Optimal resource allocation and adaptive robust control of technology innovation ecosystems based on cooperative game theory

This paper addresses a problem of optimal resource allocation for technology innovation ecosystem. Two layers goal are realized. First, an uncertain dynamical system is constructed with the Lotka-Volterra model for the description of the dynamics of the concerned innovation ecosystem, in which the growth rate and the symbiosis coefficient are specially treated as possibly (fast) time-varying uncertain parameters. Second, an adaptive robust resource allocation strategy is designed to drive the errors between the populations and the desired populations to be uniformly bounded and uniformly ultimately bounded, such that the first layer (basic) goal of resource allocation is achieved. Third, a two-player cooperative game is formulated for the seeking of the optimal control parameters, in which two (static) cost functions and a comprehensive performance index are constructed by performance analysis, and then the optimal control parameters (i.e., the Pareto-optimality) are obtained by minimizing the comprehensive performance index, such that the second layer (further) goal of optimal balance between the control inputs and the system performance is achieved. This work should be the first effort that addresses the optimal resource allocation and control for technology innovation ecosystem through a creative combination of dynamic modeling, control theory and game theory. (c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.

Automated summary

This paper addresses the problem of optimal resource allocation for technology innovation ecosystem through a creative combination of dynamic modeling, control theory, and game theory. It constructs an uncertain dynamical system to describe the dynamics of the ecosystem and designs an adaptive robust resource allocation strategy to achieve the basic goal of resource allocation. A two-player cooperative game is formulated to seek the optimal control parameters and achieve the further goal of optimal balance between control inputs and system performance.