287,421 research outputs found
Evolution of Coordination in Social Networks: A Numerical Study
Coordination games are important to explain efficient and desirable social
behavior. Here we study these games by extensive numerical simulation on
networked social structures using an evolutionary approach. We show that local
network effects may promote selection of efficient equilibria in both pure and
general coordination games and may explain social polarization. These results
are put into perspective with respect to known theoretical results. The main
insight we obtain is that clustering, and especially community structure in
social networks has a positive role in promoting socially efficient outcomes.Comment: preprint submitted to IJMP
Strategic Interaction and Networks
This paper brings a general network analysis to a wide class of economic games. A network, or interaction matrix, tells who directly interacts with whom. A major challenge is determining how network structure shapes overall outcomes. We have a striking result. Equilibrium conditions depend on a single number: the lowest eigenvalue of a network matrix. Combining tools from potential games, optimization, and spectral graph theory, we study games with linear best replies and characterize the Nash and stable equilibria for any graph and for any impact of players’ actions. When the graph is sufficiently absorptive (as measured by this eigenvalue), there is a unique equilibrium. When it is less absorptive, stable equilibria always involve extreme play where some agents take no actions at all. This paper is the first to show the importance of this measure to social and economic outcomes, and we relate it to different network link patterns.Networks, potential games, lowest eigenvalue, stable equilibria, asymmetric equilibria
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
Strategic tradeoffs in competitor dynamics on adaptive networks
Recent empirical work highlights the heterogeneity of social competitions
such as political campaigns: proponents of some ideologies seek debate and
conversation, others create echo chambers. While symmetric and static network
structure is typically used as a substrate to study such competitor dynamics,
network structure can instead be interpreted as a signature of the competitor
strategies, yielding competition dynamics on adaptive networks. Here we
demonstrate that tradeoffs between aggressiveness and defensiveness (i.e.,
targeting adversaries vs. targeting like-minded individuals) creates
paradoxical behaviour such as non-transitive dynamics. And while there is an
optimal strategy in a two competitor system, three competitor systems have no
such solution; the introduction of extreme strategies can easily affect the
outcome of a competition, even if the extreme strategies have no chance of
winning. Not only are these results reminiscent of classic paradoxical results
from evolutionary game theory, but the structure of social networks created by
our model can be mapped to particular forms of payoff matrices. Consequently,
social structure can act as a measurable metric for social games which in turn
allows us to provide a game theoretical perspective on online political
debates.Comment: 20 pages (11 pages for the main text and 9 pages of supplementary
material
Self-Organizing Flows in Social Networks
Social networks offer users new means of accessing information, essentially
relying on "social filtering", i.e. propagation and filtering of information by
social contacts. The sheer amount of data flowing in these networks, combined
with the limited budget of attention of each user, makes it difficult to ensure
that social filtering brings relevant content to the interested users. Our
motivation in this paper is to measure to what extent self-organization of the
social network results in efficient social filtering. To this end we introduce
flow games, a simple abstraction that models network formation under selfish
user dynamics, featuring user-specific interests and budget of attention. In
the context of homogeneous user interests, we show that selfish dynamics
converge to a stable network structure (namely a pure Nash equilibrium) with
close-to-optimal information dissemination. We show in contrast, for the more
realistic case of heterogeneous interests, that convergence, if it occurs, may
lead to information dissemination that can be arbitrarily inefficient, as
captured by an unbounded "price of anarchy". Nevertheless the situation differs
when users' interests exhibit a particular structure, captured by a metric
space with low doubling dimension. In that case, natural autonomous dynamics
converge to a stable configuration. Moreover, users obtain all the information
of interest to them in the corresponding dissemination, provided their budget
of attention is logarithmic in the size of their interest set
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