Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ``phantom traffic jams'' even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ``freeze by heating''? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.

Traffic and related self-driven many-particle systems

Criticism and the Growth of Knowledge. Edited by I. Lakatos and A. Musgrave. Pp. viii, 282. £3·50, paperback £1. 1970. (Cambridge University Press.)

https://www.ufz.de/export/data/2/100065_GrimmODD.pdf

A standard protocol for describing individual-based and agent-based models

What can computer simulation contribute to the social sciences? Which of the many approaches to simulation would be best for my social science project? How do I design, carry out and analyse the results from a computer simulation? This is a practical textbook on the techniques of building computer simulations to assist understanding of social and economic issues and problems. Interest in social simulation has been growing rapidly worldwide as a result of increasingly powerful hardware and software and also a rising interest in the application of ideas of complexity, evolution, adaptation and chaos in the social sciences. This authoritative book details all the common approaches to social simulation, to provide social scientists with an appreciation of the literature and allow those with some programming skills to create their own simulations.New for this edition is a chapter on how to use simulation as a tool. A new chapter on multi-agent systems has also been added to support the fact that multi-agent modelling has become the preferred approach to simulation. Social scientists in a wide range of fields will find this book an essential tool for research, particularly in sociology, economics, anthropology, geography, organizational theory, political science, social policy, cognitive psychology and cognitive science. It will also appeal to computer scientists interested in distributed artificial intelligence, multi-agent systems and agent technologies.

Simulation for the Social Scientist

This paper presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decisionmaking, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.

http://www.dpi.inpe.br/cursos/tutoriais/modelagem/referencias/agents_lucc_overview.pdf

Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review

Preface. 1. Holism, Individualism and Emergent Properties. An Approach from the Perspective of Simulation G.N. Gilbert. 2. Simulation and Rational Practice H. Kliemt. 3. Epistemic Cultures in the Social Sciences. The Modelling Dilemma Dissolved K.H. Muller. 4. Impossible Models U. Krause. 5. The World as a Process. Simulations in the Natural and Social Sciences S. Hartmann. 6. Evolutionary Explanations from a Philosophy of Science Point of View U. Mueller. 7. Some Thoughts on the Methodological Status of the Darmstadt Micro Macro Simulator (DMMS) H.-D. Heike. 8. On the Measurement of Action W. Balzer. 9. Structuralist Models, Idealization, and Approximation C.U. Moulines. 10. A Concept of Explanation for Social Interaction Models H. Westmeyer. 11. Simulation and Structuralism K.G. Troitzsch. 12. Cellular Automata in the Social Sciences. Perspectives, Restrictions, and Artefacts R. Hegselmann. 13. Computer Simulations of Sustainable Cooperation in Social Dilemmas W.B.G. Liebrand, W.M. Messick. 14. Modeling Social Change with Cellular Automata A. Nowak, M. Lewenstein. 15. Dynamic Social Impact. Robust Predictions from Simple Theory B. Latane. Index.

Modelling and simulation in the social sciences from the philosophy of science point of view

Ci-dessous se trouvent trois articles bases sur des presentations et des discussions qui ont eu lieu au Seminaire de Dagatuhl sur la microsimulation en sciences sociales : Une defi a l'informatique, Schloss Dagstuhl, 1-5 mai 1995, et ont ete publies depuis dans K. G. Troitzsch. U. Mueller, G. N. Gilbert et J.E. Doran (sous la direction de), «Social Science Microsimulation», 1996, Springer Verlag. Les trois articles sont Simulation comme une strategie de recherche et Environnements et langages informatiques pour la simulation sociale : Resume d'une discussion informelle, par G. Nigel Gilbert, et Simulation informatique et sciences sociales : Sur l'avenir d'une relation difficile - Resume d'une discussion informelle par Klaus G. Troitzsch

Social science microsimulation

This paper is about some history and some future development of computer simulation (Ostrom’s Third Symbol System) in the social sciences, as opposed to mathematical modelling (Ostrom’s Second Symbol System). Statistical modelling is mainstream, but it so often forgets about the process character of social life, whereas simulation often forgets about data. Concept driven simulation is defended, but data driven simulation is also pleaded for, taking into account that understanding a social process must precede its prediction.

Social Science Simulation — Origins, Prospects, Purposes

The simulation of social behavior in a variety of domains is an increasingly important technological tool. eference survey of social simulation work, Social Simulation: Technologies, Advances and New Discoveries comprehensively collects the most exciting developments in the field. Drawing research contributions from a vibrant community of experts on social simulation, this Premier Reference Source provides a set of unique and innovative approaches, ranging from agent-based modeling to empirically based simulations, as well as applications in business, governmental, scientific, and other contexts. This book will be a significant reference tool for researchers, educators, and practitioners in such fields as sociology, geography, economics, environmental science, artificial intelligence, machine learning, computer engineering, and networks, and a valuable, interdisciplinary addition to academic libraries.

Klaus G. Troitzsch

Papers

Simulation for the Social Scientist

Modelling and simulation in the social sciences from the philosophy of science point of view

Social science microsimulation

Social Science Simulation — Origins, Prospects, Purposes

Social Simulation: Technologies, Advances and New Discoveries