Jaakko Kuorikoski

Whether simulation models provide the right kind of understanding comparable to that of analytic models has been and remains a contentious issue. The assessment of understanding provided by simulations is often hampered by a conflation between the sense of understanding and understanding proper. This paper presents a deflationist conception of understanding and argues for the need to replace appeals to the sense of understanding with explicit criteria of explanatory relevance and for rethinking the proper way of conceptualizing the role of a single human mind in the collective scientific understanding.

ABSTRACTWe investigate the applicability of Rodrik’s accounts of model selection and horizontal progress to macroeconomic DSGE modelling in both academic and policy-oriented modelling contexts. We argue that the key step of identifying critical assumptions is complicated by the interconnectedness of the common structural core of DSGE models and by the ad hoc modifications introduced to model various rigidities and other market imperfections. We then outline alternative ways in which macroeconomic modelling could become more horizontally progressive.

Although there has been much recent discussion on mechanisms in philosophy of science and social theory, no shared understanding of the crucial concept itself has emerged. In this paper, a distinction between two core concepts of mechanism is made on the basis that the concepts correspond to two different research strategies: the concept of mechanism as a componential causal system is associated with the heuristic of functional decomposition and spatial localization and the concept of mechanism as an abstract form of interaction is associated with the strategy of abstraction and simple models. The causal facts assumed and the theoretical consequences entailed by an explanation with a given mechanism differ according to which concept of mechanism is in use. Research strategies associated with mechanism concepts also involve characteristic biases that should be taken into account when using them, especially in new areas of application

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.

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.

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.

Political science and economic science . . . make use of the same language, the same mode of abstraction, the same instruments of thought and the same method of reasoning. (Black 1998, 354) Proponents as well as opponents of economics imperialism agree that imperialism is a matter of unification; providing a unified framework for social scientific analysis. Uskali Mäki distinguishes between derivational and ontological unification and argues that the latter should serve as a constraint for the former. We explore whether, in the case of rational-choice political science, self-interested behavior can be seen as a common causal element and solution concepts as the common derivational element, and whether the former constraints the use of the latter. We find that this is not the case. Instead, what is common to economics and rational-choice political science is a set of research heuristics and a focus on institutions with similar structures and forms of organization

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

The recognition that models and simulations play a central role in the epistemology of science is about fifteen years old. Although models had long been discussed as possible foundational units in the logical analysis of scientific knowledge, the philosophical study of modelling as a distinct epistemic practice really got going in the wake of the Models as Mediators anthology edited by Margaret Morrison and Mary Morgan. In spite of the broad agreement that in fact much of science is model-based, however, there is still little agreement on pretty much anything else. What are models? Are they representations or fictions, abstract entities or concrete artifacts? Which functions do they play? Can they explain..

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