Methodological Investigations in Agent-Based Modelling : : With Applications for the Social Sciences.

Methodological Investigations in Agent-Based Modelling : : With Applications for the Social Sciences.

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Bibliographic Details
Superior document:Methodos Series ; v.13
:
Silverman, Eric.
TeilnehmendeR:
Courgeau, Daniel.
Franck, Robert.
Bijak, Jakub.
Hilton, Jason.
Noble, Jason.
Bryden, John.
Place / Publishing House:Cham : : Springer International Publishing AG,, 2018.
Ã2018.
Year of Publication:2018
Edition:1st ed.
Language:English
Series:Methodos Series
Online Access:
Physical Description:1 online resource (247 pages)
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Table of Contents:
  • 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.
  • 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.
  • 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.
  • 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.
  • 7.7 Schelling vs Doran and Axelrod.

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