Author
Itzhak Benenson
Bio: Itzhak Benenson is an academic researcher from Tel Aviv University. The author has contributed to research in topics: Population & Parking guidance and information. The author has an hindex of 30, co-authored 118 publications receiving 3834 citations.
Papers published on a yearly basis
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Book•
20 Aug 2004
TL;DR: This book defines urban Geosimulation as a framework for modelling complex spatial systems, and describes MAS models as planning and assessment tools for complex human-driven systems.
Abstract: Preface. Acknowledgements. Foreword. 1. Introduction to urban Geosimulation. 1.1 A new wave of urban geographic models is coming. 1.2 Defining urban Geosimulation. 1.3 Automata as a basis of Geosimulation. 1.4 High-resolution GIS as a driving force of Geosimulation. 1.5 The origins of support for Geosimulation. 1.6 Geosimulation of complex adaptive systems. 1.7 Book layout. 2. Formalizing Geosimulation with Geographic Automata Systems (GAS). 2.1 Cellular Automata and Multi-Agent Systems - Unite! 2.2 Geographic Automata Systems (GAS). 2.3 GAS as a tool for modelling Complex Adaptive Systems. 2.4 From Gas to software environment for urban modelling. 2.5 Object Based Environment for Urban Simulation (OBEUS) - a minimal implementation of GAS. 2.6 Universality of GAS. 2.7 Verifying GAS models. 3. System Theory, Geography, and Urban Modelling. 3.1 The basic notions of system theory. 3.2 The 1960s, geography meets system theory. 3.3 'Stocks and flows' urban modelling. 3.4 Critics of comprehensive modelling. 3.5 What next? Geosimulation of collective dynamics! 4. Modelling urban land usage with cellular automata. 4.1 Introduction. 4.2 Cellular Automata as a framework for modelling complex spatial systems. 4.3 Urban Cellular Automata. 4.4 From Markov models to urban Cellular Automata. 4.5 Integration of the CA and Markov approaches at a regional level. 4.6 Conclusions. 5. Modelling Urban Dynamics with Multi-Agent Systems. 5.1 Introduction. 5.2 MAS as a tool for modelling complex human-driven systems. 5.3 Interpreting agency. 5.4 Urban agents, urban agency, and multi-agent cities. 5.5 Agent behaviour in urban environments. 5.6 General models of agents' collectives in urban interpretation. 5.7 Abstract MAS models of urban phenomena. 5.8 Real-world agent-based simulations of urban phenomena. 5.9 MAS models as planning and assessment tools. 5.10 Conclusions. 6. Finale: Epistemology of Geosimulation. 6.1 Universal questions. 6.2 The future of Geosimulation. Bibliography. Index.
537 citations
TL;DR: The PARKAGENT model shows that additional parking supply linearly affects the occurrence of extreme values, but has only a weak impact on the average search time for a parking place or the average walking distance between the parking place and the destination.
261 citations
TL;DR: A framework that takes advantage of the formalism of automata theory and GI Science to unite cellular automata and multi‐agent systems techniques, and provides a spatial approach to bottom‐up modeling of complex geographic systems that are comprised of infrastructure and human objects is detailed.
Abstract: A novel approach to automata‐based modeling for spatial systems is described: geographic automata and Geographic Automata Systems. We detail a framework that takes advantage of the formalism of automata theory and GI Science to unite cellular automata and multi‐agent systems techniques, and provides a spatial approach to bottom‐up modeling of complex geographic systems that are comprised of infrastructure and human objects. The suitability of the framework is also discussed with reference to existing cellular automata and multi‐agent systems models used in urban studies. Practical implementation of the framework is illustrated with reference to an object‐based urban simulation environment and implementation of a popular socio‐spatial segregation model.
251 citations
TL;DR: In this article, a multi-agent simulation model of the population dynamics in a city, in which inhabitants can change their residential behavior depending on the properties of their neighborhood, neighbors and the whole city, is considered.
246 citations
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TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201
14,171 citations
TL;DR: As an example of how the current "war on terrorism" could generate a durable civic renewal, Putnam points to the burst in civic practices that occurred during and after World War II, which he says "permanently marked" the generation that lived through it and had a "terrific effect on American public life over the last half-century."
Abstract: The present historical moment may seem a particularly inopportune time to review Bowling Alone, Robert Putnam's latest exploration of civic decline in America. After all, the outpouring of volunteerism, solidarity, patriotism, and self-sacrifice displayed by Americans in the wake of the September 11 terrorist attacks appears to fly in the face of Putnam's central argument: that \"social capital\" -defined as \"social networks and the norms of reciprocity and trustworthiness that arise from them\" (p. 19)'has declined to dangerously low levels in America over the last three decades. However, Putnam is not fazed in the least by the recent effusion of solidarity. Quite the contrary, he sees in it the potential to \"reverse what has been a 30to 40-year steady decline in most measures of connectedness or community.\"' As an example of how the current \"war on terrorism\" could generate a durable civic renewal, Putnam points to the burst in civic practices that occurred during and after World War II, which he says \"permanently marked\" the generation that lived through it and had a \"terrific effect on American public life over the last half-century.\" 3 If Americans can follow this example and channel their current civic
5,309 citations
2,204 citations
TL;DR: In this paper, an overview of multi-agent system models of land-use/cover change (MAS/LUCC) is presented, which combine 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.
Abstract: 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.
1,779 citations