Predicting focal point solution in divergent interest tacit coordination games

In divergent interest tacit coordination games there is a tradeoff between selecting a solution with a high individual payoff and one which is perceptually more salient to both players, i.e., a focal point. To construct a cognitive model of decision making in such games we need to consider both the social value orientation of the players and the game features. Therefore, the goal of this study was to construct a cognitive model for predicting the probability of selecting a focal point solution in these types of games. Using bootstrap aggregated ensemble of decision trees that was trained on the “bargaining table’ game behavioural data were able to predict when players will select a focal point solution. The binary classification achieved an accuracy level of 85%. The main contribution of the current study is the ability to model players behaviour based on the interaction between different SVOs and game features. This interaction enabled us to gain different insights regarding player’s behaviour. For example, a prosocial player often showed a tendency towards focal point solutions even when their personal gains were lower than that of the co-player. Thus, SVO is not a sufficient model for explaining behaviour in different divergent interest scenarios.