Term Coding and Dispersion: A Perfect-vs-Rate Complexity Dichotomy for Information Flow

TL;DR

This paper introduces a framework for term coding in discrete mathematics, analyzing the complexity of information flow through dispersion, revealing a dichotomy between perfect and rate-based dispersion with computational implications.

Contribution

It establishes a new complexity dichotomy for dispersion in term coding, providing a polynomial-time algorithm for maximum dispersion and proving undecidability of perfect dispersion.

Findings

Maximum dispersion scales as Θ(n^D) with D as the guessing number of a related graph.

Deciding perfect dispersion occurrence is undecidable for r ≥ 3.

Asymptotic rate-threshold questions are polynomial-time decidable.

Abstract

We introduce a new framework term coding for extremal problems in discrete mathematics and information flow, where one chooses interpretations of function symbols so as to maximise the number of satisfying assignments of a finite system of term equations. We then focus on dispersion, the special case in which the system defines a term map $\Theta^\mathcal I:\A^k\to\A^r$ and the objective is the size of its image. Writing $n:=|\A|$, we show that the maximum dispersion is $\Theta(n^D)$ for an integer exponent $D$ equal to the guessing number of an associated directed graph, and we give a polynomial-time algorithm to compute $D$. In contrast, deciding whether \emph{perfect dispersion} ever occurs (i.e.\ whether $\Disp_n(\mathbf t)=n^r$ for some finite $n\ge 2$) is undecidable once $r\ge 3$, even though the corresponding asymptotic rate-threshold questions are polynomial-time decidable.