Genetic Algorithms, Learning, and the Dynamics of Corporate Takeovers

Abstract

This paper simulates, via a genetic-learning algorithm, the problems of free-riding and coordination failure when shareholders are confronted with a tender offer bid between pre-and post-takeover firm value. The outcomes produced in the simulations offer qualified support for the hypothesis that coordination to tendering strategies permitting offer success will only be partial. Further, coordination is impaired by increasing the number of shareholders. Generally increasing the divisibility of share holdings improves coordination and increases shareholder profits. Interestingly, for those parameters of the share tendering distribution which are predicted by the Nash hypothesis (e.g., the proportion of shares tendered) the results of the simulations usually approximate the results predicted by the Nash hypothesis. Moreover, those deviations from Nash outcomes which are observed are usually consistent with the biases observed in experiments on human subjects.

JEL Classification: C63, C70, H41, G34