Zeta Functions and the (Linear) Logic of Markov Processes

Fuente: arXiv
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Main Author: Seiller, Thomas
Format: Preprint
Published: 2020
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author Seiller, Thomas
author_facet Seiller, Thomas
contents The author introduced models of linear logic known as ''Interaction Graphs'' which generalise Girard's various geometry of interaction constructions. In this work, we establish how these models essentially rely on a deep connection between zeta functions and the execution of programs, expressed as a cocycle. This is first shown in the simple case of graphs, before begin lifted to dynamical systems. Focussing on probabilistic models, we then explain how the notion of graphings used in Interaction Graphs captures a natural class of sub-Markov processes. We then extend the realisability constructions and the notion of zeta function to provide a realisability model of second-order linear logic over the set of all (discrete-time) sub-Markov processes.
format Preprint
id arxiv_https___arxiv_org_abs_2001_11906
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle Zeta Functions and the (Linear) Logic of Markov Processes
Seiller, Thomas
Logic in Computer Science
Dynamical Systems
Logic
Probability
The author introduced models of linear logic known as ''Interaction Graphs'' which generalise Girard's various geometry of interaction constructions. In this work, we establish how these models essentially rely on a deep connection between zeta functions and the execution of programs, expressed as a cocycle. This is first shown in the simple case of graphs, before begin lifted to dynamical systems. Focussing on probabilistic models, we then explain how the notion of graphings used in Interaction Graphs captures a natural class of sub-Markov processes. We then extend the realisability constructions and the notion of zeta function to provide a realisability model of second-order linear logic over the set of all (discrete-time) sub-Markov processes.
title Zeta Functions and the (Linear) Logic of Markov Processes
topic Logic in Computer Science
Dynamical Systems
Logic
Probability
url https://arxiv.org/abs/2001.11906