Evolution of Cooperation among Mobile Agents

We study the effects of mobility on the evolution of cooperation among mobile players, which imitate collective motion of biological flocks and interact with neighbors within a prescribed radius $R$. Adopting the prisoner's dilemma game and the snowdrift game as metaphors, we find that cooperation can be maintained and even enhanced for low velocities and small payoff parameters, when compared with the case that all agents do not move. But such enhancement of cooperation is largely determined by the value of $R$, and for modest values of $R$, there is an optimal value of velocity to induce the maximum cooperation level. Besides, we find that intermediate values of $R$ or initial population densities are most favorable for cooperation, when the velocity is fixed. Depending on the payoff parameters, the system can reach an absorbing state of cooperation when the snowdrift game is played. Our findings may help understanding the relations between individual mobility and cooperative behavior in social systems.Comment: 15 pages, 5 figure

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Dynamics of organizational culture: Individual beliefs vs. social conformity

The complex nature of organizational culture challenges our ability to infers its underlying dynamics from observational studies. Recent computational studies have adopted a distinct different view, where plausible mechanisms are proposed to describe a wide range of social phenomena, including the onset and evolution of organizational culture. In this spirit, this work introduces an empirically-grounded, agent-based model which relaxes a set of assumptions that describes past work - (a) omittance of an individual's strive for achieving cognitive coherence, (b) limited integration of important contextual factors - by utilizing networks of beliefs and incorporating social rank into the dynamics. As a result, we illustrate that: (i) an organization may appear to be increasingly coherent in terms of organizational culture, yet be composed of individuals with reduced levels of coherence, (ii) the components of social conformity - peer-pressure and social rank - are influential at different aggregation levels.Comment: 20 pages, 8 figure

How groups can foster consensus: The case of local cultures

A local culture denotes a commonly shared behaviour within a cluster of firms. Similar to social norms or conventions, it is an emergent feature resulting from the firms' interaction in an economic network. To model these dynamics, we consider a distributed agent population, representing e.g. firms or individuals. Further, we build on a continuous opinion dynamics model with bounded confidence ($\epsilon$), which assumes that two agents only interact if differences in their behaviour are less than $\epsilon$. Interaction results in more similarity of behaviour, i.e. convergence towards a common mean. This framework is extended by two major concepts: (i) The agent's in-group consisting of acquainted interaction partners is explicitly taken into account. This leads to an effective agent behaviour reflecting that agents try to continue to interact with past partners and thus to keep sufficiently close to them. (ii) The in-group network structure changes over time, as agents can form new links to other agents with sufficiently close effective behaviour or delete links to agents no longer close in behaviour. Thus, our model provides a feedback mechanism between the agents' behaviour and their in-group structure. Studying its consequences by means of agent-based computer simulations, we find that for narrow-minded agents (low $\epsilon$) the additional feedback helps to find consensus more often, whereas for open-minded agents (high $\epsilon$) this does not hold. This counterintuitive result is explained by simulations of the network evolution

Cooperation sustainability in small groups:Exogenous and endogenous dynamics of the sustainability of cooperation

Cooperation sustainability presents a complex social phenomenon. Two common approaches have been used to study the sustainability of cooperation in small groups: endogenous processes (dynamic) and exogenous factors (static approaches). The present study integrates existing research by investigating how the interplay between exogenous and endogenous conditions affects cooperation in small groups. To uncover endogenous group dynamics in an online Public Goods experiment (n = 353), we performed multilevel latent Markov models on Bayesian estimation that allowed us to estimate latent classes on the level of rounds, individuals, and groups. We studied exogenous factors by investigating the effects of situational tightness versus looseness, and monetary versus symbolic frames on cooperation sustainability. Our key findings show that both endogenous and exogenous factors are critical to explain the variation of cooperation sustainability between groups. Second, groups exposed to tight situations reveal higher levels of cooperation sustainability than groups exposed to loose situations. Money primes did not have an impact. Among the control variables, collective intentionality showed the strongest association with cooperation. Future research may develop a more sophisticated measure of tight versus loose situations and examine the causal relationship between collective intentionality and cooperation

Aspiration Dynamics of Multi-player Games in Finite Populations

Studying strategy update rules in the framework of evolutionary game theory, one can differentiate between imitation processes and aspiration-driven dynamics. In the former case, individuals imitate the strategy of a more successful peer. In the latter case, individuals adjust their strategies based on a comparison of their payoffs from the evolutionary game to a value they aspire, called the level of aspiration. Unlike imitation processes of pairwise comparison, aspiration-driven updates do not require additional information about the strategic environment and can thus be interpreted as being more spontaneous. Recent work has mainly focused on understanding how aspiration dynamics alter the evolutionary outcome in structured populations. However, the baseline case for understanding strategy selection is the well-mixed population case, which is still lacking sufficient understanding. We explore how aspiration-driven strategy-update dynamics under imperfect rationality influence the average abundance of a strategy in multi-player evolutionary games with two strategies. We analytically derive a condition under which a strategy is more abundant than the other in the weak selection limiting case. This approach has a long standing history in evolutionary game and is mostly applied for its mathematical approachability. Hence, we also explore strong selection numerically, which shows that our weak selection condition is a robust predictor of the average abundance of a strategy. The condition turns out to differ from that of a wide class of imitation dynamics, as long as the game is not dyadic. Therefore a strategy favored under imitation dynamics can be disfavored under aspiration dynamics. This does not require any population structure thus highlights the intrinsic difference between imitation and aspiration dynamics