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Preface
1 Introduction: Doing Science in a Complex World
1.1 Science by Humans
1.2 Science in a Complex World
1.3 The Payoff: Idealizations and Many Aims
2 Complex Causality and Simplified Representation
2.1 Causal Patterns in the Face of Complexity
2.1.1 Causal Patterns
2.1.2 Causal Complexity
2.2 Simplification by Idealization
2.2.1 Reasons to Idealize
2.2.2 Idealizations’ Representational Role
2.2.3 Rampant and Unchecked Idealization
3 The Diversity of Scientific Projects
3.1 Broad Patterns: Modeling Cooperation
3.2 A Specific Phenomenon: Variation in Human Aggression
3.3 Predictions and Idealizations in the Physical Sciences
3.4 Surveying the Diversity
4 Science Isn’t after the Truth
4.1 The Aims of Science
4.1.1 Understanding as Science’s Epistemic Aim
4.1.2 Separate Pursuit of Science’s Aims
4.2 Understanding, Truth, and Knowledge
4.2.1 The Nature of Scientific Understanding
4.2.2 The Role of Truth and Scientific Knowledge
5 Causal Pattern Explanations
5.1 Explanation, Communication, and Understanding
5.2 An Account of Scientific Explanation
5.2.1 The Scope of Causal Patterns
5.2.2 The Crucial Role of the Audience
5.2.3 Adequate Explanations
6 Levels and Fields of Science
6.1 Levels in Philosophy and Science
6.2 Going without Levels
6.2.1 Against Hierarchy
6.2.2 Prizing Apart Forms of Stratification
6.3 The Fields of Science and How They Relate
7 Scientific Pluralism and Its Limits
7.1 The Entrenchment of Social Values
7.2 How Science Doesn’t Inform Metaphysics
7.3 Scientific Progress
Acknowledgments
List of Figures
List of Tables
Notes
References
Index
Description
Science is the study of our world, as it is in its messy reality. Nonetheless, science requires idealization to function—if we are to attempt to understand the world, we have to find ways to reduce its complexity.
Idealization and the Aims of Science shows just how crucial idealization is to science and why it matters. Beginning with the acknowledgment of our status as limited human agents trying to make sense of an exceedingly complex world, Angela Potochnik moves on to explain how science aims to depict and make use of causal patterns—a project that makes essential use of idealization. She offers case studies from a number of branches of science to demonstrate the ubiquity of idealization, shows how causal patterns are used to develop scientific explanations, and describes how the necessarily imperfect connection between science and truth leads to researchers’ values influencing their findings. The resulting book is a tour de force, a synthesis of the study of idealization that also offers countless new insights and avenues for future exploration.
