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A Casino Gambling Model Under Cumulative Prospect Theory: Analysis and Algorithm

We develop an approach to solve the Barberis casino gambling model [Barberis N (2012) A model of casino gambling. Management Sci. 58(1):35–51] in which a gambler whose preferences are specified by the cumulative prospect theory (CPT) must decide when to stop gambling by a prescribed deadline. We assume that the gambler can assist their decision using independent randomization. The problem is inherently time inconsistent because of the probability weighting in CPT, and we study both precommitted and naïve stopping strategies. We turn the original problem into a computationally tractable mathematical program from which we devise an algorithm to compute optimal precommitted rules that are randomized and Markovian. The analytical treatment enables us to confirm the economic insights of Barberis for much longer time horizons and to make additional predictions regarding a gambler’s behavior, including that, with randomization, a gambler may enter the casino even when allowed to play only once and that it is prevalent that a naïf never stops loss.

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Informasi Detail

Judul Seri

MANAGEMENT SCIENCE; Vol.69 No.4 April 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2474-2496

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

RANDOMIZATION
TIME INCONSISTENCY
CASINO GAMBLING
CUMULATIVE PROPECT THEORY
SKOROKHOD EMBEDDING

Info Detail Spesifik

https://doi.org/10.1287/mnsc.2022.4414

Pernyataan Tanggungjawab

Sang Hu, Jan Obloj, Xun Yu Zhou

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