Behavioral diversity, choices and noise in the iterated prisoner's dilemma
Siang Yew Chong,Xin Yao +1 more
TLDR
This paper studies the IPD game with both noise and multiple levels of cooperation (intermediate choices) in a coevolutionary environment, where players can learn and adapt their strategies through an evolutionary algorithm.Abstract:
Real-world dilemmas rarely involve just two choices and perfect interactions without mistakes. In the iterated prisoner's dilemma (IPD) game, intermediate choices or mistakes (noise) have been introduced to extend its realism. This paper studies the IPD game with both noise and multiple levels of cooperation (intermediate choices) in a coevolutionary environment, where players can learn and adapt their strategies through an evolutionary algorithm. The impact of noise on the evolution of cooperation is first examined. It is shown that the coevolutionary models presented in this paper are robust against low noise (when mistakes occur with low probability). That is, low levels of noise have little impact on the evolution of cooperation. On the other hand, high noise (when mistakes occur with high probability) creates misunderstandings and discourages cooperation. However, the evolution of cooperation in the IPD with more choices in a coevolutionary learning setting also depends on behavioral diversity. This paper further investigates the issue of behavioral diversity in the coevolution of strategies for the IPD with more choices and noise. The evolution of cooperation is more difficult to achieve if a coevolutionary model with low behavioral diversity is used for IPD games with higher levels of noise. The coevolutionary model with high behavioral diversity in the population is more resistant to noise. It is shown that strategy representations can have a significant impact on the evolutionary outcomes because of different behavioral diversities that they generate. The results further show the importance of behavioral diversity in coevolutionary learning.read more
Citations
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Computational and Game-Theoretic Approaches for Modeling Bounded Rationality
TL;DR: Four studies into the modeling of boundedly rational behavior of economic agents are presented, concerned with investigating the emergence of cooperation among boundedlyrational agents and how evolutionary algorithms can be analyzed mathematically rather than using computer simulations.
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Fingerprinting: Visualization and Automatic Analysis of Prisoner's Dilemma Strategies
Daniel Ashlock,E.-Y. Kim +1 more
TL;DR: This paper summarizes past results and introduces the following new results: Fingerprints for four new probe strategies are introduced, generalizing previous work in which tit-for-tat is the sole probe strategy.
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The Effect of Memory Size on the Evolutionary Stability of Strategies in Iterated Prisoner's Dilemma
Jiawei Li,Graham Kendall +1 more
TL;DR: It is proved that longer memory strategies outperform shorter memory strategies statistically in the sense of evolutionary stability and given an example of a memory-two strategy to show how the theoretical study of evolutionary Stability assists in developing novel strategies.
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Measuring Generalization Performance in Coevolutionary Learning
TL;DR: This paper presents a theoretical framework for generalization, the first time that generalization is defined and analyzed rigorously in coevolutionary learning, and shows that a small sample of test strategies can be used to estimate the generalization performance.
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Optimal Strategies of the Iterated Prisoner's Dilemma Problem for Multiple Conflicting Objectives
S. Mittal,Kalyanmoy Deb +1 more
TL;DR: In this article, a new paradigm of searching optimal strategies in the game of iterated prisoner's dilemma (IPD) using multiple-objective evolutionary algorithms is presented, which not only produces strategies that perform better in the iterated game but also finds a family of non-defined strategies, which can be analyzed to decipher properties a strategy should have to win the game in a more satisfactory manner.
References
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Book
The Evolution of Cooperation
TL;DR: In this paper, a model based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game was developed for cooperation in organisms, and the results of a computer tournament showed how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established.
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The Evolution of Cooperation
R. B. Greene,Robert Axelrod +1 more
TL;DR: A model is developed based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game to show how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established.
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Evolutionary programming made faster
Xin Yao,Yong Liu,Guangming Lin +2 more
TL;DR: A "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator and is proposed and tested empirically, showing that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.
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Evolving artificial neural networks
TL;DR: It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.
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TL;DR: The development of each of these procedures over the past 35 years is described and some recent efforts in these areas are reviewed.