Stochastic satisficing account of confidence in uncertain value-based decisions

Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. waiting time in a queue, affects decisions when outcome is stochastic and continuous. For example, how does one choose between an option with unreliable but high expected reward, and an option with more certain but lower expected reward? Here we used an experimental design where two choices’ payoffs took continuous values, to examine the effect of outcome variance on decisions and confidence. Inconsistent with expected utility predictions, our participants’ probability of choosing the good option decreased when both better and worse options’ payoffs were more variable. Confidence ratings were affected by outcome variability only when choosing the good option. Inspired by the satisficing heuristic, we propose a “stochastic satisficing” (SSAT) model for choosing between options with continuous uncertain outcomes. In this model, decisions are made by comparing the available options’ probability of exceeding an acceptability threshold and confidence reports scale with the chosen option’s satisficing probability. The SSAT model best explained choice behaviour and most successfully predicted confidence ratings. We further tested the model’s prediction in a second experiment where choice and confidence behaviours were found to be consistent with the SSAT simulations. Our model and experimental results generalize the cognitive heuristic of satisficing to stochastic contexts and thus provide an account of bounded rationality in the face of uncertainty.