À la fin des années 1990, plusieurs tendances convergentes en économie, psychologie et neuroscience ont préparé le terrain pour la naissance d’un nouveau champ scientifique qualifié de « neuroéconomie ». Comme pour toute discipline émergente – pensons par exemple à l’économie mathématique, l’économétrie ou l’économie expérimentale en d’autres temps – la neuroéconomie est plutôt controversée en économie. Elle soulève un grand nombre de questions d’ordre empirique, méthodologique et philosophiques donnant lieu à des débats et controverses que l’article identifie et discute (...) à travers trois questions majeures relatives aux connaissances produites : Sont-elles fiables? Sont-elles scientifiquement légitimes? Sont-elles utiles pour les économistes? Sans prétendre à l’exhaustivité, ce cadre analytique permet de comprendre l’essence des principales critiques adressées à la neuroéconomie. Dans le même temps, l’article éclaire la nature des réponses susceptibles d’être apportées à ces critiques au vu des progrès conceptuels et empiriques qu’a connus ce champ de recherche depuis son émergence et à la lumière du tournant apparu dans l’agenda de la neuroéconomie au cours de la dernière décennie. JEL Codes : A12, B41, C51, C81, C9, D03, D87. (shrink)

Reverse Inference ( RI ) is an imaging-based type of inference from brain states to mental states, which has become highly widespread in neuroscience, most especially in neuroeconomics. Recent critical studies of RI may be taken to show that, if cautiously used, RI can help achieve research goals that may be difficult to achieve by way of behavior-based procedures alone. But can RI exceed the limits of these procedures and achieve research goals that are impossible for them to achieve alone? (...) By way of answering this question we show that a conception of the mind—type identity—under which the answer is in the positive, is untenable for reasons that strongly support another conception of the mind—functionalism—under which the answer must be in the negative. On this basis we then conclude that RI cannot exceed the limits of behavior-based procedures in cognitive psychology. (shrink)

Nobody in this debate questions the point that neuroeconomics remains full of potential, and little else as yet. If so, that really is progress of sorts. I was getting afraid that we would have to open nominations for the Captain Ahab Award for obsessive work on the promotion of neuroeconomics.

Nobody in this debate questions the point that neuroeconomics remains full of potential, and little else as yet. If so, that really is progress of sorts. I was getting afraid that we would have to open nominations for the Captain Ahab Award for obsessive work on the promotion of neuroeconomics.

Neuroeconomics is a science pledged to tracing the neurobiological correlates involved in decision-making, especially in the case of economic decisions. Despite representing a recent research field that is still identifying its research objects, tools and methods, its epistemological scope and scientific relevance have already been openly questioned by several authors. Among these critics, the most influential names in the debate have been those of Faruk Gul and Wolfgang Pesendorfer, who claim that the data on neural activity cannot find place in (...) economic models, which should on the contrary be solely based on the data produced by choices. This paper aims at countering the gloomy and unsubstantiated claims of these two authors and those who believe that neuroscience cannot provide new and useful insights to the established knowledge of standard economics. The main point stressed here is that this perception is the product of a general misunderstanding of the advances made by neuroscience, which are incidentally of crucial importance. (shrink)

In traditional decision theory, utility is regarded as a mathematical representation of preferences to be inferred from agents hedonic experiences. Some go as far as to contend that utility is literally computed by specific neural areas and urge economists to complement or substitute their notion of utility with some neuro-psychological construct. In this paper, I distinguish three notions of utility that are frequently mentioned in debates about decision theory and examine some critical issues regarding their definition and measurability. Moreover, I (...) provide various empirical and conceptual reasons to doubt that economists should base decision theoretic analyses on some neuro-psychological notion of utility. (shrink)

Proponents of neuroeconomics often argue that better knowledge of the human neural architecture enables economists to improve standard models of choice. In their view, these improvements provide compelling reasons to use neural findings in constructing and evaluating economic models. In a recent article, I criticized this view by pointing to the trade-offs between the modeling desiderata valued by neuroeconomists and other economists, respectively. The present article complements my earlier critique by focusing on three modeling desiderata that figure prominently in economic (...) and neuroeconomic modeling. For each desideratum, I examine findings that neuroeconomists deem to be especially relevant for economists and argue that neuroeconomists have failed to substantiate their calls to use these findings in constructing and evaluating economic models. In doing so, I identify methodological and evidential constraints that will continue to hinder neuroeconomists’ attempts to improve such models. Moreover, I draw on the literature on scientific modeling to advance the ongoing philosophical discussion regarding the prospects of interdisciplinary models of choice. (shrink)

In the recent literature at the interface between economics, biology and neuroscience, several authors argue that by adopting an interdisciplinary approach to the analysis of decision making, economists will be able to construct predictively and explanatorily superior models. However, most economists remain quite reluctant to import biological or neural insights into their account of choice behaviour. In this paper, I reconstruct and critique one of the main arguments by means of which economists attempt to vindicate their conservative position. Furthermore, I (...) develop an alternative defense of the thesis that economists justifiably rely on a methodologically distinctive approach to the modelling of choice behaviour. (shrink)

One of the most impressive changes in economics and decision sciences is the emergence and fast growth of so-called “behavioral” economics and neuroeconomics. These fields raise several methodological issues, some of them being currently intensively discussed. Amongst those issues, the following is prominent : what is the epistemic relevance of non-behavioral or “cognitive” data, i.e. data which bear on cognitive processes and states involved in decision making ? F. Gul and W. Pesendorfer (2005/2008) have vigorously criticized the idea that these (...) data could be relevant for economics and decision sciences. Their criticisms became the focal point of a very active methodological literature. In this paper, we reconstruct and discuss Gul and Pesendorfer’s views and arguments. Although we are not convinced by some of them, we believe the authors hint a genuine issue : the “missing links” issue. (shrink)

There continues to be significant confusion about the goals, scope, and nature of modelling practice in neuroeconomics. This article aims to dispel some such confusion by using one of the most recent critiques of neuroeconomic modelling as a foil. The article argues for two claims. First, currently, for at least some economic model of choice behaviour, the benefits derivable from neurally informing an economic model do not involve special tractability costs. Second, modelling in neuroeconomics is best understood within Marr’s three-level (...) of analysis framework and in light of a co-evolutionary research ideology. The first claim is established by elucidating the relationship between the tractability of a model, its descriptive accuracy, and its number of variables. The second claim relies on an explanation of what it can take to neurally inform an economic model of choice behaviour. 1 Introduction2 Neurally Informed Models of Choice: A Case Study2.1 A case study on risk-sensitive choice2.1.1 Target and modelling framework2.1.2 Research question and hypotheses2.1.3 Competitive models of risk-sensitive behaviour2.1.4 Model-based fMRI: from economics to brains and back2.2 Neurally informed modelling3 Tractability: When Does Size Matter?4 Neural Integration and the Co-evolutionary Research Ideology5 Conclusion. (shrink)

To understand the human capacity for psychological altruism, one requires a proper understanding of how people actually think and feel. This paper addresses the possible relevance of recent findings in experimental economics and neuroeconomics to the philosophical controversy over altruism and egoism. After briefly sketching and contextualizing the controversy, we survey and discuss the results of various studies on behaviourally altruistic helping and punishing behaviour, which provide stimulating clues for the debate over psychological altruism. On closer analysis, these studies prove (...) less relevant than originally expected because the data obtained admit competing interpretations – such as people seeking fairness versus people seeking revenge. However, this mitigated conclusion does not preclude the possibility of more fruitful research in the area in the future. Throughout our analysis, we provide hints for the direction of future research on the question. (shrink)

This PhD thesis focuses on the philosophical foundations of Neuroeconomics, an innovative research program which combines findings and modelling tools from economics, psychology and neuroscience to account for human choice behaviour. The proponents of Neuroeconomics often manifest the ambition to foster radical modifications in the accounts of choice behaviour developed by its parent disciplines. This enquiry provides a philosophically informed appraisal of the potential for success and the relevance of neuroeconomic research for economics. My central claim is that neuroeconomists can (...) help other economists to build more predictive and explanatory models, yet are unlikely to foster revolutionary modifications in the economic theory of choice. The contents are organized as follows. In chapters 1-2, I present neuroeconomists’ investigative tools, distinguish the most influential approaches to neuroeconomic research and reconstruct the case in favour of a neural enrichment of economic theory. In chapters 3-7, I combine insights from neuro-psychology, economic methodology and philosophy of science to develop a systematic critique of Neuroeconomics. In particular, I articulate four lines of argument to demonstrate that economists are provisionally justified in retaining a methodologically distinctive approach to the modelling of decision making. My first argument points to several evidential and epistemological concerns which complicate the interpretation of neural data and cast doubt on the inferences neuroeconomists often make in their studies. My second argument aims to show that the trade-offs between the modelling desiderata that neuroeconomists and other economists respectively value severely constrain the incorporation of neural insights into economic models. My third argument questions neuroeconomists’ attempts to develop a unified theory of choice behaviour by identifying some central issues on which they hold contrasting positions. My fourth argument differentiates various senses of the term ‘revolution’ and illustrates that neuroeconomists are unlikely to provide revolutionary contributions to economic theory in any of these senses. (shrink)