Genetic algorithms, learning, and the dynamics of corporate takeovers

This paper simulates, via a genetic-learning algorithm, free-riding and coordination failure when shareholders are confronted with an unconditional tender-offer bid between the pre-takeover and post-takeover value of their firm. The outcomes produced by the simulations offer strong support for the hypothesis that coordination to tendering strategies permitting offer success is impaired by increasing the number of shareholders and the divisibility of shareholdings. Further, the outcomes of the simulations closely conform to the restrictions imposed by the Nash equilibrium hypothesis. When the number of shareholders and the disability of shareholdings are both small, the aggregate outcomes of the simulations converge to the aggregate outcomes produced by efficient Nash equilibria. Otherwise, the outcomes of the simulation more closely resemble the outcomes of inefficient Nash equilibria.