Emergence of Cooperation in Non-scale-free Networks

Evolutionary game theory is one of the key paradigms behind many scientific disciplines from science to engineering. Previous studies proposed a strategy updating mechanism, which successfully demonstrated that the scale-free network can provide a framework for the emergence of cooperation. Instead, individuals in random graphs and small-world networks do not favor cooperation under this updating rule. However, a recent empirical result shows the heterogeneous networks do not promote cooperation when humans play a Prisoner's Dilemma. In this paper, we propose a strategy updating rule with payoff memory. We observe that the random graphs and small-world networks can provide even better frameworks for cooperation than the scale-free networks in this scenario. Our observations suggest that the degree heterogeneity may be neither a sufficient condition nor a necessary condition for the widespread cooperation in complex networks. Also, the topological structures are not sufficed to determine the level of cooperation in complex networks.Comment: 6 pages, 5 figure

Dynamical Organization of Cooperation in Complex Topologies

In this Letter, we study how cooperation is organized in complex topologies by analyzing the evolutionary (replicator) dynamics of the Prisoner's Dilemma, a two-players game with two available strategies, defection and cooperation, whose payoff matrix favors defection. We show that, asymptotically, the population is partitioned into three subsets: individuals that always cooperate ({\em pure cooperators}), always defect ({\em pure defectors}) and those that intermittently change their strategy. In fact the size of the latter set is the biggest for a wide range of the "stimulus to defect" parameter. While in homogeneous random graphs pure cooperators are grouped into several clusters, in heterogeneous scale-free (SF) networks they always form a single cluster containing the most connected individuals (hubs). Our results give further insights into why cooperation in SF networks is favored.Comment: 4 pages and 4 figures. Final version as published in Physical Review Letter

Interdependent network reciprocity in evolutionary games

Besides the structure of interactions within networks, also the interactions between networks are of the outmost importance. We therefore study the outcome of the public goods game on two interdependent networks that are connected by means of a utility function, which determines how payoffs on both networks jointly influence the success of players in each individual network. We show that an unbiased coupling allows the spontaneous emergence of interdependent network reciprocity, which is capable to maintain healthy levels of public cooperation even in extremely adverse conditions. The mechanism, however, requires simultaneous formation of correlated cooperator clusters on both networks. If this does not emerge or if the coordination process is disturbed, network reciprocity fails, resulting in the total collapse of cooperation. Network interdependence can thus be exploited effectively to promote cooperation past the limits imposed by isolated networks, but only if the coordination between the interdependent networks is not disturbe

Evolution of cooperation on dynamical graphs

There are two key characteristic of animal and human societies: (1) degree heterogeneity, meaning that not all individual have the same number of associates; and (2) the interaction topology is not static, i.e. either individuals interact with different set of individuals at different times of their life, or at least they have different associations than their parents. Earlier works have shown that population structure is one of the mechanisms promoting cooperation. However, most studies had assumed that the interaction network can be described by a regular graph (homogeneous degree distribution). Recently there are an increasing number of studies employing degree heterogeneous graphs to model interaction topology. But mostly the interaction topology was assumed to be static. Here we investigate the fixation probability of the cooperator strategy in the prisoner’s dilemma, when interaction network is a random regular graph, a random graph or a scale-free graph and the interaction network is allowed to change. We show that the fixation probability of the cooperator strategy is lower when the interaction topology is described by a dynamical graph compared to a static graph. Even a limited network dynamics significantly decreases the fixation probability of cooperation, an effect that is mitigated stronger by degree heterogeneous networks topology than by a degree homogeneous one. We have also found that from the considered graph topologies the decrease of fixation probabilities due to graph dynamics is the lowest on scale-free graphs

Evolutionary Games in Complex Topologies: Interplay between Structure and Dynamics

En este estudio exploramos la interrelación entre la estructura subyacente de una cierta población de individuos y el resultado de la dinámica que está teniendo lugar en ella, específicamente, el Dilema del Prisionero. En la primera parte de este trabajo analizamos el caso de una topología estática, en la que la red de conexiones no cambia en el tiempo. En la segunda parte, desarrollamos dos modelos para crecer redes, donde dicho crecimiento esta íntimamente relacionado con la dinámica

Social Norms, Local Interaction, and Neighborhood Planning

This paper examines optimal social linkage when each individual's repeated interaction with each of his neighbors creates spillovers. Individuals differ across rates of time preference. A planner must choose a local interaction system or neighborhood design before observing the realization of these rates. Given the planner's choice of design and a realization of discount factors, each individual plays a repeated Prisoner's Dilemma game with his neighbors. We introduce the concept of a local trigger strategy equilibrium (LTSE) to describe a stationary sequential equilibrium in which, for any realization of discount factors, each individual conditions his cooperation on the cooperation of at least one "acceptable" group of neighbors. The presence of impatient types implies that some free riding may be tolerated in equilibrium. When residents' discount factors are known to the planner, the optimal design exhibits a cooperative "core" and an uncooperative "fringe." Uncooperative (impatient) types are connected to cooperative ones who tolerate their free riding so that social conflict is kept to a minimum. By contrast, when residents' discount factors are independently distributed, the optimal design partitions individuals into maximally connected cliques (e.g., cul-de-sacs). In that case, each person's cooperation decision becomes a pure local public good. Finally, if types are correlated, then incomplete graphs with small overlap (e.g., grids) are possible.repeated games, local interaction, social norms, neighborhood design, local trigger strategy

Evolutionary graph theory: Breaking the symmetry between interaction and replacement

We study evolutionary dynamics in a population whose structure is given by two graphs: the interaction graph determines who plays with whom in an evolutionary game; the replacement graph specifies the geometry of evolutionary competition and updating. First, we calculate the fixation probabilities of frequency dependent selection between two strategies or phenotypes. We consider three different update mechanisms: birth-death, death-birth and imitation. Then, as a particular example, we explore the evolution of cooperation. Suppose the interaction graph is a regular graph of degree h, the replacement graph is a regular graph of degree g and the overlap between the two graphs is a regular graph of degree l. We show that cooperation is favored by natural selection if b/c > hg/l. Here, b and c denote the benefit and cost of the altruistic act. This result holds for death-birth updating, weak selection and large population size. Note that the optimum population structure for cooperators is given by maximum overlap between the interaction and the replacement graph (g = h = l), which means that the two graphs are identical. We also prove that a modified replicator equation can describe how the expected values of the frequencies of an arbitrary number of strategies change on replacement and interaction graphs: the two graphs induce a transformation of the payoff matrix

How Evolutionary Dynamics Affects Network Reciprocity in Prisoner's Dilemma

Cooperation lies at the foundations of human societies, yet why people cooperate remains a conundrum. The issue, known as network reciprocity, of whether population structure can foster cooperative behavior in social dilemmas has been addressed by many, but theoretical studies have yielded contradictory results so far—as the problem is very sensitive to how players adapt their strategy. However, recent experiments with the prisoner's dilemma game played on different networks and in a specific range of payoffs suggest that humans, at least for those experimental setups, do not consider neighbors' payoffs when making their decisions, and that the network structure does not influence the final outcome. In this work we carry out an extensive analysis of different evolutionary dynamics, taking into account most of the alternatives that have been proposed so far to implement players' strategy updating process. In this manner we show that the absence of network reciprocity is a general feature of the dynamics (among those we consider) that do not take neighbors' payoffs into account. Our results, together with experimental evidence, hint at how to properly model real people's behaviorThis work was supported by the Swiss Natural Science Foundation through grant PBFRP2_145872 and by Ministerio de Economía y Competitividad (Spain) through grant PRODIEVO.Publicad