Science in Context 12, 2 (1999), pp. 247-260 SERGIO SISMONDO Models, Simulations, and Their Objects 1. Mathematical Models and Computer Simulations Mathematical models and their cousins, computer simulations, occupy an uneasy space between theory and experiment, between abstract and concrete, and often between the pressures of pure science and the needs of pragmatic action. The contributions to this volume explore those uneasy spaces, and the work that it takes to maintain positions in those spaces. Models and simulations do not, of course, form a homogeneous category. The ones considered here form a continuum, from spare symbolic entities to somewhat more complex sets of equations that are computerized largely for ease of calcula- tion and manipulation, to computer programs so large and intricate that no one person understands how they function. The differences between the endpoints of this continuum are large enough that complex computer simulations can be said to use models, of many different types, or to have some particular models at their heart. Simple models and complex simulations, then, are in at least this way different types of objects, while they are related as endpoints on a continuum. Nonetheless, in being seen as occupying a position between theories and data, simulations and models perform some similar functions, and pose some similar problems. Although there are two sections in this volume, the first mostly address- ing simulations and the second looking at economic models, some of the lessons of the papers cut across this divide of subject matter, applying to models and simulations, and to economics, physics, and physiology. Whereas theories, like local claims, can be true or false, models and simulations are typically seen in more pragmatic terms, being more or less useful, rather than more or less true. Scientific models and simulations are given the status of tools, as well as representations; they are objects, as well as ideas. They easily cross categories, such as "theory" and "experiment," the bounds of which are otherwise well-established. And modeling and simulation sit uncomfortably in science both socially and epistemically, because of the boundaries they cross. Models have become ubiquitous in public policy and corporate strategy, as well as applied and pure science. The demands of objectivity in public life have meant that decisions across a wide range of subject matters have to be accompanied by the appropriate scientific validation. Despite the fact that they do not have available at http:/www.cambridge.org/core/terms. http://dx.doi.org/10.1017/S0269889700003409 Downloaded from http:/www.cambridge.org/core. IP address: 54.146.151.240, on 09 Nov 2016 at 21:52:24, subject to the Cambridge Core terms of use,