Hyper game modeling of investor behavior in a sustainable regional innovation system taking into account possible bad faith of counterparties

One of the key problems in financing innovative projects is filtering out obviously unrealistic projects. Hyper game models decompose a complex conflict into several simple connected game models. Each player has his own idea of what game/games other players are playing, and may be mistaken or deceived regarding the preferences of other players. This leads to the fact that it is very useful method to analyze the problem of investing in an innovative project. Depending on whether the investor is dealing with a real innovation or an obviously unrealistic project, they have to solve completely different problems. Investing in innovative projects can be considered as a system of interaction between microeconomic entities with different goals. The investor in this system knows that the project may be fraudulent and has an a priori estimate of the probability that an unknown project is fraudulent. The innovator knows that the investor is aware of the existence of fraudulent projects and must convince the investor that their project is not fraudulent. We propose a hyper game model of such a system, in which an innovative project can be either a “unicorn”, or an average profitable project, or an unprofitable project. Depending on the assessment of the project type, three patterns of investor behavior are modeled. Further, the model considers the influence of the investor’s decision on the technological and marketing expertise of the project. In total, the model considers combinations of 5 project types (super - profitable projects; average - profitable projects with technological limitations; average - profitable projects with marketing limitations; unprofitable projects due to technological constraints; unprofitable projects due to marketing constraints) and 5 types of strategies available to the investor (refusal; financing without expertise; financing based on the results of technological expertise; financing based on the results of marketing expertise; financing based on the results of both technological and marketing expertise). Using the model example, we calculated payoff matrices and utility estimates for combinations of different investor strategies and project types. The dependence of expected utility on uncertainty is calculated using various methods for assessing uncertainty. The advantages of the presented model are that it is risk - neutral, encourages participants to consider the knowledge gained in a given situation as more important than the general context, and allows for modeling and analysis of strategies that involve misleading another player. The model allows for a more accurate consideration of the participants’ different perceptions of their counterparties, which improves the adaptation of strategies and is especially important in the context of high uncertainty typical of innovative projects.