Date of Award
Doctor of Philosophy (PhD)
Physics & Astronomy
Danielle S. Bassett
In the study of complex systems, it is often the case that large-scale features emerge from simple properties of the constituent units at the scale below. Nowhere is this observation more evident -- nor are the implications more important -- than in the investigation of human behavior: from the collective firing of thousands or millions of neurons arises the activity of a single brain region, from the communication between of hundreds or thousands of brain regions emerge consciousness and other cognitive functions, and from the interactions between hundreds or thousands of people appear the collective behaviors of human populations. To study such complex systems, cutting-edge research increasingly harkens back to centuries-old insights from statistical mechanics. Here, drawing inspiration from these recent efforts, we adapt and extend methods from statistical mechanics, information theory, and network science to investigate the nature of human behavior across scales.
Generally, the dissertation flows in the direction of decreasing scale, which, coincidentally, approximately corresponds to the chronological order in which the research was produced. We begin in Part I by examining the principles of emergence and control in human populations. In Part II, we study how individual humans learn and process information using networks in the world around them. Finally, in Part III, we investigate whether, and to what extent, the brain operates out of thermodynamic equilibrium. Together, these analyses aim to shed light on the statistical mechanical nature of human behavior.
Lynn, Christopher William, "The Statistical Mechanics Of Human Behavior" (2020). Publicly Accessible Penn Dissertations. 4273.