License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license (CC BY-NC-ND 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FSTTCS.2008.1741
URN: urn:nbn:de:0030-drops-17419
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2008/1741/
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Berger, Noam ; Kapur, Nevin ; Schulman, Leonard ; Vazirani, Vijay

Solvency Games

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08004.BergerNoam.1741.pdf (0.4 MB)

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.

BibTeX - Entry

@InProceedings{berger_et_al:LIPIcs:2008:1741,
  author =	{Noam Berger and Nevin Kapur and Leonard Schulman and Vijay Vazirani},
  title =	{{Solvency Games}},
  booktitle =	{IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science},
  pages =	{61--72},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-08-8},
  ISSN =	{1868-8969},
  year =	{2008},
  volume =	{2},
  editor =	{Ramesh Hariharan and Madhavan Mukund and V Vinay},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2008/1741},
  URN =		{urn:nbn:de:0030-drops-17419},
  doi =		{10.4230/LIPIcs.FSTTCS.2008.1741},
  annote =	{Keywords: Decision making under uncertainity, multi-arm bandit problems, game theory}
}

Keywords: Decision making under uncertainity, multi-arm bandit problems, game theory
Collection: IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science
Issue Date: 2008
Date of publication: 05.12.2008

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