192,551 research outputs found
Complex networks analysis in socioeconomic models
This chapter aims at reviewing complex networks models and methods that were
either developed for or applied to socioeconomic issues, and pertinent to the
theme of New Economic Geography. After an introduction to the foundations of
the field of complex networks, the present summary adds insights on the
statistical mechanical approach, and on the most relevant computational aspects
for the treatment of these systems. As the most frequently used model for
interacting agent-based systems, a brief description of the statistical
mechanics of the classical Ising model on regular lattices, together with
recent extensions of the same model on small-world Watts-Strogatz and
scale-free Albert-Barabasi complex networks is included. Other sections of the
chapter are devoted to applications of complex networks to economics, finance,
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issues, including results for opinion and citation networks.
Finally, some avenues for future research are introduced before summarizing the
main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared
for Complexity and Geographical Economics - Topics and Tools, P.
Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published
Control of the socio-economic systems using herding interactions
Collective behavior of the complex socio-economic systems is heavily
influenced by the herding, group, behavior of individuals. The importance of
the herding behavior may enable the control of the collective behavior of the
individuals. In this contribution we consider a simple agent-based herding
model modified to include agents with controlled state. We show that in certain
case even the smallest fixed number of the controlled agents might be enough to
control the behavior of a very large system.Comment: 8 pages, 3 figure
"So go downtown": simulating pedestrian movement in town centres
Pedestrian movement models have been developed since the 1970s. A review of the literature shows that such models have been developed to explain and predict macro, meso, and micro movement patterns. However, recent developments in modelling techniques, and especially advances in agent-based simulation, open up the possibility of developing integrative and complex models which use existing models as 'building blocks'. In this paper we describe such integrative, modular approach to simulating pedestrian movement behaviour. The STREETS model, developed by using Swarm and GIS, is an agent-based model that focuses on the simulation of the behavioural aspects of pedestrian movement. The modular structure of the simulation is described in detail. This is followed by a discussion of the lessons learned from the development of STREETS, especially the advantages of adopting a modular approach and other aspects of using the agent-based paradigm for modelling
The Web as an Adaptive Network: Coevolution of Web Behavior and Web Structure
Much is known about the complex network structure of the Web, and about behavioral dynamics on the Web. A number of studies address how behaviors on the Web are affected by different network topologies, whilst others address how the behavior of users on the Web alters network topology. These represent complementary directions of influence, but they are generally not combined within any one study. In network science, the study of the coupled interaction between topology and behavior, or state-topology coevolution, is known as 'adaptive networks', and is a rapidly developing area of research. In this paper, we review the case for considering the Web as an adaptive network and several examples of state-topology coevolution on the Web. We also review some abstract results from recent literature in adaptive networks and discuss their implications for Web Science. We conclude that adaptive networks provide a formal framework for characterizing processes acting 'on' and 'of' the Web, and offers potential for identifying general organizing principles that seem otherwise illusive in Web Scienc
Microeconomic Structure determines Macroeconomic Dynamics. Aoki defeats the Representative Agent
Masanao Aoki developed a new methodology for a basic problem of economics:
deducing rigorously the macroeconomic dynamics as emerging from the
interactions of many individual agents. This includes deduction of the fractal
/ intermittent fluctuations of macroeconomic quantities from the granularity of
the mezo-economic collective objects (large individual wealth, highly
productive geographical locations, emergent technologies, emergent economic
sectors) in which the micro-economic agents self-organize.
In particular, we present some theoretical predictions, which also met
extensive validation from empirical data in a wide range of systems: - The
fractal Levy exponent of the stock market index fluctuations equals the Pareto
exponent of the investors wealth distribution. The origin of the macroeconomic
dynamics is therefore found in the granularity induced by the wealth / capital
of the wealthiest investors. - Economic cycles consist of a Schumpeter
'creative destruction' pattern whereby the maxima are cusp-shaped while the
minima are smooth. In between the cusps, the cycle consists of the sum of 2
'crossing exponentials': one decaying and the other increasing.
This unification within the same theoretical framework of short term market
fluctuations and long term economic cycles offers the perspective of a genuine
conceptual synthesis between micro- and macroeconomics. Joining another giant
of contemporary science - Phil Anderson - Aoki emphasized the role of rare,
large fluctuations in the emergence of macroeconomic phenomena out of
microscopic interactions and in particular their non self-averaging, in the
language of statistical physics. In this light, we present a simple stochastic
multi-sector growth model.Comment: 42 pages, 6 figure
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