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The dual problems of how an idealized model can represent and provide information about its target have become a central topic of in the philosophy of science. We argue that several current views are misguided in assuming that the... more
The dual problems of how an idealized model can represent and provide information about its target have become a central topic of in the philosophy of science. We argue that several current views are misguided in assuming that the epistemology of modeling and simulation must build on a philosophical theory of the representation relation (e.g. isomorphism, similarity). We extend Robert Brandom’s inferentialist account of meaning into scientific representation to argue that representational language is explicatory, not explanatory, in nature. We provide a broader philosophical rationale for inferential accounts of scientific representation, and an epistemologically modest account of the role of models in terms of inferential scorekeeping. We apply these views to the contested case of computer simulations to argue that, although the praxis of simulation modeling resembles that of scientific experimentation, simulations alone cannot lead to genuinely novel discoveries about the world, a...
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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... more
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.
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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... more
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 paper 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 both for reflecting on current science policy and envisioning more effective scientific practices with respect to interdisciplinary problem-solving.
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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... more
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.
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In this paper we argue that, despite its influence, critical realism is not the most promising version of scientific realism for economics. The main problem with critical realism is its hermetic insulation from the mainstream of the... more
In this paper we argue that, despite its influence, critical realism is not the most promising version of scientific realism for economics. The main problem with critical realism is its hermetic insulation from the mainstream of the philosophy of science. We argue that this intellectual isolation is unfortunate, as it has meant that critical realism has missed many opportunities to develop its central concepts, such as causal mechanism, emergence, and explanation. At the same time, we argue, critical realists have missed some crucial aspects of the intellectual strategy of modern economics. Our point is not to defend mainstream economics, rather it is to show that a better understanding of modeling as a scientific research strategy opens up the possibility of a more penetrating analysis of its possible shortcomings.
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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... more
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,...
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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,... more
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.
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Rationality and self-interest are routinely attributed an explanatory priority as an inherently understandable basis - as an ideal of natural order - for all social scientific explanation. We argue that this is not consistent with a... more
Rationality and self-interest are routinely attributed an explanatory priority as an inherently understandable basis - as an ideal of natural order - for all social scientific explanation. We argue that this is not consistent with a causal-mechanistic understanding of science and that using self-interest and rationality heuristically as a default baseline biases social scientific research. From a naturalist perspective, both rationality and self-interest are empirical objects of explanation. We discuss one such explanatory hypothesis, according to which consistent self-interested behavior is sustained by a social norm.
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Computer simulation is widely taken to be the best, and sometimes the only, tool with which to study highly complex phenomena. However, in many fields, whether simulation models provide the
If ontic dependence is the basis of explanation, there cannot be mathematical explanations. Accounting for the explanatory dependency between mathematical properties and empirical phenomena poses insurmountable metaphysical and epistemic... more
If ontic dependence is the basis of explanation, there cannot be mathematical explanations. Accounting for the explanatory dependency between mathematical properties and empirical phenomena poses insurmountable metaphysical and epistemic difficulties, and the proposed amendments to the counterfactual theory of explanation invariably violate core commitments of the theory. Instead, mathematical explanations are either abstract mechanistic constitutive explanations or reconceptualizations of the explanandum phenomenon in which mathematics as such does not have an explanatory role. Explanation-like reasoning within mathematics, distinction between explanatory and nonexplanatory proofs, and comparative judgments of mathematical depth can be fully accounted for by a concept of formal understanding.
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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... more
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.
Research Interests: Metaphysics and Causality
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,... more
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.
If ontic dependence is the basis of explanation, there cannot be mathematical explanations. Accounting for the explanatory dependency between mathematical properties and empirical phenomena poses insurmountable metaphysical and epistemic... more
If ontic dependence is the basis of explanation, there cannot be mathematical explanations. Accounting for the explanatory dependency between mathematical properties and empirical phenomena poses insurmountable metaphysical and epistemic difficulties, and the proposed amendments to the counterfactual theory of explanation invariably violate core commitments of the theory. Instead, mathematical explanations are either abstract mechanistic constitutive explanations or reconceptualizations of the explanandum phenomenon in which mathematics as such does not have an explanatory role. Explanation-like reasoning within mathematics, distinction between explanatory and nonexplanatory proofs, and comparative judgements of mathematical depth can be fully accounted for by a concept of formal understanding.
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We review prominent modeling approaches in social epistemology aimed at understanding the functioning of epistemic communities. We provide a philosophy-of-science perspective on the use and interpretation of such simple models, and... more
We review prominent modeling approaches in social epistemology aimed at understanding the functioning of epistemic communities. We provide a philosophy-of-science perspective on the use and interpretation of such simple models, and highlight the need for better integration with relevant findings from other research fields studying collective problem solving.
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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... more
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
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In this paper we argue that, despite its influence, critical realism is not the most promising version of scientific realism for economics. The main problem with critical realism is its hermetic insulation from the mainstream of the... more
In this paper we argue that, despite its influence, critical realism is not the most promising version of scientific realism for economics. The main problem with critical realism is its hermetic insulation from the mainstream of the philosophy of science. We argue that this intellectual isolation is unfortunate, as it has meant that critical realism has missed many opportunities to develop its central concepts, such as causal mechanism, emergence, and explanation. At the same time, we argue, critical realists have missed some crucial aspects of the intellectual strategy of modern economics. Our point is not to defend mainstream economics, rather it is to show that a better understanding of modeling as a scientific research strategy opens up the possibility of a more penetrating analysis of its possible shortcomings.
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Robert Sugden argues that robustness analysis cannot play an epistemic role in grounding model-world relationships because the procedure is only a matter of comparing models with each other. We posit that this argument is based on a view... more
Robert Sugden argues that robustness analysis cannot play an epistemic role in grounding model-world relationships because the procedure is only a matter of comparing models with each other. We posit that this argument is based on a view of models as being surrogate systems in too literal a sense. In contrast, the epistemic importance of robustness analysis is easy to explicate if modelling is viewed as extended cognition, as inference from assumptions to conclusions. Robustness analysis is about assessing the reliability of our extended inferences, and when our confidence in these inferences changes, so does our confidence in the results. Furthermore, we argue that Sugden’s inductive account relies tacitly on robustness considerations.
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Simulations are often so complex that they in turn become difficult to understand. Assessment of the intelligibility of simulation models is often based on the psychological sense of understanding, rather than on any explicitly discussed... more
Simulations are often so complex that they in turn become difficult to understand. Assessment of the intelligibility of simulation models is often based on the psychological sense of understanding, rather than on any explicitly discussed criteria. This is problematic, because the sense of understanding is only a fallible indicator of understanding and there are reasons to expect it to be systematically biased in the context of simulations. Distinguishing between the sense of understanding and understanding proper is necessary when thinking about the possible ways of coping with the unavoidable complexity of simulation models with respect to our limited human minds.
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Probabilistic phenomena are often perceived as being problematic targets for contrastive explanation. It is usually thought that the possibility of contrastive explanation hinges on whether or not the probabilistic behaviour is... more
Probabilistic phenomena are often perceived as being problematic targets for contrastive explanation. It is usually thought that the possibility of contrastive explanation hinges on whether or not the probabilistic behaviour is irreducibly indeterministic, and that the possible remaining contrastive explananda are token event probabilities or complete probability distributions over such token outcomes. This paper uses the invariance-under-interventions account of contrastive explanation to argue against both ideas. First, the problem of contrastive explanation also arises in cases in which the probabilistic behaviour of the explanandum is due to unobserved causal heterogeneity. Second, it turns out that, in contrast to the case of pure indeterminism, the plausible contrastive explananda under causal heterogeneity are not token event probabilities, but population-level statistical facts.
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Mechanisms are often characterized as causal structures and the interventionist account of causation then used to characterize what it is to be a causal structure. The associated modularity constraint on causal structures has evoked... more
Mechanisms are often characterized as causal structures and the interventionist account of causation then used to characterize what it is to be a causal structure. The associated modularity constraint on causal structures has evoked criticism against using the theory as an account of mechanisms, since many mechanisms seem to violate modularity. This paper answers to this criticism by making a distinction between a causal system and a causal structure. It makes sense to ask what the modularity properties of a given causal structure are, but not whether a causal system is modular tout court. The counter-examples to the interventionist account are systems in which a particular structure is modular in variables, but not in parameters. A failure of parameter-modularity does not by itself threaten the interventionist interpretation of the structure and the possibility of causally explaining with that structure, but it does mean that knowledge of the structure is not sufficient to constitutively explain system-level properties of the embedding system.
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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... more
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.
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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,... more
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
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Robert Sugden argues that robustness analysis cannot play an epistemic role in grounding model-world relationships because the procedure is only a matter of comparing models with each other. We posit that this argument is based on a view... more
Robert Sugden argues that robustness analysis cannot play an epistemic role in grounding model-world relationships because the procedure is only a matter of comparing models with each other. We posit that this argument is based on a view of models as being surrogate systems in too literal a sense. In contrast, the epistemic importance of robustness analysis is easy to