Strategy Selection in Networked Evolutionary Games: Structural Effect and the Evolution of Cooperation

2015 ◽  
pp. 439-458
Author(s):  
Shaolin Tan ◽  
Jinhu Lü
Keyword(s):  
Evolutionary Games ◽  
Structural Effect ◽  
Evolution Of Cooperation ◽  
Strategy Selection

scholarly journals Dynamic utility: the sixth reciprocity mechanism for the evolution of cooperation

2020 ◽  
Vol 7 (8) ◽  
pp. 200891 ◽  
Author(s):  
Hiromu Ito ◽  
Jun Tanimoto
Keyword(s):  
Game Theory ◽  
Dynamic Game ◽  
Evolutionary Games ◽  
Evolution Of Cooperation ◽  
Current Status ◽  
Cooperative Behaviour ◽  
Evolutionary Mechanism ◽  
Static Game ◽  
Chicken Game ◽  
The Difference

Game theory has been extensively applied to elucidate the evolutionary mechanism of cooperative behaviour. Dilemmas in game theory are important elements that disturb the promotion of cooperation. An important question is how to escape from dilemmas. Recently, a dynamic utility function (DUF) that considers an individual's current status (wealth) and that can be applied to game theory was developed. The DUF is different from the famous five reciprocity mechanisms called Nowak's five rules. Under the DUF, cooperation is promoted by poor players in the chicken game, with no changes in the prisoner's dilemma and stag-hunt games. In this paper, by comparing the strengths of the two dilemmas, we show that the DUF is a novel reciprocity mechanism (sixth rule) that differs from Nowak's five rules. We also show the difference in dilemma relaxation between dynamic game theory and (traditional) static game theory when the DUF and one of the five rules are combined. Our results indicate that poor players unequivocally promote cooperation in any dynamic game. Unlike conventional rules that have to be brought into game settings, this sixth rule is universally (canonical form) applicable to any game because all repeated/evolutionary games are dynamic in principle.

scholarly journals Eco-evolutionary spatial dynamics of non-linear social dilemmas

2019 ◽  
Author(s):  
Chaitanya S. Gokhale ◽  
Hye Jin Park
Keyword(s):  
Spatial Structure ◽  
Public Goods ◽  
Public Good ◽  
Spatial Dynamics ◽  
Social Dilemmas ◽  
Evolutionary Games ◽  
Evolution Of Cooperation ◽  
Evolutionary Trajectory ◽  
The Public ◽  
Non Linear

AbstractSpatial dynamics can promote the evolution of cooperation. While dispersal processes have been studied in simple evolutionary games, real-world social dilemmas are much more complicated. The public good, in many cases, does not increase linearly as per the investment in it. When the investment is low, for example, every additional unit of the investment may help a lot to increase the public good, but the effect vanishes as the number of investments increase. Such non-linear behaviour is the norm rather than an exception in a variety of social as well as biological systems. We take into account the non-linearity in the payoffs of the public goods game as well as the natural demographic effects of population densities. Population density has also been shown to impact the evolution of co-operation. Coupling these non-linear games and population size effect together with an explicitly defined spatial structure brings us one step closer to the complexity of real eco-evolutionary spatial systems. We show how the non-linearity in payoffs, resulting in synergy or discounting of public goods can alter the effective rate of return on the cooperative investment. Synergy or discounting in public goods accumulation affects the resulting spatial structure, not just quantitatively but in some cases, drastically changing the outcomes. In cases where a linear payoff structure would lead to extinction, synergy can support the coexistence of cooperators and defectors. The combined eco-evolutionary trajectory can thus be qualitatively different in cases on non-linear social dilemmas.

scholarly journals When and How Does Mutation-Generated Variation Promote the Evolution of Cooperation?

Games ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 4
Author(s):  
Mathias Spichtig ◽  
Martijn Egas
Keyword(s):  
Direct Effect ◽  
Indirect Effect ◽  
Evolutionary Games ◽  
Evolution Of Cooperation ◽  
Mutation Rates ◽  
Direct And Indirect Effects ◽  
Cooperative Strategies ◽  
New Perspective ◽  
Asexual Populations ◽  
Positive Direct

Mutation-generated variation in behavior is thought to promote the evolution of cooperation. Here, we study this by distinguishing two effects of mutation in evolutionary games of the finitely repeated Prisoner’s Dilemma in infinite asexual populations. First, we show how cooperation can evolve through the direct effect of mutation, i.e., the fitness impact that individuals experience from interactions with mutants before selection acts upon these mutants. Whereas this direct effect suffices to explain earlier findings, we question its generality because mutational variation usually generates the highest direct fitness impact on unconditional defectors (AllD). We identify special conditions (e.g., intermediate mutation rates) for which cooperation can be favored by an indirect effect of mutation, i.e., the fitness impact that individuals experience from interactions with descendants of mutants. Simulations confirm that AllD-dominated populations can be invaded by cooperative strategies despite the positive direct effect of mutation on AllD. Thus, here the indirect effect of mutation drives the evolution of cooperation. The higher level of cooperation, however, is not achieved by individuals triggering reciprocity (‘genuine cooperation’), but by individuals exploiting the willingness of others to cooperate (‘exploitative cooperation’). Our distinction between direct and indirect effects of mutation provides a new perspective on how mutation-generated variation alters frequency-dependent selection.

THE EFFECT OF A HISTORY-FITNESS-BASED UPDATING RULE ON EVOLUTIONARY GAMES

2010 ◽  
Vol 21 (12) ◽  
pp. 1433-1442 ◽  
Author(s):  
WEN-BO DU ◽  
XIAN-BIN CAO ◽  
RUN-RAN LIU ◽  
CHUN-XIAO JIA
Keyword(s):  
Prisoner's Dilemma ◽  
Prisoner’S Dilemma ◽  
Evolutionary Games ◽  
Numerical Results ◽  
Evolution Of Cooperation ◽  
Cooperation Level ◽  
Mixing Proportion ◽  
Cooperative Behaviors ◽  
Updating Rule ◽  
Nature And Society

In this paper, we introduce a history-fitness-based updating rule into the evolutionary prisoner's dilemma game (PDG) on square lattices, and study how it works on the evolution of cooperation level. Under this updating rule, the player i will firstly select player j from its direct neighbors at random and then compare their fitness which is determined by the current payoff and history fitness. If player i's fitness is larger than that of j, player i will be more likely to keep its own strategy. Numerical results show that the cooperation level is remarkably promoted by the history-fitness-based updating rule. Moreover, there exists a moderate mixing proportion of current payoff and history fitness that can induce the optimal fitness, where the highest cooperation level is obtained. Our work may shed some new light on the ubiquitous cooperative behaviors in nature and society induced by the history factor.

scholarly journals Evolution of cooperation in evolutionary games for Sanitation Boards

2014 ◽  
Vol 17 (2) ◽  
Author(s):  
Rocío Botta ◽  
Gerardo Blanco ◽  
Christian E. Schaerer
Keyword(s):  
Game Theory ◽  
Public Good ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Evolutionary Games ◽  
Evolution Of Cooperation ◽  
Cost Models ◽  
Policy Makers ◽  
Community Projects ◽  
The Cost

In a group of individuals that come together to produce a good or provide a service, the cooperators (who pay to produce the good) are often exploited by those who receive the benefit without paying the cost. Models were developed over time using incentives (reward or punishment) and the option of abandoning the initiative to promote and stabilize the cooperation. In this paper we analyze several models based on the evolutionary game theory and public good games. We compare and organize them in a taxonomic table following their main characteristics to select the most suitable for a specific problem. The analyzed models are compared by using a public good problem in community projects for water supply. We have reasonable assurance that phenomena that appear on mod- els also occurs in these community projects. Therefore, we propose that evolutionary game theory can be a useful tool for policy-makers in order to improve cooperation and discourage defection in sanitation boards.

scholarly journals Markov Evolutionary Games for Network Defense Strategy Selection

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 19505-19516 ◽  
Author(s):  
Jianming Huang ◽  
Hengwei Zhang ◽  
Jindong Wang
Keyword(s):  
Evolutionary Games ◽  
Strategy Selection ◽  
Defense Strategy ◽  
Network Defense

EVOLUTIONARY GAMES ON VISIBILITY GRAPHS

2011 ◽  
Vol 14 (03) ◽  
pp. 307-315 ◽  
Author(s):  
ALEKSANDRA MURKS ◽  
MATJAŽ PERC
Keyword(s):  
Time Series ◽  
Logistic Map ◽  
Evolutionary Process ◽  
Interaction Network ◽  
Evolutionary Games ◽  
Evolution Of Cooperation ◽  
Square Lattice ◽  
Scale Free ◽  
Heterogeneous Interaction ◽  
Visibility Algorithm

We show that time series of different complexities can be transformed into networks that host individuals playing evolutionary games. The irregularity of the time series is thereby faithfully reflected in the fraction of cooperators surviving the evolutionary process, thus effectively linking time series with evolutionary games. Pivotal to the linkage is a simple visibility algorithm that transforms time series into networks. More specifically, periodic series yield regular networks, chaotic series yield random networks, while fractal series yield scale-free networks. As an example, we use a chaotic time series from the Logistic map and a fractal time series of Brownian motion, yielding an interaction network with an exponential and a power-law degree distribution, respectively. By employing the prisoner's dilemma and the snowdrift game, we demonstrate that such heterogeneous interaction networks facilitate the evolution of cooperation if compared to the traditional square lattice topology. Due to the simplicity of the employed methodology, newcomers with a basic command of nonlinear dynamics or stochastic processes can become easily acquainted with evolutionary games, and moreover, integrate these interesting and vibrant subfields of physics more effectively into their research.

RISK PREFERENCE AND SEQUENTIAL CHOICE IN EVOLUTIONARY GAMES

2010 ◽  
Vol 13 (04) ◽  
pp. 559-578 ◽  
Author(s):  
PATRICK ROOS ◽  
DANA NAU
Keyword(s):  
Decision Making ◽  
Evolutionary Game ◽  
Evolutionary Games ◽  
Expected Value ◽  
Evolution Of Cooperation ◽  
Value Maximization ◽  
Stag Hunt Game ◽  
Human Decision ◽  
Open Question ◽  
Descriptive Theories

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.

A Strategy with Novel Evolutionary Features for the Iterated Prisoner's Dilemma

2009 ◽  
Vol 17 (2) ◽  
pp. 257-274 ◽  
Author(s):  
Jiawei Li ◽  
Graham Kendall
Keyword(s):  
Prisoner's Dilemma ◽  
Evolutionary Dynamics ◽  
Prisoner’S Dilemma ◽  
Evolutionary Games ◽  
Evolution Of Cooperation ◽  
Iterated Prisoner's Dilemma ◽  
Evolutionary Features ◽  
Group Members ◽  
Identification Mechanism ◽  
Iterated Prisoner’S Dilemma

In recent iterated prisoner's dilemma tournaments, the most successful strategies were those that had identification mechanisms. By playing a predetermined sequence of moves and learning from their opponents' responses, these strategies managed to identify their opponents. We believe that these identification mechanisms may be very useful in evolutionary games. In this paper one such strategy, which we call collective strategy, is analyzed. Collective strategies apply a simple but efficient identification mechanism (that just distinguishes themselves from other strategies), and this mechanism allows them to only cooperate with their group members and defect against any others. In this way, collective strategies are able to maintain a stable population in evolutionary iterated prisoner's dilemma. By means of an invasion barrier, this strategy is compared with other strategies in evolutionary dynamics in order to demonstrate its evolutionary features. We also find that this collective behavior assists the evolution of cooperation in specific evolutionary environments.

scholarly journals Cooperation on Interdependent Networks by Means of Migration and Stochastic Imitation

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 485 ◽  
Author(s):  
Sayantan Nag Chowdhury ◽  
Srilena Kundu ◽  
Maja Duh ◽  
Matjaž Perc ◽  
Dibakar Ghosh
Keyword(s):  
Social Systems ◽  
Evolutionary Game ◽  
Evolutionary Games ◽  
Evolution Of Cooperation ◽  
Adverse Conditions ◽  
Interdependent Networks ◽  
Network Layers ◽  
Interdependent Network ◽  
Human Decision

Evolutionary game theory in the realm of network science appeals to a lot of research communities, as it constitutes a popular theoretical framework for studying the evolution of cooperation in social dilemmas. Recent research has shown that cooperation is markedly more resistant in interdependent networks, where traditional network reciprocity can be further enhanced due to various forms of interdependence between different network layers. However, the role of mobility in interdependent networks is yet to gain its well-deserved attention. Here we consider an interdependent network model, where individuals in each layer follow different evolutionary games, and where each player is considered as a mobile agent that can move locally inside its own layer to improve its fitness. Probabilistically, we also consider an imitation possibility from a neighbor on the other layer. We show that, by considering migration and stochastic imitation, further fascinating gateways to cooperation on interdependent networks can be observed. Notably, cooperation can be promoted on both layers, even if cooperation without interdependence would be improbable on one of the layers due to adverse conditions. Our results provide a rationale for engineering better social systems at the interface of networks and human decision making under testing dilemmas.

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