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.

This paper is an attempt to further our understanding of mechanisms conceived of as ontologically separable from laws. What opportunities are there for a mechanistic perspective to be independent of, or even more fundamental than, a law perspective? Advocates of the mechanistic view often play with the possibility of internal and external reliability, or with the paralleling possibilities of enforcing, counteracting, redirecting, etc., the mechanisms’ power to produce To further this discussion I adopt a trope ontology. It is independent of the notion of law, and can easily be adapted to account for such characteristics of mechanisms. The idea of tropes as mechanisms is worked out in some detail. According to the resulting picture, there is still an opportunity to link mechanisms and laws. But while the predominant law view conceives of mechanistic approaches as special kinds of law accounts, this study indicates that the converse may be true. Law accounts are special cases of mechanistic accounts, and they work only in those worlds where the mechanisms are of the right kind.

Realism in Action is a selection of essays written by leading representatives in the fields of action theory and philosophy of mind, philosophy of the social sciences and especially the nature of social action, and of epistemology and philosophy of science. Practical reason, reasons and causes in action theory, intending and trying, and folk-psychological explanation are some of the topics discussed by these leading participants. A particular emphasis is laid on trust, commitments and social institutions, on the possibility of grounding social notions in individual social attitudes, on the nature of social groups, institutions and collective intentionality, and on common belief and common knowledge. Applications to the social sciences include, e.g., a look at the Erklären-Verstehen controversy in economics, and at constructivist and realist views on archeological reconstructions of the past

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.