36,197 research outputs found
Minimizing Message Size in Stochastic Communication Patterns: Fast Self-Stabilizing Protocols with 3 bits
This paper considers the basic model of communication, in
which in each round, each agent extracts information from few randomly chosen
agents. We seek to identify the smallest amount of information revealed in each
interaction (message size) that nevertheless allows for efficient and robust
computations of fundamental information dissemination tasks. We focus on the
Majority Bit Dissemination problem that considers a population of agents,
with a designated subset of source agents. Each source agent holds an input bit
and each agent holds an output bit. The goal is to let all agents converge
their output bits on the most frequent input bit of the sources (the majority
bit). Note that the particular case of a single source agent corresponds to the
classical problem of Broadcast. We concentrate on the severe fault-tolerant
context of self-stabilization, in which a correct configuration must be reached
eventually, despite all agents starting the execution with arbitrary initial
states.
We first design a general compiler which can essentially transform any
self-stabilizing algorithm with a certain property that uses -bits
messages to one that uses only -bits messages, while paying only a
small penalty in the running time. By applying this compiler recursively we
then obtain a self-stabilizing Clock Synchronization protocol, in which agents
synchronize their clocks modulo some given integer , within rounds w.h.p., and using messages that contain bits only.
We then employ the new Clock Synchronization tool to obtain a
self-stabilizing Majority Bit Dissemination protocol which converges in time, w.h.p., on every initial configuration, provided that the
ratio of sources supporting the minority opinion is bounded away from half.
Moreover, this protocol also uses only 3 bits per interaction.Comment: 28 pages, 4 figure
Continuous opinion model in small world directed networks
In the compromise model of continuous opinions proposed by Deffuant et al,
the states of two agents in a network can start to converge if they are
neighbors and if their opinions are sufficiently close to each other, below a
given threshold of tolerance . In directed networks, if agent i is a
neighbor of agent j, j need not be a neighbor of i. In Watts-Strogatz networks
we performed simulations to find the averaged number of final opinions
and their distribution as a function of $\epsilon$ and of the network
structural disorder. In directed networks exhibits a rich structure,
being larger than in undirected networks for higher values of , and
smaller for lower values of .Comment: 15 pages, 6 figure
Advances towards a General-Purpose Societal-Scale Human-Collective Problem-Solving Engine
Human collective intelligence has proved itself as an important factor in a
society's ability to accomplish large-scale behavioral feats. As societies have
grown in population-size, individuals have seen a decrease in their ability to
activeily participate in the problem-solving processes of the group.
Representative decision-making structures have been used as a modern solution
to society's inadequate information-processing infrastructure. With computer
and network technologies being further embedded within the fabric of society,
the implementation of a general-purpose societal-scale human-collective
problem-solving engine is envisioned as a means of furthering the
collective-intelligence potential of society. This paper provides both a novel
framework for creating collective intelligence systems and a method for
implementing a representative and expertise system based on social-network
theory.Comment: Collective Problem Solving Theory and Social-Network algorith
Local Convergence and Global Diversity: The Robustness of Cultural Homophily
Recent extensions of the Axelrod model of cultural dissemination (Klemm et al
2003) showed that global diversity is extremely fragile with small amounts of
cultural mutation. This seemed to undermine the original Axelrod theory that
homophily preserves diversity. We show that cultural diversity is surprisingly
robust if we increase the tendency towards homophily as follows. First, we
raised the threshold of similarity below which influence is precluded. Second,
we allowed agents to be influenced by all neighbors simultaneously, instead of
only one neighbor as assumed in the orginal model. Computational experiments
show how both modifications strongly increase the robustness of diversity
against mutation. We also find that our extensions may reverse at least one of
the main results of Axelrod. While Axelrod predicted that a larger number of
cultural dimensions (features) reduces diversity, we find that more features
may entail higher levels of diversity.Comment: 21 pages, 8 figures, Submitted for presentation in Mathematical
Sociology Session, Annual Meeting of the American Sociological Association
(ASA), 200
Dynamics of organizational culture: Individual beliefs vs. social conformity
The complex nature of organizational culture challenges our ability to infers
its underlying dynamics from observational studies. Recent computational
studies have adopted a distinct different view, where plausible mechanisms are
proposed to describe a wide range of social phenomena, including the onset and
evolution of organizational culture. In this spirit, this work introduces an
empirically-grounded, agent-based model which relaxes a set of assumptions that
describes past work - (a) omittance of an individual's strive for achieving
cognitive coherence, (b) limited integration of important contextual factors -
by utilizing networks of beliefs and incorporating social rank into the
dynamics. As a result, we illustrate that: (i) an organization may appear to be
increasingly coherent in terms of organizational culture, yet be composed of
individuals with reduced levels of coherence, (ii) the components of social
conformity - peer-pressure and social rank - are influential at different
aggregation levels.Comment: 20 pages, 8 figure
Coordination of Decisions in a Spatial Agent Model
For a binary choice problem, the spatial coordination of decisions in an
agent community is investigated both analytically and by means of stochastic
computer simulations. The individual decisions are based on different local
information generated by the agents with a finite lifetime and disseminated in
the system with a finite velocity. We derive critical parameters for the
emergence of minorities and majorities of agents making opposite decisions and
investigate their spatial organization. We find that dependent on two essential
parameters describing the local impact and the spatial dissemination of
information, either a definite stable minority/majority relation
(single-attractor regime) or a broad range of possible values (multi-attractor
regime) occurs. In the latter case, the outcome of the decision process becomes
rather diverse and hard to predict, both with respect to the share of the
majority and their spatial distribution. We further investigate how a
dissemination of information on different time scales affects the outcome of
the decision process. We find that a more ``efficient'' information exchange
within a subpopulation provides a suitable way to stabilize their majority
status and to reduce ``diversity'' and uncertainty in the decision process.Comment: submitted for publication in Physica A (31 pages incl. 17 multi-part
figures
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