scholarly journals Dynamic Model of Collaboration in Multi-Agent System Based on Evolutionary Game Theory

Games ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 75
Author(s):  
Zhuozhuo Gou ◽  
Yansong Deng
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Multi Agent Systems ◽  
Exchange Of Information ◽  
Message Communication ◽  
Stable Conditions ◽  
Agent Collaboration ◽  
Multi Agent ◽  
Reducing Costs

Multi-agent collaboration is greatly important in order to reduce the frequency of errors in message communication and enhance the consistency of exchanging information. This study explores the process of evolutionary decision and stable strategies among multi-agent systems, including followers, leaders, and loners, involved in collaboration based on evolutionary game theory (EGT). The main elements that affected the strategies are discussed, and a 3D evolution model is established. The evolutionary stability strategy (ESS) and stable conditions were analyzed subsequently. Numerical simulation results were obtained through MATLAB simulation, and they manifested that leaders play an important role in exchanging information with other agents, accepting agents’ state information, and sending messages to agents. Then, with the positivity of receiving and feeding back messages for followers, implementing message communication is profitable for the system, and the high positivity can accelerate the exchange of information. At the behavior level, reducing costs can strengthen the punishment of impeding the exchange of information and improve the positivity of collaboration to facilitate the evolutionary convergence toward the ideal state. Finally, the EGT results revealed that the possibility of collaboration between loners and others is improved, and the rewards are increased, thereby promoting the implementation of message communication that encourages leaders to send all messages, improve the feedback positivity of followers, and reduce the hindering degree of loners.

Evolutionary game theory and multi-agent reinforcement learning

2005 ◽  
Vol 20 (1) ◽  
pp. 63-90 ◽  
Author(s):  
KARL TUYLS ◽  
ANN NOWÉ
Keyword(s):  
Game Theory ◽  
Reinforcement Learning ◽  
Evolutionary Game Theory ◽  
State Of The Art ◽  
Evolutionary Game ◽  
The State ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Mathematical Connection

In this paper we survey the basics of reinforcement learning and (evolutionary) game theory, applied to the field of multi-agent systems. This paper contains three parts. We start with an overview on the fundamentals of reinforcement learning. Next we summarize the most important aspects of evolutionary game theory. Finally, we discuss the state-of-the-art of multi-agent reinforcement learning and the mathematical connection with evolutionary game theory.

PATENTING OR SECRET? THE INTERACTION BETWEEN LEADING FIRMS AND FOLLOWING FIRMS BASED ON EVOLUTIONARY GAME THEORY AND MULTI-AGENT SIMULATION

2019 ◽  
Vol 23 (07) ◽  
pp. 1950068 ◽  
Author(s):  
XIAOYANG ZHAO
Keyword(s):  
Game Theory ◽  
Bounded Rationality ◽  
Evolutionary Game Theory ◽  
Patent Protection ◽  
Evolutionary Game ◽  
Equilibrium Solutions ◽  
Strategic Choices ◽  
Trade Off ◽  
Multi Agent ◽  
Imitation Strategy

Firms who want to appropriate innovation often need to make decision facing the trade-off between patenting and secret. This paper explores how leading firms make trade-off between patenting and secret through the view of the interaction between leading firms and following firms who have the option of imitation or substitution, based on the evolutionary game theory. Then, a simulation model is built combining the evolutionary game model and agent-based modelling method, which allows us to implement bounded rationality and interactivities. The simulation is run with different gain parameters and the results are checked by cross-validation. It is found that leading firms are more likely to adopt patenting strategy with well developed patent protection regime. While depending on variations on patent protection effectiveness, technological characteristics, and leading firms’ investment in patent portfolio development, following firms may choose imitation strategy or substitution strategy. Considering bounded rationality, firms could choose sub-optional strategies and leads to scenarios other than evolutionary equilibrium solutions, which provide deeper insights of strategic choices of leading firms and following firms. This paper makes contributions to theory by using the perspective of multi-agent view and integrating bounded rationality in the simulation. Finally, this paper draws conclusion and puts forward some suggestions.

Sign in / Sign up

Export Citation Format

Share Document