This paper sets out a framework for understanding how the scientific community constructs computer simulation as an epistemically and pragmatically useful methodology. The framework is based on comparisons between simulation and the loosely-defined categories of “theoretical work” and “experimental work.” Within that framework, the epistemological adequacy of simulation arises from its role as a mathematical manipulation of a complex, abstract theoretical model. To establish that adequacy demands a detailed “theoretical” grasp of the internal structure of the computer program. Simultaneously, the pragmatic usefulness of simulation arises from its role as a “virtual laboratory.” That role is made possible by black-boxing the internal structure of the program, such that the scientist can interact with the computer in an intuitive, “experimental” manner. Thus simulation is rendered authoritative, opening up encoded theories to a novel, “experimental” type of manipulation.

In its reconstruction of scientific practice, philosophy of science has traditionally placed scientific theories in a central role, and has reduced the problem of mediating between theories and the world to formal considerations. Many applications of scientific theories, however, involve complex mathematical models whose constitutive equations are analytically unsolvable. The study of these applications often consists in developing representations of the underlying physics on a computer, and using the techniques of computer simulation in order to learn about the behavior of these systems. In many instances, these computer simulations are not simple number-crunching techniques. They involve a complex chain of inferences that serve to transform theoretical structures into specific concrete knowledge of physical systems. In this paper I argue that this process of transformation has its own epistemology. I also argue that this kind of epistemology is unfamiliar to most philosophy of science, which has traditionally concerned itself with the justification of theories, not with their application. Finally, I urge that the nature of this epistemology suggests that the end results of some simulations do not bear a simple, straightforward relation to the theories from which they stem.

What kind of objects are computer programs used for simulation purposes in scientific settings? The current investigation treats a special case. It focuses on “event generators,” the program packages that particle physicists construct and use to simulate mechanisms of particle production. The paper is an attempt to bring the multiplex and unfolding character of such knowledge objects to the fore: Multiple meanings and functions are embodied in the object and can be drawn out selectively according to the requirements of a work setting. The object's conceptual complexity governs its application in some contexts, while the object is considered a mere “black box,” transparent and ready-to-hand, in others. These two poles span a full spectrum of object aspects, functions, and conceptions. Event generators are ideas turned into software, testing grounds for models, just a tool to study the performance of a detector, etc. The object's multiplex nature is submitted to negotiation among different actors.

When economists report on research using mathematical models, they use a literary form similar to the experimental report in the laboratory sciences. This form consists of a narrative of a series of events, with a clear temporal segregation of the agency of the author and the agency of the objects of study. Existing explanations of this literary form treat it as a rhetorical device that either conceals the agency of the author in constructing and interpreting the findings, or simply appropriates the appearance of accepted (natural-)scientific method. This article — based on analysis of a research program in economics, a single article that issued from that program, and in-depth interviews with the authors — proposes an alternate interpretation. Drawing on the praxeological “laboratory studies” tradition in science studies, we treat work with mathematical models as involving the interaction of economists with objects (models) that act independently of the analyst's will. The clear separation of the economist's and the models agency, as depicted in the published report, is not the result of a rhetorical rewriting of actual events, but is a practical accomplishment. Every step in the analytical work that preceded the paper is devoted to developing a procedure in which the economists' agency will be completely accountable in terms of accepted practices, and the performance of the model will be distinct and compelling.

The Netherlands has been a pioneering country in the development of macroeconometric modeling and its use in economic policy. The paper shows that the model was used to overcome the fragmented culture of Dutch pillarization. It proves that the specific use (and institutionalization) of modeling in the policy process is at least partly shaped by a nation's (historical) social structure. The case study relates to the outcome of a controversy within the social democratic pillar in the Netherlands in the period 1930–50 as to how to plan the economic system in the context of the social developments leading up to the crisis, World War II, and the postwar recovery.

This paper examines the interaction between economic models and policy advice through a case study of the U.K. government's Panel of Independent Forecasters. The Panel, which met for the first time in February 1993, was part of the government's response to the policy vacuum created by its departure from the European Exchange Rate Mechanism. The paper focuses on the policy recommendations made by the Panel and their foundation in economic models. It is argued that, because of their ambiguity, economic models do not provide an “objective” basis for policy making. Rather, they provide a level epistemological basis for debating the various social, political, and moral theories that can be used to frame economic policy. The paper concludes that although economic models often serve to depoliticize economic issues, they also have the potential to do exactly the opposite — namely, repoliticize them by connecting economics to wider social and moral debates.