The Real Option Game Model to the Technology Innovation Investment Strategy

The real option game theory is a new effective method to analyze the project investment decision containing uncertain factors in the incomplete competition circumstance. Based on the introduction of the development of real option game method, this paper puts forward a real option game model to research on the investment opportunity and investment strategy of technology innovation involved two competitors. The varies of the value of project bring on three kinds of game equilibrium, that is the corporate investment equilibrium strategy, the leader ＆ follower investment equilibrium strategy and the corporate delay equilibrium strategy.

Markov decision evolutionary game theoretic learning for cooperative sensing of unmanned aerial vehicles

As one of the major contributions of biology to competitive decision making, evolutionary game theory provides a useful tool for studying the evolution of cooperation. To achieve the optimal solution for unmanned aerial vehicles （UAVs） that are car- rying out a sensing task, this paper presents a Markov decision evolutionary game （MDEG） based learning algorithm. Each in- dividual in the algorithm follows a Markov decision strategy to maximize its payoff against the well known Tit-for-Tat strate- gy. Simulation results demonstrate that the MDEG theory based approach effectively improves the collective payoff of the roam. The proposed algorithm can not only obtain the best action sequence but also a sub-optimal Markov policy that is inde- pendent of the game duration. Furthermore, the paper also studies the emergence of cooperation in the evolution of self-regarded UAVs. The results show that it is the adaptive ability of the MDEG based approach as well as the perfect balance between revenge and forgiveness of the Tit-for-Tat strategy that the emergence of cooperation should be attributed to.