  We derive an optimal strategy in the popular Deal or No Deal game show.
Q-learning quantifies the continuation value inherent in sequential decision
making and we use this to analyze contestants risky choices. Given their
choices and optimal strategy, we invert to find implied bounds on their levels
of risk aversion. In risky decision making, previous empirical evidence has
suggested that past outcomes affect future choices and that contestants have
time-varying risk aversion. We demonstrate that the strategies of two players
(Suzanne and Frank) from the European version of the game are consistent with
constant risk aversion levels except for their last risk-seeking choice.
