Evolution of coordination in pairwise and multi-player interactions via prior commitments

Weak selection, which means a phenotype is slightly advantageous over another, is an important limiting case in evolutionary biology. Recently, it has been introduced into evolutionary game theory. In evolutionary game dynamics, the probability to be imitated or to reproduce depends on the performance in a game. The influence of the game on the stochastic dynamics in finite populations is governed by the intensity of selection. In many models of both unstructured and structured populations, a key assumption allowing analytical calculations is weak selection, which means that all individuals perform approximately equally well. In the weak selection limit many different microscopic evolutionary models have the same or similar properties. How universal is weak selection for those microscopic evolutionary processes? We answer this question by investigating the fixation probability and the average fixation time not only up to linear but also up to higher orders in selection intensity. We find universal higher order expansions, which allow a rescaling of the selection intensity. With this, we can identify specific models which violate (linear) weak selection results, such as the one-third rule of coordination games in finite but large populations.

Many real-world applications of multiagent systems require independently designed (heterogeneous) and operated (autonomous) agents to interoperate. We consider agents who offer business services and collaborate in interesting business service engagements. We formalize notions of interoperability and conformance, which appropriately support agent heterogeneity and autonomy. With respect to autonomy, our approach considers the choices that each agent has, and how their choices are coordinated so that at any time one agent leads and its counterpart follows, but with initiative fluidly shifting among the participants. With respect to heterogeneity, we characterize the variations in the agents' designs, and show how an agent may conform to a specification or substitute for another agent. Our approach addresses a challenging problem with multi-party interactions that existing approaches cannot solve. Further, we introduce a set of edit operations by which to modify an agent design so as to ensure its conformance with others.

Words are not wind - how public joint commitment and reputation solve the Prisoner's Dilemma

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources. Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work are long-running continuous (LRC) queries, that we also term as "open-world" queries. These queries are neither short-lived nor infinitely long lived. They live long enough to make the prospect of multi-query optimization meaningful. But queries may also terminate at any time, requiring re-optimization of the query plans. The queries are "open" from the grid perspective as the grid cannot control or predict: (1) arrival of a query with time, location, required data and, (2) query revocations. Query optimization in such an environment has two major challenges: (a) optimizing in a multi-query environment and (b) continuous optimization due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization, where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns. Changing of strategies is also autonomously decided by each grid node based on how its strategy is faring with respect to other strategies in the grid.

Diverse selection intensities resolve the cooperation dilemma induced by breaking the symmetry between interaction and learning

Evolutionary cooperation dynamics of combining imitation and super-rational aspiration induced strategy updating

Evolutionary Game Theory and the Prisoner's Dilemma (PD) Game in particular have been used to study the evolution of cooperation. We consider a population of asexually reproducing, age-structured individuals in a two-dimensional square lattice structure. The individuals employ fixed cooperative or defecting strategies towards their neighbors in repeating interactions to accumulate reproductive fitness. We focus on the effects of the persistence of past interactions and interactive neighborhood size on the evolution of cooperation. We show that larger neighborhood sizes are generally detrimental to cooperation and that the persistence of fitness effects decreases the likelihood of the evolution of cooperation in small neighborhoods. However, for larger neighborhood sizes the persistence effect is reversed. Thus, our study corroborates earlier studies that population structure increases the evolutionary potential for cooperative behavior in a PD paradigm. This finding may explain the heterogeneity of previous results on the effect of neighborhood size and cautions that the persistence of fitness outcomes needs to be considered in analyses of the evolution of cooperative behavior. The persistence of fitness outcomes of pairwise interactions may vary dramatically in biological and social systems and could have profound effects on the evolution of cooperation in various contexts.

Journal Article Leadership and the aggregation of international collective action Get access DG Arce M DG Arce M Search for other works by this author on: Oxford Academic Google Scholar Oxford Economic Papers, Volume 53, Issue 1, January 2001, Pages 114–137, https://doi.org/10.1093/oep/53.1.114 Published: 01 January 2001

RIMNet: Recommendation Incentive Mechanism based on evolutionary game dynamics in peer-to-peer service networks

We develop a multi-level selection model in the framework of indirect reciprocity. Using two levels of selection, one at the individual level and another at the group level, we propose a competitive scenario among social norms, in which all individuals in each group undergo pairwise interactions, whereas all groups also engage in pairwise conflicts, modeled by different games. Norms evolve as a result of groups' conflicts whereas evolution inside each group promotes the selection of best strategies for each ruling social norm. Different types of inter-group conflict and intensities of selection are considered. The proposed evolutionary model leads to the emergence of one of the recently obtained leading-eight social norms, irrespective of the type of conflict between groups employed. We also compared the individual performance of the norm obtained in the evolutionary process with several other popular norms, showing that it performs better than any the other norms. This reputation assignment rule gives rise to a stern and unambiguous response to each individual behavior, where prompt forgiving coexists with implacable punishment.

Evolutionary game theory and the concept of an evolutionarily stable strategy have been not only extensively developed and successfully applied to explain the evolution of animal behavior, but also widely used in economics and social sciences. Recently, in order to reveal the stochastic dynamical properties of evolutionary games in randomly fluctuating environments, the concept of stochastic evolutionary stability based on conditions for stochastic local stability for a fixation state was developed in the context of a symmetric matrix game with two phenotypes and random payoffs in pairwise interactions [Zheng et al., Phys. Rev. E 96, 032414 (2017)2470-004510.1103/PhysRevE.96.032414]. In this paper, we extend this study to more general situations, namely, multiphenotype symmetric as well as asymmetric matrix games with random payoffs. Conditions for stochastic local stability and stochastic evolutionary stability are established. Conditions for a fixation state to be stochastically unstable and almost everywhere stochastically unstable are distinguished in a multiphenotype setting according to the initial population state. Our results provide some alternative perspective and a more general theoretical framework for a better understanding of the evolution of animal behavior in a stochastic environment.

We study a natural extension of classical evolutionary game theory to a setting in which pairwise interactions are restricted to the edges of an undirected graph or network. We generalize the definition of an evolutionary stable strategy (ESS), and show a pair of complementary results that exhibit the power of randomization in our setting: subject to degree or edge density conditions, the classical ESS of any game are preserved when the graph is chosen randomly and the mutation set is chosen adversarially, or when the graph is chosen adversarially and the mutation set is chosen randomly. We examine natural strengthenings of our generalized ESS definition, and show that similarly strong resultsnare not possible for them.

From an evolutionary point of view, trees have at least one intriguing feature: they tend to have high levels of genetic diversity, but at the same time, they are known for their low evolutionary rates. Thus, trees are characterized by a counterintuitive combination of rapid micro-evolutionary change and a low macro-evolutionary change (Petit and Hampe 2006). Trees experience highly heterogeneous environmental conditions and are exposed to extreme climatic events within their lifetime, which could contribute to the maintenance of their typically high genetic diversity (Gutschick and BassiriRad 2003; Petit and Hampe 2006). Trees are not only highly diverse but also highly fecund over their extended lifetime, allowing them to respond to high selection intensity and to adapt quickly to local conditions (Petit and Hampe 2006). Mean antiquity of tree species is one order of magnitude higher than for herbs, which implies low rates of extinction to compensate for their low rates of speciation. However, forest species are more vulnerable to environmental change than this combination of evolutionary features may suggest (Jump and Penuelas 2005). Recent studies of Spanish populations of beech (Fagus sylvatica) are showing that the fragmentation of the forests that took place several centuries ago has led to a high genetic divergence of the populations and a reduced genetic diversity despite the fact that the species is wind-pollinated and the fragments are very near to each other (Jump and Penuelas 2006). These studies show the negative genetic impact of forest fragmentation , demonstrating that trees are not at reduced risk from environmental change (Fig. 2.1). This rather unexpected sensitivity of trees to forest management is particularly important under the current climate change since it can exacerbate the impact of human activities on forest dynamics and natural regeneration (Castro et al. 2004a).

A unified framework for reinforcement learning, co-learning and meta-learning how to coordinate in collaborative multi-agent systems

Human societies are organized and developed through collective cooperative behaviours. Based on the information in their environment, individuals can form collective cooperation by strategically changing unfavourable surroundings and imitating superior behaviours. However, facing the rampant proliferation and spreading of misinformation, we still lack systematic investigations into the impact of misinformation on the evolution of collective cooperation. Here, we study this problem by classical evolutionary game theory. We find that the existence of misinformation generally impedes the emergence of collective cooperation on networks, although the level of cooperation is slightly higher for weak social cooperative dilemma below a proven threshold. We further show that this possible advantage diminishes as social connections become denser, suggesting that the detrimental effect of misinformation further increases when 'social viscosity' is low. Our results uncover the quantitative effect of misinformation on suppressing collective cooperation, and pave the way for designing possible mechanisms to improve collective cooperation.