174,152 research outputs found
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
Heterogeneous resource allocation can change social hierarchy in public goods games
Public Goods Games represent one of the most useful tools to study group
interactions between individuals. However, even if they could provide an
explanation for the emergence and stability of cooperation in modern societies,
they are not able to reproduce some key features observed in social and
economical interactions. The typical shape of wealth distribution - known as
Pareto Law - and the microscopic organization of wealth production are two of
them. Here, we introduce a modification to the classical formulation of Public
Goods Games that allows for the emergence of both of these features from first
principles. Unlike traditional Public Goods Games on networks, where players
contribute equally to all the games in which they participate, we allow
individuals to redistribute their contribution according to what they earned in
previous rounds. Results from numerical simulations show that not only a Pareto
distribution for the payoffs naturally emerges but also that if players don't
invest enough in one round they can act as defectors even if they are formally
cooperators. Finally, we also show that the players self-organize in a very
productive backbone that covers almost perfectly the minimum spanning tree of
the underlying interaction network. Our results not only give an explanation
for the presence of the wealth heterogeneity observed in real data but also
points to a conceptual change regarding how cooperation is defined in
collective dilemmas.Comment: 8 pages, 5 figures, 55 reference
Image scoring in ad-hoc networks : an investigation on realistic settings
Encouraging cooperation in distributed Multi-Agent Systems (MAS) remains an open problem. Emergent application domains such as Mobile Ad-hoc Networks (MANETs) are characterised by constraints including sparse connectivity and a lack of direct interaction history. Image scoring, a simple model of reputation proposed by Nowak and Sigmund, exhibits low space and time complexity and promotes cooperation through indirect reciprocity, in which an agent can expect cooperation in the future without repeat interactions with the same partners. The low overheads of image scoring make it a promising technique for ad-hoc networking domains. However, the original investigation of Nowak and Sigmund is limited in that it (i) used a simple idealised setting, (ii) did not consider the effects of incomplete information on the mechanism’s efficacy, and (iii) did not consider the impact of the network topology connecting agents. We address these limitations by investigating more realistic values for the number of interactions agents engage in, and show that incomplete information can cause significant errors in decision making. As the proportion of incorrect decisions rises, the efficacy of image scoring falls and selfishness becomes more dominant. We evaluate image scoring on three different connection topologies: (i) completely connected, which closely approximates Nowak and Sigmund’s original setup, (ii) random, with each pair of nodes connected with a constant probability, and (iii) scale-free, which is known to model a number of real world environments including MANETs
Participation costs dismiss the advantage of heterogeneous networks in evolution of cooperation
Real social interactions occur on networks in which each individual is
connected to some, but not all, of others. In social dilemma games with a fixed
population size, heterogeneity in the number of contacts per player is known to
promote evolution of cooperation. Under a common assumption of positively
biased payoff structure, well-connected players earn much by playing
frequently, and cooperation once adopted by well-connected players is
unbeatable and spreads to others. However, maintaining a social contact can be
costly, which would prevent local payoffs from being positively biased. In
replicator-type evolutionary dynamics, it is shown that even a relatively small
participation cost extinguishes the merit of heterogeneous networks in terms of
cooperation. In this situation, more connected players earn less so that they
are no longer spreaders of cooperation. Instead, those with fewer contacts win
and guide the evolution. The participation cost, or the baseline payoff, is
irrelevant in homogeneous populations but is essential for evolutionary games
on heterogeneous networks.Comment: 4 figures + 3 supplementary figure
Social Evolution: New Horizons
Cooperation is a widespread natural phenomenon yet current evolutionary
thinking is dominated by the paradigm of selfish competition. Recent advanced
in many fronts of Biology and Non-linear Physics are helping to bring
cooperation to its proper place. In this contribution, the most important
controversies and open research avenues in the field of social evolution are
reviewed. It is argued that a novel theory of social evolution must integrate
the concepts of the science of Complex Systems with those of the Darwinian
tradition. Current gene-centric approaches should be reviewed and com-
plemented with evidence from multilevel phenomena (group selection), the
constrains given by the non-linear nature of biological dynamical systems and
the emergent nature of dissipative phenomena.Comment: 16 pages 5 figures, chapter in forthcoming open access book
"Frontiers in Ecology, Evolution and Complexity" CopIt-arXives 2014, Mexic
Emergence of communities and diversity in social networks
Communities are common in complex networks and play a significant role in the functioning of social, biological, economic,
and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the
effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social
networks is still lacking. Addressing this fundamental problem
is of paramount importance in understanding, predicting, and
controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here,
we answer this question using the ultimatum game, which has
been a paradigm for characterizing altruism and fairness. We
experimentally show that stable local communities with different
internal agreements emerge spontaneously and induce social
diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social
norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community
leaders. This result indicates that networks are significant in the
emergence and stabilization of communities and social diversity.
Our experimental results also provide valuable information about
strategies for developing network models and theories of evolutionary games and social dynamics.This work was supported by the National Nature Science Foundation of China under Grants 61573064, 71631002, 71401037, and 11301032; the Fundamental Research Funds for the Central Universities and Beijing Nova Programme; and the Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant). The Boston University work was supported by NSF Grants PHY-1505000, CMMI-1125290, and CHE- 1213217, and by Defense Threat Reduction Agency Grant HDTRA1-14-1-0017, and Department of Energy Contract DE-AC07-05Id14517. (61573064 - National Nature Science Foundation of China; 71631002 - National Nature Science Foundation of China; 71401037 - National Nature Science Foundation of China; 11301032 - National Nature Science Foundation of China; Fundamental Research Funds for the Central Universities and Beijing Nova Programme; Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant); PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - Defense Threat Reduction Agency; DE-AC07-05Id14517 - Department of Energy)Published versio
Random Topologies and the emergence of cooperation: the role of short-cuts
We study in detail the role of short-cuts in promoting the emergence of
cooperation in a network of agents playing the Prisoner's Dilemma Game (PDG).
We introduce a model whose topology interpolates between the one-dimensional
euclidean lattice (a ring) and the complete graph by changing the value of one
parameter (the probability p to add a link between two nodes not already
connected in the euclidean configuration). We show that there is a region of
values of p in which cooperation is largely enhanced, whilst for smaller values
of p only a few cooperators are present in the final state, and for p
\rightarrow 1- cooperation is totally suppressed. We present analytical
arguments that provide a very plausible interpretation of the simulation
results, thus unveiling the mechanism by which short-cuts contribute to promote
(or suppress) cooperation
Mesoscopic structure conditions the emergence of cooperation on social networks
We study the evolutionary Prisoner's Dilemma on two social networks obtained
from actual relational data. We find very different cooperation levels on each
of them that can not be easily understood in terms of global statistical
properties of both networks. We claim that the result can be understood at the
mesoscopic scale, by studying the community structure of the networks. We
explain the dependence of the cooperation level on the temptation parameter in
terms of the internal structure of the communities and their interconnections.
We then test our results on community-structured, specifically designed
artificial networks, finding perfect agreement with the observations in the
real networks. Our results support the conclusion that studies of evolutionary
games on model networks and their interpretation in terms of global properties
may not be sufficient to study specific, real social systems. In addition, the
community perspective may be helpful to interpret the origin and behavior of
existing networks as well as to design structures that show resilient
cooperative behavior.Comment: Largely improved version, includes an artificial network model that
fully confirms the explanation of the results in terms of inter- and
intra-community structur
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