Eliciting Risk Aversion with Inverse Reinforcement Learning via
  Interactive Questioning
Ziteng Cheng Anthony Coache Sebastian Jaimungal
This paper proposes a novel framework for identifying an agent's risk
aversion using interactive questioning. Our study is conducted in two
scenarios: a one-period case and an infinite horizon case. In the one-period
case, we assume that the agent's risk aversion is characterized by a cost
function of the state and a distortion risk measure. In the infinite horizon
case, we model risk aversion with an additional component, a discount factor.
Assuming the access to a finite set of candidates containing the agent's true
risk aversion, we show that asking the agent to demonstrate her optimal
policies in various environment, which may depend on their previous answers, is
an effective means of identifying the agent's risk aversion. Specifically, we
prove that the agent's risk aversion can be identified as the number of
questions tends to infinity, and the questions are randomly designed. We also
develop an algorithm for designing optimal questions and provide empirical
evidence that our method learns risk aversion significantly faster than
randomly designed questions in simulations. Our framework has important
applications in robo-advising and provides a new approach for identifying an
agent's risk preferences.