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

Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded

Methodological Issues of Spatial Agent-Based Models

Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling

Policy and Place: A Spatial Data Science Framework for Research and Decision-Making

abstract: A major challenge in health-related policy and program evaluation research is attributing underlying causal relationships where complicated processes may exist in natural or quasi-experimental settings. Spatial interaction and heterogeneity between units at individual or group levels can violate both components of the Stable-Unit-Treatment-Value-Assumption (SUTVA) that are core to the counterfactual framework, making treatment effects difficult to assess. New approaches are needed in health studies to develop spatially dynamic causal modeling methods to both derive insights from data that are sensitive to spatial differences and dependencies, and also be able to rely on a more robust, dynamic technical infrastructure needed for decision-making. To address this gap with a focus on causal applications theoretically, methodologically and technologically, I (1) develop a theoretical spatial framework (within single-level panel econometric methodology) that extends existing theories and methods of causal inference, which tend to ignore spatial dynamics; (2) demonstrate how this spatial framework can be applied in empirical research; and (3) implement a new spatial infrastructure framework that integrates and manages the required data for health systems evaluation. The new spatially explicit counterfactual framework considers how spatial effects impact treatment choice, treatment variation, and treatment effects. To illustrate this new methodological framework, I first replicate a classic quasi-experimental study that evaluates the effect of drinking age policy on mortality in the United States from 1970 to 1984, and further extend it with a spatial perspective. In another example, I evaluate food access dynamics in Chicago from 2007 to 2014 by implementing advanced spatial analytics that better account for the complex patterns of food access, and quasi-experimental research design to distill the impact of the Great Recession on the foodscape. Inference interpretation is sensitive to both research design framing and underlying processes that drive geographically distributed relationships. Finally, I advance a new Spatial Data Science Infrastructure to integrate and manage data in dynamic, open environments for public health systems research and decision- making. I demonstrate an infrastructure prototype in a final case study, developed in collaboration with health department officials and community organizations.Dissertation/ThesisDoctoral Dissertation Geography 201

Key challenges in agent-based modelling for geo-spatial simulation

Agent-based modelling (ABM) is fast becoming the dominant paradigm in social simulation due primarily to a worldview that suggests that complex systems emerge from the bottom-up, are highly decentralised, and are composed of a multitude of heterogeneous objects called agents. These agents act with some purpose and their interaction, usually through time and space, generates emergent order, often at higher levels than those at which such agents operate. ABM however raises as many challenges as it seeks to resolve. It is the purpose of this paper to catalogue these challenges and to illustrate them using three somewhat different agent-based models applied to city systems. The seven challenges we pose involve: the purpose for which the model is built, the extent to which the model is rooted in independent theory, the extent to which the model can be replicated, the ways the model might be verified, calibrated and validated, the way model dynamics are represented in terms of agent interactions, the extent to which the model is operational, and the way the model can be communicated and shared with others. Once catalogued, we then illustrate these challenges with a pedestrian model for emergency evacuation in central London, a hypothetical model of residential segregation tuned to London data which elaborates the standard Schelling (1971) model, and an agent-based residential location built according to spatial interactions principles, calibrated to trip data for Greater London. The ambiguities posed by this new style of modelling are drawn out as conclusions

Socio-economic models to assess and policy instruments to steer the impact of nature-based solutions: a review

Urban challenges, such as climate change, economic development and land conversion, are increasing and attracting more attention, consequently widening the complexity of urban planning and decisionmaking processes. Nature-based solutions (NBS) are considered to contribute to resolving these emerging urban problems. While models are available to assess the impacts of NBS on urban heat, air quality, flooding and water quality, there are few models that evaluate their socio-economic impacts. Even though statistical models could provide insight in their actual (ex-post) socio-economic impacts, simulation models represent a key tool to urban planning as they provide the opportunity to assess the expected (ex-ante) socio-economic impacts of NBS and, thus, allow better informed decision making before implementation. This paper provides, first, a review of socio-economic models that can assess the impact of NBS (either statistical or simulation) and, second, a review of socio-economic models that assess the effectiveness of policy instruments to steer urban development patterns. Results show that there is a lack of spatially explicit simulation models with the ability to assess the socio-economic impacts of different NBS. Most models that assess socio-economic impacts include statistical (expost), non-spatially explicit or use non-European case studies. Socio-economic impacts evaluated include urban sprawl, housing prices and gentrification. Furthermore, there is a lack of models that have the potential to assess socio-economic impacts of NBS as well as the impact of policy instruments that influence urban development patterns. Hence, it is concluded that there is need for simulation models that allow to assess the expected (ex-ante) socio-economic impacts of NBS as well as the effectiveness of land use policy instruments

Urban segregation as a complex system : an agent-based simulation approach

Urban segregation represents a significant barrier for achieving social inclusion in cities. To overcome this, it is necessary to implement policies founded upon a better understanding of segregation dynamics. However, a crucial challenge for achieving such understanding lies in the fact that segregation is a complex system. It emerges from local interactions able to produce unexpected and counterintuitive outcomes that cannot be defined a priori. This study adopts an agent-based simulation approach that addresses the complex nature of segregation. It proposes a model named MASUS, Multi-Agent Simulator for Urban Segregation, which provides a virtual laboratory for exploring theoretical issues and policy approaches concerning segregation. The MASUS model was first implemented for São José dos Campos, a medium-sized Brazilian city. Based on the data of this city, the model was parameterized and calibrated. The potential of MASUS is demonstrated through three different sets of simulation experiments. The first compares simulated data with real data, the second tests theories about segregation, and the third explores the impact of anti-segregation policies. The first set of experiments provides a retrospective validation of the model by simulating the segregation dynamics of São José dos Campos during the period 1991-2000. In general, simulated and real data reveal the same trends, a result that demonstrates that the model is able to accurately represent the segregation dynamics of the study area. The second set of experiments aims at demonstrating the potential of the model to explore and test theoretical issues about urban segregation. These experiments explore the impact of two mechanisms on segregation: income inequality and personal preferences. To test the impact of income inequality, scenarios considering different income distributions were simulated and compared. The results show how decreasing levels of income inequality promote the spatial integration of different social groups in the city. Additional tests were conducted to explore how the preferences of high-income families regarding the presence of other income groups could affect segregation patterns. The results reveal that the high levels of segregation were maintained even in a scenario where affluent households did not take into account the income composition of neighborhoods when selecting their residential location. Finally, the third set of experiments provides new insights about the impact of different urban policies on segregation. One experiment tests whether the regularization of clandestine settlements and equitable distribution of infrastructure would affect the segregation trends in the city. The simulated outputs indicate that they had no significant impact on the segregation patterns. Besides this test focusing on a general urban policy, two specific social-mix policy approaches were explored: poverty dispersion and wealth dispersion. The results suggest that policies based on poverty dispersion, which have been adopted in cities in Europe and the United States, are less effective in developing countries, where poor families represent a large share of the population. On the other hand, the policy based on wealth dispersion was able to produce substantial and long-term improvements in the segregation patterns of the city.Städtische Segregation als komplexes System : Ein agentenbasierter Simulationsansatz Die städtische Segregation stellt eine bedeutende Barriere für die Erreichung der sozialen Inclusion in den Städten dar. Um diese zu überwinden, ist es notwendig, eine Politik zu betreiben, die die Dynamiken der Segregation besser versteht und berücksichtigt. Eine besondere Herausforderung für ein besseres Verständnis dieser Dynamik ist die Tatsache, dass Segregation ein komplexes System ist. Dieses System entsteht aus lokalen Interaktionen, die zu unerwarteten und nicht eingängigen Ergebnissen führt, die nicht von vornherein bestimmt werden können. Diese Studie wendet einen multi-agenten Simulationsmodel an, das die komplexe Natur der Segregation berücksichtigt. Es schlägt ein Modell mit dem Namen MASUS (Multi-Agent Simulator for Urban Segregation) vor. Dieses bietet ein virtuelles Labor für die Untersuchung der theoretischen Aspekte und Politikansätze der Segregation. Das Modell wurde für São José dos Campos, eine mittelgroße brasilianische Stadt, eingesetzt. Das Modell wurde auf der Grundlage der Daten dieser Stadt parametisiert und kallibriert. Das Potenzial von MASUS wird durch drei verschiedene Arten von Simulationsexperimente dargestellt. Die erste vergleicht simulierte Daten mit realen Daten, die zweite prüft Segregationstheorien, und die dritte untersucht die Auswirkungen von Antisegregationspolitik. Die erste Gruppe von Experimenten liefert eine rückblickende Validierung des Modells durch die Simulation der Segregationsdynamiken von São José dos Campos im Zeitraum 1991-2000. Die simulierten und realen Daten zeigen im Allgemeinen die gleichen Trends. Dies zeigt, dass das Modell in der Lage ist, die Segregationsdynamik im Untersuchungsgebiet korrekt darzustellen. Die zweite Gruppe von Experimenten hat zum Ziel, das Potenzial des Modells hinsichtlich der Untersuchung und Prüfung der theoretischen Aspekte städtischer Segregation darzustellen. Diese Experimente untersuchen die Auswirkung von zwei Mechanismen auf Segregation: Einkommensungleichheit und persönliche Präferenzen. Um die Auswirkungen von Einkommensungleichheit zu prüfen, wurden Szenarien mit unterschiedlichen Einkommensverteilungen simuliert und verglichen. Die Ergebnisse zeigen wie abnehmende Einkommenshöhen die räumliche Integration von verschiedenen sozialen Gruppen in der Stadt fördern. Zusätzliche Tests wurden durchgeführt, um zu untersuchen wie die Präferenzen von Haushalten mit hohen Einkommen im Bezug auf das Vorhandensein anderer Einkommensgruppen die Segregationsmuster beeinflussen könnten. Die Ergebnisse zeigen, dass die Segregation auf hohem Niveau blieb sogar in einem Szenario wo wohlhabende Haushalte das Einkommensgefüge der Nachbarschaft bei der Wahl ihrer Wohngegend nicht berücksichtigten. Die dritte Gruppe von Experimenten führt zu neuen Einsichten über die Auswirkungen von verschiedenen städtischen politischen Maßnahmen auf die Segregation. Ein Experiment prüft ob die Regulierung von illegalen Siedlungen und die gleichmäßige Verteilung der Infrastruktur die Segregationstrends in der Stadt beeinflussen. Die Ergebnisse der Simulation zeigen, dass diese keine signifikante Auswirkung auf die Segregationsmuster haben. Neben diesem Test, der die allgemeine städtische Politik zum Inhalt hat, wurden zwei Ansätze der spezifischen Sozialen-Mix-Politik untersucht: Armutsverteilung und Wohlstandsverteilung. Die Ergebnisse deuten daraufhin, dass eine Politik der Armutsverteilung, die aus europäischen und nordamerikanischen Städten bekannt ist, weniger wirkungsvoll in Entwicklungsländern ist, wo arme Familien einen Großteil der Bevölkerung darstellen. Auf der anderen Seite führte eine Politik der Wohlstandsverteilung zu erheblichen und langfristigen Verbesserungen der Segregationsmuster der Stadt

Ethnic segregation and spatial patterns of attitudes:studying the link using register data and social simulation

We theorize the causal link between ethnic residential segregation and polarization of ethnic attitudes within and between ethnic groups (e.g. attitudes towards immigration policies, multiculturalism, tolerance or trust in certain ethnic groups). We propose that the complex relationship between segregation and polarization might be explained by three assumptions: (1) ethnic membership moderates social influence–residents influence each other’s attitudes and their ethnic background moderates this influence; (2) spatial proximity between residents increases opportunities for influence; (3) the degree of ethnic segregation varies across space–and therefore, the mix of intra- and inter-ethnic influence also varies across space. We borrow and extend an (agent-based) simulation model of social influence to systematically explore how these three assumptions affect the polarization of ethnic attitudes within and between ethnic groups under the assumptions made in the model. We simulate neighborly interactions and social influence dynamics in the districts of Rotterdam, using empirically observed segregation patterns as input of our simulations. According to our model, polarization in ethnic attitudes is stronger in districts and parts of districts where mixing of ethnic groups allows for many opportunities to interact with both the ethnic ingroup and the outgroup. Our study provides a new theoretical perspective on polarization of ethnic attitudes by demonstrating that the segregation-polarization link can emerge as an unintended outcome from repeated intra- and inter-ethnic interactions in segregated spaces.</p