the equilibrium of venture capital incentive contract: optimization and q-learning approaches

This paper presents an incentive contract model for allocating the income of venture projects. venture capital (vc), as one of the main sources of financing innovative projects, faces challenges like moral hazards, information asymmetry and interest conflicts (three agency problems). in addition to identifying the items that may affect the income of venture projects and the introduction of cost functions, we present an optimal incentive contract model from the perspective of both venture capitalists and entrepreneurs. in this model, a venture capitalist, as an active investor, provides managerial and training assistance to the entrepreneur. the results showed that the higher the initial ability of the entrepreneur, the less money the venture capitalist pays for training. furthermore, the wealth that the contract parties can obtain if the venture contract is not accepted, is an influential factor in the contract payment function. this model has also been studied with bounded rationality hypothesis and has been implemented using the q-learning algorithm. in addition, the results obtained from the q-learning approach, are reasonably convergent with the nash equilibrium.