- Introduction: Doing Philosophy of Social Science
- Micro, Macro, and Mechanisms
- Mechanisms, Causal Modeling, and the Limitations of Traditional Multiple Regression
- Process Tracing and Causal Mechanisms
- Descriptive-Causal Generalizations: “Empirical Laws” in the Social Sciences?
- Useful Complex Causality
- Partial Explanations in Social Science
- Mechanistic Social Probability: How Individual Choices and Varying Circumstances Produce Stable Social Patterns
- The Impact of Duhemian Principles on Social Science Testing and Progress
- Philosophy and the Practice of Bayesian Statistics in the Social Sciences
- Sciences of Historical Tokens and Theoretical Types: History and the Social Sciences
- RCTs, Evidence, and Predicting Policy Effectiveness
- Bringing Context and Variability Back into Causal Analysis
- The Potential Value of Computational Models in Social Science Research
- Models of Culture
- The Evolutionary Program in Social Philosophy
- Cultural Evolution: Integration and Skepticism
- Coordination and the Foundations of Social Intelligence
- Making Race Out of Nothing: Psychologically Constrained Social Roles
- A Feminist Empirical and Integrative Approach in Political Science: Breaking Down the Glass Wall?
- Social Constructions of Mental Illness
- Cooperation and Reciprocity: Empirical Evidence and Normative Implications
- Evaluating Social Policy
- Values and the Science of Well-Being: A Recipe for Mixing
Abstract and Keywords
This article investigates a philosophical hypothesis about the nature of (some) probabilities encountered in social sciences, and also explains how to use a new interpretation of probability, far flung frequency (FFF) mechanistic probability, to central cases in social sciences. Probabilities in error terms can reflect both methodological and social probabilities. Some well-known interpretations of probability are then highlighted. “Mechanistic probability” interpretations depend on the same causal structure, but there is no established term for such interpretations at present. Furthermore, the article reviews the core concepts of mechanistic probability using the idea of a wheel of fortune. The wheel of fortune is called a causal map device. The evidence for FFF mechanistic probability in social contexts is not as strong as one would like. It should be taken seriously as an account of the kind of probability to which many claims in social sciences implicitly refer.
Marshall Abrams is assistant professor in the Department of Philosophy at the University of Alabama at Birmingham. He received his PhD from the University of Chicago and was an NSF-sponsored postdoctoral fellow at Duke University’s Center for Philosophy of Biology. His philosophical research focuses on the nature and role of probability and causation in evolutionary biology and the social sciences, and on interactions between biological evolution and social processes with emphasis on modeling of cognitive coherence relations in cultural change. He is also engaged in purely scientific research on the evolution of obesity and diabetes, and is an associate editor at the journal Frontiers in Evolutionary and Population Genetics.
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