190,780 research outputs found
Emergence of communities and diversity in social networks
Communities are common in complex networks and play a significant role in the functioning of social, biological, economic,
and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the
effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social
networks is still lacking. Addressing this fundamental problem
is of paramount importance in understanding, predicting, and
controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here,
we answer this question using the ultimatum game, which has
been a paradigm for characterizing altruism and fairness. We
experimentally show that stable local communities with different
internal agreements emerge spontaneously and induce social
diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social
norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community
leaders. This result indicates that networks are significant in the
emergence and stabilization of communities and social diversity.
Our experimental results also provide valuable information about
strategies for developing network models and theories of evolutionary games and social dynamics.This work was supported by the National Nature Science Foundation of China under Grants 61573064, 71631002, 71401037, and 11301032; the Fundamental Research Funds for the Central Universities and Beijing Nova Programme; and the Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant). The Boston University work was supported by NSF Grants PHY-1505000, CMMI-1125290, and CHE- 1213217, and by Defense Threat Reduction Agency Grant HDTRA1-14-1-0017, and Department of Energy Contract DE-AC07-05Id14517. (61573064 - National Nature Science Foundation of China; 71631002 - National Nature Science Foundation of China; 71401037 - National Nature Science Foundation of China; 11301032 - National Nature Science Foundation of China; Fundamental Research Funds for the Central Universities and Beijing Nova Programme; Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant); PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - Defense Threat Reduction Agency; DE-AC07-05Id14517 - Department of Energy)Published versio
Collective intelligence: aggregation of information from neighbors in a guessing game
Complex systems show the capacity to aggregate information and to display
coordinated activity. In the case of social systems the interaction of
different individuals leads to the emergence of norms, trends in political
positions, opinions, cultural traits, and even scientific progress. Examples of
collective behavior can be observed in activities like the Wikipedia and Linux,
where individuals aggregate their knowledge for the benefit of the community,
and citizen science, where the potential of collectives to solve complex
problems is exploited. Here, we conducted an online experiment to investigate
the performance of a collective when solving a guessing problem in which each
actor is endowed with partial information and placed as the nodes of an
interaction network. We measure the performance of the collective in terms of
the temporal evolution of the accuracy, finding no statistical difference in
the performance for two classes of networks, regular lattices and random
networks. We also determine that a Bayesian description captures the behavior
pattern the individuals follow in aggregating information from neighbors to
make decisions. In comparison with other simple decision models, the strategy
followed by the players reveals a suboptimal performance of the collective. Our
contribution provides the basis for the micro-macro connection between
individual based descriptions and collective phenomena.Comment: 9 pages, 9 figure
Modeling social norms in real-world agent-based simulations
Studying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans\u27 actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors. Studies on norms are much older than the age of computer science, since normative studies have been a classic topic in sociology, psychology, philosophy and law. Various theories have been put forth about the functioning of social norms. Although an extensive amount of research on norms has been performed during the recent years, there remains a significant gap between current models and models that can explain real-world normative behaviors. Most of the existing work on norms focuses on abstract applications, and very few realistic normative simulations of human societies can be found. The contributions of this dissertation include the following: 1) a new hybrid technique based on agent-based modeling and Markov Chain Monte Carlo is introduced. This method is used to prepare a smoking case study for applying normative models. 2) This hybrid technique is described using category theory, which is a mathematical theory focusing on relations rather than objects. 3) The relationship between norm emergence in social networks and the theory of tipping points is studied. 4) A new lightweight normative architecture for studying smoking cessation trends is introduced. This architecture is then extended to a more general normative framework that can be used to model real-world normative behaviors. The final normative architecture considers cognitive and social aspects of norm formation in human societies. Normative architectures based on only one of these two aspects exist in the literature, but a normative architecture that effectively includes both of these two is missing
The Emergence of Norms via Contextual Agreements in Open Societies
This paper explores the emergence of norms in agents' societies when agents
play multiple -even incompatible- roles in their social contexts
simultaneously, and have limited interaction ranges. Specifically, this article
proposes two reinforcement learning methods for agents to compute agreements on
strategies for using common resources to perform joint tasks. The computation
of norms by considering agents' playing multiple roles in their social contexts
has not been studied before. To make the problem even more realistic for open
societies, we do not assume that agents share knowledge on their common
resources. So, they have to compute semantic agreements towards performing
their joint actions. %The paper reports on an empirical study of whether and
how efficiently societies of agents converge to norms, exploring the proposed
social learning processes w.r.t. different society sizes, and the ways agents
are connected. The results reported are very encouraging, regarding the speed
of the learning process as well as the convergence rate, even in quite complex
settings
How groups can foster consensus: The case of local cultures
A local culture denotes a commonly shared behaviour within a cluster of
firms. Similar to social norms or conventions, it is an emergent feature
resulting from the firms' interaction in an economic network. To model these
dynamics, we consider a distributed agent population, representing e.g. firms
or individuals. Further, we build on a continuous opinion dynamics model with
bounded confidence (), which assumes that two agents only interact if
differences in their behaviour are less than . Interaction results in
more similarity of behaviour, i.e. convergence towards a common mean. This
framework is extended by two major concepts: (i) The agent's in-group
consisting of acquainted interaction partners is explicitly taken into account.
This leads to an effective agent behaviour reflecting that agents try to
continue to interact with past partners and thus to keep sufficiently close to
them. (ii) The in-group network structure changes over time, as agents can form
new links to other agents with sufficiently close effective behaviour or delete
links to agents no longer close in behaviour. Thus, our model provides a
feedback mechanism between the agents' behaviour and their in-group structure.
Studying its consequences by means of agent-based computer simulations, we find
that for narrow-minded agents (low ) the additional feedback helps to
find consensus more often, whereas for open-minded agents (high )
this does not hold. This counterintuitive result is explained by simulations of
the network evolution
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