Self-assembling of networks in an agent-based model

Schweitzer, Frank; Tilch, Benno

doi:10.1103/physreve.66.026113

Cited by 21 publications

(13 citation statements)

References 22 publications

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“…Schweitzer has done work using the concepts of Brownian motion to develop theoretical models of self organisation within urban and human processes (Schweitzer and Holyst 2000;Schweitzer and Tilch 2002;Schweitzer 2002Schweitzer , 2003. Theoretical models are based on mathematics and physics; and attempt to make general theories about urban processes.…”

Section: Theoretical Modelsmentioning

confidence: 99%

Analysis of Urban Growth and Sprawl from Remote Sensing Data

Bhatta

2010

Advances in Geographic Information Science

411

297

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The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: Bauer, ThomasPrinted on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Chandrani and Bagmi Preface Urban growth and sprawl is a pertinent topic for analysis and assessment towards the sustainable development of a city. Environmental impacts of urban growth and extent of urban problems have been growing in complexity and relevance, generating strong imbalances between the city and its hinterland. The need to address this complexity in assessing and monitoring the urban planning and management processes and practices is strongly felt in the recent years.Determining the rate of urban growth and urban spatial configuration, from remote sensing data, is a prevalent approach in contemporary urban geographic studies. Maps of growth and a classified urban structure derived from remotely sensed data can assist planners to visualise the trajectories of their cities, their underlying systems, functions, and structures. There are currently a number of applications of analytical methods and models available to cities by using the remote sensing data and geographic information system (GIS) techniques, in specific-for mapping, monitoring, measuring, analysing, and modelling.The international participants are increasingly engaged with the urgent environmental tasks for the sustainable development of their urban regions, the planning challenges faced by the local authorities, and the application of remote sensing data and GIS techniques in the analysis of urban growth to meet these challenges. However, despite the promise of new and fast-developing remote sensing technologies, a gap exists between the research-focused results offered by the urban remote sensing community and the application of these data and methods/models by the governments of urban regions. There is no end of interesting scientific questions to ask about cities and their growth, but sometimes these questions do not match the operational problems and concerns of a given city. This necessitates more focused research and debate in the areas covered by this title. Kolkata, India B. Bhatta vii Acknowledgments I am grateful to all the authors of the numerous books and research publications mentioned in the list of references of this book. I have consulted these literatures, picked up the relevant materials, synthesised them and put them in an organised manner with simple language in this book. I express my gratitude to the teachers, researchers and organisations who have contributed a lot to the quality and information content of this book. I am very much thankful to

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Section: Theoretical Modelsmentioning

confidence: 99%

Analysis of Urban Growth and Sprawl from Remote Sensing Data

Bhatta

2010

Advances in Geographic Information Science

411

297

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“…The concept of Brownian agents [42] has found a vast range of applications at different levels of organization, physical, biological and social. Specifically, active motion and clustering in biological systems [10,11,17,51], self-wiring of networks and trail formation based on chemotactic interactions [21,47,52] and emotional influence in online communications [13-15, 46, 57] are studied both from a modeling and a data-driven perspective.…”

Section: /31mentioning

confidence: 99%

The law of proportionate growth and its siblings: Applications in agent-based modeling of socio-economic systems

Schweitzer¹

2019

Preprint

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The law of proportionate growth simply states that the time dependent change of a quantity x is proportional to x. Its applicability to a wide range of dynamic phenomena is based on various assumptions for the proportionality factor, which can be random or deterministic, constant or time dependent. Further, the dynamics can be combined with additional additive growth terms, which can be constants, aggregated quantities, or interaction terms. This allows to extent the core dynamics into an agent-based modeling framework with vast applications in social and economic systems. The paper adopts this overarching perspective to discuss phenomena as diverse as saturated growth, competition, stochastic growth, investments in random environments, wealth redistribution, opinion dynamics and the wisdom of crowds, reputation dynamics, knowledge growth, and the combination with network dynamics.Such achievements made Aoki a forerunner in agent-based modeling, the way we want it to be: Away from mere, and often arbitrary, computer simulations based on ad-hoc assumptions about agent's behavior, towards a formal, tractable and still insightful analysis of interacting systems. Current trends in econophysics [4] and sociophysics [43] point into this direction.In the spirit of Aoki's approch, I will sketch how a class of multiplicative models can be fruitfully applied to various dynamic problems in the socio-economic domain. The core of this model class is the law of proportionate growth proposed by R. Gibrat in 1931 to describe the growth of firms. The size of a firm is described by a (positive) variable x i (t). The law of proportionate growth then 1/31

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“…In one such study, reinforcement learning is used as a mechanism for discovering which agents to follow in the minority game [4]. Other work focuses on the stability of dynamic processes on networks, self-assembly, and models that replicate properties of networks found in nature [11,25]. Finally, in a study at the intersection of social science and statistics, a simple model of a "stochastically evolving social network" is used to guide the pairings for repeated games [26].…”

Section: Related Workmentioning

confidence: 99%

Agent-organized networks for dynamic team formation

Gaston

desJardins

2005

Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems

122

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Many multi-agent systems consist of a complex network of autonomous yet interdependent agents. Examples of such networked multi-agent systems include supply chains and sensor networks. In these systems, agents have a select set of other agents with whom they interact based on environmental knowledge, cognitive capabilities, resource limitations, and communications constraints. Previous findings have demonstrated that the structure of the artificial social network governing the agent interactions is strongly correlated with organizational performance. As multi-agent systems are typically embedded in dynamic environments, we wish to develop distributed, on-line network adaptation mechanisms for discovering effective network structures. Therefore, within the context of dynamic team formation, we propose several strategies for agentorganized networks (AONs) and evaluate their effectiveness for increasing organizational performance.

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Self-assembling of networks in an agent-based model

Cited by 21 publications

References 22 publications

Analysis of Urban Growth and Sprawl from Remote Sensing Data

Analysis of Urban Growth and Sprawl from Remote Sensing Data

The law of proportionate growth and its siblings: Applications in agent-based modeling of socio-economic systems

Agent-organized networks for dynamic team formation

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