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EB

ONLINE

Cham : Springer International Publishing AG, 2018

1 online resource (247 pages)

ISBN 9783319724089 (electronic bk.)

ISBN 9783319724065

Methodos Ser. ; v.13

Print version: Silverman, Eric Methodological Investigations in Agent-Based Modelling Cham : Springer International Publishing AG,c2018 ISBN 9783319724065

Intro -- Foreword -- References -- Acknowledgements -- Contents -- Acronyms -- Part I Agent-Based Models -- 1 Introduction -- 1.1 Overview -- 1.2 Artificial Life as Digital Biology -- 1.2.1 Artificial Life as Empirical Data-Point -- 1.3 Social Simulation and Sociological Relevance -- 1.3.1 Methodological Concerns in Social Simulation -- 1.4 Case Study: Schelling’s Residential Segregation Model -- 1.4.1 Implications of Schelling’s Model -- 1.5 Social Simulation in Application: The Case of Demography -- 1.5.1 Building Model-Based Demography -- 1.6 General Summary -- 1.6.1 Alife Modelling -- 1.6.2 Simulation for the Social Sciences -- 1.6.3 Schelling’s Model as a Case Study in Modelling -- 1.6.4 Developing a Model-Based Demography -- 1.6.5 General Conclusions of the Text: Messages for the Modeller -- 1.6.6 Chapter Summaries -- 1.6.7 Contributions -- References -- 2 Simulation and Artificial Life -- 2.1 Overview -- 2.2 Introduction to Simulation Methodology -- 2.2.1 The Goals of Scientific Modelling -- 2.2.2 Mathematical Models -- 2.2.3 Computational Models -- 2.2.4 The Science Versus Engineering Distinction -- 2.2.5 Connectionism: Scientific Modelling in Psychology -- 2.2.6 Bottom-Up Modelling and Emergence -- 2.3 Evolutionary Simulation Models and Artificial Life -- 2.3.1 Genetic Algorithms and Genetic Programming -- 2.3.2 Evolutionary Simulations and Artificial Life -- 2.3.3 Bedau and the Challenges Facing ALife -- 2.4 Truth in Simulation: The Validation Problem -- 2.4.1 Validation and Verification in Simulation -- 2.4.2 The Validation Process in Engineering Simulations -- 2.4.3 Validation in Scientific Simulations: Concepts of Truth -- 2.4.4 Validation in Scientific Models: Kuppers and Lenhard Case Study -- 2.5 The Connection Between Theory and Simulation -- 2.5.1 Simulation as `Miniature Theories’.

2.5.2 Simulations as Theory and Popperian Falsificationism -- 2.5.3 The Quinean View of Science -- 2.5.4 Simulation and the Quinean View -- 2.6 ALife and Scientific Explanation -- 2.6.1 Explanation Through Emergence -- 2.6.2 Strong vs Weak Emergence -- 2.6.3 Simulation as Thought Experiment -- 2.6.4 Explanation Compared: Simulations vs Mathematical Models -- 2.7 Summary and Conclusions -- References -- 3 Making the Artificial Real -- 3.1 Overview -- 3.2 Strong vs. Weak Alife and AI -- 3.2.1 Strong vs. Weak AI: Creating Intelligence -- 3.2.2 Strong vs. Weak Alife: Creating Life? -- 3.2.3 Defining Life and Mind -- 3.3 Levels of Artificiality -- 3.3.1 The Need for Definitions of Artificiality -- 3.3.2 Artificial1: Examples and Analysis -- 3.3.3 Artificial2: Examples and Analysis -- 3.3.4 Keeley’s Relationships Between Entities -- 3.4 `Real’ AI: Embodiment and Real-World Functionality -- 3.4.1 Rodney Brooks and `Intelligence Without Reason’ -- 3.4.2 Real-World Functionality in Vision and Cognitive Research -- 3.4.3 The Differing Goals of AI and Alife: Real-World Constraints -- 3.5 `Real’ Alife: Langton and the Information Ecology -- 3.5.1 Early Alife Work and Justifications for Research -- 3.5.2 Ray and Langton: Creating Digital Life? -- 3.5.3 Langton’s Information Ecology -- 3.6 Toward a Framework for Empirical Alife -- 3.6.1 A Framework for Empirical Science in AI -- 3.6.2 Newell and Simon Lead the Way -- 3.6.3 Theory-Dependence in Empirical Science -- 3.6.4 Artificial Data in Empirical Science -- 3.6.4.1 Trans-Cranial Magnetic Stimulation -- 3.6.4.2 Neuroscience Studies of Rats -- 3.6.5 Artificial Data and the `Backstory’ -- 3.6.6 Silverman and Bullock’s Framework: A PSS Hypothesis for Life -- 3.6.7 The Importance of Backstory for the Modeller -- 3.6.8 Where to Go from Here -- 3.7 Summary and Conclusions -- References.

5.3.3 Areas of Contention: The Lack of `Real’ Data -- 5.4 Cederman’s Model Types: Examples and Analysis -- 5.4.1 Type 1: Behavioural Aspects of Social Systems -- 5.4.2 Type 2: Emerging Configurations -- 5.4.3 Type 3: Interaction Networks -- 5.4.4 Overlap in Cederman’s Categories -- 5.5 Methodological Peculiarities of the Political Sciences -- 5.5.1 A Lack of Data: Relating Results to the Real World -- 5.5.2 A Lack of Hierarchy: Interdependence of Levels of Analysis -- 5.5.3 A Lack of Clarity: Problematic Theories -- 5.6 In Search of a Fundamental Theory of Society -- 5.6.1 The Need for a Fundamental Theory -- 5.6.2 Modelling the Fundamentals -- 5.7 Systems Sociology: A New Approach for Social Simulation? -- 5.7.1 Niklas Luhmann and Social Systems -- 5.7.2 Systems Sociology vs. Social Simulation -- 5.8 Promises and Pitfalls of the Systems Sociology Approach -- 5.8.1 Digital Societies? -- 5.8.2 Rejecting the PSS Hypothesis for Society -- 5.9 Social Explanation and Social Simulation -- 5.9.1 Sawyer’s Analysis of Social Explanation -- 5.9.2 Non-reductive Individualism -- 5.9.3 Macy and Miller’s View of Explanation -- 5.9.4 Alife and Strong Emergence -- 5.9.5 Synthesis -- 5.10 Summary and Conclusion -- References -- 6 Analysis: Frameworks and Theories for Social Simulation -- 6.1 Overview -- 6.2 Frameworks and ALife: Strong ALife -- 6.2.1 Strong ALife and the Lack of `Real’ Data -- 6.2.2 Artificial1 vs Artificial2: Avoiding the Distinction -- 6.2.3 Information Ecologies: The Importance ofBack-stories -- 6.3 Frameworks and ALife: Weak ALife -- 6.3.1 Artificial1 vs. Artificial2: Embracing the Distinction -- 6.3.2 Integration of Real Data: Case Studies -- 6.3.3 Backstory: Allowing the Artificial -- 6.4 The Legacy of Levins -- 6.4.1 The 3 Types: A Useful Hierarchy? -- 6.4.2 Constraints of the Fourth Factor -- 6.5 Frameworks and Social Science.

7.7 Schelling vs Doran and Axelrod.

4 Modelling in Population Biology -- 4.1 Overview -- 4.2 Levins’ Framework: Precision, Generality, and Realism -- 4.2.1 Description of Levins’ Three Dimensions -- 4.3 Levins’ L1, L2 and L3 Models: Examples and Analysis -- 4.3.1 L1 Models: Sacrificing Generality -- 4.3.2 L2 Models: Sacrificing Realism -- 4.3.3 L3 Models: Sacrificing Precision -- 4.4 Orzack and Sober’s Rebuttal -- 4.4.1 The Fallacy of Clearly Delineated Model Dimensions -- 4.4.2 Special Cases: The Inseparability of Levins’ Three Factors -- 4.5 Resolving the Debate: Intractability as the Fourth Factor -- 4.5.1 Missing the Point? Levins’ Framework as Pragmatic Guideline -- 4.5.2 Odenbaugh’s Defence of Levins -- 4.5.3 Intractability as the Fourth Factor: A Refinement -- 4.6 A Levinsian Framework for Alife -- 4.6.1 Population Biology vs. Alife: A Lack of Data -- 4.6.2 Levinsian Alife: A Framework for Artificial Data? -- 4.6.3 Resembling Reality and Sites of Sociality -- 4.6.4 Theory-Dependence Revisited -- 4.7 Tractability Revisited -- 4.7.1 Tractability and Braitenberg’s Law -- 4.7.2 David Marr’s Classical Cascade -- 4.7.3 Recovering Algorithmic Understanding -- 4.7.4 Randall Beer and Recovering AlgorithmicUnderstanding -- 4.7.5 The Lure of Artificial Worlds -- 4.8 Saving Simulation: Finding a Place for Artificial Worlds -- 4.8.1 Shifting the Tractability Ceiling -- 4.8.2 Simulation as Hypothesis-Testing -- 4.9 Summary and Conclusion -- References -- Part II Modelling Social Systems -- 5 Modelling for the Social Sciences -- 5.1 Overview -- 5.2 Agent-Based Models in Political Science -- 5.2.1 Simulation in Social Science: The Role of Models -- 5.2.2 Axelrod’s Complexity of Cooperation -- 5.3 Lars-Erik Cederman and Political Actors as Agents -- 5.3.1 Emergent Actors in World Politics a Modelling Manifesto -- 5.3.2 Criticism from the Political Science Community.

6.5.1 Artificial1 vs. Artificial2: A Useful Distinction? -- 6.5.2 Levins: Still Useful for Social Scientists? -- 6.5.3 Cederman’s 3 Types: Restating the Problem -- 6.5.4 Building the Framework: Unifying Principles for Biology and Social Science Models -- 6.5.5 Integration of Real Data -- 6.6 Views from Within Social Simulation -- 6.6.1 Finding a Direction for Social Simulation -- 6.6.2 Doran’s Perspective on the Methodology of Artificial Societies -- 6.6.3 Axelrod and Tesfatsion’s Perspective: The Beginner’s Guide to Social Simulation -- 6.7 Summary and Conclusions -- References -- 7 Schelling’s Model: A Success for Simplicity -- 7.1 Overview -- 7.2 The Problem of Residential Segregation -- 7.2.1 Residential Segregation as a Social Phenomenon -- 7.2.1.1 The Importance of the Problem -- 7.2.2 Theories Regarding Residential Segregation -- 7.3 The Chequerboard Model: Individual Motives in Segregation -- 7.3.1 The Rules and Justifications of the Model -- 7.3.2 Results of the Model: Looking to the Individual -- 7.3.3 Problems of the Model: A Lack of Social Structure -- 7.4 Emergence by Any Other Name: Micromotives and Macrobehaviour -- 7.4.1 Schelling’s Justifications: A Valid View of Social Behaviour? -- 7.4.2 Limiting the Domain: The Acceptance of Schelling’s Result -- 7.4.3 Taylor’s Sites of Sociality: One View of the Acceptance of Models -- 7.4.4 The Significance of Taylor: Communicabilityand Impact -- 7.5 Fitting Schelling to the Modelling Frameworks -- 7.5.1 Schelling and Silverman-Bullock: Backstory -- 7.5.2 Schelling and Levins-Silverman: Tractability -- 7.5.3 Schelling and Cederman: Avoiding Complexity -- 7.6 Lessons from Schelling -- 7.6.1 Frameworks: Varying in Usefulness -- 7.6.2 Tractability: A Useful Constraint -- 7.6.3 Backstory: Providing a Basis -- 7.6.4 Artificiality: When it Matters -- 7.6.5 The Practical Advantages of Simplicity.

001895026

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(Au-PeEL)EBL6422582

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(OCoLC)1231607926