Rationally Biased Learning
This work addresses the problem of understanding human decision-making biases for researchers in psychology and economics, offering a rational grounding that is incremental by linking biases to optimization theory.
The paper tackles the problem of explaining human cognitive biases, such as disproportionate attention to bad outcomes and status quo bias, by showing they can arise as optimal solutions in infinite horizon stationary optimization problems under imperfect state observation, and provides conditions for their occurrence and robustness analysis.
Humans display a tendency to pay more attention to bad outcomes, often in a disproportionate way relative to their statistical occurrence. They also display euphorism, as well as a preference for the current state of affairs (status quo bias). Based on the analysis of optimal solutions of infinite horizon stationary optimization problems under imperfect state observation, we show that such human perception and decision biases can be grounded in a form of rationality. We also provide conditions (boundaries) for their possible occurence and an analysis of their robustness.Thus, biases can be the product of rational behavior.