The Analytics of Robust Satisficing

Satisficers, in contrast to maximizers, are content with attaining the reasonable target that they set for themselves. Such behavior is prevalent in decision makers especially when they are facing complex situations under risks and uncertainty (Simon 1955). While there is an abundance of prescriptive analytics tools for maximizers, it is not the case for satisficers. To fill this gap, we develop a new prescriptive analytics tool called robust satisficing that uses data to help a satisficer achieve her target expected reward or consumption as well as possible under ambiguous risks and prediction uncertainty. It builds upon the robustness optimization framework recently proposed by Long et al. (2021), and we extend it to incorporate aspects of predictive analytics. We adopt linear regression as the underlying predictive model and propose a new estimator uncertainty and residual ambiguity set to characterize the relations between the underlying regression coefficients, which is uncertain but non-stochastic, and the stochastic random variables representing residuals that have ambiguous distributions. The robust satisficing model is also useful in allocating resources for multiple satisficing agents to meet their expected reward targets. We present some useful robust satisficing models that can be solved efficiently, and provide tractable approximations to tackle adaptive linear optimization problems. The simulation studies for newsvendor problems elucidate the benefits of the robust satisficing framework in helping the firm attain the target expected profits, mitigate shortfalls, and limit target surplus, if desired. The robust satisficing model can also improve solutions over one that is obtained by solving a baseline empirical optimization model using estimated parameters. The improvement is also more pronounced when data availability is limited. Paradoxically, maximizers can also benefit from the analytics of robust satisficing.