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Mixed-methods research (MMR)—the combination of qualitative and quantitative data within the same design to strengthen causal inference—is gaining prominence in the social sciences but its benefits are contested. There remains confusion over which methods to mix and what is the point of mixing them. We argue that variety of evidence is what matters, not of data or methods, and that distinct epistemic principles underlie its added value for causal inference. The centrality of evidential variety also implies that strong causal pluralism is untenable as a foundation for MMR.

The article argues for the epistemic rationale of triangulation, namely, the use of multiple and independent sources of evidence. It claims that triangulation is to be understood as causal reasoning from data to phenomenon, and it rationalizes its epistemic value in terms of controlling for likely errors and biases of particular data-generating procedures. This perspective is employed to address objections against triangulation concerning the fallibility and scope of the inference, as well as problems of independence, incomparability, and discordance of evidence. The debate on the existence of social preferences is used as an illustrative case.

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: nonsensitivity, 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.

This paper provides an inferentialist account of model-based understanding by combining a counterfactual account of explanation and an inferentialist account of representation with a view of modeling as extended cognition. This account makes it understandable how the manipulation of surrogate systems like models can provide genuinely new empirical understanding about the world. Similarly, the account provides an answer to the question how models, that always incorporate assumptions that are literally untrue of the model target, can still provide factive explanations. Finally, the paper shows how the contrastive counterfactual theory of explanation can provide tools for assessing the explanatory power of models.

Interdisciplinarity is strongly promoted in science policy across the world. It is seen as a necessary condition for providing practical solutions to many pressing complex problems for which no single disciplinary approach is adequate alone. In this article we model multi- and interdisciplinary research as an instance of collective problem solving. Our goal is to provide a basic representation of this type of problem solving and chart the epistemic benefits and costs of researchers engaging in different forms of cognitive coordination. Our findings suggest that typical forms of interdisciplinary collaboration are unlikely to find optimal solutions to complex problems within short time frames and can lead to methodological conservatism. This provides some grounds for both reflecting on current science policy and envisioning more effective scientific practices with respect to interdisciplinary problem solving.

In this introduction to the Topical Collection on Social and Cognitive Diversity in Science , we map the questions that have guided social epistemological approaches to diversity in science. Both social and cognitive diversity of different types is claimed to be epistemically beneficial. The challenge is to understand how an increase in a group’s diversity can bring about epistemic benefits and whether there are limits beyond which diversity can no longer improve a group’s epistemic performance. The contributions to the Topical Collection discuss various proposals to maintain an appropriate amount of cognitive diversity in science, for instance, by recruiting and retaining practitioners from underrepresented social groups, providing incentives for explorative and risky research, encouraging interdisciplinary collaborations and stakeholder participation in research, requiring industry scientists to share their evidence, and developing strategies to encounter politically motivated attempts to manufacture doubt. To be successful, efforts to promote diversity in science should anticipate risks related to institutional interventions, navigate trade-offs between different types of epistemically good outcomes, and identify hidden costs that such policies may cause for various actors. Such efforts need to be assessed not only from an epistemic perspective but also from the point of view of fairness and the political legitimacy of scientific institutions.

This book provides the first comprehensive and critical examination of Mäki’s realist philosophy of economics. Introduction: Uskali Mäki’s realist philosophy of economics Aki Lehtinen (Univ. of Helsinki) Part I Isolating Truth in Economic Models 1 Saving Truth for Economics Frank Hindriks (Univ. of Groningen) 2 The verisimilitude of economic models Ilkka Niiniluoto (Univ. of Helsinki) 3 Mäki’s MISS Daniel Hausman (Univ. of Wisconsin-Madison) 4 Models as isolators: for and against Till Grüne-Yanoff (Univ. of Helsinki) 5 Isolated economic mechanisms: motivational crowding, multi-argument utility-functions and mixing mechanisms Jack Vromen (Erasmus University Rotterdam) Part II The Commonsensical Basis of Economics 6 Realism, Commonsensibles, and Economics: The Case of Contemporary Revealed Preference Theory D. Wade Hands (Univ. of Puget Sound) 7 Are Preferences for Real? Choice Theory, Folk Psychology, and the Hard Case for Commonsensible Realism Francesco Guala (Univ. of Milan) Part III The proper domain of economics 8 Mäki’s realism and the scope of economics Don Ross (Univ. of Cape Town) 9 Mäki on Economics Imperialism John Davis (Univ. of Amsterdam) Part IV Rethinking Realism(s) 10 Pragmatism, Perspectival Realism, and Econometrics Kevin Hoover (Duke University) 11 Getting Real with the Critical Petri Ylikoski and Jaakko Kuorikoski (Univ. of Helsinki) 12 Realism, conversation and inference Jesús Zamora-Bonilla (UNED, Madrid) Aki Lehtinen is a post-doctoral researcher at the University of Helsinki, Finland Jaakko Kuorikoski is a post-doctoral researcher at the University of Helsinki, Finland Petri Ylikoski is Deputy Director of Trends and Tensions in Intellectual Integration (TINT), Department of Social and Moral Philosophy, University of Helsinki, Finland

This paper aims to provide Humean metaphysics for the interventionist theory of causation. This is done by appealing to the hierarchical picture of causal relations as being realized by mechanisms, which in turn are identified with lower-level causal structures. The modal content of invariances at the lowest level of this hierarchy, at which mechanisms are reduced to strict natural laws, is then explained in terms of projectivism based on the best-system view of laws.

We claim that the process of theoretical model refinement in economics is best characterised as robustness analysis: the systematic examination of the robustness of modelling results with respect to particular modelling assumptions. We argue that this practise has epistemic value by extending William Wimsatt’s account of robustness analysis as triangulation via independent means of determination. For economists robustness analysis is a crucial methodological strategy because their models are often based on idealisations and abstractions, and it is usually difficult to tell which idealisations are truly harmful. Introduction Making Sense of Robustness Robustness in Economics The Epistemic Import of Robustness Analysis An Illustration: Geographical Economics Models Independence of Derivations Concluding Remarks

This book provides the first comprehensive and critical examination of Mäki’s realist philosophy of economics.