Micro, Macro, and Mechanisms

This article, which takes a fresh look at micro–macro relations in the social sciences from the point of view of the mechanistic account of explanation, introduces the distinction between causal and constitutive explanation. It then discusses the intentional fundamentalism, and challenges the idea that intentional explanations have a privileged position in the social sciences. A mechanism-based explanation describes the causal process selectively. The properties of social networks serve both as the explananda and the explanantia in sociology. Knowledge of the causal mechanisms is vital in the justification of historical causal claims. The intentional attitudes of individuals are also important in most mechanism-based explanations of social phenomena. It is important to pay closer attention to how real macro social facts figure in social scientific theories and explanations.Peer reviewe

Review of Individuals and Identity in Economics by John B. Davis

Book review. Reviewed work: Individuals and Identity in Economics / John B. Davis. - Cambridge University Press, 2011.Non peer reviewe

Understanding the Coleman boat

Peer reviewe

Generative Explanation and Individualism in Agent-Based Simulation

Social scientists associate agent-based simulation (ABS) models with three ideas about explanation: they provide generative explanations, they are models of mechanisms, and they implement methodological individualism. In light of a philosophical account of explanation, we show that these ideas are not necessarily related and offer an account of the explanatory import of ABS models. We also argue that their bottom-up research strategy should be distinguished from methodological individualism.Peer reviewe

How to be critical and realist about economics

Peer reviewe

Explanatory relevance across disciplinary boundaries

Many of the arguments for neuroeconomics rely on mistaken assumptions about criteria of explanatory relevance across disciplinary boundaries and fail to distinguish between evidential and explanatory relevance. Building on recent philosophical work on mechanistic research programmes and the contrastive counterfactual theory of explanation, we argue that explaining an explanatory presupposition or providing a lower-level explanation does not necessarily constitute explanatory improvement. Neuroscientific findings have explanatory relevance only when they inform a causal and explanatory account of the psychology of human decision-making.Peer reviewe

Sociological Individualism

Peer reviewe

Humanistic interpretation and machine learning

This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not eliminate the researchers’ judgments from the process of producing evidence for social scientific theories. The paper shows this by distinguishing between two prevalent attitudes toward topic modeling, i.e., topic realism and topic instrumentalism. Under neither can modeling provide social scientific evidence without the researchers’ interpretive engagement with the original text materials. Thus the unsupervised text analysis cannot improve the objectivity of interpretation by alleviating the problem of underdetermination in interpretive debate. The paper argues that the sense in which unsupervised methods can improve objectivity is by providing researchers with the resources to justify to others that their interpretations are correct. This kind of objectivity seeks to reduce suspicions in collective debate that interpretations are the products of arbitrary processes influenced by the researchers’ idiosyncratic decisions or starting points. The paper discusses this view in relation to alternative approaches to formalizing interpretation and identifies several limitations on what unsupervised learning can be expected to achieve in terms of supporting interpretive work.This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not eliminate the researchers’ judgments from the process of producing evidence for social scientific theories. The paper shows this by distinguishing between two prevalent attitudes toward topic modeling, i.e., topic realism and topic instrumentalism. Under neither can modeling provide social scientific evidence without the researchers’ interpretive engagement with the original text materials. Thus the unsupervised text analysis cannot improve the objectivity of interpretation by alleviating the problem of underdetermination in interpretive debate. The paper argues that the sense in which unsupervised methods can improve objectivity is by providing researchers with the resources to justify to others that their interpretations are correct. This kind of objectivity seeks to reduce suspicions in collective debate that interpretations are the products of arbitrary processes influenced by the researchers’ idiosyncratic decisions or starting points. The paper discusses this view in relation to alternative approaches to formalizing interpretation and identifies several limitations on what unsupervised learning can be expected to achieve in terms of supporting interpretive work.Peer reviewe

Causal and Constitutive Explanation Compared

This article compares causal and constitutive explanation. While scientific inquiry usually addresses both causal and constitutive questions, making the distinction is crucial for a detailed understanding of scientific questions and their interrelations. These explanations have different kinds of explananda and they track different sorts of dependencies. Constitutive explanations do not address events or behaviors, but causal capacities. While there are some interesting relations between building and causal manipulation, causation and constitution are not to be confused. Constitution is a synchronous and asymmetric relation between relata that cannot be conceived as independent existences. However, despite their metaphysical differences, the same key ideas about explanation largely apply to both. Causal and constitutive explanations face similar challenges (such as the problems of relevance and explanatory regress) and both are in the business of mapping networks of counterfactual dependence—i.e. mechanisms—although the relevant counterfactuals are of a different sort. In the final section the issue of developmental explanation is discussed. It is argued that developmental explanations deserve their own place in taxonomy of explanations, although ultimately developmental dependencies can be analyzed as combinations of causal and constitutive dependencies. Hence, causal and constitutive explanation are distinct, but not always completely separate forms of explanation.Peer reviewe

The (hopefully) last stand of the covering-law theory: A reply to Opp

In his paper Karl-Dieter Opp heroically sets out to defend both the adequacy and socio- logical fruitfulness of the covering-law account of explanation (the HO scheme). The attempt is bold, as he is not only defending the HO scheme as a theory of explanation but also as a scheme for finding and establishing causal relationships. In this reply I argue that the defense is not successful; quite the contrary, it clearly demonstrates why mecha- nism-based reasoning is important in the social sciences. I also argue that this change in metatheoretical perspective has implications for thinking about the role of rational choice theory in sociology, which should not be seen as a foundational theory but rather as a version of commonsense psychology that can be used for modeling purposes.Peer reviewe