Really Real Patterns

Abstract

In the philosophy of science, there have been a number of attempts to characterize high-level phenomena as patterns in lower-level phenomena. In computer science, there has been a great deal of effort devoted to recognizing patterns and to representing those patterns using formal models. This dissertation engages with the philosophical literature on patterns from a perspective informed by relevant work in computer science. My aim is not to correct philosophers' errors or misconceptions by appeal to results in computer science---though that may occasionally be appropriate. Rather, my aim is to bolster the philosophical literature by introducing helpful conceptual tools drawn from fields like machine learning, computational modeling, pattern recognition, and algorithmic information theory. As I argue, these conceptual tools allow us to place pattern-based accounts of non-fundamental ontology (esp. Dennett, 1991) on a firmer theoretical foundation. More concretely, I propose a novel, rigorous, and scientifically-informed criterion for the reality of patterns in the physical world. I also propose a more general approach to understanding non-fundamental ontology using ideas from machine learning, such as features and feature selection. This approach to non-fundamental ontology, I argue, will allow us to better understand scientific modeling, inter-level relations, scientific reduction, and other important issues in the philosophy of science.