| Description |
1 online resource |
| Series |
Springer Proceedings in Complexity |
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Springer proceedings in complexity.
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| Contents |
Intro -- Preface -- Organization -- General Chair -- Program Committee Chairs -- Program Committee -- Additional Reviewers -- Contents -- 1 How Social Simulation Could Help Social Science Dealwith Context -- 1.1 Introduction -- 1.2 Talking About "Context" -- 1.2.1 Situational Context -- 1.2.2 Cognitive Context -- 1.2.3 Social Context -- 1.3 How Social Science Deals with Context -- 1.3.1 Quantitative Social Science -- 1.3.1.1 Context-Dependency and Randomness -- 1.3.1.2 Over-generic Cognitive Models -- 1.3.2 Qualitative Social Science |
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1.4 Some Ways Agent-Based Social Simulation Could Deal with Context -- 1.4.1 Implementing Context-Sensitive Agents in Social Simulations -- 1.4.2 Approaching Context from Qualitative Narratives -- 1.5 Concluding Discussion -- References -- 2 Agent-Based Modeling with and Without Methodological Individualism -- 2.1 Introduction -- 2.2 Methodological Individualism -- 2.3 Agent-Based Modeling -- 2.4 Are Agent-Based Models Individualistic? -- 2.5 Should Agent-Based Models Be Individualistic? -- 2.6 Conclusion -- References -- 3 Inflation Expectations in a Small Open Economy -- 3.1 Introduction |
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3.2 Results -- 3.2.1 Stability Under Households Decision -- 3.3 Concluding Remarks -- References -- 4 Qualitative Data in the Service of Model Building: The Case of Structural Shirking -- 4.1 Introduction -- 4.2 The Phenomenon Under Investigation: Shirking -- 4.3 Starting from Theories: Simulation Model of Shirking #1 -- 4.4 Findings of Model #1 and Shortcomings -- 4.5 Goal of the Study -- 4.6 Turning to the Empirical World: Developing Model #2 -- 4.6.1 Addressing Gap 1: Legislation Analysis to Define the Concept of Shirking |
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4.6.2 Addressing Gap 2: Individual In-Depth Interviews to Discover Mechanisms -- 4.6.3 Supplementing Empirical Findings with Social Scientific Theories -- 4.6.4 Bringing It All Together: Implementing and Running Model #2 -- 4.7 Discussion -- References -- 5 Causation in Agent-Based Computational Social Science -- 5.1 Introduction -- 5.2 Why a Causal Theory of Explanation? -- 5.3 Existing Accounts of Causation in Agent-Based Computational Social Science -- 5.3.1 Agent Causation -- 5.3.2 Algorithmic Causation -- 5.4 Alternative Accounts of Causation -- 5.4.1 Interventions -- 5.4.2 Mechanisms |
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5.5 Conclusion -- References -- 6 Times of Crises and Labour Market Reforms -- 6.1 Introduction and (Short) Model Description -- 6.2 Results -- References -- 7 Selecting the Right Game Concept for Social Simulationof Real-World Systems -- 7.1 Introduction -- 7.2 Taxonomy of Game Concepts -- 7.2.1 List of Game Concepts -- 7.2.2 Criteria -- 7.2.3 Selection of Game Concepts -- 7.3 Discussion -- 7.4 Future Work -- Appendix -- References -- 8 Physician, Heal Thyself! The Prospects for Using ABM to Target Interventions to Raise ABM Engagement -- 8.1 Introduction -- 8.2 A Model Proposal |
| Summary |
This book presents the state-of-the-art in social simulation as presented at the Social Simulation Conference 2018 in Stockholm, Sweden. It covers the developments in applications and methods of social simulation, addressing societal issues such as socio-ecological systems and policy making. Methodological issues discussed include large-scale empirical calibration, model sharing and interdisciplinary research, as well as decision making models, validation and the use of qualitative data in simulation modeling. Research areas covered include archaeology, cognitive science, economics, organization science, and social simulation education. This collection gives readers insight into the increasing use of social simulation in both its theoretical development and in practical applications such as policy making whereby modelling and the behavior of complex systems is key. The book will appeal to students, researchers and professionals in the various fields |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Sociology -- Simulation methods
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Society & social sciences.
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Artificial intelligence.
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Operational research.
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Computer modelling & simulation.
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Maths for scientists.
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Social Science -- General.
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Technology & Engineering -- Engineering (General)
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Business & Economics -- Operations Research.
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Computers -- Computer Simulation.
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Mathematics -- Applied.
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| Genre/Form |
Electronic books
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| Form |
Electronic book
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| Author |
Verhagen, Harko.
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Borit, Melania
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Bravo, Giangiacomo.
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Wijermans, Nanda
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| ISBN |
9783030341275 |
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3030341275 |
|
9783030341282 |
|
3030341283 |
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9783030341299 |
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3030341291 |
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