Computing and Philosophy: Selected Papers from IACAP 2014 (Synthese Library, 375)

✍ Scribed by Vincent C. Müller (editor)

• Publisher: Springer
• Year: 2015
• Tongue: English
• Leaves: 279
• Category: Library

✦ Synopsis

This volume offers very selected papers from the 2014 conference of the “International Association for Computing and Philosophy” (IACAP) - a conference tradition of 28 years. The theme of the papers is the two-way relation between computing technologies and philosophical questions: Computing technologies both raise new philosophical questions, and shed light on traditional philosophical problems. The chapters cover: 1) philosophy of computing, 2) philosophy of computer science & discovery, 3) philosophy of cognition & intelligence, 4) computing & society, and 5) ethics of computation.

✦ Table of Contents

Editorial
IACAP 2014
Review Process
Contents
Part I Philosophy of Computing
1 What Is a Computational Constraint?
1.1 Introduction
1.2 Turing-Deutsch Resources
1.3 Trans-Turing Resources
1.4 Computationalist Explanation
1.5 Computational Constraint
1.6 Computational Constraint Versus Instrumental and Cognitivist Constraints
1.7 Conclusion
References
2 Computing Mechanisms and Autopoietic Systems
2.1 Introduction
2.2 Computing Mechanisms
2.3 Autopoietic Systems
2.4 Representation
2.5 Inputs, Outputs, and Perturbations
2.6 Thinking Outside the Box
2.7 Conclusion
References
3 Are Gandy Machines Really Local?
3.1 Church, Turing and Gandy
3.2 The Realization
3.3 Gandy Machines: A Case Study
3.4 The Physical Sense of Gandy Machines and Locality: An Inaccuracy
3.5 The Case of Quantum Mechanics
3.6 Conclusion
References
4 A Refutation of the Church-Turing Thesis According to Some Interpretation of What the Thesis Says
4.1 What a Function Is
4.2 What an “Effective Calculation” of a (Numeric) Function Is
4.3 What the Church-Turing Thesis Says
4.4 A Non Turing Machine Equivalent Way to Calculate the Ackermann Function
4.5 Conclusions
4.6 An Explanatory Conjecture
Appendix
References
5 In What Sense Does the Brain Compute?
5.1 Engineered Implementation
5.2 Natural Computation?
5.3 Three Different Senses
5.4 Critique of Senses (1) and (2)
5.5 Computation Is Non-intrinsic
5.6 Computational Ascriptions Are Normative, Not Causal
5.7 Error and Malfunction
5.8 Computation Versus Explanation
5.9 The Brain as Cognitive Computer
References
Part II Philosophy of Computer Science & Discovery
6 Computational Scientific Discovery and Cognitive ScienceTheories
6.1 Introduction
6.2 Scientific Discovery
6.3 Theories in Behavioural and Cognitive Science
6.4 A Computational System for Theory Discovery in Cognitive Science
6.5 Example of Theory Discovery
6.6 Discussion
References
7 Discovering Empirical Theories of Modular Software Systems. An Algebraic Approach
7.1 Introduction: The Need of Empirical Theories of Software Systems
7.2 Discovering Empirical Theories of Software Systems
7.3 Modular Semantic Theories and Empirical Structuralism
7.4 Using Institutions to Build Modular Semantic Theories
7.5 Concluding Remarks
References
8 Introducing the Doxastically Centered Approach to Formalizing Relevance Bonds in Conditionals
8.1 Introduction and Plan
8.2 Quick Overview of DCEC
8.3 Analogical Version of the New Approach
8.3.1 An Example of Kernel K in Action
8.4 Deductive Version of the Doxastic Approach
8.4.1 Standard Machinery Presupposed
8.4.2 Doxastic Context
8.4.3 Example
8.4.4 Approach Subsumes Relevance Proof Theory
8.4.5 Remark: The Stunning Elasticity of Relevance
8.5 Conclusion: Next Steps
References
9 From Silico to Vitro: Computational Models of Complex Biological Systems Reveal Real-World Emergent Phenomena
9.1 Introduction
9.2 Can Models Explain?
9.2.1 Simulations Are More Than Models
9.3 Constructing an Explanatory Predictive Simulation
9.3.1 The Bottom-Up/Top-Down Conflict
9.3.2 Validation and Verification Against Real-World Data
9.4 The Reactive Animation Simulation Environment
9.4.1 General
9.4.2 Examples of Emerging Behaviors in RA Simulations
9.4.2.1 Model #1: Thymocyte Development
9.4.2.2 Model #2: Pancreas Organogenesis
9.4.3 Bottom-Up Data Integration with Top-Down Environmental Constraints
9.5 Summary – Why are These Simulations Explanatory?
References
Part III Philosophy of Cognition & Intelligence
10 Why We Shouldn't Reason Classically, and the Implications for Artificial Intelligence
10.1 Introduction
10.2 Perverse Arguments
10.3 Reticent Logic (RL)
10.3.1 Basic RL
10.3.2 Stepwise RL
10.3.3 Other Reticent Logics
10.3.4 Classifying RL
10.4 Can a Consistent Artificially Intelligent Machine Prove It Is Consistent?
10.5 A Rebuttal of the Mathematical Argument Against Artificial Intelligence
10.6 Conclusion
References
11 Cognition as Higher-Order Regulation
11.1 Thinking and Breathing
11.2 On the Insufficiency of Regulation for Life
11.3 Meta-regulation
References
12 Eliminativisms, Languages of Thought, & the Philosophy of Computational Cognitive Modeling
12.1 Introduction
12.2 Eliminativisms
12.3 Paul Churchland: A Folk Psychological Eliminativist, Not an Intentional Eliminativist
12.4 Languages of Thought
12.5 Maps, Intentional Attitudes, LOT, and Eliminativism
12.6 Languages of Thought, Intentional Attitudes, and Eliminativism
12.7 A Role for Language in Thought Without the Belief Box
12.8 Still Further Possibilities
References
13 A Mechanistic Account of Computational Explanation in Cognitive Science and Computational Neuroscience
13.1 Basic Assumptions of the Framework
13.1.1 Explanandum Phenomenon
13.1.2 Explanatory Focus and Scaffolding
13.1.3 Three Levels of Constitutive Explanation
13.1.4 Mechanistically Adequate Model of Computation
13.1.5 Marr's Cash Register
13.2 Case study: Predictive Coding in Mirror Neurons
13.3 Conclusion
References
14 Internal Supervision & Clustering: A New Lesson from `Old' Findings?
14.1 Introduction
14.1.1 What Is Learning?
14.2 Preliminaries
14.3 What Does It Take to Recognize a Pattern?
14.3.1 Informational Inputs
14.3.2 Abstraction
14.3.3 The Product of the Abstraction Process
14.3.4 Probabilistic and Diagnostic Information
14.3.5 Reactivation of Matching Stored Representations: Evidence in Support
14.3.6 On Similarity Yet Again
14.4 Top-Down Effects and Machine Learning
14.4.1 Are There Really Top-Down Effects in Perception?
14.5 Conclusions: Minimum Requirements for Successful Clustering
References
Part IV Computing & Society
15 Floridi/Flusser: Parallel Lives in Hyper/Posthistory
15.1 Introduction
15.1.1 Methodology – Background – Scope
15.2 Information Contra Entropy
15.3 Functionaries/Inforgs
15.4 Information Revolution, Topology of Hyper/Post-history
15.5 Nature/Culture – E-nvironmentalism
15.6 Game Theory – Interpretations of Homo Ludens
15.7 Conclusions
References
16 Machine Ethics and Modal Psychology
16.1 Introduction and Motivation
16.2 Causes, Counterfactuals, and Context
16.2.1 Norm Violations and Counterfactual Possibilities
16.3 An Example and Associated Computational Treatment
16.3.1 Counterfactuals and Computation
16.3.2 Polyscheme: Formal Preliminaries
16.3.2.1 Syntax and Semantics
16.3.2.2 Syntax: Composition, Constraints, and Variables
16.3.2.3 Situations and Inheritance
16.3.2.4 Counterfactual Inference
16.4 The Pen and Professor Formalized
16.4.1 Normality in the Pen and Professor Vignette
16.5 Discussion and Future Work
References
17 My Liver Is Broken, Can You Print Me a New One?
17.1 Introduction
17.2 Setting the Stage
17.3 Informational Practices
17.4 Personal Identity Questions
17.5 Concluding Remarks
References
18 Robots, Ethics and Software – FOSS vs. Proprietary Licenses
18.1 Introduction
18.2 Background and Definitions
18.3 Robot Actions and Their Moral Significance
18.4 Ethical Concerns
18.4.1 FOSS as Fragmenter and Aggregator
18.4.2 PS as Fragmenter and Aggregator
18.5 Conclusions
References