Truthful Elicitation of Imprecise Forecasts
This addresses the challenge of managing uncertainty in safety-critical domains where existing scoring rules fail, offering a novel solution for decision-makers who rely on accurate probabilistic forecasts.
The paper tackles the problem of truthful elicitation of imprecise forecasts under epistemic uncertainty by proposing a two-way communication framework that connects to social choice theory and uses randomized proper scoring rules, enabling forecasters to report sets of beliefs truthfully and allowing decision-makers to integrate this uncertainty into decisions, thus improving credibility.
The quality of probabilistic forecasts is crucial for decision-making under uncertainty. While proper scoring rules incentivize truthful reporting of precise forecasts, they fall short when forecasters face epistemic uncertainty about their beliefs, limiting their use in safety-critical domains where decision-makers (DMs) prioritize proper uncertainty management. To address this, we propose a framework for scoring imprecise forecasts -- forecasts given as a set of beliefs. Despite existing impossibility results for deterministic scoring rules, we enable truthful elicitation by drawing connection to social choice theory and introducing a two-way communication framework where DMs first share their aggregation rules (e.g., averaging or min-max) used in downstream decisions for resolving forecast ambiguity. This, in turn, helps forecasters resolve indecision during elicitation. We further show that truthful elicitation of imprecise forecasts is achievable using proper scoring rules randomized over the aggregation procedure. Our approach allows DM to elicit and integrate the forecaster's epistemic uncertainty into their decision-making process, thus improving credibility.