22,690 research outputs found
Strategic Learning and the Topology of Social Networks
We consider a group of strategic agents who must each repeatedly take one of
two possible actions. They learn which of the two actions is preferable from
initial private signals, and by observing the actions of their neighbors in a
social network.
We show that the question of whether or not the agents learn efficiently
depends on the topology of the social network. In particular, we identify a
geometric "egalitarianism" condition on the social network that guarantees
learning in infinite networks, or learning with high probability in large
finite networks, in any equilibrium. We also give examples of non-egalitarian
networks with equilibria in which learning fails.Comment: 30 pages, one figur
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
The Philanthropic Landscape in the United States: A Topology of Trends
Over the last decade, the field of philanthropy has been in a constant state of evolution. New wealth has brought new philanthropists into the field, many seeking to apply their business acumen to their philanthropic work. There also has been a corresponding growth in consultants and advisors providing guidance and assistance on all aspects of giving. The growth of new technologies has revolutionized communications, social organizing, data collection, and program delivery. Additionally, the line between sectors is blurring and many funders and donors are exploring partnerships across sectors, if not focusing their philanthropic efforts solely on private sector driven initiatives. This paper was commissioned as part of the process undertaken by the Africa Grantmakers' Affinity Group (AGAG) to develop a new strategic plan that responds to changes int he landscapes in Africa and in philanthropy. The changes in philanthropy are vast and a full cataloging of them is outside of the scope of this brief paper. What this paper strives to provide is a brief overview of the major trends that have been driving philanthropy over the last three to five years and where possible, provide specific examples of these various types of philanthropy at work in Africa with the hope of fostering reflection and coversation as AGAG moves into its strategic planning process
Controllability of Social Networks and the Strategic Use of Random Information
This work is aimed at studying realistic social control strategies for social
networks based on the introduction of random information into the state of
selected driver agents. Deliberately exposing selected agents to random
information is a technique already experimented in recommender systems or
search engines, and represents one of the few options for influencing the
behavior of a social context that could be accepted as ethical, could be fully
disclosed to members, and does not involve the use of force or of deception.
Our research is based on a model of knowledge diffusion applied to a
time-varying adaptive network, and considers two well-known strategies for
influencing social contexts. One is the selection of few influencers for
manipulating their actions in order to drive the whole network to a certain
behavior; the other, instead, drives the network behavior acting on the state
of a large subset of ordinary, scarcely influencing users. The two approaches
have been studied in terms of network and diffusion effects. The network effect
is analyzed through the changes induced on network average degree and
clustering coefficient, while the diffusion effect is based on two ad-hoc
metrics defined to measure the degree of knowledge diffusion and skill level,
as well as the polarization of agent interests. The results, obtained through
simulations on synthetic networks, show a rich dynamics and strong effects on
the communication structure and on the distribution of knowledge and skills,
supporting our hypothesis that the strategic use of random information could
represent a realistic approach to social network controllability, and that with
both strategies, in principle, the control effect could be remarkable
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
Learning and coordinating in a multilayer network
We introduce a two layer network model for social coordination incorporating
two relevant ingredients: a) different networks of interaction to learn and to
obtain a payoff , and b) decision making processes based both on social and
strategic motivations. Two populations of agents are distributed in two layers
with intralayer learning processes and playing interlayer a coordination game.
We find that the skepticism about the wisdom of crowd and the local
connectivity are the driving forces to accomplish full coordination of the two
populations, while polarized coordinated layers are only possible for
all-to-all interactions. Local interactions also allow for full coordination in
the socially efficient Pareto-dominant strategy in spite of being the riskier
one
Individual and global adaptation in networks
The structure of complex biological and socio-economic networks affects the selective pressures or behavioural incentives of components in that network, and reflexively, the evolution/behaviour of individuals in those networks changes the structure of such networks over time. Such âadaptive networksâ underlie how gene-regulation networks evolve, how ecological networks self-organise, and how networks of strategic agents co-create social organisations. Although such domains are different in the details, they can each be characterised as networks of self-interested agents where agents alter network connections in the direction that increases their individual utility. Recent work shows that such dynamics are equivalent to associative learning, well-understood in the context of neural networks. Associative learning in neural substrates is the result of mandated learning rules (e.g. Hebbian learning), but in networks of autonomous agents âassociative inductionâ occurs as a result of local individual incentives to alter connections. Using results from a number of recent studies, here we review the theoretical principles that can be transferred between disciplines as a result of this isomorphism, and the implications for the organisation of genetic, social and ecological networks
An integrated core competence evaluation framework for portfolio management in the oil industry
Drawing upon resource-based theory, this paper presents a core competence evaluation framework for managing the competence portfolio of an oil company. It introduces a network typology to illustrate how to form different types of strategic alliance relations with partnering firms to manage and grow the competence portfolio. A framework is tested using a case study approach involving face-to-face structured interviews. We identified purchasing, refining and sales and marketing as strong candidates to be the core competencies. However, despite the company's core business of refining oil, the core competencies were identified to be their research and development and performance management (PM) capabilities. We further provide a procedure to determine different kinds of physical, intellectual and cultural resources making a dominant impact on company's competence portfolio. In addition, we provide a comprehensive set of guidelines on how to develop core competence further by forging a partnership alliance choosing an appropriate network topology
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