Metric-first & entropy-first surprises

TL;DR

This paper explores how models that are least surprised by observations can enhance understanding of paradigm shifts in physics, using Bayesian model-selection principles and entropy-based measures to analyze historical and educational examples.

Contribution

It introduces a metric-first and entropy-first approach to understanding scientific paradigm shifts and provides educational strategies based on these concepts.

Findings

Bayesian model-selection emphasizes models least surprised by data.

Entropy measures relate to available work and complexity in physical systems.

Educational strategies for physics based on surprise and entropy concepts.

Abstract

Established idea-sets don't update seamlessly. The tension between new and old views of nature is e.g. documented in Galileo's dialogs and now present in many fields. However the science of Bayesian model-selection has made recent strides in both life & physical sciences, in effect suggesting that we look to models which are quantitatively {\em surprised least} by present-day observations. We illustrate the relevance of this to physics-education with a qualitative look at two paradigm-shifts, namely from {\bf Lorentz-transform to metric-equation} descriptions of motion in space-time, and from {\bf classical to statistical thermodynamics} with help from Boltzmann's choice-multiplicity & Shannon's uncertainty. Connections of the latter to {\bf correlation measures} behind available-work, evolving complexity, and model-selection relevant to physics undergrads are also explored. New strategies are exemplified with Appendices {\em for teachers} on: anyspeed traffic-laws & 3-vector velocity-addition, the energy-momentum half-plane lost to finite lightspeed, the modern distinction between proper & geometric accelerations, metric-first kinematics with acceleration & differential-aging, quantifying risk with a handful of coins, effective number of choices, available work in bits, reversible-thermalization of life's power-stream, and choice-multiplicity measures of layered complex-system health.