model Uncertainty

Robust Data-Driven Decisions Under Model Uncertainty

Mislearning from censored data: The gambler's fallacy and other correlational mistakes in optimal‐stopping problems

Which misspecifications persist?

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