Jaakko Kuorikoski

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

Unrealistic Assumptions in Rational Choice Theory

The most common argument against the use of rational choice models outside economics is that they make unrealistic assumptions about individual behavior. We argue that whether the falsity of assumptions matters in a given model depends on which factors are explanatorily relevant. Since the explanatory factors may vary from application to application, effective criticism of economic model building should be based on model-specific arguments showing how the result really depends on the false assumptions. However, some modeling results in imperialistic applications are relatively robust with respect to unrealistic assumptions.

Explanatory relevance across disciplinary boundaries: the case of neuroeconomics

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.

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.

Looping Kinds and Social Mechanisms

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.

Economics Imperialism and Solution Concepts in Political Science

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.

Understanding Nonmodular Functionality: Lessons from Genetic Algorithms

Evolution is often characterized as a tinkerer creating efficient but messy solutions. We analyze the nature of the problems that arise when trying to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem—solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show, first, that evolutionary designs are often hard to understand because they exhibit nonmodular functionality and, second, that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation.

How Organization Explains

Constitutive mechanistic explanations explain a property of a whole with 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.

Limitations of Behaviorally Informed Policy Under Social Interaction

Nudge and boost are two competing approaches to applying the psychology of reasoning and decision making to improve policy. Proponents of both the approaches claim capacity to enhance social welfare through better individual decisions. We question the validity of this claim. First, individual rationality is neither sufficient nor necessary for improving collective outcomes. Second, collective outcomes of complex 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 norms can sometimes lead to better collective outcomes than nudge and boost. More generally, we present conditions under which the three approaches enhance social welfare. Furthermore, we argue that to reliably improve collective outcomes that depend on the aggregation of many decisions, it is necessary to understand the interface between the psychology of reasoning and decision making on the one hand and economics and policy on the other.