Maximin Safety: When Failing to Lose Is Preferable to Trying to Win

DOI10.1007/978-3-642-39091-3_22zbMATH Open1390.68656arXiv1501.05031OpenAlexW1825135573MaRDI QIDQ5326424

Publication date: 5 August 2013

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Abstract: We present a new decision rule, emph{maximin safety}, that seeks to maintain a large margin from the worst outcome, in much the same way minimax regret seeks to minimize distance from the best. We argue that maximin safety is valuable both descriptively and normatively. Descriptively, maximin safety explains the well-known emph{decoy effect}, in which the introduction of a dominated option changes preferences among the other options. Normatively, we provide an axiomatization that characterizes preferences induced by maximin safety, and show that maximin safety shares much of the same behavioral basis with minimax regret.

Full work available at URL: https://arxiv.org/abs/1501.05031

Mathematics Subject Classification ID

Decision theory (91B06) Reasoning under uncertainty in the context of artificial intelligence (68T37)