Maximin Safety: When Failing to Lose is Preferable to Trying to Win
This work addresses decision-making under uncertainty, offering a new theoretical framework that could influence fields like economics and psychology, though it appears incremental as an extension of minimax regret.
The paper introduces maximin safety, a decision rule that prioritizes maintaining a large margin from the worst outcome, analogous to minimax regret's focus on minimizing distance from the best. It demonstrates its value by explaining the decoy effect descriptively and providing an axiomatization that aligns it with minimax regret normatively.
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.