Generative systems

Abstract: I. INTRODUCTION 1. Myths of LaPlacean Omniscience Realism for Limited Beings in a Rich Messy World Social Natures Heuristics as Adaptations for the Real World Nature as Backwoods Mechanic and Used-Parts Dealer Error and Change Organization and Aims of This Book 2. Normative Idealizations versus the Metabolism of Error Inadequacies of Our Normative Idealizations Satisficing, Heuristics, and Possible Behavior for Real Agents The Productive Use of Error-Prone Procedures 3. Toward a Philosophy for Limited Beings The Stance and Outlook of a Scientifically Informed Philosophy of Science Ceteris Paribus, Complexity, and Philosophical Method Our Present and Future Naturalistic Philosophical Methods II. PROBLEM-SOLVING STRATEGIES FOR COMPLEX SYSTEMS 4. Robustness, Reliability, and Overdetermination Common Features of Concepts of Robustness Robustness and the Structure of Theories Robustness, Testability, and the Nature of Theoretical Terms Robustness, Redundancy, and Discovery Robustness, Objectification, and Realism Robustness and Levels of Organization Heuristics and Robustness Robustness, Independence, and Pseudo-Robustness: A Case Study 5. Heuristics and the Study of Human Behavior Heuristics Reductionist Research Strategies and Their Biases An Example of Reductionist Biases: Models of Group Selection Heuristics Can Hide Their Tracks Two Strategies for Correcting Reductionist Biases The Importance of Heuristics in the Study of Human Behavior 6. False Models as Means to Truer Theories Even the Best Models Have "Biases" The Concept of a "Neutral Model" How Models Can Misrepresent Twelve Things To Do with False Models Background of the Debate over Linkage Mapping in Genetics Castle's Attack on the "Linear Linkage" Model Muller's Data and the Haldane Mapping Function Muller's "Two-Dimensional" Arguments against Castle Multiply-Counterfactual Uses of False Models False Models Can Provide New Predictive Tests Highlighting Features of a Preferred Model False Models and Adaptive Design Arguments Summary and Conclusions 7. Robustness and Entrenchment: How the Contingent Becomes Necessary Generative Entrenchment and the Architecture of Adaptive Design Generative Systems Come To Dominate in Evolutionary Processes Resistance to Foundational Revisions Bootstrapping Feedbacks: Differential Dependencies and Stable Generators Implications of Generative Entrenchment Generative Entrenchment and Robustness Conclusion 8. Lewontin's Evidence (That There Isn't Any) Is Evidence Impotent, or Just Inconstant? False Models as Means to Truer Theories Narrative Accounts and Theory as Montage III. REDUCTIONISM(S) IN PRACTICE 9. Complexity and Organization Reductionism and the Analysis of Complex Systems Complexity Evolution, Complexity, and Organization Complexity and the Localization of Function 10. The Ontology of Complex Systems: Levels of Organization, Perspectives, and Causal Thickets Robustness and Reality Levels of Organization Perspectives: A Preliminary Characterization Causal Thickets 11. Reductive Explanation: A Functional Account Two Kinds of Rational Reconstruction Successional versus Inter-Level Reduction Levels of Organization and the Co-Evolution and Development of Interlevel Theories Two Views of Explanation: Major Factors and Mechanisms versus Laws and Deductive Completeness Levels of Organization and Explanatory Costs and Benefits An Example: The Assumption of "the Purity of the Gametes" in the Heterozygote Identificatory Hypotheses as Tools in the Search for Explanations Appendix: Modifications Appropriate to a Cost-Benefit Version of Salmon's Account of Explanation 12. Emergence as Non-Aggregativity and the Biases of Reductionism(s) Reduction and Emergence Aggregativity Perspectival, Contextual, and Representational Complexities or, "It Ain't Quite So Simple as That!" Adaptation to Fine- and Coarse-Grained Environments: Derivational Paradoxes for a Formal Account of Aggregativity Aggregativity and Dimensionality Aggregativity as a Heuristic for Evaluating Decompositions, and Our Concepts of Natural Kinds Reductionisms and Biases Revisited IV. ENGINEERING AN EVOLUTIONARY VIEW OF SCIENCE 13. Epilogue: On the Softening of the "Hard" Sciences From Straw-Man Reductionist to Lover of Complexity Messiness in State-of-the-Art Theoretical Physics Hidden Elegance and Revelations in Run-of-the-Mill Applied Science "Pure" versus Applied Science, and What Difference Should It Make? Hortatory Closure Appendix A. Important Properties of Heuristics Appendix B. Common Reductionistic Heuristics Appendix C. Glossary of Key Concepts and Assumptions Appendix D. A Panoply of LaPlacean and Leibnizian Demons Notes Bibliography Credits Index

Abstract: 1: Introduction.- 1.1. Systems Science.- 1.2. Systems Problem Solving.- 1.3. Hierarchy of Epistemological Levels of Systems.- 1.4. The Role of Mathematics.- 1.5. The Role of Computer Technology.- 1.6. Architecture of Systems Problem Solving.- Notes.- 2: Source and Data Systems.- 2.1. Objects and Object Systems.- 2.2. Variables and Supports.- 2.3. Methodological Distinctions.- 2.4. Discrete Versus Continuous.- 2.5. Image Systems and Source Systems.- 2.6. Data Systems.- Notes.- Exercises.- 3: Generative Systems.- 3.1. Empirical Investigation.- 3.2. Behavior Systems.- 3.3. Methodological Distinctions.- 3.4. From Data Systems to Behavior Systems.- 3.5. Measures of Uncertainty.- 3.6. Search for Admissible Behavior Systems.- 3.7. State-Transition Systems.- 3.8. Generative Systems.- 3.9. Simplification of Generative Systems.- 3.10. Systems Inquiry and Systems Design.- Notes.- Exercises.- 4: Structure Systems.- 4.1. Wholes and Parts.- 4.2. Systems, Subsystems, Supersystems.- 4.3. Structure Source Systems and Structure Data Systems.- 4.4. Structure Behavior Systems.- 4.5. Problems of Systems Design.- 4.6. Identification Problem.- 4.7. Reconstruction Problem.- 4.8. Reconstructability Analysis.- 4.9. Simulation Experiments.- 4.10. Inductive Reasoning.- 4.11. Inconsistent Structure Systems.- Notes.- Exercises.- 5: Metasystems.- 5.1. Change versus Invariance.- 5.2. Primary and Secondary Systems Traits.- 5.3. Metasystems.- 5.4. Metasystems versus Structure Systems.- 5.5. Multilevel Metasystems.- 5.6. Identification of Change.- Notes.- Exercises.- 6: Complexity.- 6.1. Complexity in Systems Problem Solving.- 6.2. Three Ranges of Complexity.- 6.3. Measures of Systems Complexity.- 6.4. Bremermann's Limit.- 6.5. Computational Complexity.- 6.6. Complexity Within GSPS.- Notes.- Exercises.- 7: Goal-Oriented Systems.- 7.1. Primitive, Basic, and Supplementary Concepts.- 7.2. Goal and Performance.- 7.3. Goal-Oriented Systems.- 7.4. Structure Systems as Paradigms of Goal-Oriented Behavior Systems.- 7.5. Design of Goal-Oriented Systems.- 7.6. Adaptive Systems.- 7.7. Autopoietic Systems.- Notes.- Exercises.- 8: Systems Similarity.- 8.1. Similarity.- 8.2. Similarity and Models of Systems.- 8.3. Models of Source Systems.- 8.4. Models of Data Systems.- 8.5. Models of Generative Systems.- 8.6. Models of Structure Systems.- 8.7. Models of Metasystems.- Notes.- Exercises.- 9: GSPS: Architecture, USE, Evolution.- 9.1. Epistemological Hierarchy of Systems: Formal Definition.- 9.2. Methodological Distinctions: A Summary.- 9.3. Problem Requirements.- 9.4. Systems Problems.- 9.5. GSPS Conceptual Framework: Formal Definition.- 9.6. Overview of GSPS Architecture.- 9.7. GSPS Use: Some Case Studies.- 9.8. GSPS Evolution.- Notes.- Exercises.- Appendices.- A: List of Symbols.- B: Glossary of Relevant Mathematical Terms.- C: Some Relevant Theorems.- D: Refinement Lattices.- E: Classes of Structures Relevant to Reconstructability Analysis.- References.- Author Index.