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Proceedings Papers
. isal2020, ALIFE 2020: The 2020 Conference on Artificial Life171-179, (July 13–18, 2020) 10.1162/isal_a_00264
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The evolution of social institutions (e.g. institutions of political decision making or joint resource administration) is an important question in the context of understanding of how societies develop and evolve. In principle, social institutions can be conceptualized as abstract games with multiple players and rules about individual decision making and individual and joint outcomes. Here we propose a formal approach for the composition of games (e.g. Prisoner's Dilemma – PD) to model the evolution of social institutions. Following a generalized description of the approach, we describe two examples of application for the composition of PD games. We assess the impact of the composed games on the level of cooperation. We discuss the implications of the proposed approach and how it may help to develop effective models of social institution evolution.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life300-307, (July 29–August 2, 2019) 10.1162/isal_a_00179
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Fitness improving innovations occur in populations of organisms as genetic changes (mutations) that allow better fit with the environmental niche of the organisms. Similarly, fitness improving innovations may occur in the context of human communities as well in terms of socio-economic innovations (e.g. new ways of organizing the military, new products or services) that lead to more efficient use of available resources. Here we explore the link between such innovations and the harshness of the environment, where the populations live. Environmental harshness characterizes the availability of population growth supporting resources in the environment. Our analysis shows that if the harshness of the environment varies smoothly with the distance, the expected extent of fitness improving innovations and of the resource utilization efficiency of populations depends in a combined linear and harmonic manner on the harshness of the environment at the location of origin of the populations. We explore the implications of this result for particular cases of both biological and social environments (e.g. gene drives, business innovation).
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life404-411, (July 23–27, 2018) 10.1162/isal_a_00078
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Cooperation among selfish individuals provides the fundamentals for social organization among animals and humans. Cooperation games capture this behavior at an abstract level and provide the tools for the analysis of the evolution of cooperation. Here we use the Rock-Paper-Scissors (RPS) game with positive and negative draw outcomes (i.e. when the draw outcome has a positive or negative impact on the players) to study the evolution of cooperative behavior in communities of simulated selfish agents. The agents communicate to each other using a probabilistic language and the cooperation game is set in an uncertain resource generation context. The offspring of the agents may clump together or may spread out, simulating the easy and difficult identification of possible cooperation partners. The results show that more uncertainty leads to more cooperation both in positive and negative draw games. Surprisingly we found that in negative draw games the level of cooperation is statistically significantly higher, although close to, the level that would be expected from random choice of RPS decisions. We also analyzed language complexity correlates of cooperation. The agent-based simulations and the results described here are applicable to social institutions or ecological systems with more than two, non-transitively comparable, decision states that can be described abstractly as RPS games.
Proceedings Papers
. ecal2017, ECAL 2017, the Fourteenth European Conference on Artificial Life438-445, (September 4–8, 2017) 10.1162/isal_a_073
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Open-ended evolution is a fundamental issue in artificial life research. We consider biological and social systems as a flux of interacting components that transiently participate in interactions with other system components as part of these systems. This approach and the corresponding reasoning suggest that systems able to deliver open-ended evolution must have a representation equivalent of Turing machines. Here we provide an implementation of a such model of evolving systems using a cellular automata world. We analyze the simulated world using a set of metrics based on criteria of open-ended evolution suggested by Bedau et al. We show that the cellular automata world has significantly more evolutionary activity than a corresponding random shadow world. Our work indicates that the proposed cellular automata worlds have the potential to generate open-ended evolution according to the criteria that we have considered.
Proceedings Papers
. alif2016, ALIFE 2016, the Fifteenth International Conference on the Synthesis and Simulation of Living Systems290-297, (July 4–6, 2016) 10.1162/978-0-262-33936-0-ch051
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Social learning plays a key role in the evolution of cooperation in humans and other animals. It has also been shown both theoretically and experimentally that environmental adversity is also a key determinant of the evolution of cooperation among individuals. Here we investigate the impact of social learning on the evolution of cooperation in the context of a range of levels of environmental adversity. We used an agent-based simulated world of asexual individuals that communicate and play a probabilistic version of the Prisoners Dilemma game. We considered simulated worlds either with or without random spreading of the offspring and two variants of social learning, either copying to some extent all communication rules or copying fully some of the communication rules of the best performing neighbor individual. The results show that in the case of spreading of the offspring, social learning increases the level of cooperation and reverses the association between this and the level of environmental adversity, i.e. low adversity with social learning implies higher level of cooperation. Copying fully some communication rules also increases the steady-state level of communication complexity in the simulated agent communities. The results suggest that the level of cooperation in communities of individuals may get boosted alternately by highly adverse environments and by layers of social learning in low adversity environments.
Proceedings Papers
. ecal2015, ECAL 2015: the 13th European Conference on Artificial Life130-137, (July 20–24, 2015) 10.1162/978-0-262-33027-5-ch028