2021/06/10 by Zengjing Chen, Larry G. Epstein, Chen, Zengjing +3 · 1 citation
Decision Sciences · #Advanced Bandit Algorithms Research #Decision-Making and Behavioral Economics #FOS: Computer and information sciences #FOS: Economics and business #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probability (math.PR) #Risk and Portfolio Optimization #Theoretical Economics (econ.TH)
paper · pdf · doi:10.48550/arxiv.2106.05472
openalex publication_date 2021/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper studies a multi-armed bandit problem where the decision-maker is loss averse, in particular she is risk averse in the domain of gains and risk loving in the domain of losses. The focus is on large horizons. Consequences of loss aversion for asymptotic (large horizon) properties are derived in a number of analytical results. The analysis is based on a new central limit theorem for a set of measures under which conditional variances can vary in a largely unstructured history-dependent way subject only to the restriction that they lie in a fixed interval.