The Generative Programs Framework

TL;DR

This paper proposes a general framework for representing physical theories as generative programs with DAGs, extending beyond causal models to encompass broader structural relations like ontological priority.

Contribution

It introduces a novel structural framework representing physical theories as generative programs and DAGs, generalizing causation to ontological priority.

Findings

Generative programs can model any physical theory's data generation process.

Directed acyclic graphs encode ontological priority relations.

Framework applies to philosophical debates on realism and locality.

Abstract

Recently there has been significant interest in using causal modelling techniques to understand the structure of physical theories. However, the notion of `causation' is limiting - insisting that a physical theory must involve causal structure already places significant constraints on the form that theory may take. Thus in this paper, we aim to set out a more general structural framework. We argue that any quantitative physical theory can be represented in the form of a generative program, i.e. a list of instructions showing how to generate the empirical data; the information-processing structure associated with this program can be represented by a directed acyclic graph (DAG). We suggest that these graphs can be interpreted as encoding relations of `ontological priority,' and that ontological priority is a suitable generalisation of causation which applies even to theories that don't have a natural causal structure. We discuss some applications of our framework to philosophical questions about realism, operationalism, free will, locality and fine-tuning.