Caterina Marchionni

Economic Modelling as Robustness Analysis

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. 1. Introduction2. Making Sense of Robustness3. Robustness in Economics4. The Epistemic Import of Robustness Analysis5. An Illustration: Geographical Economics Models6. Independence of Derivations7. Concluding Remarks Introduction Making Sense of Robustness Robustness in Economics The Epistemic Import of Robustness Analysis An Illustration: Geographical Economics Models Independence of Derivations Concluding Remarks

Explanatory Pluralism and Complementarity From Autonomy to Integration

Philosophers of the social sciences are increasingly convinced that macro-and micro-explanations are complementary. Whereas macro-explanations are broad, micro-explanations are deep. I distinguish between weak and strong complementarity: Strongly complementary explanations improve one another when integrated, weakly complementary explanations do not. To demonstrate the explanatory autonomy of different levels of explanation, explanatory pluralists mostly presuppose the weak form of complementarity. By scrutinizing the notions of explanatory depth and breadth, I argue that macro- and micro-accounts of the same phenomenon are more often strongly complementary. This invites a revision of the pluralist position in which integration promotes explanatory progress.

Evidential Diversity and the Triangulation of Phenomena

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.

Generative Explanation and Individualism in Agent-Based Simulation

Social scientists associate agent-based simulation (ABS) models with three ideas about explanation: they provide generative explanations, they are models of mechanisms, and they implement methodological individualism. In light of a philosophical account of explanation, we show that these ideas are not necessarily related and offer an account of the explanatory import of ABS models. We also argue that their bottom-up research strategy should be distinguished from methodological individualism.

Introduction: methodologies of bounded rationality

The modelling of bounded rationality is currently pursued by approaches that exhibit a wide diversity of methodologies. This special issue collects five contributions that discuss different methodological aspects of these approaches. In our introduction, we map the variety of methodological positions with respect to three questions. First, what kinds of evidence do the respective approaches consider relevant for modelling bounded rationality? Second, what kind of modelling desiderata do the respective approaches focus on? And third, how do the respective approaches justify the normative validity of bounded rationality? To broaden the picture, we not only discusss the five contributions of this issue, but also include relevant positions from the extant literature.

Model-based Explanation in the Social Sciences: Modeling Kinship Terminologies and Romantic Networks

I compare Reads model of kinship terminologies to a sociological model of a romantic and sexual network. This comparison leads to an account of how models function to construe explanations that complement Reads own account.

Neuroeconomics: hype or hope?

1. Introduction Caterina Marchionni and Jack Vromen 2. When economics meet neuroscience: Hype and hope Uskali Maki 3. The disunity of neuroeconomics: a methodological approach Roberto Fumagalli 4. Inductive modeling using causal studies in neuroeconomics: brains on drugs Moana Vercoe and Paul J. Zak 5. The philosopher in the scanner (or: How can neuroscience contribute to social ontology?) Francesco Guala and Tim Hodgson 6. Why neuroeconomics is relevant for economics, despite different questions, abstractions and all that Emarh Aydinonat 7. Where economics and neuroscience might meet Jack Vromen 8. The methodologies of neuroeconomics Glenn Harrison and Don Ross 9. Neuroeconomics: a radical replacement of economics? Michiru Nagatsu 10. Do neurobiological data help us to understand economic decisions better? Alessandro Antonietti 11. Explanatory relevance across disciplinary boundaries - the case of neuroeconomics Jaakko Kuorikoski and Petri Ylikoski

How to make progress in theories of spatial clustering: a case study of Malmberg and Maskell’s emerging theory

Many social science disciplines suffer from a tradition of tolerating vaguely formulated theoretical claims. The authors report a case study of the explanatory claims made in an emerging knowledge-based theory of clusters proposed by the economic geographers Malmberg and Maskell. In doing so they reinterpret and reconstruct Malmberg and Maskellss theory by applying what is called the ‘contrastive approach’ to explanation from contemporary philosophy of science literature. This approach is proposed as a means of enhancing explanatory clarity and thereby of fostering explanatory progress. The contrastive approach is useful in specifying the exact explanation-seeking questions, and answers to them. Specifying the explanatory claims of a theory also makes it easier to identify questions that are not posed and hence remain unanswered; those constitute a challenge for further theorizing. The case study supports the argument that the precise formulation of explanatory questions promotes explanatory progress.

Modeling model selection in model pluralism

ABSTRACT In his recent book, Rodrik [(2015). Economics rules. Why economics works, when it fails, and how to tell the difference. Oxford University Press] proposes an account of model pluralism according to which multiple models of the same target are acceptable as long as one model is more useful for one purpose and another is more useful for another purpose. How, then, is the right model for the purpose selected? Rodrik roughly outlines a selection procedure, which we formalize to enhance understanding of his account of model pluralism and to advance the critical discussion.

What is mechanistic evidence, and why do we need it for evidence-based policy?

It has recently been argued that successful evidence-based policy should rely on two kinds of evidence: statistical and mechanistic. The former is held to be evidence that a policy brings about the desired outcome, and the latter concerns how it does so. Although agreeing with the spirit of this proposal, we argue that the underlying conception of mechanistic evidence as evidence that is different in kind from correlational, difference-making or statistical evidence, does not correctly capture the role that information about mechanisms should play in evidence-based policy. We offer an alternative account of mechanistic evidence as information concerning the causal pathway connecting the policy intervention to its outcome. Not only can this be analyzed as evidence of difference-making, it is also to be found at any level and is obtainable by a broad range of methods, both experimental and observational. Using behavioral policy as an illustration, we draw the implications of this revised understanding of mechanistic evidence for debates concerning policy extrapolation, evidence hierarchies, and evidence integration.