A Theory for Discrete-time Boolean Finite Dynamical Systems with Uncertainty

TL;DR

This paper develops a mathematical theory for discrete-time Boolean finite dynamical systems incorporating uncertainty, addressing a gap in understanding how randomness affects system behavior.

Contribution

It introduces a formal framework for modeling uncertainty in DT-BFDS and proves fundamental theoretical results, expanding the scope beyond deterministic models.

Findings

Established a formal model for DT-BFDS with uncertainty

Proved key theoretical properties of uncertain systems

Extended understanding of system dynamics under uncertainty

Abstract

Dynamical Systems is a field that studies the collective behavior of objects that update their states according to some rules. Discrete-time Boolean Finite Dynamical System (DT-BFDS) is a subfield where the systems have some finite number of objects whose states are Boolean values, and the state updates occur in discrete time. In the subfield of DT-BFDS, researchers aim to (i) design models for capturing real-world phenomena and using the models to make predictions and (ii) develop simulation techniques for acquiring insights about the systems' behavior. Useful for both aims is understanding the system dynamics mathematically before executing the systems. Obtaining a mathematical understanding of BFDS is quite challenging, even for simple systems, because the state space of a system grows exponentially in the number of objects. Researchers have used computational complexity to circumvent the challenge. The complexity theoretic research in DT-BFDS has successfully produced complete characterizations for many dynamical problems. The DT-BFDS studies have mainly dealt with deterministic models, where the update at each time step is deterministic, so the system dynamics are completely determinable from the initial setting. However, natural systems have uncertainty. Models having uncertainty may lead to far-better understandings of nature. Although a few attempts have explored DT-BFDS with uncertainty, including stochastic initialization and tie-breaking, they have scratched only a tiny surface of models with uncertainty. The introduction of uncertainty can be through two schemes. One is the introduction of alternate update functions. The other is the introduction of alternate update schedules. 37This paper establishes a theory of models with uncertainty and proves some fundamental results.