A salience account of explanatory power

Abstract

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] When we ask questions like "why won't my car engine start?" or "why is John late?" we are searching for explanations. We search for explanations in a variety of contexts and for a variety of reasons. Sometimes we want a friend to give us just enough information to enable us to make a quick decision (e.g. "How do you know this left turn is correct?"). At other times, we want more precise, detailed information (e.g. "Why exactly does water transition directly from solid phase to gas phase?"). We search for explanations because we are curious and because we want to understand reality correctly. Moreover, we search for explanations because they help us solve problems, organize information, make predictions, design useful tools, and intervene on natural systems in countless ways. For this reason, explanation is a primary aim of science. Good explanations, it is argued, are intrinsically valuable because they give us a correct understanding of the world. For example, explanations give us understanding of causes, of necessity, of possibility, and of unification (Lipton 2009, 43). But, explanations can also provide a sort of "know-how" or skill-based understanding (Elgin 2007, de Regt 2009). For instance, explanation can provide the abilities needed to apply a model in different contexts or re-interpret various parts of a model. Because these cognitive and skill-based abilities are central to scientific goals (e.g. prediction, manipulation, formulation of laws), successful science is marked by its ability to provide us with good explanations (Salmon 1984, 259). Philosophy of science, then, is concerned with articulating a theory of explanation. Although the literature does not suggest a unique model for "scientific" explanation, one important test for a satisfactory theory of explanation is that it accurately characterize our best scientific explanations. Articulating a theory of explanation includes giving an account of at least two things: (1) the nature of explanatory information and (2) the value of explanatory information. Additionally, a good theory of explanation should provide criteria for judging an explanation's degree of success - a framework to help us distinguish between better and worse explanations. The aim of my dissertation research was to answer the following questions: Is there a bright-line between explanation and non-explanation that is context independent? Are there context independent criteria for successful explanations? What is the explanatory role of idealizations in models that explain phenomena in physics and biology? What is the relationship between explanation and understanding? Answers to these questions impact the way we view scientific practice in general and the standards by which we judge explanations (in both scientific and non-scientific contexts). For example, if the success of an explanation is context dependent, then it is evaluated of the basis of facts about the explanatory context in which it is offered. If so, then individual disciplines must justify their own appropriate standards for explanatory success. Thus, an explanation in biology (or history) is not necessarily less successful or less correct simply because it does not meet the criteria for an explanation in physics. The aim of my dissertation is to defend a strategy for evaluating an explanation's success - a salience account of explanatory power. According to this account, there is no context independent bright-line between explanation and non-explanation. Nor are there context independent criteria for successful explanations. To test this account, I apply it to two debates. The first debate is about how to characterize the explanatory power of idealization. The second is a debate about how to characterize the relationship between explanation and understanding. If the salience account can provide a solution to both debates, I take this as evidence that we should continue to develop this account.