The equilibrium of venture capital incentive contract: Optimization and Q-learning approaches

Abstract. The current study presents an incentive contract model to allocate income to venture projects. In this respect, Venture Capital (VC), as one of the main sources of nancing innovative projects, faces several challenges such as moral hazards, information asymmetry, and interest con icts, often referred to as three agency problems. In addition to the identication of the factors that may affect the income of venture projects and elaboration of cost functions, this study presented an optimal incentive contract model from the perspectives of venture capitalists and entrepreneurs. In this model, a venture capitalist, as an active investor, provides entrepreneurs with managerial and training assistance. The results revealed that the higher the initial ability of the entrepreneur was, the less money the venture capitalist would pay for training. Of note, in case the venture contract was not accepted, the wealth that the contract parties would obtain would become an in uential factor in the contract payment function. This model was studied considering the bounded rationality hypothesis and implemented using the Q-learning algorithm. In addition, the results obtained from the Q-learning approach were found to be reasonably convergent with the Nash equilibrium.