Patterns and Parsimony
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Abstract: The ability to develop good high-level representations is essential for modeling one's environment successfully. This ability is also central to modeling in science, where scientists seek to identify high-level regularities in physical systems (e.g., psychological regularities realized by the human brain). Philosophers, cognitive scientists, and data scientists have, in different ways, proposed a central role for compression in understanding what distinguishes good high-level representations from bad ones. This idea is facially plausible. Good high-level representations are often highly informative despite their simplicity—simplicity they achieve by abstracting away from the fine-grained details of their target systems. Despite its plausibility, I argue that this account of high-level representations is substantially incomplete and that a more complete account requires careful attention to the details of specific models and their relationships to possible representations. Ultimately, I conclude that good high-level representations are those under which the target system exhibits model-relevant patterns.
Speaker Bio: Tyler Millhouse is a philosopher of science who works at the intersection of philosophy, cognitive science, and data science. Central to his research is the close analogy between how data scientists model data, how scientists model physical systems, and how intelligent agents model their environments. Building on this analogy, Tyler’s work draws insights about natural intelligence and scientific modeling from work on modeling in artificial intelligence and machine learning. Tyler has also conducted research in computer vision using data from the Mars Reconnaissance Orbiter and research in moral cognition with Liane Young and Shaun Nichols. He received his MA in Philosophy from Tufts University and will receive his PhD in Philosophy from the University of Arizona. When he's not working, he enjoys cooking, astrophotography, and watching terrible movies.