Halfway through the 1930s, a new practice was born that was based on instruments called 'models'. This practice is characterized by building and applying empirical models, i.e. representations of (aspects of) the world. The aim of this chapter is to explore these kinds of representations.

In several accounts of what models are and how they function a specific view dominates. This view contains the following characteristics. First, there is a clear-cut distinction between theories, models and data and secondly, empirical assessment takes place after the model is built. This view in which discovery and justification are disconnected is not in accordance with several practices of mathematical business-cycle model building. What these practices show is that models have to meet implicit criteria of adequacy, such as satisfying (...) theoretical, mathematical and statistical requirements, and be useful for policy. In order to be adequate, models have to integrate enough items to satisfy such criteria. These items include besides theoretical notions, policy views, mathematisations of the cycle and metaphors also empirical data and facts. So, the main thesis of this chapter is that the context of discovery is the successful integration of those items that satisfy the criteria of adequacy. Because certain items are empirical data and facts, justification can be built-in. (shrink)

The assessment of models in an experiment depends on their material nature and their function in the experiment. Models that are used to make the phenomenon under investigation visible - sensors - are assessed by calibration. However, calibration strategies assume material intervention. The experiment discssed in this paper is an experiment in economics to measure the influence of technology shocks on business cycles. It uses immaterial, mathematical instruments. It appears that calibration did not work for these kinds of models, it (...) did not provide reliable evidence for the facts of the business cycle. (shrink)

In economics, models are built to answer specific questions. Each type of question requires its own type of models; in other words, it defines the requirements that a model should meet and thereby instructs how the models should be built. An explanation is an answer to a ‘why’-question. In economics, this answer is provided by a white-box model. To answer a ‘how much’-question, which is asking for a measurement, economists can make use of black-box models. Economic phenomena are often (...) so complex that white-box models that should function as explanations for them appear not to be intelligible. So, for questions concerning understanding - e.g. ‘Can you clarify this phenomenon‘’ ‘Can you tell me what would happen if‘’ - it would appear that grey-box models are more adequate. This implies a twofold revision of de Regt and Dieks’s (2005) criteria for understanding phenomena. First, whenever the term ‘theory’ is used it should be substituted by ‘model’. Secondly, the Criterion for the Intelligibility of Models (CIM) is now simplified as: ‘A model M is intelligible for economists (in context C) if they have built M as a grey-box construction’. A grey-box construction implies that they ‘can recognize qualitatively characteristic consequences of M without performing exact calculations’. (shrink)

The Representational Theory of Measurement conceives measurement as establishing homomorphisms from empirical relational structures into numerical relation structures, called models. There are two different approaches to deal with the justification of a model: an axiomatic and an empirical approach. The axiomatic approach verifies whether a given relational structure satisfies certain axioms to secure homomorphic mapping. The empirical approach conceives models to function as measuring instruments by transferring observations of an economic system into quantitative facts about that system. These facts are (...) evaluated by their accuracy and precision. Precision is achieved by least squares methods and accuracy by calibration. For calibration standards are needed. Then two strategies can be distinguished. One aims at estimating the invariant (structural) equations of the system. The other strategy is to use known stable facts about the system to adjust the model parameters. For this strategy, the requirement of models as homomorphic mappings has been dropped. (shrink)

The paper argues that the Nash Equilibrium Refinement Programme in game theory was less successful than its competitor, the Epistemic Programme (Interactive Epistemology). The prime criterion of success is the extent to which the programmes were able to reach the key objective guiding non-cooperative game theory for much of the 20th century, namely, to develop a complete characterisation of the strategic rationality of economic agents in the form of the ultimate game theoretic solution concept for any normal form and extensive (...) game. The paper explains this in terms of unjustified degrees of mathematisation in the Nash Equilibrium Refinement Programme. While this programme's mathematical models were often inspired by purely mathematical concerns rather than the economic phenomena they were intended to be mathematical models of, the Epistemic Programme's mathematical models were developed with a keen eye to the role beliefs and desires play in strategic interaction between rational economic agents playing games; that is, their Interactive Epistemology. The Epistemic Programme succeeded in developing mathematical models formalising aspects of strategic interaction that remained implicit in the Nash Equilibrium Refinement Programme due to an unjustified degree of mathematisation. As a result, the Epistemic Programme is more successful in game theory . (shrink)

I argue that game theoretic explanations of human actions make implausible epistemological assumptions. A logical analysis of game theoretic explanations shows that they do not conform to the belief-desire framework of action explanation. Epistemic characterization theorems (specifying sufficient conditions for game theoretic solution concepts to obtain) are argued to be the canonical way to make game theory conform to that framework. The belief formation practices implicit in epistemic characterization theorems, however, disregard all information about players except what can be found (...) in the game itself. And such a practice of belief formation is, I show, implausible. (shrink)

Models treating the simple properties of social groups have a common shortcoming. Typically, they focus on the local properties of group members and the features of the world with which group members interact. I consider economic models of bureaucratic corruption, to show that (a) simple properties of groups are often constituted by the properties of the wider population, and (b) even sophisticated models are commonly inadequate to account for many simple social properties. Adequate models and social policies must treat certain (...) factors that are not local to individual members of the group, even if those factors are not causally connected to those individuals. Key Words: individualism • corruption • supervenience • model • cause. (shrink)

This volume presents mathematical game theory as an interface between logic and philosophy.

A long-standing tradition presents economic activity in terms of the flow of fluids. This metaphor lies behind a small but influential practice of hydraulic modelling in economics. Yet turning the metaphor into a three-dimensional hydraulic model of the economic system entails making numerous and detailed commitments about the analogy between hydraulics and the economy. The most famous 3-D model in economics is probably the Phillips machine, the central object of this paper.

Biologists and economists use models to study complex systems. This similarity between these disciplines has led to an interesting development: the borrowing of various components of model-based theorizing between the two domains. A major recent example of this strategy is economists’ utilization of the resources of evolutionary biology in order to construct models of economic systems. This general strategy has come to be called evolutionary economics and has been a source of much debate among economists. Although philosophers have developed literatures (...) on the nature of models and modeling, the unique issues surrounding this kind of interdisciplinary model building have yet to be independently investigated. In this paper, we utilize evolutionary economics as a case study in the investigation of more general issues concerning interdisciplinary modeling. We begin by critiquing the distinctions currently used within the evolutionary economics literature and propose an alternative carving of the conceptual terrain. We then argue that the three types of evolutionary economics we distinguish capture distinctions that will be important whenever resources of model-based theorizing are borrowed across distinct scientific domains. Our analysis of these model-building strategies identifies several of the unique methodological and philosophical issues that confront interdisciplinary modeling. (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)