Abstract:
Our classical statistical arsenal for extracting truth from data often fails to produce correct
predictions. Uncertainty, blurry evidence and multiple possible solutions may trip up even the
best interrogator. Info-metrics – the science of modeling, reasoning, and drawing inferences
under conditions of noisy and insufficient information – provides a consistent and efficient
framework for constructing models and theories with minimal assumptions. It reveals the
simplest solution, model or story, that is hidden in the observed information. Technically, infometrics is at the intersection of information-theory and statistical inference. It combines the tools
and principles of information theory, within a constrained optimization framework.
My talk will be based on my new book ‘Foundations of Info-Metrics: Modeling, Inference, and
Imperfect Information,’ http://info-metrics.org/ in which I develop and examine the theoretical
underpinning of info-metrics and provide extensive interdisciplinary applications. In this talk I
will discuss the basic ideas via a small number of graphical representations of the model and
theory and will then present a number of interdisciplinary real-world examples for using the
framework for inference. These examples include finance, network aggregation, predicting
election, option pricing, and more. Depending on time, I will also discuss using that framework
for basic modeling.
About the Speaker:
Amos Golan is a professor of economics and directs the Info-Metrics Institute at American University. He is also an External Professor at the Santa Fe Institute and a Senior Associate at Pembroke College, Oxford. His research is primarily in the interdisciplinary field of info-metrics - the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. He has published in economics, econometrics, statistics, mathematics, physics, visualization and philosophy journals. His most recent book is ‘Foundations of InfoMetrics: Modeling, Inference, and Imperfect Information,’ Oxford University Press (2018). Web page: https://www.american.edu/cas/faculty/agolan.cfm.