Monotonic entropy growth for a nonlinear model of random exchanges

TL;DR

This paper proves that a nonlinear discrete-time model of a random market exhibits monotonic entropy growth, using a combination of linear evolution and coarse-graining analogous to renormalization group transformations.

Contribution

It introduces a novel proof of entropy monotonicity for a nonlinear market model by combining linear evolution and coarse-graining techniques.

Findings

Entropy increases monotonically in the model.

The proof utilizes information theory inequalities.

The model relates to Ulam's energy redistribution problem.

Abstract

We present a proof of the monotonic entropy growth for a nonlinear discrete-time model of a random market. This model, based on binary collisions, also may be viewed as a particular case of Ulam's redistribution of energy problem. We represent each step of this dynamics as a combination of two processes. The first one is a linear energy-conserving evolution of the two-particle distribution, for which the entropy growth can be easily verified. The original nonlinear process is actually a result of a specific `coarse-graining' of this linear evolution, when after the collision one variable is integrated away. This coarse-graining is of the same type as the real space renormalization group transformation and leads to an additional entropy growth. The combination of these two factors produces the required result which is obtained only by means of information theory inequalities.