An Experimental Study of Open Innovation using MASTERMIND

This paper presents the first experimental results on open innovation, which is defined to be a method to solve problems with other people by revealing some or the complete history of algorithm already used. An important example is open source. Our data from human subjects show that non-modular payoff structure drives the convergence to a Nash equilibrium, in which commission price to helpers converge to zero but helpers will not stop solving problems for others. By non-modularity, we mean that the total production (or payoff) of a team is zero if either one of its members fails to produce at least at a certain level. In the experiment, subjects produce by solving a variant of a popular board game called MASTERMIND. Theoretically, free-riding leads to zero commission price. This removes a signaling function of price for the difficulty levels of work remaining. Empirically, however, it is not sufficient to cause the catastrophic outcome of zero payoff. This provides a basis for us to hypothesize that open innovation is a key explanation because it allows subjects to directly observe the history of work already done and potentially direct more resources to the more difficult tasks.