Info-Gap Robust-Satisficing and the Probability of Survival

In this paper we study why, and when, and in what form, a satisficing strategy is a better bet for survival, than a strategy which uses the best available information in attempting to optimize the outcome. We prove that, under severe uncertainty, a robust-satisficing decision has a better probability of survival than a best-model outcome-optimizing decision. These results are based on non-probabilistic info-gap decision theory, which provides a quantification of Knightian uncertainty. We show that our results are applicable to Bayesian mixing of two models, allocation between a risky and a risk-free asset, foraging behavior, explaining Ellsberg's `paradox', satisfying multiple requirements, forecasting in dynamical systems, and managing exogenous uncertainties.