Black-boxing and cause-effect power

TL;DR

This paper challenges reductionism by demonstrating that macro-level systems, modeled as black boxes, can exhibit higher cause-effect power than micro-level systems using integrated information ({}), revealing emergent causation.

Contribution

It introduces a framework for measuring cause-effect power at macro levels via black-boxing, showing macro systems can surpass micro systems in integrated information.

Findings

Macro black-box systems can have higher {} than micro systems.

Emergent macro cause-effect properties are identified at multiple scales.

The framework applied to a biological model reveals stable macro cause-effect maxima.

Abstract

Reductionism assumes that causation in the physical world occurs at the micro level, excluding the emergence of macro-level causation. We challenge this reductionist assumption by employing a principled, well-defined measure of intrinsic cause-effect power - integrated information ({\Phi}), and showing that, according to this measure, it is possible for a macro level to "beat" the micro level. Simple systems were evaluated for {\Phi} across different spatial and temporal scales by systematically considering all possible black boxes. These are macro elements that consist of one or more micro elements over one or more micro updates. Cause-effect power was evaluated based on the inputs and outputs of the black boxes, ignoring the internal micro elements that support their input-output function. We show how black-box elements can have more common inputs and outputs than the corresponding micro elements, revealing the emergence of high-order mechanisms and joint constraints that are not apparent at the micro level. As a consequence, a macro, black-box system can have higher {\Phi} than its micro constituents by having more mechanisms (higher composition) that are more interconnected (higher integration). We also show that, for a given micro system, one can identify local maxima of {\Phi} across several spatiotemporal scales. The framework is demonstrated on a simple biological system, the Boolean network model of the fission-yeast cell-cycle, for which we identify stable local maxima during the course of its simulated biological function. These local maxima correspond to macro levels of organization at which emergent cause-effect properties of physical systems come into focus, and provide a natural vantage point for scientific inquiries.