Physical grounds for causal perspectivalism

TL;DR

This paper proposes a physical basis for causal perspectivalism by linking causal relations to the internal states of thermodynamically constrained agents, emphasizing the role of hardware and learning in establishing causality.

Contribution

It introduces a thermodynamic model of causal agents, showing how causal relations emerge from physical states and are perspectival based on hardware and environment.

Findings

Causal relations are grounded in the physical states of agents.

Learning minimizes error rate by reducing dissipated power.

Causal relations are shared among agents with identical hardware in the same environment.

Abstract

We ground the asymmetry of causal relations in the internal physical states of a special kind of open and irreversible physical system, a causal agent. A causal agent is an autonomous physical system, maintained in a steady state, far from thermal equilibrium, with special subsystems: sensors, actuators, and learning machines. Using feedback, the learning machine, driven purely by thermodynamic constraints, changes its internal states to learn probabilistic functional relations inherent in correlations between sensor and actuator records. We argue that these functional relations just are causal relations learned by the agent, and so such causal relations are simply relations between the internal physical states of a causal agent. We show that learning is driven by a thermodynamic principle: the error rate is minimised when the dissipated power is minimised. While the internal states of a causal agent are necessarily stochastic, the learned causal relations are shared by all machines with the same hardware embedded in the same environment. We argue that this dependence of causal relations on such `hardware' is a novel demonstration of causal perspectivalism.