Network regularity and the influence of asycnhronism on the evolution of cooperation

In a population of interacting agents, the update dynamics defines the temporal relation between the moments at which agents update the strategies they use when they interact with other agents. The update dynamics is said to be synchronous if this process occurs simultaneously for all the agents and asynchronous if this is not the case. On the other hand, the network of contacts defines who may interact with whom. In this paper, we investigate the features of the network of contacts that play an important role in the influence of the update dynamics on the evolution of cooperative behaviors in a population of agents. First we show that asynchronous dynamics is detrimental to cooperation only when 1) the network of contacts is highly regular and 2) there is no noise in the strategy update process. We then show that, among the different features of the network of contacts, network regularity plays indeed a major role in the influence of the update dynamics, in combination with the temporal scale at which clusters of cooperator agents grow

Update dynamics, strategy exchanges and the evolution of cooperation in the snowdrift game

We verify through numerical simulations that the influence of the update dynamics on the evolution of cooperation in the Snowdrift game is closely related to the number of strategy exchanges between agents. The results show that strategy exchanges contribute to the destruction of compact clusters favorable to cooperator agents. In general, strategy exchanges decrease as the synchrony rate decreases. This explains why smaller synchrony rates are beneficial to cooperators in situations where a large number of exchanges occur with synchronous updating. On the other hand, this is coherent with the fact that the Snowdrift game is completely insensitive to the synchrony rate when the replicator dynamics transition rule is used: there are almost no strategy exchanges when this rule is used

The influence of the update dynamics on the evolution of cooperation

We investigate the influence of the update dynamics on the evolution of cooperation. Three of the most studied games in this area are used: Prisoner’s Dilemma, Snowdrift and the Stag Hunt. Previous studies with the Prisoner’s Dilemma game reported that less cooperators survive with the asynchronous version of the game than with the synchronous one. On the other side, studies with the Snowdrift game are not conclusive about this subject. Based on simulations with these three games, played on different types of networks and using different levels of noise in the choice of the next strategy to be adopted by the agents, we conclude that, in general, an asynchronous dynamics favors the evolution of cooperation. Results concerning the monotonicity of these models and their sensitivity to small changes in the synchrony rate are also reported. This work is a contribution to a better understanding of the conditions under which cooperation can emerge and how different parameters may influence this emergence

The influence of asynchronous dynamics in the spatial prisioner's dilemma game

We examine the influence of asynchronism in the Spatial Prisoner’s Dilemma game. Previous studies reported that less cooperation is achieved with the asynchronous version of the game than with the synchronous one. Here, we show that, in general, the opposite is the most common outcome. This conclusion is only possible because a larger number of scenarios was tested, namely, different interaction topologies, a transition rule that can be tuned to emulate different levels of determinism in the choice of the next strategy to be adopted and different rates of asynchronism. The influence of stochastic and deterministic periodic updating in the outcome of the system is also compared. We found that these two update disciplines lead basically to the same result. This is an important issue in the simulation of social and biological behavior

Asynchronous stochastic dynamics and the spatial prisioner's dilemma game

We argue that intermediate levels of asynchronism should be explored when one uses evolutionary games to model biological and sociological systems. Usually, only perfect synchronism and continuous asynchronism are used, assuming that it is enough to test the model under these two opposite update methods. We believe that biological and social systems lie somewhere between these two extremes and that we should inquire how the models used in these situations behave when the update method allows more than one element to be active at the same time but not necessarily all of them. Here, we use an update method called Asynchronous Stochastic Dynamics which allows us to explore intermediate levels of asynchronism and we apply it to the Spatial Prisoner’s Dilemma game. We report some results concerning the way the system changes its behaviour as the synchrony rate of the update method varies

Musical pattern extraction using genetic algorithms

This paper describes a research work in which we study the possibility of applying genetic algorithms to the extraction of musical patterns in monophonic musical pieces. Each individual in the population represents a possible segmentation of the piece being analysed. The goal is to find a segmentation that allows the identification of the most significant patterns of the piece. In order to calculate an individual’s fitness, all its segments are compared among each other. The bigger the area occupied by similar segments the better the quality of the segmentation

Aplicação de Algoritmos Evolucionários à Extracção de Padrões Musicais

Dissertação de Mestrado em Engenharia Informática apresentada á Faculdade de Ciências e Tecnologia da Universidade de Coimbra.A extracção de padrões é um problema que se coloca em várias áreas como, por exemplo, a biologia molecular ou a área financeira, e que pode ser considerado, do ponto de vista da inteligência artificial, como uma forma de aprendizagem não supervisionada. No domínio musical, o problema pode ser definido, informalmente, da seguinte forma: dada uma peça musical (ou várias), identificar as partes dessa peça que se repitam, aproximadamente ou não, e que possuam um significado relevante no contexto dessa peça. O objectivo deste trabalho consistiu em estudar a viabilidade da aplicação de algoritmos evolucionários ao problema da extracção de padrões musicais. Para levar a cabo o estudo proposto desenvolvemos duas abordagens utilizando dois tipos diferentes de algoritmos evolucionários: a programação genética e os algoritmos genéticos. Em cada uma das abordagens o objectivo é essencialmente o mesmo: encontrar uma segmentação de uma peça que permita identificar os padrões mais importantes nela existentes. Devido às características de cada um dos algoritmos, a representação utilizada para os indivíduos é diferente. Assim, enquanto que na abordagem baseada em programação genética cada indivíduo é um programa que produz como resultado uma determinada peça, constituindo ao mesmo tempo uma descrição da sua estrutura de segmentos, na abordagem baseada em algoritmos genéticos cada indivíduo consiste numa sequência de símbolos que representa uma hipótese de segmentação da peça a analisar. Embora as funções de avaliação utilizadas nas duas abordagens também sejam diferentes, ambas beneficiam os indivíduos que apresentem o conjunto dos padrões mais importantes existentes na peça. Para ambas as abordagens foi também desenvolvido um método que permite realizar uma segunda segmentação de uma peça a partir dos segmentos identificados na primeira segmentação. Os resultados experimentais obtidos com a abordagem baseada em programação genética que desenvolvemos permitem-nos verificar que esta abordagem apresenta bastantes dificuldades na resolução deste tipo de problemas. Pelo contrário, a abordagem baseada em algoritmos genéticos permitiu obter resultados que nos levam a considerar que a aplicação desta abordagem a este tipo de problemas é viável.Pattern extraction is a problem that occurs in several areas like, for example, molecular biology and finance, and can be viewed, from the point of view of artificial intelligence, as a kind of unsupervised learning. In the musical domain, the problem can be informally defined in the following way: given a musical piece (or more), identify the meaningful recurrent parts of that piece. The goal of this work is to study the viability of applying evolutionary algorithms to the problem of musical pattern extraction. In order to take this study, we develop two approaches based on two different types of evolutionary algorithms: genetic programming and genetic algorithms. The goal in both approaches is essentially the same: find a segmentation of a musical piece that allows the identification of the most meaningful patterns that exist in that piece. Due to the character of each type of algorithm, the representation used to represent individuals in each approach its different. Hence, while in the genetic programming based approach an individual is a program that produces as a result a musical piece, being at the same time a description of the structure of that piece, in the genetic algorithms based approach each individual is a sequence of symbols that represent a possible segmentation of the musical piece that is being analyzed. The two approaches also use different fitness functions, but both have in common the fact that they give a better fitness value to individuals that present the set of most meaningful patterns. For both approaches we also developed a method to make a second segmentation of a musical piece using the segments identified in the first segmentation. The experimental results obtained with the genetic programming based approach allowed us to verify that this approach has great difficulties in the resolution of this type of problems. On the contrary, with the genetic algorithms based approach we obtained results that allow us to believe that this approach can be useful in the resolution of this type of problems

Paradigmatic analysis using genetic programming

Paradigmatic analysis consists in the segmentation of a musical piece through the identification of relations between different parts of the piece, and the classification of the identified segments into categories. In this paper we describe how a genetic programming system can be used to make the paradigmatic analysis of monophonic musical pieces, using a simple fitness function inspired in the Kolmogorov complexity estimation. We make use of automatically defined functions in order to represent segments. Relations are made explicit through the reuse of segments and the application of transformations to these segments

How to build the network of contacts : selecting the cooperative partners

We address the problem of finding the correct agents to interact with from a general standpoint. We take the payout obtained by agents in any game with dilemma as an input to our model. Its output is a probability distribution used in the partner selection that increasingly favours cooperative agents. Our approach contrasts with others designed for specific games without concerns of generality. We show both theoretically and experimentally that the major factor affecting cooperators selecting only themselves is the agents' strategies. This result does not depend on game nature or the initial probability distribution

Selection of cooperative partners in n-player games

We address the problem of finding the appropriate agents to interact with in n-player games. In our model an agent only requires knowledge about the payoff and identification of its partners. This information is used to update a probability distribution over candidate partners. As such, our model is applicable in any situation, be it a cooperative dilemma or a game where a Nash Equilibrium is equal to a Pareto Optimal profile