170,370 research outputs found
Peer-assisted online games with social reciprocity
Online games and social networks are cross-pollinating rapidly in today's Internet: Online social network sites are deploying more and more games in their systems, while online game providers are leveraging social networks to power their games. An intriguing development as it is, the operational challenge in the previous game persists, i.e., the large server operational cost remains a non-negligible obstacle for deploying high-quality multi-player games. Peer-to-peer based game network design could be a rescue, only if the game players' mutual resource contribution has been fully incentivized and efficiently scheduled. Exploring the unique advantage of social network based games (social games), we advocate to utilize social reciprocities among peers with social relationships for efficient contribution incentivization and scheduling, so as to power a high-quality online game with low server cost. In this paper, social reciprocity is exploited with two give-and-take ratios at each peer: (1) peer contribution ratio (PCR), which evaluates the reciprocity level between a pair of social friends, and (2) system contribution ratio (SCR), which records the give-and-take level of the player to and from the entire network. We design efficient peer-to-peer mechanisms for game state distribution using the two ratios, where each player optimally decides which other players to seek relay help from and help in relaying game states, respectively, based on combined evaluations of their social relationship and historical reciprocity levels. Our design achieves effective incentives for resource contribution, load balancing among relay peers, as well as efficient social-aware resource scheduling. We also discuss practical implementation concerns and implement our design in a prototype online social game. Our extensive evaluations based on experiments on PlanetLab verify that high-quality large-scale social games can be achieved with conservative server costs. © 2011 IEEE.published_or_final_versionThe 19th IEEE International Workshop on Quality of Service (IWQoS 2011), San Jose, CA., 6-7 June 2011. In Proceedings of 19th IWQoS, 2011, p. 1-
An approach about health games to social network environment
The need for contact, sharing and socializing is
part of human nature, so that the community's role is vital to
the survival of the species. With the development of
Information and Communication Technologies and their
influence on society and everyday life, led the search for new
ways to build relationships and create communities among
people, creating virtual communities; turn social networks
emerge as new forms of association that respond to more
complex understanding of human interaction in a way that
the wider community; these to promote your main goal,
more integrated into the games as a tool of socialization. The
impact of current games at various levels, as the social and
economic development, and its applicability in various fields,
it becomes important to analyze the contribution of the
community. It is intended in this article, a description of the
factors that motivate this research to a later stage to develop
the proposed objectives: to analyze the new social reality and
develop a model that allows adapting the health games to
social games in order to foster the creation / sustainability of
communities based on social networks, aiming to raise
awareness of health issues, increasing knowledge and
sharing of experiences of members of communities
Compositional Modelling of Network Games
The analysis of games played on graph-like structures is of increasing importance due to the prevalence of social networks, both virtual and physical, in our daily life. As well as being relevant in computer science, mathematical analysis and computer simulations of such distributed games are vital methodologies in economics, politics and epidemiology, amongst other fields. Our contribution is to give compositional semantics of a family of such games as a well-behaved mapping, a strict monoidal functor, from a category of open graphs (syntax) to a category of open games (semantics). As well as introducing the theoretical framework, we identify some applications of compositionality
Collaboration in Social Networks
The very notion of social network implies that linked individuals interact
repeatedly with each other. This allows them not only to learn successful
strategies and adapt to them, but also to condition their own behavior on the
behavior of others, in a strategic forward looking manner. Game theory of
repeated games shows that these circumstances are conducive to the emergence of
collaboration in simple games of two players. We investigate the extension of
this concept to the case where players are engaged in a local contribution game
and show that rationality and credibility of threats identify a class of Nash
equilibria -- that we call "collaborative equilibria" -- that have a precise
interpretation in terms of sub-graphs of the social network. For large network
games, the number of such equilibria is exponentially large in the number of
players. When incentives to defect are small, equilibria are supported by local
structures whereas when incentives exceed a threshold they acquire a non-local
nature, which requires a "critical mass" of more than a given fraction of the
players to collaborate. Therefore, when incentives are high, an individual
deviation typically causes the collapse of collaboration across the whole
system. At the same time, higher incentives to defect typically support
equilibria with a higher density of collaborators. The resulting picture
conforms with several results in sociology and in the experimental literature
on game theory, such as the prevalence of collaboration in denser groups and in
the structural hubs of sparse networks
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
Multi-Player Diffusion Games on Graph Classes
We study competitive diffusion games on graphs introduced by Alon et al. [1]
to model the spread of influence in social networks. Extending results of
Roshanbin [8] for two players, we investigate the existence of pure Nash
equilibria for at least three players on different classes of graphs including
paths, cycles, grid graphs and hypercubes; as a main contribution, we answer an
open question proving that there is no Nash equilibrium for three players on (m
x n) grids with min(m, n) >= 5. Further, extending results of Etesami and Basar
[3] for two players, we prove the existence of pure Nash equilibria for four
players on every d-dimensional hypercube.Comment: Extended version of the TAMC 2015 conference version now discussing
hypercube results (added details for the proof of Proposition 1
Maximizing Social Welfare in Score-Based Social Distance Games
Social distance games have been extensively studied as a coalition formation
model where the utilities of agents in each coalition were captured using a
utility function u that took into account distances in a given social network.
In this paper, we consider a non-normalized score-based definition of social
distance games where the utility function u_v depends on a generic scoring
vector v, which may be customized to match the specifics of each individual
application scenario.
As our main technical contribution, we establish the tractability of
computing a welfare-maximizing partitioning of the agents into coalitions on
tree-like networks, for every score-based function u_v. We provide more
efficient algorithms when dealing with specific choices of u_v or simpler
networks, and also extend all of these results to computing coalitions that are
Nash stable or individually rational. We view these results as a further strong
indication of the usefulness of the proposed score-based utility function: even
on very simple networks, the problem of computing a welfare-maximizing
partitioning into coalitions remains open for the originally considered
canonical function u.Comment: In Proceedings TARK 2023, arXiv:2307.0400
Maximizing Social Welfare in Score-Based Social Distance Games
Social distance games have been extensively studied as a coalition formation
model where the utilities of agents in each coalition were captured using a
utility function that took into account distances in a given social
network. In this paper, we consider a non-normalized score-based definition of
social distance games where the utility function depends on a generic
scoring vector , which may be customized to match the specifics of each
individual application scenario.
As our main technical contribution, we establish the tractability of
computing a welfare-maximizing partitioning of the agents into coalitions on
tree-like networks, for every score-based function . We provide more
efficient algorithms when dealing with specific choices of or simpler
networks, and also extend all of these results to computing coalitions that are
Nash stable or individually rational. We view these results as a further strong
indication of the usefulness of the proposed score-based utility function: even
on very simple networks, the problem of computing a welfare-maximizing
partitioning into coalitions remains open for the originally considered
canonical function .Comment: Short version appeared at TARK 2023. arXiv admin note: substantial
text overlap with arXiv:2307.0506
Cooperative Behavior Cascades in Human Social Networks
Theoretical models suggest that social networks influence the evolution of
cooperation, but to date there have been few experimental studies.
Observational data suggest that a wide variety of behaviors may spread in human
social networks, but subjects in such studies can choose to befriend people
with similar behaviors, posing difficulty for causal inference. Here, we
exploit a seminal set of laboratory experiments that originally showed that
voluntary costly punishment can help sustain cooperation. In these experiments,
subjects were randomly assigned to a sequence of different groups in order to
play a series of single-shot public goods games with strangers; this feature
allowed us to draw networks of interactions to explore how cooperative and
uncooperative behavior spreads from person to person to person. We show that,
in both an ordinary public goods game and in a public goods game with
punishment, focal individuals are influenced by fellow group members'
contribution behavior in future interactions with other individuals who were
not a party to the initial interaction. Furthermore, this influence persists
for multiple periods and spreads up to three degrees of separation (from person
to person to person to person). The results suggest that each additional
contribution a subject makes to the public good in the first period is tripled
over the course of the experiment by other subjects who are directly or
indirectly influenced to contribute more as a consequence. These are the first
results to show experimentally that cooperative behavior cascades in human
social networks
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