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Abstract
This document proposes a phenomenological method of listening to algorithmic music that accounts for the possible variations that are found in different instances or performances of the work. After an initial discussion of the wide range of music that falls under the umbrella of algorithmic composition, Robert Hatten’s approach to semiotic markedness in music is introduced and related to terms commonly employed in the analysis of algorithmic works. This translation of musical markedness into algorithmic composition is followed by an introduction to Philip Johnson-Laird’s concept of mental models. The author combines facets of these concepts to create a listening approach titled Abductive Mental Modeling in which listeners create mental models of the algorithm behind the composition, further refining and correcting these models as they listen to additional versions of the work. The introduction of this approach is followed by two case study chapters. The first case study chapter centers around algorithmic works that primarily feature a defined structure, and includes Alvin Lucier’s I Am Sitting in a Room, Steve Reich’s Piano Phase, and the Jared Bradley Tubbs’ Chemically Bound. The second chapter centers around works that feature a certain level of interactivity from outside sources, and includes Cornelius Cardew’s The Great Learning: Paragraph 7, Sam Pluta’s Matrix (for George Lewis), and Jared Bradley Tubbs’ Atomically Arranged. A concluding chapter examines possible future applications for Abductive Mental Modeling in fields such as sonification, AI generated music, video game music, and sound installations.