Ebook: Model-Based Reasoning: Science, Technology, Values

The study of diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help of traditional notions of reasoning, such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science.
There are several key ingredients common to the various forms of model-based reasoning considered in this book. The term `model' comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in the light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors.
The various contributions of the book are written by interdisciplinary researchers who are active in the area of creative reasoning in science and technology: the most recent results and achievements in the topics above are illustrated in the chapters.

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The study of diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help of traditional notions of reasoning, such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science.
There are several key ingredients common to the various forms of model-based reasoning considered in this book. The term `model' comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in the light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors.
The various contributions of the book are written by interdisciplinary researchers who are active in the area of creative reasoning in science and technology: the most recent results and achievements in the topics above are illustrated in the chapters.
Content:
Front Matter....Pages i-xv
Metaphor-Based Values in Scientific Models....Pages 1-19
Analogy in Scientific Discovery: The Case of Johannes Kepler....Pages 21-39
Model Experiments and Models in Experiments....Pages 41-58
Models, Simulations, and Experiments....Pages 59-74
Calibration of Models in Experiments....Pages 75-93
The Development of Scientific Taxonomies....Pages 95-111
Production, Science and Epistemology. An Overview on New Models and Scenarios....Pages 113-125
Modeling Practices and “Tradition”....Pages 127-146
Modeling Data: Analogies in Neural Networks, Simulated Annealing and Genetic Algorithms....Pages 147-165
Perceptual Simulation in Analogical Problem Solving....Pages 167-189
Building Demand Models to Improve Environmental Policy Process....Pages 191-208
Toward a Computational Model of Hypothesis Formation and Model Building in Science....Pages 209-225
Models as Parts of Distributed Cognitive Systems....Pages 227-241
Conceptual Models, Inquiry and the Problem of Deriving Normative Claims from a Naturalistic Base....Pages 243-257
Dynamic Imagery: A Computational Model of Motion and Visual Analogy....Pages 259-274
Model-Based Reasoning and Similarity in the World....Pages 275-285
Epistemic Artifacts: Michael Faraday’s Search for the Optical Effects of Gold....Pages 287-303
Epistemic Mediators and Model-Based Discovery in Science....Pages 305-329
Deterministic Models and the “Unimportance of the Inevitable”....Pages 331-351
Mental Models in Conceptual Development....Pages 353-368
Back Matter....Pages 391-404
Modeling Core Knowledge and Practices in a Computational Approach to Innovation Process....Pages 369-390

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The study of diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help of traditional notions of reasoning, such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science.
There are several key ingredients common to the various forms of model-based reasoning considered in this book. The term `model' comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in the light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors.
The various contributions of the book are written by interdisciplinary researchers who are active in the area of creative reasoning in science and technology: the most recent results and achievements in the topics above are illustrated in the chapters.
Content:
Front Matter....Pages i-xv
Metaphor-Based Values in Scientific Models....Pages 1-19
Analogy in Scientific Discovery: The Case of Johannes Kepler....Pages 21-39
Model Experiments and Models in Experiments....Pages 41-58
Models, Simulations, and Experiments....Pages 59-74
Calibration of Models in Experiments....Pages 75-93
The Development of Scientific Taxonomies....Pages 95-111
Production, Science and Epistemology. An Overview on New Models and Scenarios....Pages 113-125
Modeling Practices and “Tradition”....Pages 127-146
Modeling Data: Analogies in Neural Networks, Simulated Annealing and Genetic Algorithms....Pages 147-165
Perceptual Simulation in Analogical Problem Solving....Pages 167-189
Building Demand Models to Improve Environmental Policy Process....Pages 191-208
Toward a Computational Model of Hypothesis Formation and Model Building in Science....Pages 209-225
Models as Parts of Distributed Cognitive Systems....Pages 227-241
Conceptual Models, Inquiry and the Problem of Deriving Normative Claims from a Naturalistic Base....Pages 243-257
Dynamic Imagery: A Computational Model of Motion and Visual Analogy....Pages 259-274
Model-Based Reasoning and Similarity in the World....Pages 275-285
Epistemic Artifacts: Michael Faraday’s Search for the Optical Effects of Gold....Pages 287-303
Epistemic Mediators and Model-Based Discovery in Science....Pages 305-329
Deterministic Models and the “Unimportance of the Inevitable”....Pages 331-351
Mental Models in Conceptual Development....Pages 353-368
Back Matter....Pages 391-404
Modeling Core Knowledge and Practices in a Computational Approach to Innovation Process....Pages 369-390
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