Petri Ylikoski

Understanding causation in social-ecological systems (SES) is indispensable for promoting sustainable outcomes. However, the study of such causal relations is challenging because they are often complex and intertwined, and their analysis involves diverse disciplines. Although there is agreement that no single research approach (RA) can comprehensively explain SES phenomena, there is a lack of ability to deal with this diversity. Underlying this diversity and the challenge of dealing with it are different causal reasonings that are rarely explicit. Awareness of hidden assumptions is essential for understanding how the causal reasoning of an RA is constituted, and for promoting the integration, translation, or juxtaposition of different RAs. We identify the following elements as particularly relevant for understanding causal reasoning: methods, frameworks and theories, accounts of causation, analytical focus, and causal notions. We begin with the idea that one of these elements typically figures as an entry point to an RA. This entry point is particularly important because it generates a path dependence that orients causal reasoning. In a subsequent step, when an approach is applied, causal reasoning concretizes as a result of a particular constellation of the remaining elements. We come to these insights by studying the application of four different RAs to the same social-ecological case (the collapse of Baltic cod stocks in the 1980s). On the basis of our findings we developed a guide for the analysis of causal reasoning by raising awareness of the assumptions, key elements, and the relations between these key elements for a given RA. The guide can be used to elicit the causal reasoning of RAs, facilitate interdisciplinary collaboration, and support disclosure of ethical/political dimensions that underlie management/governance interventions that are formulated on the basis of causal findings of research studies.

In this Chapter, we address the challenge of explaining institutional change, asking whether the much-criticized rational choice perspective can contribute to the understanding of institutional change in political science. We discuss the methodological reasons why rational choice institutionalism (RCI) often assumes that institutional change is exogenous and discontinuous. We then identify and explore the possible pathways along which RCI can be extended to be more useful in understanding institutional change in political science. Finally, we reflect on what RCI theorizing would look like if it started to take endogenous change seriously: by giving up some of its simplifying assumptions, RCI can be a useful tool for analyzing institutional change, but choosing this path has consequences for the generality of the models in RCI as well as for the style of its theorizing.

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.

We argue that the appraisal of models in social epistemology requires conceiving of them as argumentative devices, taking into account the argumentative context and adopting a family-of-models perspective. We draw up such an account and show how it makes it easier to see the value and limits of the use of models in social epistemology. To illustrate our points, we document and explicate the argumentative role of epistemic landscape models in social epistemology and highlight their limitations. We also claim that our account could be fruitfully used in appraising other models in philosophy and science.

Yhteiskuntatieteellinen tutkimus on pohjimmiltaan kysymyksiin vastaamista. Kysymysten avulla hahmotellaan yhteiskunnallisille ilmiöille syitä ja seurauksia. Mikä rooli syy-seuraussuhteiden ymmärtämisellä sitten on arvioitaessa ja täsmennettäessä yhteiskuntatieteellisiä selitysmalleja? Kausaalinen järkeily ja selittäminen läpäisee kaikkea yhteiskuntatieteellistä tutkimusta tutkimusmenetelmistä ja -asetelmista riippumatta. Kausaalisuuden käsitettä, kausaalisuhteita ja kausaalisia päätelmiä koskevien oletusten hahmottaminen on tärkeää, kun alan tutkimuskäytäntöjä kehitetään. Syistä selityksiin tarjoaa tieteenfilosofisia ja metodologisia välineitä, joita tarvitaan yhteiskuntatieteellisessä selittämisessä, kausaalianalyysissa ja pyrittäessä ratkaisemaan tiedon soveltamiseen liittyviä kysymyksiä. Kirjassa tarkastellaan kausaalisen järkeilyn merkitystä erilaisia tutkimusasetelmia ja -menetelmiä käyttävissä yhteiskuntatieteellisissä tutkimuksissa. Teorioita, käsitteitä ja metodologisia näkemyksiä havainnollistetaan lukuisilla konkreettisilla tutkimusesimerkeillä. Teos on suunnattu erityisesti yhteiskuntatieteiden ja niiden filosofian opiskelijoille sekä alan tutkijoille.

Generalization from a case study is a perennial issue in the methodology of the social sciences. The case study is one of the most important research designs in many social scientific fields, but no shared understanding exists of the epistemic import of case studies. This article suggests that the idea of mechanism-based theorizing provides a fruitful basis for understanding how case studies contribute to a general understanding of social phenomena. This approach is illustrated with a re- construction of Espeland and Sauder's case study of the effects of rankings on US legal education. On the basis of the reconstruction, it is argued that, at least with respect to sociology, the idea of mechanism-based theorizing captures many of the generalizable elements of case studies.

We compare Guala’s unified theory of institutions with that of Searle and Greif. We show that unification can be many things and it may be associated with diverse explanatory goals. We also highlight some of the important shortcomings of Guala’s account: it does not capture all social institutions, its ability to bridge social ontology and game theory is based on a problematic interpretation of the type-token distinction, and its ability to make social ontology useful for social sciences is hindered by Guala’s interpretation of social institution types as social kinds akin to natural kinds.

This comment discusses Kaidesoja and raises the issue whether his analysis justifies stronger conclusions than he presents in the book. My comments focus on four issues. First, I argue that his naturalistic reconstruction of critical realist transcendental arguments shows that transcendental arguments should be treated as a rare curiosity rather than a general argumentative strategy. Second, I suggest that Kaidesoja’s analysis does not really justify his optimism about the usefulness of causal powers ontology in the social sciences. Third, I raise some doubts about the heuristic value of Mario Bunge’s social ontology that Kaidesoja presents as a replacement for critical realist ontology. Finally, I propose an alternative way to analyze failures of aggregativity that might better serve Kaidesoja’s purposes than the Wimsattian scheme he employs in the book.

In this paper I examine the role of thought experiments in the social studies of science. More specifically, I will focus on two strands of social studies of science: the so-called sociology of scientific knowledge and the naturalistically oriented philosophy of science with interest in social dimensions of science. I begin by discussing David Hull's views on thought experiments in the study of science. His account serves as a foil that helps me to make some points about thought experiments. As an illustration I discuss two example of thought experimenting in the social studies of science. The first example is the use of thought experiments by the sociologists of scientific knowledge, and the second case will be the recent work by Philip Kitcher on division of cognitive labour. With the help of these cases, I argue that Hull's negative attitude towards the use of thought experiments requires some, tempering. The notion of thought experiment will be understood broadly in this paper. One could also talk about imaginary or hypothetical examples. In social sciences the contrast for thought experiments is not an experiment, but an empirical case study. Accordingly, thought experiment in social science in an imaginary case study or a made up historical scenario. The focus of my discussion is on the various functions and argumentative roles thought experiments have or could have in social studies of science. An empirical experiment (or a case study) is not an argument by itself, nor is a thought experiment. However, they both are used in making arguments and my aim is to look how they are used.

This paper discusses the epistemic import of highly abstract and simplified theoretical models using Thomas Schelling’s checkerboard model as an example. We argue that the epistemic contribution of theoretical models can be better understood in the context of a cluster of models relevant to the explanatory task at hand. The central claim of the paper is that theoretical models make better sense in the context of a menu of possible explanations. In order to justify this claim, we introduce a distinction between causal scenarios and causal mechanism schemes. These conceptual tools help us to articulate the basis for modelers’ intuitive confidence that their models make an important epistemic contribution. By focusing on the role of the menu of possible explanations in the evaluation of explanatory hypotheses, it is possible to understand how a causal mechanism scheme can improve our explanatory understanding even in cases where it does not describe the actual cause of a particular phenomenon.

Comparisons of rival explanations or theories often involve vague appeals to explanatory power. In this paper, we dissect this metaphor by distinguishing between different dimensions of the goodness of an explanation: non-sensitivity, cognitive salience, precision, factual accuracy and degree of integration. These dimensions are partially independent and often come into conflict. Our main contribution is to go beyond simple stipulation or description by explicating why these factors are taken to be explanatory virtues. We accomplish this by using the contrastive-counterfactual approach to explanation and the view of understanding as an inferential ability. By combining these perspectives, we show how the explanatory power of an explanation in a given dimension can be assessed by showing the range of answers it provides to what-if-things-had-been-different questions and the theoretical and pragmatic importance of these questions. Our account also explains intuitions linking explanation to unification or to exhibition of a mechanism.

The psychiatric category of addiction has recently been broadened to include new behaviors. This has prompted critical discussion about the value of a concept that covers so many different substances and activities. Many of the debates surrounding the notion of addiction stem from different views concerning what kind of a thing addiction fundamentally is. In this essay, we put forward an account that conceptualizes different addictions as sharing a cluster of relevant properties (the syndrome) that is supported by a matrix of causal mechanisms. According to this “addiction-as-a-kind” hypothesis, several different kinds of substance and behavioral addictions can be thought of as instantiations of the same thing – addiction. We show how a clearly articulated account of addiction can facilitate empirical research and the theoretical integration of different perspectives on addiction. The causal matrix approach provides a promising alternative to existing accounts of the nature of psychiatric disorders, the traditional disease model, and its competitors. It is a positive addition to discussions about diagnostic criteria, and sheds light on how psychiatric classification may be integrated with research done in other scientific fields. We argue that it also provides a plausible approach to understanding comorbidity.

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.

During the past decade, social mechanisms and mechanism-based ex- planations have received considerable attention in the social sciences as well as in the philosophy of science. This article critically reviews the most important philosophical and social science contributions to the mechanism approach. The first part discusses the idea of mechanism- based explanation from the point of view of philosophy of science and relates it to causation and to the covering-law account of explanation. The second part focuses on how the idea of mechanisms has been used in the social sciences. The final part discusses recent developments in analytical sociology, covering the nature of sociological explananda, the role of theory of action in mechanism-based explanations, Merton’s idea of middle-range theory, and the role of agent-based simulations in the development of mechanism-based explanations.

is chapter takes a fresh look at micro-macro relations in the social sciences from the point of view of the mechanistic account of explanation. Traditionally, micro- macro issues have been assimilated to the problem of methodological individualism. It is not my intention to resurrect this notoriously unfruitful controversy. On the contrary, the main thrust of this chapter is to show that the cul-de-sac of that debate can be avoided if we give up some of its presuppositions. The debate about methodological individualism is based on assumptions about explanation, and once we change those assumptions, the whole argumenta- tive landscape changes.

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.

In this paper I will discuss the idea of the invisible hand in the connection of its recent use in the philosophy of science. It has been invoked by some philosophers of science with a naturalistic bent as a part of their account of science. Some have made explicit references to the idea (Hull, 1988a) and others have only presupposed it (Giere, 1988; Goldman, 1991; Kitcher, 1993). I will argue that there are some problematic features in the way the idea of the invisible hand isused inthese accounts. I will first discuss some general properties of the invisible hand explanations and then present some motives for its use in the theory of science. Then I will show how one particular philosopher of science, David Hull, uses the idea. I will use Hull's account as a practising target and offer some comments and criticism in order to promote more disciplined use of this model of explanation in science studies.

This paper provides a conceptual analysis of the notion of interests as it is used in the social studies of science. After describing the theoretical background behind the Strong Program's adoption of the concept of interest, the paper outlines a reconstruction of the everyday notion of interest and argues that this same notion is used also by the sociologists of scientific knowledge. However, there are a couple of important differences between the everyday use of this notion and the way in which it used by the sociologists. The sociologists do not use the term in evaluative context and they do not regard interests as purely non-epistemic factors. Finally, it is argued that most of the usual critiques of interest explanations, by both philosophers and fellow sociologists, are misguided.

This article discusses agent-based simulation (ABS) as a tool of sociological understanding. I argue that agent-based simulations can play an important role in the expansion of explanatory understanding in the social sciences. The argument is based on an inferential account of understanding (Ylikoski 2009, Ylikoski & Kuorikoski 2010), according to which computer simulations increase our explanatory understanding by expanding our ability to make what-if inferences about social processes and by making these inferences more reliable. The inferential account also suggests a number of ways in which the use of simulation methodology might give rise to illusory understanding. The structure of the article is as ..