This paper examines two recent approaches to the nature and functioning of economic models: models as isolating representations and models as credible constructions. The isolationist view conceives of economic models as surrogate systems that isolate some of the causal mechanisms or tendencies of their respective target systems, while the constructionist approach treats them rather like pure constructions or fictional entities that nevertheless license different kinds of inferences. I will argue that whereas the isolationist view is still tied to the representationalist (...) understanding of models that takes the model-target dyad as the basic unit of analysis, the constructionist perspective can better accommodate the way we actually acquire knowledge through them. Using the example of Tobin’s ultra-Keynesian model I will show how many of the epistemic characteristics of modelling tend to go unrecognised if too much focus is placed on the model-target dyad. (shrink)

In recent years, the emergence of a new trend in contemporary philosophy has been observed in the increasing usage of empirical research methods to conduct philosophical inquiries. Although philosophers primarily use secondary data from other disciplines or apply quantitative methods (experiments, surveys, etc.), the rise of qualitative methods (e.g., in-depth interviews, participant observations and qualitative text analysis) can also be observed. In this paper, I focus on how qualitative research methods can be applied within philosophy of science, namely within the (...) philosophical debate on modeling. Specifically, I review my empirical investigations into the issues of model de-idealization, model justification and performativity. (shrink)

The paper reviews the philosophical literature on the epistemology of modelling in contemporary economics. In particular, it focuses on open questions concerning the epistemic role of models, the validity of inferences from the models to the world, and the legitimacy of their use for purposes of explanation, prediction and intervention.

An otherwise lawlike generalisation hedged by a ceteris paribus (CP) clause qualifies as a law of nature, if the CP clause can be substituted with a set of conditions derived from the multivariate regression model used to interpret the empirical data in support of the gen- eralisation. Three studies in human biology that use regression analysis are surveyed, showing that standard objections to cashing out CP clauses in this way—based on alleged vagueness, vacuity, or lack of testability—do not apply. CP (...) laws also cannot be said to be simply false due to the indefinitely many conditions not explicitly stated in their associated model: scientific CP clauses imply that these are, given the evidence, not nomically relevant. (shrink)

Building upon work by Mary Hesse (1974), this paper aims to show that a single method of investigation lies behind Maxwell’s use of physical analogies in his major scientific works before the Treatise on Electricity and Magnetism. Key to understanding the operation of this method is to recognize that Maxwell’s physical analogies are intended to possess an ‘inductive’ function in addition to an ‘illustrative’ one. That is to say, they not only serve to clarify the equations proposed for an unfamiliar (...) domain with a working interpretation drawn from a more familiar science, but can also be sources of defeasible yet relatively strong arguments from features of the more familiar domain to features of the less. Compared with the reconstructions by Achinstein (1991), Siegel (1991), Harman (1998) and others, which postulate a discontinuity in Maxwell’s approach to physical analogy, the account defended in this paper i) makes sense of the continuity in Maxwell’s remarks on scientific methodology, ii) explains his quest for a “mathematical classification of physical quantities” and iii) offers a new and more plausible interpretation of the debated episode of the introduction of the displacement current in Maxwell’s “On Physical Lines of Forces”. (shrink)

In book II of Plato’s Republic, Socrates discusses the cities of necessity and luxury (372d-373a). Discussions of these cities have often focused on citizens desiring more than they need, which creates a demand for luxury. Yet the second part of the equation, which is not usually recognized, is that there must be sufficient supply to meet this demand. The focus of this article is on the importance of supply in the discussion of the first two cities in book II of (...) the Republic. This article argues that the way Plato models the cities makes it the case that a surplus above levels of necessity will be generated from time to time. That the unwanted surplus cannot be spontaneously disposed of entails that the first two cities are institutionally incomplete. A government is needed in order to coordinate the disposal of the surplus supply the city will produce. (shrink)

We argue that the appraisal of models in social epistemology requires conceiving of them as argumentative devices, taking into account the argumentative context and adopting a family-of-models perspective. We draw up such an account and show how it makes it easier to see the value and limits of the use of models in social epistemology. To illustrate our points, we document and explicate the argumentative role of epistemic landscape models in social epistemology and highlight their limitations. We also claim that (...) our account could be fruitfully used in appraising other models in philosophy and science. (shrink)

Unlike any other field, the science of morality has drawn attention from an extraordinarily diverse set of disciplines. An interdisciplinary research program has formed in which economists, biologists, neuroscientists, psychologists, and even philosophers have been eager to provide answers to puzzling questions raised by the existence of human morality. Models and simulations, for a variety of reasons, have played various important roles in this endeavor. Their use, however, has sometimes been deemed as useless, trivial and inadequate. The role of models (...) in the science of morality has been vastly underappreciated. This omission shall be remedied here, offering a much more positive picture on the contributions modelers made to our understanding of morality. (shrink)

This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: any successful analysis of models must target sets of (...) models, their multiplicity of functions within science, and their scientific context and history and for almost any aspect x of phenomenon y, scientists require multiple models to achieve scientific goal z. (shrink)

There is a striking contrast between the significance of Harold Hotelling’s contribution to industrial economics and the fact that his location model was invalid, unrealistic and non-robust. It is difficult to make sense of the explanatory value of Hotelling’s model based on philosophical accounts that emphasize logical validity, representational adequacy, and robustness as determinants of explanatory value. However, these accounts are misleading because they overlook the context within which the explanatory value added of a model is apprehensible. We present Hotelling’s (...) model in its historical context and show why it is an important and explanatory model despite its apparent deficiencies. (shrink)

The question of whether the idealized models of theoretical economics are explanatory has been the subject of intense philosophical debate. It is sometimes presupposed that either a model provides the actual explanation or it does not provide an explanation at all. Yet, two sets of issues are relevant to the evaluation of model-based explanation: what conditions should a model satisfy in order to count as explanatory and does the model satisfy those conditions. My aim in this paper is to unpack (...) this distinction and show that separating the first set of issues from the second is crucial to an accurate diagnosis of the distinctive challenges that economic models pose. Along the way I sketch a view of model-based explanation in economics that focuses on the role that non-empirical and empirical strategies play in increasing confidence in the adequacy of a given model-based explanation. (shrink)

In this article we defend the inferential view of scientific models and idealisation. Models are seen as “inferential prostheses” (instruments for surrogative reasoning) construed by means of an idealisation-concretisation process, which we essentially understand as a kind of counterfactual deformation procedure (also analysed in inferential terms). The value of scientific representation is understood in terms not only of the success of the inferential outcomes arrived at with its help, but also of the heuristic power of representation and their capacity to (...) correct and improve our models. This provides us with an argument against Sugden’s account of credible models: the likelihood or realisticness (their “credibility”) is not always a good measure of their acceptability. As opposed to “credibility” we propose the notion of “enlightening”, which is the capacity of giving us understanding in the sense of an inferential ability. (shrink)

In this paper we investigate with a case study from chemistry under what conditions a simulation can serve as a surrogate for an experiment. The case-study concerns a simulation of H2-formation in outer space. We find that in this case the simulation can act as a surrogate for an experiment, because there exists comprehensive theoretical background knowledge in form of quantum mechanics about the range of phenomena to which the investigated process belongs and because any particular modelling assumptions as can (...) be justified. If these requirements are met then direct empirical validation may even be dispensable. We conjecture that this is not the case in the absence of comprehensive theoretical background knowledge. (shrink)

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. (shrink)

Philosophers of science have developed several accounts of how consideration of scientific models can prompt learning about real-world targets. In recent years, various authors advocated the thesis that consideration of so-called minimal models can prompt learning about such targets. In this paper, I draw on the philosophical literature on scientific modelling and on widely cited illustrations from economics and biology to argue that this thesis fails to withstand scrutiny. More specifically, I criticize leading proponents of such thesis for failing to (...) explicate in virtue of what properties or features minimal models supposedly prompt learning and for substantially overstating the epistemic import of minimal models. I then examine and refute several arguments one may put forward to demonstrate that consideration of minimal models can prompt learning about real-world targets. In doing so, I illustrate the implications of my critique for the wider debate on the epistemology of scientific modelling. (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)

Mathematical models are a well established tool in most natural sciences. Although models have been neglected by the philosophy of science for a long time, their epistemological status as a link between theory and reality is now fairly well understood. However, regarding the epistemological status of mathematical models in the social sciences, there still exists a considerable unclarity. In my paper I argue that this results from specific challenges that mathematical models and especially computer simulations face in the social sciences. (...) The most important difference between the social sciences and the natural sciences with respect to modeling is that in the social sciences powerful and well confirmed background theories (like Newtonian mechanics, quantum mechanics or the theory of relativity in physics) do not exist in the social sciences. Therefore, an epistemology of models that is formed on the role model of physics may not be appropriate for the social sciences. I discuss the challenges that modeling faces in the social sciences and point out their epistemological consequences. The most important consequences are that greater emphasis must be placed on empirical validation than on theoretical validation and that the relevance of purely theoretical simulations is strongly limited. (shrink)

This essay examines the issue of false assumptions in models via a case study of a prominent economic model of sustainable development, wherein the assumption of an infinite future plays a central role. Two proposals are found to be helpful for this case, one based on the concept of derivational robustness and the other on understanding. Both suggest that the assumption of an infinite future, while arguably legitimate in some applications of the model, is problematic with respect to what I (...) call “Parfitian” welfare functions. This result is relevant to debates about discounting the future in economics and environmental ethics. (shrink)

It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world, do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing and analysing the model affects one’s confidence in hypotheses about the world. Economic models, I argue, are often assessed for their credibility. If a model is judged credible, it is considered to be a relevant possibility. Considering (...) such relevant possibilities may affect one’s confidence in necessity or impossibility hypotheses. Thus, one can learn from minimal economic models. (shrink)

This paper evaluates Nancy Cartwright’s critique of economic models. Cartwright argues that economics fails to build relevant “nomological machines” able to isolate capacities. In this paper, I contend that many economic models are not used as nomological machines. I give some evidence for this claim and build on an inferential and pragmatic approach to economic modeling. Modeling in economics responds to peculiar inferential norms where a “good” model is essentially a model that enhances our knowledge about possible worlds. As a (...) consequence, models and experiments are very different knowledge-producing devices, at least in economics. (shrink)

This paper asks how, in science in general and in economics in particular, theoretical models aid the understanding of real-world phenomena. Using specific models in economics and biology as test cases, it considers three alternative answers: that models are tools for isolating the ‘capacities’ of causal factors in the real world; that modelling is ‘conceptual exploration’ which ultimately contributes to the development of genuinely explanatory theories; and that models are credible counterfactual worlds from which inductive inferences can be made. The (...) paper argues that the ‘credible worlds’ account captures significant aspects of scientific practice, even if many modellers see their work as conceptual exploration. (shrink)

This paper discusses the epistemic import of highly abstract and simplified theoretical models using Thomas Schelling’s checkerboard model as an example. We argue that the epistemic contribution of theoretical models can be better understood in the context of a cluster of models relevant to the explanatory task at hand. The central claim of the paper is that theoretical models make better sense in the context of a menu of possible explanations. In order to justify this claim, we introduce a distinction (...) between causal scenarios and causal mechanism schemes. These conceptual tools help us to articulate the basis for modelers’ intuitive confidence that their models make an important epistemic contribution. By focusing on the role of the menu of possible explanations in the evaluation of explanatory hypotheses, it is possible to understand how a causal mechanism scheme can improve our explanatory understanding even in cases where it does not describe the actual cause of a particular phenomenon. (shrink)

In this note, I comment on Julian Reiss's paper ?The explanation paradox?. I argue in support of two of the propositions that make up that paradox (that economic models are false, and that they are explanatory) but challenge the third proposition, that only true accounts can explain. I defend the ?credible worlds? account of models as fictions that are explanatory by virtue of similarity relations with real-world phenomena. I argue that Reiss's objections to the role of subjective similarity judgements in (...) explanation illegitimately presuppose the existence of rational criteria by which science can certify the objective validity of its explanation. (shrink)

This paper defends the thesis of learning from non-causal models: viz. that the study of some model can prompt justified changes in one’s confidence in empirical hypotheses about a real-world target in the absence of any known or predicted similarity between model and target with regards to their causal features. Recognizing that we can learn from non-causal models matters not only to our understanding of past scientific achievements, but also to contemporary debates in the philosophy of science. At one end (...) of the philosophical spectrum, my thesis undermines the views of those who, like Cartwright (Erkenntnis 70:45–58, 2009), follow Hesse (Models and Analogies in Science, Notre Dame, University of Notre Dame Press, 1963) in restricting the possibility of learning from models to only those situations where a model identifies some causal factors present in the target. At the other end of the spectrum, my thesis also helps undermine some extremely permissive positions, e.g., Grüne-Yanoff’s (Erkenntnis 70(1):81–99, 2009, Philos Sci 80(5): 850–861, 2013) claim that learning from a model is possible even in the absence of any similarity at all between model and target. The thesis that we can learn from non-causal models offers a cautious middle ground between these two extremes. (shrink)

How is it possible that models from game theory, which are typically highly idealised, can be harnessed for designing institutions through which we interact? I argue that game theory assumes that social interactions have a specific structure, which is uncovered with the help of directed graphs. The graphs make explicit how game theory encodes counterfactual information in natural collections of its models and can therefore be used to track how model-interventions change model-outcomes. For model-interventions to inform real-world design requires the (...) truth of a causal hypothesis, namely that structural relations specified in a model approximate causal relations in the target interaction; or in other words, that the directed graph can be interpreted causally. In order to increase their confidence in this hypothesis, market designers complement their models with natural and laboratory experiments, and computational methods. Throughout the paper, the reform of a matching market for medical residents provides a case study for my proposed view, which hasn't been previously considered in the philosophy of science. (shrink)

What does it mean to say that a theory represents the targeted phenomenon that it aims to explain? Our interpretation of “representation” is closely related to the methodological position that we would adopt in answering the question of realism in science. As is pointed out by Nancy Cartwright, according to the traditional syntactic approach of explaining scientific theorization, the question of realism is about how accurately the sciences can represent the world; in the semantic approach, however, the focus of the (...) question shifts to a concern about the range of science—i.e., how much of the world the sciences can represent. This shift in the methodological concern is by no means trivial; it indicates that there is a change of content in the concept of representation from a static idea to a dynamic one. The static idea of representation concerns how reliably the formal structure of a class of sentences—i.e., the formal structure of a theory—can stand for the targeted phenomenon. The dynamic idea, however, perceives a theory as a class of models and explores the development of these models; that is, the dynamic idea of representation investigates how a theorizer uses these models to stand for reality. As a consequence of this shift from a static to a dynamic mode of thinking, it seems that model-building constitutes the main content of the concept of representation. By comparing two differing contemporary accounts of the nature of economic models and presenting a case study in economic theorizing, this paper argues that representation is a process of economists’ repeatedly using “realistic representation of the isolated unrealistic world” at each step of their theorizing to build up a class of “unrealistic constructed credible worlds.”. (shrink)

Odenbaugh and Alexandrova provide a challenging critique of the epistemic benefits of robustness analysis, singling out for particular criticism the account we articulated in Kuorikoski et al.. Odenbaugh and Alexandrova offer two arguments against the confirmatory value of robustness analysis: robust theorems cannot specify causal mechanisms and models are rarely independent in the way required by robustness analysis. We address Odenbaugh and Alexandrova’s criticisms in order to clarify some of our original arguments and to shed further light on the properties (...) of robustness analysis and its epistemic rationale. (shrink)

In this thesis I present a rights-based theory of exploitation. I argue that successful conceptions of exploitation should begin with the ordinary language claim that exploitation involves `taking unfair advantage'. Consequently, they must combine an account of what it means to take advantage of another with an account of when transactions are unfair. Existing conceptions of exploitation fail to provide adequate accounts of both aspects of exploitation. -/- Hillel Steiner and John Roemer provide convincing accounts of the unfairness involved in (...) exploitation, but because they fail to provide restrictive `advantage taking' conditions, their theories of exploitation include cases that we would not ordinarily describe as exploitations. Ruth Sample and Robert Goodin provide accounts that place a stronger emphasis on the attitudes involved in exploitation and the processes that bring it about. Unfortunately, these theories do not properly incorporate the unfairness aspect of exploitation. Consequently, they are either self-frustrating or incomplete. -/- I provide a conception of exploitation that combines both aspects. I claim that a bilateral, strictly Pareto improving, and voluntary transaction t between A and B is exploitative just in case (1) there exists an unrectified property rights violation at some point in the history of exchanges leading to t that (2) reduces B's bargaining power by altering her status quo point, and (3) the degree to which B's preferences are satisfied in t is less than it would be had B's bargaining power not been reduced (by the alteration of her status quo point). Further, (4a) A's egoistic preferences are better satisfied in t because B's bargaining power is limited, and (4b) if the previous conditions also hold for A, then the reduction in A's bargaining power is less than the reduction in B's bargaining power. Finally, (5) A truly believes the previous conditions hold. (shrink)

Se critican dos tesis de Nancy Cartwright. Por un lado, se examina el enfoque de las “capacidades” en el campo de lo económico para argumentar que estas responden a la lógica de los “árboles de posibilidades” o resultados de final abierto; por el otro, se ofrece una alternativa al problema de “sobre-restricción” de los modelos económicos para mostrar cómo el gran número de supuestos auxiliares acota las posibilidades de extrapolar las conclusiones obtenidas en el modelo a condiciones distintas de las (...) explicitadas en él. Se muestra la utilidad del análisis de robustez para descubrir las condiciones esenciales que cierran el árbol de posibilidades. (shrink)

N. Emrah Aydinonat offre une caractérisation des explications par la main invisible. Celles-ci seraient des explications partielles potentielles et seraient en mesure de nous indiquer des capacités à l’œuvre dans le monde et élargiraient notre horizon intellectuel en conceptualisant des possibilités jusqu’alors inédites. Je montre que Nancy Cartwright offre un argument permettant de douter de cette première possibilité. La science économique n’ayant que peu de principes sûrs à sa disposition et reposant ainsi sur des hypothèses auxiliaires, il est impossible d’effectuer (...) des expériences galiléennes avec les modèles et de mettre au jour les capacités. La conception d’Uskali Mäki des modèles comme isolations et systèmes de représentation laisse penser que la deuxième possibilité est néanmoins fondée. (shrink)

This article examines the issue of whether consideration of so-called minimal models can prompt learning about real-world targets. Using a widely cited example as a test case, it argues against the increasingly popular view that consideration of minimal models can prompt learning about such targets. The article criticizes influential defenses of this view for failing to explicate by virtue of what properties or features minimal models supposedly prompt learning. It then argues that consideration of minimal models cannot prompt learning about (...) real-world targets unless one supplements these models with additional information or presuppositions concerning such targets. (shrink)

This is my personal homepage. Find my philosophical papers under "Philosophy".

Many scientific models lack an established representation relation to actual targets and instead refer to merely possible processes, background conditions, and results. This article shows how such models can be appraised. On the basis of the discussion of how-possibly explanations, five types of learning opportunities are distinguished. For each of these types, an example—from economics, biology, psychology, and sociology—is discussed. Contexts and purposes are identified in which the use of a model offers a genuine opportunity to learn. These learning opportunities (...) offer novel justifications for modeling practices that fall between the cracks of standard representationalist appraisals of models. (shrink)

Some authors claim that minimal models have limited epistemic value (Fumagalli, 2016; Grüne-Yanoff, 2009a). Others defend the epistemic benefits of modelling by invoking the role of robustness analysis for hypothesis confirmation (see, e.g., Levins, 1966; Kuorikoski et al., 2010) but such arguments find much resistance (see, e.g., Odenbaugh & Alexandrova, 2011). In this paper, we offer a Bayesian rationalization and defence of the view that robustness analysis can play a confirmatory role, and thereby shed light on the potential of minimal (...) models for hypothesis confirmation. We illustrate our argument by reference to a case study from macroeconomics. At the same time, we also show that there are cases in which robustness analysis is detrimental to confirmation. We characterize these cases and link them to recent investigations on evidential variety (Landes, 2020b, 2021; Osimani and Landes, forthcoming). We conclude that robustness analysis over minimal models can confirm, but its confirmatory value depends on concrete circumstances. (shrink)

Highly idealized models may serve various epistemic functions, notably explanation, in virtue of representing the world. Inferentialism provides a prima facie compelling characterization of what constitutes the representation relation. In this paper, I argue that what I call factive inferentialism does not provide a satisfactory solution to the puzzle of model-based—factive—explanation. In particular, I show that making explanatory counterfactual inferences is not a sufficient guide for accurate representation, factivity, or realism. I conclude by calling for a more explicit specification of (...) model-world mismatches and properties imputation. (shrink)

This paper supports the literature which argues that derivational robustness can have epistemic import in highly idealized economic models. The defense is based on a particular example from mathematical economic theory, the dynamic Walrasian general equilibrium model. It is argued that derivational robustness first increased and later decreased the credibility of the Walrasian model. The example demonstrates that derivational robustness correctly describes the practices of a particular group of influential economic theorists and provides support for the arguments of philosophers who (...) have offered a general epistemic justification of such practices. (shrink)

Is there a unique way to de-idealize models? If not, how might the possible ways of reducing the distortion between models and reality differ from each other? Based on an empirical case study conducted in financial markets, this paper discusses how a popular valuation model (the Discounted Cash Flow model) idealizes reality and how the market participants de-idealize it in concrete market situations. In contrast to Cartwright's view that economic models are generally over-constrained, this paper suggests that valuation models are (...) under-constrained. This serves as the reason why the relaxation of simplifying assumptions and concretization do not work as methods of de-idealization. The paper finds that financial market participants de-idealize models using commentary that takes the form of judgment. As a conclusion, a hypothesis is formulated that proposes that the more underdetermined the model is the bigger role narrative and other pragmatic elements play in the process of model application. (shrink)

Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of robustness analysis has (...) been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation. (shrink)

The paper explores how, in economics and biology, theoretical models are used as explanatory devices. It focuses on a modelling strategy by which, instead of starting with an unexplained regularity in the world, the modeller begins by creating a credible model world. The model world exhibits a regularity, induced by a mechanism in that world. The modeller concludes that there may be a part of the real world in which a similar regularity occurs and that, were that the case, the (...) model would offer an explanation. Little concrete guidance is given about where such a regularity might be found. Three modelling exercises in evolutionary game theory—one from economics and two from biology—are used as case studies. Two of these (one from each discipline) exemplify ‘explanation in search of observation’. The third goes a step further, analysing a regularity in a model world and treating it as informative about the real world, but without saying anything about real phenomena. The paper argues that if the relation between the model and real worlds is understood in terms of similarity, and if modelling is understood as an ongoing discovery process rather than as the demonstration of empirical truths, there can be value in creating explanations before finding the regularities that are to be explained. (shrink)

Popper’s ‘Situational Analysis’ (SA) constitutes his methodological proposal for the social sciences. We claim that the two hallmarks of SA are: (i) that scientists assume they possess a ‘wider’ view of the problem-situation than actors do, and (ii) use the model as an ideal ‘benchmark’ scenario to identify thedeviationof actors’ actual behaviour from the former. We argue that SA is not a generalization of the neoclassical theory of individual behaviour but captures instead the methodology adopted by modern behavioural economists. Last, (...) we argue that SA highlights a way of acquiring knowledge that has gone unnoticed in the literature. (shrink)

:In this article, we begin by identifying three main neuroethical approaches: neurobioethics, empirical neuroethics, and conceptual neuroethics. Our focus is on conceptual approaches that generally emphasize the need to develop and use a methodological modus operandi for effectively linking scientific and philosophical interpretations. We explain and assess the value of conceptual neuroethics approaches and explain and defend one such approach that we propose as being particularly fruitful for addressing the various issues raised by neuroscience: fundamental neuroethics.

In a broad brush, biological models are often constructed in two general types: as a concrete model; as an abstract model. A concrete model is a material model such as model organisms, while an abstract model is a mathematical or computational model consists of equations or algorithms. Though there are types of biological models that cannot be strictly categorized as either concrete or abstract, they are falling somewhere in between this spectrum. In view of the fact that biological phenomena are (...) sui generis and distinct from physical phenomena in many respects, using the concepts in standard modeling fails to account for the differences between biological sciences and physical sciences. Drawing from the resources of a credible-world account of models in economic modeling, I propose to reinterpret abstract models and concrete models in the light of the credible world account of models. (shrink)

RESUMEN El conocimiento utilizado para armar e implementar políticas socioeconómicas refiere por lo general a aseveraciones causales que pueden ser conceptuadas de diferentes maneras; sin embargo, estas suelen omitir un tema central: las consecuencias alternativas o los desvíos que emergen en caso de fracasar, lo que puede acarrear consecuencias negativas. Se argumenta que, para una buena implementación, es fundamental tener en cuenta dichas consecuencias alternativas, lo cual implica un cambio sustancial en el modo de tomar decisiones, donde la asimetría de (...) resultados es relevante. ABSTRACT The knowledge used to structure and implement socioeconomic policies generally refers to causal assertions that can be conceptualized differently; however, they tend to disregard the central issue of alternative consequences or deviations that arise in case of failure, which can cause negative effects. The paper argues that a successful implementation entails taking these alternative consequences into account. This implies a substantial change in the way decisions are made, in which the asymmetry of results becomes relevant. (shrink)