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Solvency Games

Authors Noam Berger, Nevin Kapur, Leonard Schulman, Vijay Vazirani

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LIPIcs.FSTTCS.2008.1741.pdf
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  • Decision making under uncertainity
  • multi-arm bandit problems
  • game theory

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Abstract

We study the decision theory of a maximally risk-averse investor ---
one whose objective, in the face of stochastic uncertainties, is to
minimize the probability of ever going broke. With a view to
developing the mathematical basics of such a theory, we start with a
very simple model and obtain the following results: a characterization
of best play by investors; an explanation of why poor and rich players
may have different best strategies; an explanation of why
expectation-maximization is not necessarily the best strategy even for
rich players. For computation of optimal play, we show how to apply
the Value Iteration method, and prove a bound on its convergence
rate.

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Noam Berger, Nevin Kapur, Leonard Schulman, and Vijay Vazirani. Solvency Games. In IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science. Leibniz International Proceedings in Informatics (LIPIcs), Volume 2, pp. 61-72, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008) https://doi.org/10.4230/LIPIcs.FSTTCS.2008.1741

Author Details

Noam Berger
Nevin Kapur
Leonard Schulman
Vijay Vazirani
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