Evolutionarily Optimal Risk Aversion

In an experimental choice situation, we identify risk-acceptability thresholds and show how such thresholds are updated in response to benchmark information, a recurrent feature of health, safety, and environmental (HS&E) risk governance. We present a theoretical framework linking the observed behavior to an underlying evolutionary parameter, which in this case is (an abstract notion of) risk aversion. The theoretical model allows to predict how the experimental subjects adjust their risk aversion when informed about the risky choices of others. Applications of the framework arise naturally in HS&E settings, where individuals and organizations revise risk thresholds by observing peers, experienced coworkers, or acknowledged experts. By distinguishing confidence-driven inertia from trust-driven overreaction, the paper provides actionable guidance for HS&E risk communication and adaptive risk management.