25,688 research outputs found
Coevolution of Cooperation and Partner Rewiring Range in Spatial Social Networks
In recent years, there has been growing interest in the study of
coevolutionary games on networks. Despite much progress, little attention has
been paid to spatially embedded networks, where the underlying geographic
distance, rather than the graph distance, is an important and relevant aspect
of the partner rewiring process. It thus remains largely unclear how individual
partner rewiring range preference, local vs. global, emerges and affects
cooperation. Here we explicitly address this issue using a coevolutionary model
of cooperation and partner rewiring range preference in spatially embedded
social networks. In contrast to local rewiring, global rewiring has no distance
restriction but incurs a one-time cost upon establishing any long range link.
We find that under a wide range of model parameters, global partner switching
preference can coevolve with cooperation. Moreover, the resulting partner
network is highly degree-heterogeneous with small average shortest path length
while maintaining high clustering, thereby possessing small-world properties.
We also discover an optimum availability of reputation information for the
emergence of global cooperators, who form distant partnerships at a cost to
themselves. From the coevolutionary perspective, our work may help explain the
ubiquity of small-world topologies arising alongside cooperation in the real
world
Coevolutionary games - a mini review
Prevalence of cooperation within groups of selfish individuals is puzzling in
that it contradicts with the basic premise of natural selection. Favoring
players with higher fitness, the latter is key for understanding the challenges
faced by cooperators when competing with defectors. Evolutionary game theory
provides a competent theoretical framework for addressing the subtleties of
cooperation in such situations, which are known as social dilemmas. Recent
advances point towards the fact that the evolution of strategies alone may be
insufficient to fully exploit the benefits offered by cooperative behavior.
Indeed, while spatial structure and heterogeneity, for example, have been
recognized as potent promoters of cooperation, coevolutionary rules can extend
the potentials of such entities further, and even more importantly, lead to the
understanding of their emergence. The introduction of coevolutionary rules to
evolutionary games implies, that besides the evolution of strategies, another
property may simultaneously be subject to evolution as well. Coevolutionary
rules may affect the interaction network, the reproduction capability of
players, their reputation, mobility or age. Here we review recent works on
evolutionary games incorporating coevolutionary rules, as well as give a
didactic description of potential pitfalls and misconceptions associated with
the subject. In addition, we briefly outline directions for future research
that we feel are promising, thereby particularly focusing on dynamical effects
of coevolutionary rules on the evolution of cooperation, which are still widely
open to research and thus hold promise of exciting new discoveries.Comment: 24 two-column pages, 10 figures; accepted for publication in
BioSystem
Emergence of social networks via direct and indirect reciprocity
Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals' degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to
strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores ("indirect reciprocity"), which is known to play an important role in many economic interactions. In
order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. "tit-for-tat") as well as indirect
reciprocity (helping strangers in order to increase one's reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which
are dynamic at the individual level but stable at the network level
Learning to Reach Agreement in a Continuous Ultimatum Game
It is well-known that acting in an individually rational manner, according to
the principles of classical game theory, may lead to sub-optimal solutions in a
class of problems named social dilemmas. In contrast, humans generally do not
have much difficulty with social dilemmas, as they are able to balance personal
benefit and group benefit. As agents in multi-agent systems are regularly
confronted with social dilemmas, for instance in tasks such as resource
allocation, these agents may benefit from the inclusion of mechanisms thought
to facilitate human fairness. Although many of such mechanisms have already
been implemented in a multi-agent systems context, their application is usually
limited to rather abstract social dilemmas with a discrete set of available
strategies (usually two). Given that many real-world examples of social
dilemmas are actually continuous in nature, we extend this previous work to
more general dilemmas, in which agents operate in a continuous strategy space.
The social dilemma under study here is the well-known Ultimatum Game, in which
an optimal solution is achieved if agents agree on a common strategy. We
investigate whether a scale-free interaction network facilitates agents to
reach agreement, especially in the presence of fixed-strategy agents that
represent a desired (e.g. human) outcome. Moreover, we study the influence of
rewiring in the interaction network. The agents are equipped with
continuous-action learning automata and play a large number of random pairwise
games in order to establish a common strategy. From our experiments, we may
conclude that results obtained in discrete-strategy games can be generalized to
continuous-strategy games to a certain extent: a scale-free interaction network
structure allows agents to achieve agreement on a common strategy, and rewiring
in the interaction network greatly enhances the agents ability to reach
agreement. However, it also becomes clear that some alternative mechanisms,
such as reputation and volunteering, have many subtleties involved and do not
have convincing beneficial effects in the continuous case
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
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
Recommended from our members
Smile asymmetries and reputation as reliable indicators of likelihood to cooperate: An evolutionary analysis
Cooperating with individuals whose altruism is not motivated by genuine prosocial emotions could have been costly in ancestral division of labour partnerships. How do humans ‘know’ whether or not an individual has the prosocial emotions committing future cooperation? Frank (1988) has hypothesized two pathways for altruist-detection: (a) facial expressions of emotions signalling character; and (b) gossip regarding the target individual’s reputation. Detecting non-verbal cues signalling commitment to cooperate may be one way to avoid the costs of exploitation. Spontaneous smiles while cooperating may be reliable index cues because of the physiological constraints controlling the neural pathways mediating involuntary emotional expressions. Specifically, it is hypothesized that individuals whose help is mediated by a genuine sympathy will express involuntary smiles (which are observably different from posed smiles). To investigate this idea, 38 participants played dictator games (i.e. a unilateral resource allocation task) against cartoon faces with a benevolent emotional expression (i.e. concern furrows and smile). The faces were presented with information regarding reputation (e.g. descriptions of an altruistic character vs. a non-altruistic character). Half of the sample played against icons with symmetrical smiles (representing a spontaneous smile) while the other half played against asymmetrically smiling icons (representing a posed smile). Icons described as having altruistic motives received more resources than icons described as self-interested helpers. Faces with symmetrical smiles received more resources than faces with asymmetrical smiles. These results suggest that reputation and smile asymmetry influence the likelihood of cooperation and thus may be reliable cues to altruism. These cues may allow for altruists to garner more resources in division of labour situations
Partner selection supports reputation-based cooperation in a Public Goods Game
In dyadic models of indirect reciprocity, the receivers' history of giving
has a significant impact on the donor's decision. When the interaction involves
more than two agents things become more complicated, and in large groups
cooperation can hardly emerge. In this work we use a Public Goods Game to
investigate whether publicly available reputation scores may support the
evolution of cooperation and whether this is affected by the kind of network
structure adopted. Moreover, if agents interact on a bipartite graph with
partner selection cooperation can thrive in large groups and in a small amount
of time.Comment: 6 pages, 10 figures. In press for Springer E
- …