Multi-agent evolutionary game in the recycling utilization of construction waste

2020 ◽  
Vol 738 ◽  
pp. 139826 ◽  
Author(s):  
Yongbo Su
Keyword(s):  
Evolutionary Game ◽  
Construction Waste ◽  
Multi Agent

scholarly journals GAME THEORY BASED MULTI CRITERIA DECISION MAKING PROBLEM UNDER UNCERTAINTY: A CASE STUDY ON INDIAN TEA INDUSTRY

2018 ◽  
Vol 19 (1) ◽  
pp. 154-175 ◽  
Author(s):  
Animesh DEBNATH ◽  
Abhirup BANDYOPADHYAY ◽  
Jagannath ROY ◽  
Samarjit KAR
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Evolutionary Game ◽  
Market Volatility ◽  
Evolutionary Strategies ◽  
Multi Criteria Decision Making ◽  
Tea Industry ◽  
Multi Agent ◽  
The Impact ◽  
Indian Tea

The long-term evolution of multi agent multi criteria decision making (MCDM) and to obtain sustainable decision a novel methodology is proposed based on evolutionary game theory. In this paper multi agent MCDM is represented as an evolutionary game and the evolutionary strategies are defined as sustainable decisions. Here we consider the problem of decision making in Indian Tea Industry. The agents in this game are essentially Indian Tea Estate owner and Indian Tea board. The replicator dynamics of the evolutionary game are studied to obtain evolutionary strategies which could be defined as sustainable strategies. The multi agent MCDM in Indian Tea Industry is considered under different socio-political and Corporate Social Responsibility scenario and groups of Indian Tea Industry. Again, the impacts of imprecision and market volatility on the outcome of some strategies (decisions) are studied. In this paper the imprecision on the impact of the strategies are modelled as fuzzy numbers whereas the market volatility is taken into account as white noise. Hence the MCDM problem for Indian Tea Industry is modelled as a hybrid evolutionary game. The probabilities of strategies are obtained by solving hybrid evolutionary game and could be represented as a Dempster-Shafer belief structure. The simulation results facilitate the Decision Makers to choose the strategies (decisions) under different type of uncertainty.

An Evolutionary Game Theoretic Perspective on Learning in Multi-Agent Systems

Synthese ◽  
2004 ◽  
Vol 139 (2) ◽  
pp. 297-330 ◽  
Author(s):  
Karl Tuyls ◽  
Ann Nowe ◽  
Tom Lenaerts ◽  
Bernard Manderick
Keyword(s):  
Evolutionary Game ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Game Theoretic

scholarly journals The stabilization of equilibria in evolutionary game dynamics through mutation: mutation limits in evolutionary games

2019 ◽  
Vol 475 (2231) ◽  
pp. 20190355 ◽  
Author(s):  
Johann Bauer ◽  
Mark Broom ◽  
Eduardo Alonso
Keyword(s):  
Nash Equilibrium ◽  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Solution Concept ◽  
Evolutionary Games ◽  
Dynamic Approach ◽  
Q Learning ◽  
Interior Equilibrium ◽  
Dynamic Solution ◽  
Multi Agent

The multi-population replicator dynamics is a dynamic approach to coevolving populations and multi-player games and is related to Cross learning. In general, not every equilibrium is a Nash equilibrium of the underlying game, and the convergence is not guaranteed. In particular, no interior equilibrium can be asymptotically stable in the multi-population replicator dynamics, e.g. resulting in cyclic orbits around a single interior Nash equilibrium. We introduce a new notion of equilibria of replicator dynamics, called mutation limits, based on a naturally arising, simple form of mutation, which is invariant under the specific choice of mutation parameters. We prove the existence of mutation limits for a large class of games, and consider a particularly interesting subclass called attracting mutation limits. Attracting mutation limits are approximated in every (mutation-)perturbed replicator dynamics, hence they offer an approximate dynamic solution to the underlying game even if the original dynamic is not convergent. Thus, mutation stabilizes the system in certain cases and makes attracting mutation limits near attainable. Hence, attracting mutation limits are relevant as a dynamic solution concept of games. We observe that they have some similarity to Q-learning in multi-agent reinforcement learning. Attracting mutation limits do not exist in all games, however, raising the question of their characterization.

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