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Chapter 8: Conclusion

Chapter 8: Conclusion. Wilensky and Rand. Roadmap. What are ABMs? Chapter 0, 1, and 2 How to build ABMs? Chapter 3, 4, and 5 How to examine ABMs? Chapter 6 and 7 Advanced topics in ABM Chapter 8. Advanced topics in ABM. Model design guidelines Full spectrum modeling

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Chapter 8: Conclusion

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  1. Chapter 8: Conclusion Wilensky and Rand

  2. Roadmap • What are ABMs? • Chapter 0, 1, and 2 • How to build ABMs? • Chapter 3, 4, and 5 • How to examine ABMs? • Chapter 6 and 7 • Advanced topics in ABM • Chapter 8

  3. Advanced topics in ABM • Model design guidelines • Full spectrum modeling • Between simple models and elaborated realistic (ER) models • Simple models: lack every mechanism that exists in the real world • ER: impossible to understand • Iterative modeling • Iterative feedbacks between (1) the conceptual model design process and (2) the model implementation process • To benefit model building and data collection/ theory generation process

  4. Advanced topics in ABM • Calibrating ABMs: two ways • Extraction of rules from empirical data • Use machine learning approach to examine large datasets • Decision trees extract minimum decision points from training data • Create an ABM rule that represents a decision tree * Other approaches (e.g., equation-based modeling)? • Extraction of rules from participatory simulation • Participatory simulation: type of social simulation where individuals participate in models of a complex system. • HubNet

  5. Advanced topics in ABM • Using ABM for communication, persuasion, and education • A platform that allows people from different disciplines to communicate their decision rules and understand the tradeoffs • Human, embedded and virtual agents through mediation • Integration of Human agents, Embedded sensory-enabled robotic agents, and autonomous Virtual agents (HEV-M) • To accomplish some mutual goals • NetLogoLab

  6. Advanced topics in ABM • Hybrid computational methods • System dynamics modeling (SDM) and ABM • Both model the same complexity system and compare results • SDM examines one part of the system and ABM examines another part • Machine learning and ABM • ABM cycle + ML cycle  Integrated cycle • Initialize the world and agents  Agents observe the world  agents update (through ML)  agents take actions * Equation-based modeling and ABM?

  7. Advanced topics in ABM • Integration of advanced data sources and output • Geographic information systems (GIS) toolkits • Social network analysis (SNA) toolkits • Physical sensor data • Advanced mathematical analysis • Mathematica Link * Matlab?

  8. Advanced topics in ABM • Applications of ABM • Natural sciences: chemistry and physics • Biology • Medecine • Economics • Organizations and politics • Anthropology • Engineering • Math and computer science * Marketing (e.g., Type II spatial models + ABM)?

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