On the Predictive Power of Theories of One-Shot Play

We propose a novel challenge for assessing the predictive power of a theory of one shotplay in games (subjects playing a game exactly once): we test the predictive power of theories in situations for which we do not (yet) have any data. To do so, we consider a variety of such theories and fix their parameter estimates from the recent large scale meta-analysis of Wright and Leyton-Brown (2017). We then compare the predictive power of these theories, measured in terms of log-likelihood, for a series of symmetric hawk-dove games played in the lab. We find that even for such a narrow class of games, no theory is uniformly better than all others across all treatments. Furthermore, the theory that provides the highest overall log-likelihood for our data is Nash equilibrium with risk aversion, with an estimated risk aversion parameter taken from Hey and Orme (1994) and its replication in Harrison and Rutstr m (2009). In particular, it significantly beats the two theories (based on quantal level k and cognitive hierarchy models) which performed best in Wright and Leyton-Brown s (2017) standard out-of-sample prediction task.