ORCID

https://orcid.org/0000-0003-1770-3119

Date of Award

Summer 2026

Language

English

Embargo Period

5-23-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Physics

Program

Physics

First Advisor

Ariel Caticha

Committee Members

Ariel Caticha, Lewis Segal, Oleg Lunin, Kevin Knuth, Daniel Robbins

Keywords

Entropic Inference, Quantum Physics, Quantitative Finance

Subject Categories

Data Science | Dynamic Systems | Econometrics | Finance | Finance and Financial Management | Management Sciences and Quantitative Methods | Partial Differential Equations | Quantum Physics | Statistical Models

Abstract

In many scientific and financial contexts, we must reason and make predictions under conditions of incomplete information. This dissertation develops Entropic Dynamics (ED) as a unified framework for deriving dynamical laws directly from principles of inference. Within this approach, probability distributions represent states of knowledge, and their evolution is determined through entropy maximization subject to relevant constraints. This leads to a novel concept of entropic time and a formulation of dynamics as an inferential process. In this talk, I will present how ED provides a common foundation across multiple domains. In physics, quantum dynamics for particles and scalar fields in curved space-time emerge from entropic inference, leading to formulations equivalent to the Schrödinger equation. In finance, the same framework derives the Geometric Brownian Motion model and the Black–Scholes–Merton equation for option pricing, as well as extensions to foreign exchange models. These results illustrate how Entropic Dynamics offers a unified methodology for modeling complex systems across physics and financial markets.

License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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