Making Models Work

Abstract

<p>Scientific models are used to investigate reality. Here “model” refers to a representation which is created by an agent for a particular inferential purpose. These purposes include but are not limited to explanation, prediction, exploration, classification, and measurement. Through modeling, scientists become capable of understanding the composition and structure of natural systems and social systems in a systematic manner constitutive of scientific research. This process of understanding is underwritten by a logical structure distinctive to scientific modeling.</p><p>Throughout this dissertation, I articulate, justify, and defend a specific account of the logical structure of scientific modeling. In order to do so, I detail economic models, which I contend are representative of general scientific modeling. Broadly, my account of scientific modeling can be decomposed into three distinct claims. First, I argue for understanding scientific modeling in terms representation. Following others, I then conceptualize representation in terms of purpose and relevant similarity. However, against this conceptualization are numerous counterarguments, which I proceed to detail and then disarm.</p><p>Second, I argue that the ideal scientific model is a useful model. Connectedly, I contend that in order for a scientific model to be useful, it must first be idealized. In order to demonstrate the necessity of idealizations for scientific modeling, I begin by detailing a number of idealization strategies and demonstrate how they are integral to the use of scientific models across the natural and social sciences. But in order to demonstrate that idealized models are not only useful but are ideal, I dismantle the putative ideal of completeness which holds that the ideal model completely represents reality in all its detail and complexity. However, as I demonstrate, completeness is neither achievable nor a legitimate aspiration for working scientist.</p><p>Third, I argue that in order to use scientific models, it is often necessary for scientists to alter them in order to better fit particular target systems. In order to explain the alteration process, I detail the representational continuum found across the sciences which stretches from highly concrete data models to highly abstract principles. Between these extremes are theoretical models and empirical models. In order to construct such models, scientists must engage in an exploratory process by which possibilities are mapped and relative likelihoods estimated. In this way, scientists can construct highly specialized models which can allow them to better pursue specific inferential purposes. All of this results in a division of inferential labor and associated efficiency gains which, I argue, are constitutive of scientific progress. </p>