MATEC Web Conf.
Volume 226, 2018XIV International Scientific-Technical Conference “Dynamic of Technical Systems” (DTS-2018)
|Number of page(s)||6|
|Section||4 Fundamental methods of system analysis, modeling and optimization of dynamic systems|
|Published online||07 November 2018|
Direct incentive-compatible mechanism for innovator-investor bargain in an innovation investment system
Institute of computer science and problems of regional management, KBSC RAS, 360004, Nalchik, Russia
* Corresponding author: email@example.com
The functioning of the system of innovation investment, where an objective evaluation of an investment project containing new knowledge is impossible both for the investor and the innovator, currently is a matter of great interest. While for traditional industries, the use of statistical data is a satisfactory approach to the problem of uncertainty, for innovation projects this method isn’t applicable due to the natural absence of a valid statistical base. The practice shows that the empirical mechanisms used for innovation projects are rarely used for investing in traditional industries and vice versa. In this connection, there arises the problem of development an effective mechanism for financing innovation projects. We developed such a mechanism for the innovator-investor system in the form of a Bayesian non-cooperative repeating game with recalculated payments. The equilibrium parameters for any period of the given game are obtained. It is shown that, depending on the a priori estimates of the type of project, four different equilibria are possible in any particular period of that game. Therefore, using the strategy of adjusting a priori estimates of the investor and innovator on the basis of the Regret Matching rule, equilibrium for this finite Bayesian game is obtained.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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