Naïve versus Sophisticated Mixing: Experimental Evidence

We identify a new behavioral bias. We define naïve players as those who, when indifferent between optimal choices, assign an equal probability to each. We test for their existence. In a first session, we sort participants... [ view full abstract ]

We identify a new behavioral bias. We define naïve players as those who, when indifferent between optimal choices, assign an equal probability to each. We test for their existence. In a first session, we sort participants into naïve players and their sophisticated counterparts. Two weeks later, each group plays against varying proportions of automated players (bots) that follow varying off-equilibrium mixed strategies. We find evidence of the existence of players that are relatively naïve and of the reaction by sophisticated players. This compensation is not large enough to restore equilibria, implying there are predictable methods to attain above-equilibrium payoffs. We also isolate altruistic components of players' strategies: behavior gets closer to Nash equilibria by adding transparent bots that do not directly incentivize any change in behavior but decrease the benefits of surplus maximizing behavior. Lastly, the analysis suggests that the probability of being naïve can be partially predicted by a quantitative test.