This Element introduces the philosophical literature on models, with an emphasis on normative considerations relevant to models for decision-making. Chapter 1 gives an overview of core questions in the philosophy of modeling. Chapter 2 examines the concept of model adequacy for purpose, using three examples of models from the atmospheric sciences to describe how this sort of adequacy is determined in practice. Chapter 3 explores the significance of using models that are not adequate for purpose, including the purpose of informing public decisions. Chapter 4 provides a basic framework for values in modelling, using a case study to highlight the ethical challenges in building models for decision making. It concludes by establishing the need for strategies to manage value judgments in modelling, including the potential for public participation in the process.
Author(s): Eric Winsberg; Stephanie Harvard
Series: Elements in the Philosophy of Science
Publisher: Cambridge University Press
Year: 2024
Language: English
City: Cambridge
Cover
Title page
Copyright page
Scientific Models and Decision-Making
Contents
1 Introduction
1.1 What Is a Model?
1.2 Are There ‘Types’ of Models?
1.2.1 Abstract/Mental Models
1.2.2 Concrete Models
1.2.3 Data Models
1.2.4 Mathematical (including Computational) Models
1.3 Mathematical and Computational Models: A Closer Look
1.3.1 Idealization
1.3.2 Articulation
1.3.3 Sensitivity
1.3.4 Credentials
1.3.5 Skill
1.4 Conclusion
2 Adequacy for Purpose
2.1 Introduction
2.2 A Zero-Dimensional Energy Balance Model
2.3 Weather Forecasting Model
2.4 Climate Models
2.5 Conclusion
3 Inadequacy for Purpose
3.1 Introduction
3.2 SIR Models: From Overshoot to Lockdown?
3.3 Two Kinds of Risk
3.4 Models for Public Decisions
3.5 Conclusion
4 Models and Values
4.1 Introduction
4.2 A Basic ‘Values in Modelling’ Framework
4.2.1 What to Represent: A High-Level View
4.2.2 Fine-Grained Representational Decisions
4.2.3 Inferential Decisions
4.3 The Ethical Significance of Including and Excluding Variables
4.4 The Expected Utility of Representational Decisions
4.5 Case Study: Imperial College London Model
4.5.1 Background
4.5.2 What Is Represented? Study Topic and End Points in Context
4.5.3 Finer-Grained Representational Decisions
Other Variables
Parameters
4.5.4 Uncertainty
4.5.5 Choice of Counterfactuals for Projection
4.6 Moral Responsibilities in Modelling
4.7 Public Participation in Modelling
4.8 Conclusion
References
