Improving success probability of innovation through multi-agent collaboration: a differential game model

Abstract

ABSTRACT New product or technology innovation is always facing many uncontrollable factors, which lead to the uncertainty of the success probability of an innovation project and its developing time. Multi-agent collaboration is an effective way to improve the success probability and reduce the developing time of an innovation project. In this paper, we employ the knowledge sharing model and the success probability model to formulate a differential game for the multi-agent collaborative innovation (MACI) mechanism under uncertainty. Firstly, the equilibrium policy for each agent is obtained by applying the optimal control theory. Secondly, we observe that the derived equilibrium policy can not only effectively reduce the developing time of the innovation procedure, but also improve the success probability of the innovation project. Thirdly, we show that the optimal profit allocation contract for the collaborative mechanism can be obtained by solving a linear programming problem. Finally, this paper investigates how governments’ subsidy policies affect the equilibrium of the collaborative mechanism. Numerical experiments are conducted to test the performance of different collaborative innovation mechanisms. This proposed model and its derived managerial insights provide new theoretical guidance for establishing and implementing the multi-agent collaborative mechanism of new product or technology innovations under uncertain environments.