Evolutionary simulation modelling is presented as a methodology involving the application of modelling techniques developed within the artificial sciences to evolutionary problems. Although modelling work employing this methodology has a long and interesting history, it has remained, until recently, a relatively underdeveloped practice, lacking a unifying theoretical framework.

This report is the result of a panel discussion at the Third Workshop of the UK's Special Interest Group on Multi-Agent Systems

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This paper surveys the evolutionary game theoretic literature on reciprocity in human interactions, dealing both with long-term relationships and with sporadic interactions. Four basic themes, repetition, commitment, assortation, and parochialism, appear repeatedly throughout the literature. Repetition can give rise to the evolution of behavior that exhibits reciprocity-like features but a vast array of other behaviors are also stable. In sporadic interactions, reciprocity can be stable if the propensity to punish selfish actions can induce opportunists to cooperate, if reciprocators themselves behave opportunistically when they expect others to do so, or if matching is sufficiently assortative.

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Through imitation the behaviours exhibited by one generation of organisms are passed on to the next generation. To allow for the evolutionary emergence of previously non-existing behaviours it is necessary to introduce some mechanism to add new variants. One way is to select only the best organisms of each generation as "teachers" for the organisms of the next generation and to add some random noise during the imitation process so that by chance at least some "students" turn out to be better than their "teachers". Another way is to allow organisms to learn during their life individually, e.g. through a trial-and-error procedure, so that what they "teach" their "students" is an improved version of the behaviour they learnt by imitating their own "teachers". In this paper we describe some simulations of the cultural evolution of a simple food-getting capacity in a population of artificial organisms living in an environment that contains food. A neural network controls the behaviour of each organism and the organism learns to look for food by imitating the behaviour of an organism of the preceding generation. We manipulate the variables we have described (selection of "teachers", random noise, individual learning) and observe the consequences of these manipulations. Results show that both random noise in the transmission process and individual learning during life lead to the emergence of previously non-existing behaviours and that adding selection of "teachers" to the scenario with individual learning leads to a population with higher performance and lower inter-individual variability.

We provide an overview of recent research on belief and opinion dynamics in social networks. We discuss both Bayesian and non-Bayesian models of social learning and focus on the implications of the form of learning (e.g., Bayesian vs. non-Bayesian), the sources of information (e.g., observation vs. communication), and the structure of social networks in which individuals are situated on three key questions: (1) whether social learning will lead to consensus, i.e., to agreement among individuals starting with different views; (2) whether social learning will effectively aggregate dispersed information and thus weed out incorrect beliefs; (3) whether media sources, prominent agents, politicians and the state will be able to manipulate beliefs and spread misinformation in a society.

There is much empirical evidence that human decision-making under risk does not coincide with expected value maximization, and much effort has been invested into the development of descriptive theories of human decision-making involving risk (e.g. Prospect Theory). An open question is how behavior corresponding to these descriptive models could have been learned or arisen evolutionarily, as the described behavior differs from expected value maximization. We believe that the answer to this question lies, at least in part, in the interplay between risk-taking, sequentiality of choice, and population dynamics in evolutionary environments. In this paper we provide the results of several evolutionary game simulations designed to study the risk behavior of agents in evolutionary environments. These include several evolutionary lottery games where sequential decisions are made between risky and safe choices, and an evolutionary version of the well-known stag hunt game. Our results show how agents that are sometimes risk-prone and sometimes risk-averse can outperform agents that make decisions solely based on the maximization of the local expected values of the outcomes, and how this can facilitate the evolution of cooperation in situations where cooperation entails risk. 1.

ISBN#: 0-87724-032-9 These essays are the result of an interdisciplinary study program organized by the American Academy of Arts and Sciences and supported by the National Science Foundation under grant number BCS-0083721. The views expressed in this volume are those held by each contributor. They do not necessarily represent the position of the Officers and Fellows of the American Academy of Arts and Sciences or the National Science Foundation. Please direct inquiries to:

Social learning is distinguished from innate behaviour and individual learning as a behavioural strategy. We investigate simple mechanisms for social learning in an evolutionary simulation of food-preference copying in Norway rats. These animals learn preferences by interacting with conspeci cs, but, unexpectedly, they fail to learn aversions after interacting with a poisoned demonstrator.