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This paper proposes a novel framework for identifying an agent's riskaversion using interactive questioning. Our study is conducted in twoscenarios: a one-period case and an infinite horizon case. In the one-periodcase, we assume that the agent's risk aversion is characterized by a costfunction of the state and a distortion risk measure. In the infinite horizoncase, we model risk aversion with an additional component, a discount factor.Assuming the access to a finite set of candidates containing the agent's truerisk aversion, we show that asking the agent to demonstrate her optimalpolicies in various environment, which may depend on their previous answers, isan effective means of identifying the agent's risk aversion. Specifically, weprove that the agent's risk aversion can be identified as the number ofquestions tends to infinity, and the questions are randomly designed. We alsodevelop an algorithm for designing optimal questions and provide empiricalevidence that our method learns risk aversion significantly faster thanrandomly designed questions in simulations. Our framework has importantapplications in robo-advising and provides a new approach for identifying anagent's risk preferences.

Eliciting Risk Aversion with Inverse Reinforcement Learning via Interactive Questioning