ISBN:
9783540938132
Language:
English
Pages:
1 Online-Ressource (745 pages)
Series Statement:
Understanding Complex Systems
Series Statement:
Understanding Complex Systems Ser.
Parallel Title:
Print version Edmonds, Bruce Simulating Social Complexity : A Handbook
DDC:
302.0113
Keywords:
Complexity (Philosophy)
;
Social structure-Computer simulation..
;
Social interaction-Computer simulation
;
Electronic books
;
Aufsatzsammlung
;
Aufsatzsammlung
Abstract:
Simulating Social Complexity -- A Handbook -- Contents -- Part I: Introductory Material -- Chapter 1: Introduction to the Handbook -- 1.1 Simulating Social Complexity -- 1.2 The Context, Going Back to Herbert Simon -- 1.3 The Structure of the Handbook -- 1.3.1 Introductory Section -- 1.3.2 Methodology Section -- 1.3.3 Mechanisms Section -- 1.3.4 Applications Section -- References -- Chapter 2: Historical Introduction -- 2.1 Overview -- 2.2 The First Two Decades -- 2.3 Computer Simulation in Its Own Right -- 2.4 Conclusion -- Further Reading -- References -- Chapter 3: Types of Simulation -- 3.1 Introduction -- 3.2 Purposes of Simulation -- 3.3 Types of Systems Simulated -- 3.4 Modelling -- 3.4.1 Individuals -- 3.4.2 Interaction Between Individuals -- 3.4.3 The Environment -- 3.4.4 Factors to Consider When Choosing a Model -- 3.5 Implementation -- 3.6 Conclusion -- Further Reading -- References -- Part II: Methodology -- Chapter 4: Informal Approaches to Developing Simulation Models -- 4.1 Introduction: Exploration and Consolidation Modelling Phases -- 4.2 Knowing the Purpose of the Model -- 4.3 Modelling Assumptions -- 4.4 Maintaining Control of the Model While Exploring -- 4.5 Understanding the Model -- 4.6 The Consolidation Phase -- 4.7 Tools to Aid Model Development -- 4.8 Conclusion -- Further Reading -- References -- Chapter 5: A Formal Approach to Building Compositional Agent-Based Simulations -- 5.1 Introduction -- 5.2 Principles of Compositional Design of Multi-agent Systems -- 5.2.1 The Design Process -- 5.2.2 Compositionality of Processes and Knowledge -- 5.2.3 Problem Description -- 5.2.4 Conceptual and Detailed Design -- 5.2.4.1 Process Composition -- Identification of Processes at Different Levels of Abstraction -- Relevant Aspects of a Process -- Modelling Process Abstraction Levels -- Composition -- Information Exchange
Abstract:
5.2.4.2 Knowledge Composition -- Identification of Knowledge Structures at Different Abstraction Levels -- Information Types -- Knowledge Bases -- Composition of Knowledge Structures -- 5.2.4.3 Relation Between Process Composition and Knowledge Composition -- 5.2.5 Design Rationale -- 5.2.6 Multi-agent Systems in the Simulation of Social Phenomena -- 5.3 Organisations -- 5.3.1 Specification of Organisation Structure -- 5.3.2 Organisation Structure -- 5.3.3 Dynamic Properties of an Organisation -- 5.3.3.1 Role Dynamic Properties -- 5.3.3.2 Transfer Dynamic Properties -- 5.3.3.3 Group Dynamic Properties -- 5.3.3.4 Intergroup Interaction Dynamic Properties -- 5.3.3.5 Organisation Dynamic Properties -- 5.3.4 Organisation Realisation -- 5.3.5 Organisational Example -- 5.3.5.1 Groups and Roles in Organisational Example -- 5.3.5.2 Dynamic Properties in Organisational Example -- Role Dynamic Properties -- Group Dynamic Properties -- Intergroup Interaction Dynamic Properties -- Organisational Dynamic Properties -- Realisation -- 5.3.5.3 Conclusion of Organisational Example -- 5.4 Organisation Design by Requirements Refinement -- 5.4.1 Designing by Requirements Refinement -- 5.4.1.1 Role Behaviour Design -- 5.4.1.2 Interaction Protocol Design -- 5.5 The Agent Approach -- 5.5.1 Some Agent Notions -- 5.5.2 Representative Agents -- 5.5.3 Agent Properties -- 5.5.3.1 External Primitive Concepts -- Interaction with the External World -- Observation -- Execution of Actions in the External World -- Communication with Other Agents -- 5.5.3.2 Internal Primitive Concepts -- World and Agent Models -- Self Model and History -- Goals and Plans -- Group Concepts -- 5.5.4 Example of the Agent Approach: An Elevator -- 5.5.4.1 External Primitive Concepts (Table 5.2) -- Observation -- Performing Actions -- Incoming Communication -- Outgoing Communication
Abstract:
5.5.4.2 Internal Primitive Concepts (Table 5.3) -- World and Agent Models -- Self Model and History -- Goals and Plans -- Group Concepts -- 5.5.4.3 Types of Behaviour (Table 5.4) -- Autonomy -- Pro-activeness -- Reactiveness -- Social Behaviour -- Own Adaptation and Learning -- 5.5.4.4 Conclusion of Elevator Example -- 5.6 Conclusion -- Further Reading -- References -- Chapter 6: Checking Simulations: Detecting and Avoiding Errors and Artefacts -- 6.1 Introduction -- 6.2 Three Symbolic Systems Used to Model Social Processes -- 6.3 Agent Based Modelling -- 6.3.1 Concept -- 6.3.2 Design, Implementation, and Use of an Agent-Based Model -- 6.4 Errors and Artefacts -- 6.4.1 Definition of Error and Artefact and Their Relevance for Validation and Verification -- 6.4.2 Appearance of Errors and Artefacts -- 6.4.3 Activities Aimed at Detecting Errors and Artefacts -- 6.4.3.1 Modeller´s Activities -- 6.4.3.2 Computer Scientist´s Activities -- 6.4.3.3 Programmer´s Activities -- 6.5 Summary -- Further Reading -- References -- Chapter 7: Documenting Social Simulation Models: The ODD Protocol as a Standard -- 7.1 Introduction and History -- 7.2 The Purpose of ODD -- 7.3 The ODD Protocol -- 7.4 How to Use ODD -- 7.5 An Example -- 7.5.1 Purpose -- 7.5.2 Entities, State Variables, and Scales -- 7.5.3 Process Overview and Scheduling -- 7.5.4 Design Concepts -- 7.5.5 Initialization -- 7.5.6 Input Data -- 7.5.7 Submodels -- 7.6 Discussion -- 7.7 Conclusion -- Further Reading -- References -- Chapter 8: Validating Simulations -- 8.1 Introduction -- 8.2 The Simulation Development Process -- 8.2.1 What Does It Mean to Verify a Computerised Model? -- 8.2.2 What Does It Mean to Validate a Model? -- 8.3 Verification Methods and Techniques -- 8.3.1 Static and Dynamic Methods -- 8.3.2 Good Programming Practices -- 8.3.2.1 Modularity and Encapsulation
Abstract:
8.3.2.2 High-Level Memory Management -- 8.3.2.3 Software Reuse -- Model Architecture Level -- Module or Object Level -- Programming Language API Level -- Generic-Type Level -- Routine Level -- 8.3.3 Defensive Programming -- 8.3.4 Replication for Model Alignment -- 8.3.4.1 Types of Model Equivalence -- 8.3.4.2 Programming for Replication -- 8.3.5 Participative-Based Methods -- 8.4 Validation Approaches -- 8.4.1 The Goal of Validation: Goodness of Description -- 8.4.2 Broad Types of Validity -- 8.4.2.1 Validation Through Prediction -- 8.4.2.2 Validation Through Retrodiction -- 8.4.2.3 Validation Through Structural Similarity -- 8.4.3 Validation Techniques -- 8.4.3.1 Face Validity -- 8.4.3.2 Turing Tests -- 8.4.3.3 Historical Validity -- 8.4.3.4 Event Validity -- 8.4.3.5 Extreme Condition Tests -- 8.4.3.6 Sensitivity Analysis -- 8.4.3.7 Cross-Sectional Validity -- 8.4.3.8 Comparison to Other Models -- 8.4.3.9 Cross-Element Validity -- 8.4.3.10 Participatory Approaches for Validation -- 8.4.4 Relationship to Modelling Strategies -- 8.4.4.1 Subjunctive Agent-Based Models -- 8.4.4.2 Context-Specific Agent-Based Models -- 8.4.4.3 Modus Operandi: Formal and Informal Approaches -- Further Reading -- References -- Chapter 9: Understanding Simulation Results -- 9.1 Introduction -- 9.2 Aggregate Patterns and Conventional Representations of Model Dynamics -- 9.3 Individual Patterns, Novel Approaches and Visualisation -- 9.3.1 Phase Maps -- 9.3.2 Recurrence Plots -- 9.4 Explanation, Understanding and Causality -- 9.5 Future Directions -- Further Reading -- References -- Chapter 10: Participatory Approaches -- 10.1 Introduction -- 10.2 Expectations of Using Participatory Approaches with Simulation of Social Complexity -- 10.2.1 Increasing Quality of Simulation Models of Social Complexity -- 10.2.1.1 Taking Social Diversity and Capacity to Evolve into Account
Abstract:
10.2.1.2 Distribution of Control -- 10.2.2 Improving Suitability of Simulation Model´s Use -- 10.2.2.1 Case of Increasing Knowledge -- 10.2.2.2 Case of Policy Making -- 10.2.3 Simulation as a Means to Support Participation -- 10.2.3.1 Dynamics and Uncertainties -- 10.2.3.2 Towards Social Learning -- 10.2.4 Synthesis: A Key Role of the Interaction Pattern Between Model and Stakeholders -- 10.3 A Diversity of Settings -- 10.3.1 From System Science and Cybernetics -- 10.3.2 Knowledge Engineering: Between Artificial Intelligence and Social Psychology -- 10.3.3 From Software Engineering -- 10.3.4 From Statistical Modelling -- 10.3.5 From the Social Sciences -- 10.4 Participation in the Modelling Process: Diversity of Phases and Intensity -- 10.4.1 Stages in the Modelling Process -- 10.4.2 Level of Involvement -- 10.4.2.1 Information and No Control -- 10.4.2.2 Consultation and No Control -- 10.4.2.3 Dialogue with Modellers and No Control -- 10.4.2.4 Dialogue with Modellers and Control -- 10.4.2.5 Co-building of a Model and No Control -- 10.4.2.6 Co-building of a Model and Control -- 10.4.3 Heterogeneity of Actors -- 10.4.4 Which Configurations Can Meet the Expectations of the First Section? -- 10.5 Combining Approaches and Techniques at Work -- 10.5.1 The Fire Hazard Case Study -- 10.5.2 The AtollGame Experiment -- 10.6 Discussion: Relations Between Participants and Models -- 10.7 Conclusion -- Further Reading -- References -- Chapter 11: Combining Mathematical and Simulation Approaches to Understand the Dynamics of Computer Models -- 11.1 Introduction -- 11.2 Computer Models as Input-Output Functions -- 11.3 Different Ways of Representing the Same Formal Model -- 11.4 `Stochastic´ Computer Models as Stochastic Processes -- 11.5 Tools to Understand the Behaviour of Formal Models
Abstract:
11.6 Computer Simulation: Approximating the Exact Probability Distribution by Running the Model
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