527 research outputs found
Cooperation and Contagion in Web-Based, Networked Public Goods Experiments
A longstanding idea in the literature on human cooperation is that
cooperation should be reinforced when conditional cooperators are more likely
to interact. In the context of social networks, this idea implies that
cooperation should fare better in highly clustered networks such as cliques
than in networks with low clustering such as random networks. To test this
hypothesis, we conducted a series of web-based experiments, in which 24
individuals played a local public goods game arranged on one of five network
topologies that varied between disconnected cliques and a random regular graph.
In contrast with previous theoretical work, we found that network topology had
no significant effect on average contributions. This result implies either that
individuals are not conditional cooperators, or else that cooperation does not
benefit from positive reinforcement between connected neighbors. We then tested
both of these possibilities in two subsequent series of experiments in which
artificial seed players were introduced, making either full or zero
contributions. First, we found that although players did generally behave like
conditional cooperators, they were as likely to decrease their contributions in
response to low contributing neighbors as they were to increase their
contributions in response to high contributing neighbors. Second, we found that
positive effects of cooperation were contagious only to direct neighbors in the
network. In total we report on 113 human subjects experiments, highlighting the
speed, flexibility, and cost-effectiveness of web-based experiments over those
conducted in physical labs
The diplomat's dilemma: Maximal power for minimal effort in social networks
Closeness is a global measure of centrality in networks, and a proxy for how
influential actors are in social networks. In most network models, and many
empirical networks, closeness is strongly correlated with degree. However, in
social networks there is a cost of maintaining social ties. This leads to a
situation (that can occur in the professional social networks of executives,
lobbyists, diplomats and so on) where agents have the conflicting objectives of
aiming for centrality while simultaneously keeping the degree low. We
investigate this situation in an adaptive network-evolution model where agents
optimize their positions in the network following individual strategies, and
using only local information. The strategies are also optimized, based on the
success of the agent and its neighbors. We measure and describe the time
evolution of the network and the agents' strategies.Comment: Submitted to Adaptive Networks: Theory, Models and Applications, to
be published from Springe
Learning and innovative elements of strategy adoption rules expand cooperative network topologies
Cooperation plays a key role in the evolution of complex systems. However,
the level of cooperation extensively varies with the topology of agent networks
in the widely used models of repeated games. Here we show that cooperation
remains rather stable by applying the reinforcement learning strategy adoption
rule, Q-learning on a variety of random, regular, small-word, scale-free and
modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove
games. Furthermore, we found that using the above model systems other long-term
learning strategy adoption rules also promote cooperation, while introducing a
low level of noise (as a model of innovation) to the strategy adoption rules
makes the level of cooperation less dependent on the actual network topology.
Our results demonstrate that long-term learning and random elements in the
strategy adoption rules, when acting together, extend the range of network
topologies enabling the development of cooperation at a wider range of costs
and temptations. These results suggest that a balanced duo of learning and
innovation may help to preserve cooperation during the re-organization of
real-world networks, and may play a prominent role in the evolution of
self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3
Tables, 12 Figures and 116 reference
The Naming Game in Social Networks: Community Formation and Consensus Engineering
We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat.
Mech.: Theory Exp. P06014] in empirical social networks. This stylized
agent-based model captures essential features of agreement dynamics in a
network of autonomous agents, corresponding to the development of shared
classification schemes in a network of artificial agents or opinion spreading
and social dynamics in social networks. Our study focuses on the impact that
communities in the underlying social graphs have on the outcome of the
agreement process. We find that networks with strong community structure hinder
the system from reaching global agreement; the evolution of the Naming Game in
these networks maintains clusters of coexisting opinions indefinitely. Further,
we investigate agent-based network strategies to facilitate convergence to
global consensus.Comment: The original publication is available at
http://www.springerlink.com/content/70370l311m1u0ng3
Altruism can proliferate through group/kin selection despite high random gene flow
The ways in which natural selection can allow the proliferation of
cooperative behavior have long been seen as a central problem in evolutionary
biology. Most of the literature has focused on interactions between pairs of
individuals and on linear public goods games. This emphasis led to the
conclusion that even modest levels of migration would pose a serious problem to
the spread of altruism in group structured populations. Here we challenge this
conclusion, by analyzing evolution in a framework which allows for complex
group interactions and random migration among groups. We conclude that
contingent forms of strong altruism can spread when rare under realistic group
sizes and levels of migration. Our analysis combines group-centric and
gene-centric perspectives, allows for arbitrary strength of selection, and
leads to extensions of Hamilton's rule for the spread of altruistic alleles,
applicable under broad conditions.Comment: 5 pages, 2 figures. Supplementary material with 50 pages and 26
figure
Individualization as driving force of clustering phenomena in humans
One of the most intriguing dynamics in biological systems is the emergence of
clustering, the self-organization into separated agglomerations of individuals.
Several theories have been developed to explain clustering in, for instance,
multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of
fish, and animal herds. A persistent puzzle, however, is clustering of opinions
in human populations. The puzzle is particularly pressing if opinions vary
continuously, such as the degree to which citizens are in favor of or against a
vaccination program. Existing opinion formation models suggest that
"monoculture" is unavoidable in the long run, unless subsets of the population
are perfectly separated from each other. Yet, social diversity is a robust
empirical phenomenon, although perfect separation is hardly possible in an
increasingly connected world. Considering randomness did not overcome the
theoretical shortcomings so far. Small perturbations of individual opinions
trigger social influence cascades that inevitably lead to monoculture, while
larger noise disrupts opinion clusters and results in rampant individualism
without any social structure. Our solution of the puzzle builds on recent
empirical research, combining the integrative tendencies of social influence
with the disintegrative effects of individualization. A key element of the new
computational model is an adaptive kind of noise. We conduct simulation
experiments to demonstrate that with this kind of noise, a third phase besides
individualism and monoculture becomes possible, characterized by the formation
of metastable clusters with diversity between and consensus within clusters.
When clusters are small, individualization tendencies are too weak to prohibit
a fusion of clusters. When clusters grow too large, however, individualization
increases in strength, which promotes their splitting.Comment: 12 pages, 4 figure
Predicting language diversity with complex network
Evolution and propagation of the world's languages is a complex phenomenon,
driven, to a large extent, by social interactions. Multilingual society can be
seen as a system of interacting agents, where the interaction leads to a
modification of the language spoken by the individuals. Two people can reach
the state of full linguistic compatibility due to the positive interactions,
like transfer of loanwords. But, on the other hand, if they speak entirely
different languages, they will separate from each other. These simple
observations make the network science the most suitable framework to describe
and analyze dynamics of language change. Although many mechanisms have been
explained, we lack a qualitative description of the scaling behavior for
different sizes of a population. Here we address the issue of the language
diversity in societies of different sizes, and we show that local interactions
are crucial to capture characteristics of the empirical data. We propose a
model of social interactions, extending the idea from, that explains the growth
of the language diversity with the size of a population of country or society.
We argue that high clustering and network disintegration are the most important
characteristics of models properly describing empirical data. Furthermore, we
cancel the contradiction between previous models and the Solomon Islands case.
Our results demonstrate the importance of the topology of the network, and the
rewiring mechanism in the process of language change
Beyond aridification: multiple explanations for the elevated diversification of cacti in the New World Succulent Biome
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106989/1/nph12752.pd
Emergence of metapopulations and echo chambers in mobile agents
Multi-agent models often describe populations segregated either in the physical space, i.e. subdivided in metapopulations, or in the ecology of opinions, i.e. partitioned in echo chambers. Here we show how the interplay between homophily and social influence controls the emergence of both kinds of segregation in a simple model of mobile agents, endowed with a continuous opinion variable. In the model, physical proximity determines a progressive convergence of opinions but differing opinions result in agents moving away from each others. This feedback between mobility and social dynamics determines to the onset of a stable dynamical metapopulation scenario where physically separated groups of like-minded individuals interact with each other through the exchange of agents. The further introduction of confirmation bias in social interactions, defined as the tendency of an individual to favor opinions that match his own, leads to the emergence of echo chambers where different opinions can coexist also within the same group. We believe that the model may be of interest to researchers investigating the origin of segregation in the offline and online world
Network Homophily and the Evolution of the Pay-It-Forward Reciprocity
The pay-it-forward reciprocity is a type of cooperative behavior that people who have benefited from others return favors to third parties other than the benefactors, thus pushing forward a cascade of kindness. The phenomenon of the pay-it-forward reciprocity is ubiquitous, yet how it evolves to be part of human sociality has not been fully understood. We develop an evolutionary dynamics model to investigate how network homophily influences the evolution of the pay-it-forward reciprocity. Manipulating the extent to which actors carrying the same behavioral trait are linked in networks, the computer simulation model shows that strong network homophily helps consolidate the adaptive advantage of cooperation, yet introducing some heterophily to the formation of network helps advance cooperation's scale further. Our model enriches the literature of inclusive fitness theory by demonstrating the conditions under which cooperation or reciprocity can be selected for in evolution when social interaction is not confined exclusively to relatives
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