15,754 research outputs found
Multi-choice opinion dynamics model based on Latane theory
In this paper Nowak--Szamrej-Latan\'e model is reconsidered. This
computerised model of opinion formation bases on Latan\'e theory of social
impact. We modify this model to allow for multi (more than two) opinions. With
computer simulations we show that in the modified model the signatures of
order/disorder phase transition are still observed. The transition may be
observed in the average fraction of actors sharing the -th opinion, its
variation and also average number of clusters of actors with the same opinion
and the average size of the largest cluster of actors sharing the same opinion.
Also an influence of model control parameters on simulation results is shortly
reviewed. For a homogeneous society with identical actors' supportiveness and
persuasiveness the critical social temperature decreases with an increase
of available opinions from () via 4.7, 4.1 to for
, 4, 5, respectively.Comment: 12 page
Spatial interactions in agent-based modeling
Agent Based Modeling (ABM) has become a widespread approach to model complex
interactions. In this chapter after briefly summarizing some features of ABM
the different approaches in modeling spatial interactions are discussed.
It is stressed that agents can interact either indirectly through a shared
environment and/or directly with each other. In such an approach, higher-order
variables such as commodity prices, population dynamics or even institutions,
are not exogenously specified but instead are seen as the results of
interactions. It is highlighted in the chapter that the understanding of
patterns emerging from such spatial interaction between agents is a key problem
as much as their description through analytical or simulation means.
The chapter reviews different approaches for modeling agents' behavior,
taking into account either explicit spatial (lattice based) structures or
networks. Some emphasis is placed on recent ABM as applied to the description
of the dynamics of the geographical distribution of economic activities, - out
of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with
spatial structure, is used to illustrate the potential of such an approach for
spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book
"Complexity and Geographical Economics - Topics and Tools", P. Commendatore,
S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014
Macroscopic Noisy Bounded Confidence Models with Distributed Radical Opinions
In this article, we study the nonlinear Fokker-Planck (FP) equation that
arises as a mean-field (macroscopic) approximation of bounded confidence
opinion dynamics, where opinions are influenced by environmental noises and
opinions of radicals (stubborn individuals). The distribution of radical
opinions serves as an infinite-dimensional exogenous input to the FP equation,
visibly influencing the steady opinion profile. We establish mathematical
properties of the FP equation. In particular, we (i) show the well-posedness of
the dynamic equation, (ii) provide existence result accompanied by a
quantitative global estimate for the corresponding stationary solution, and
(iii) establish an explicit lower bound on the noise level that guarantees
exponential convergence of the dynamics to stationary state. Combining the
results in (ii) and (iii) readily yields the input-output stability of the
system for sufficiently large noises. Next, using Fourier analysis, the
structure of opinion clusters under the uniform initial distribution is
examined. Specifically, two numerical schemes for identification of
order-disorder transition and characterization of initial clustering behavior
are provided. The results of analysis are validated through several numerical
simulations of the continuum-agent model (partial differential equation) and
the corresponding discrete-agent model (interacting stochastic differential
equations) for a particular distribution of radicals
Organization of Multi-Agent Systems: An Overview
In complex, open, and heterogeneous environments, agents must be able to
reorganize towards the most appropriate organizations to adapt unpredictable
environment changes within Multi-Agent Systems (MAS). Types of reorganization
can be seen from two different levels. The individual agents level
(micro-level) in which an agent changes its behaviors and interactions with
other agents to adapt its local environment. And the organizational level
(macro-level) in which the whole system changes it structure by adding or
removing agents. This chapter is dedicated to overview different aspects of
what is called MAS Organization including its motivations, paradigms, models,
and techniques adopted for statically or dynamically organizing agents in MAS.Comment: 12 page
Dynamical strategies for obstacle avoidance during Dictyostelium discoideum aggregation: a Multi-agent system model
Chemotaxis, the movement of an organism in response to chemical stimuli, is a
typical feature of many microbiological systems. In particular, the social
amoeba \textit{Disctyostelium discoideum} is widely used as a model organism,
but it is not still clear how it behaves in heterogeneous environments. A few
models focusing on mechanical features have already addressed the question;
however, we suggest that phenomenological models focusing on the population
dynamics may provide new meaningful data. Consequently, by means of a specific
Multi-agent system model, we study the dynamical features emerging from complex
social interactions among individuals belonging to amoeba colonies.\\ After
defining an appropriate metric to quantitatively estimate the gathering
process, we find that: a) obstacles play the role of local topological
perturbation, as they alter the flux of chemical signals; b) physical obstacles
(blocking the cellular motion and the chemical flux) and purely chemical
obstacles (only interfering with chemical flux) elicit similar dynamical
behaviors; c) a minimal program for robustly gathering simulated cells does not
involve mechanisms for obstacle sensing and avoidance; d) fluctuations of the
dynamics concur in preventing multiple stable clusters. Comparing those
findings with previous results, we speculate about the fact that chemotactic
cells can avoid obstacles by simply following the altered chemical gradient.
Social interactions are sufficient to guarantee the aggregation of the whole
colony past numerous obstacles
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