Jaakko Kuorikoski

Naturalistic social epistemology has gone mainstream, but two of its central methods, case studies and modelling, are poorly understood. Even though case studies and modelling can be seen as two distinct approaches to social epistemology, both can be seen as mutually complementary methods for investigating social mechanisms relevant to the epistemic functioning of science. We argue, therefore, that naturalistic social epistemology of science should be seen as continuous with theoretical social science.

Constitutivemechanisticexplanationsexplainapropertyofawholewith the properties of its parts and their organization. Carl Craver’s mutual manipulability criterion for constitutive relevance only captures the explanatory relevance of causal properties of parts and leaves the organization side of mechanistic explanation unaccounted for. We use the contrastive counterfactual theory of explanation and an account of the dimensions of organization to build a typology of organizational dependence. We analyse organizational explanations in terms of such dependencies and emphasize the importance of modular organizational motifs. We apply this framework to two cases from social science and systems biology, both fields in which organization plays a crucial explanatory role: agent-based simulations of residential segregation and the recent work on network motifs in transcription regulation networks.

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.

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.

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

Human behavior is not always independent of the ways in which humans are scientifically classified. That there are looping effects of human kinds has been used as an argument for the methodological separation of the natural and the human sciences and to justify social constructionist claims. We suggest that these arguments rely on false presuppositions and present a mechanisms-based account of looping that provides a better way to understand the phenomenon and its theoretical and philosophical implications.

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers (Hong and Page 2004). This striking claim about the power of cognitive diversity is highly influential within and outside academia, from democratic theory to management of teams in professional organizations. Our replication and analysis of the models used by Hong and Page suggests, however, that both the binary string model and its one-dimensional variant are inadequate for exploring the trade-off between cognitive diversity and ability. Diversity may sometimes beat ability, but the models fail to provide reliable evidence of if and when it does so. We suggest ways in which these important model templates can be improved.

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.

All economic models involve abstractions and idealisations. Economic theory itself does not tell which idealizations are truly fatal or harmful for the result and which are not. This is why much of what is seen as theoretical contribution in economics is constituted by deriving familiar results from different modelling assumptions. If a modelling result is robust with respect to particular modelling assumptions, the empirical falsity of these particular assumptions does not provide grounds for criticizing the result. In this paper we demonstrate how derivational robustness analysis does carry epistemic weight and answer criticism concerning its non-empirical nature and the problematic form of the required independence of the ways of derivation. The epistemic rationale and importance of robustness analysis also challenge some common conceptions of the role of theory in economics.

We mobilise contemporary philosophy of science to further clarify observations on economic modelling made by Gilboa et al. (2023). We adopt a normative stance towards these modelling practices to identify the extent to which they are epistemically justified. Our message is simple: many of the distinctions proposed by Gilboa et al. (2023) are useful, but without the proper qualifications, too much flexibility in choosing the right interpretation risks downplaying the crucial role that empirical evidence should play in any modelling endeavour.

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.

This paper deals with the evidential value of neuroeconomic experiments for the triangulation of economically relevant phenomena. We examine the case of social preferences, which involves bringing together evidence from behavioural experiments, neuroeconomic experiments, and observational studies from other social sciences. We present an account of triangulation and identify the conditions under which neuroeconomic evidence is diverse in the way required for successful triangulation. We also show that the successful triangulation of phenomena does not necessarily afford additional confirmation to general theories about those phenomena.

Odenbaugh and Alexandrova provide a challenging critique of the epistemic benefits of robustness analysis, singling out for particular criticism the account we articulated in Kuorikoski et al.. Odenbaugh and Alexandrova offer two arguments against the confirmatory value of robustness analysis: robust theorems cannot specify causal mechanisms and models are rarely independent in the way required by robustness analysis. We address Odenbaugh and Alexandrova’s criticisms in order to clarify some of our original arguments and to shed further light on the properties of robustness analysis and its epistemic rationale.

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.

Mixed methods research - 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 the 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 mixed methods research.

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model that captures the role of ‘ability’ in a meaningful way, and we use it to explore the trade-offs between diversity and ability in scientific problem solving.

I argue that non-epistemic values are necessarily embedded in the measure of evidential strength of contrastive evidence. When evidence is contrastive, evidence is stronger the more it favours a hypothesis over a set of plausible, mutually exclusive alternative hypotheses. In such a contrastive epistemic setting, evidence has an effect not only on a particular hypothesis, but on the whole probability distribution over the set of alternative hypotheses. A natural way of analysing the incremental impact of new evidence on a set of alternative hypotheses is in terms of uncertainty or ‘entropy’ reduction. There is no unique single measure of uncertainty/entropy, however, and, consequently, no single unique measure of uncertainty reduction. Finally, I argue that the ‘right’ measure of entropy reduction, and hence of evidential strength, depends on the pragmatic context and non-epistemic values.

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model which captures the role of ‘ability’ in a meaningful way, and use it to explore the trade-offs between diversity and ability in scientific problem solving.

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

Human behavior is not always independent of the ways in which humans are scientifically classified. That there are looping effects of human kinds has been used as an argument for the methodological separation of the natural and the human sciences and to justify social constructionist claims. We suggest that these arguments rely on false presuppositions and present a mechanisms-based account of looping that provides a better way to understand the phenomenon and its theoretical and philosophical implications.

Nudge and boost are two competing approaches to applying the psychology of reasoning and decision making to improve policy. Whereas nudges rely on manipulation of choice architecture to steer people towards better choices, the objective of boosts is to develop good decision-making competences. Proponents of both approaches claim capacity to enhance social welfare through better individual decisions. We suggest that such efforts should involve a more careful analysis of how individual and social welfare are related in the policy context. First, individual rationality is not always sufficient or necessary for improving collective outcomes. Second, collective outcomes of complex social interactions among individuals are largely ignored by the focus of both nudge and boost on individual decisions. We suggest that the design of mechanisms and social norms can sometimes lead to better collective outcomes than nudge and boost, and present conditions under which the three approaches (nudge, boost, and design) can be expected to enhance social welfare.

Tässä luvussa tarkastelemme hypoteesien testaamista ja kokeellista kausaalista järkeilyä tieteenfilosofisesta näkökulmasta. Arvioimme kokeellisen menetelmän mahdollisuuksia ja rajoituksia yhteiskuntatieteellisen tutkimuksen kontekstissa, jossa luonnontieteille ominaisia yleispäteviä teorioita harvoin on saatavilla ja jossa suoraviivaisiin kausaaliväitteisiin suhtaudutaan usein epäillen. Tämä luku ei siis ole menetelmäopas, joka kädestä pitäen opastaisi, kuinka yhteiskuntatieteellisiä kokeita tulisi rakentaa, vaan katsaus niihin perustaviin metodologisiin kysymyksiin ja periaatteisiin, joihin varsinaiset menetelmät nojaavat.

The invariance under interventions –account of causal explanation imposes a modularity constraint on causal systems: a local intervention on a part of the system should not change other causal relations in that system. This constraint has generated criticism against the account, since many ordinary causal systems seem to break this condition. This paper answers to this criticism by noting that explanatory models are always models of specific causal structures, not causal systems as a whole, and that models of causal structures can have different modularity properties which determine what can and what cannot be explained with the model.