Emergence as the conversion of information: a unifying theory

Abstract

Is reduction always a good scientific strategy? The existence of the special sciences above physics suggests not. Previous research has shown that dimensionality reduction (macroscales) can increase the dependency between elements of a system (a phenomenon called ‘causal emergence’). Here, we provide an umbrella mathematical framework for emergence based on information conversion. We show evidence that coarse-graining can convert information from one ‘type’ to another. We demonstrate this using the well-understood mutual information measure applied to Boolean networks. Using partial information decomposition, the mutual information can be decomposed into redundant, unique and synergistic information atoms. Then by introducing a novel measure of the synergy bias of a given decomposition, we are able to show that the synergy component of a Boolean network’s mutual information can increase at macroscales. This can occur even when there is no difference in the total mutual information between a macroscale and its underlying microscale, proving information conversion. We relate this broad framework to previous work, compare it to other theories, and argue it complexifies any notion of universal reduction in the sciences, since such reduction would likely lead to a loss of synergistic information in scientific models. This article is part of the theme issue ‘Emergent phenomena in complex physical and socio-technical systems: from cells to societies’.

Information processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior

“…In addition to the notion of synergy-as-information-modification, the partial information decomposition also provides insights into what joint information is redundantly shared between parents [40]. It is known that different systems can be variously dominated by redundant or synergistic information dynamics [58, 94], although this is a very new area of study. Previous work has suggested that different behavioral states may be associated with different distributions of redundant and synergistic information processing [59], and we hypothesized that the fixation/cue states would have a different ratio of synergistic and redundant information dynamics.…”

“…Previous work has suggested that different behavioral states may be associated with different distributions of redundant and synergistic information processing [59], and we hypothesized that the fixation/cue states would have a different ratio of synergistic and redundant information dynamics. The idea of an explicitly normalized redundancy/synergy ratio has been proposed in [94], who found that topologically similar systems can nevertheless have strongly different distributions of partial information over the PI lattice. The calculate the redundancy/synergy ratio ( RSR ), we first normalize the relevant PI-atoms by the overall joint mutual information, which gives us the proportion of the total information that is purely synergistic and the proportion of the total information that is purely redundant: …”

“…Following the convention established in [58, 94], for the log-redundancy/synergy ratio, a value greater than zero indicates a redundancy-dominated dynamic, while a value less than zero indicates a synergy dominated dynamic.…”

From the origin of life to pandemics: Emergent phenomena in complex systems

“…In the search for a solid and convincing theory of emergence, Varley and Hoel's contribution, Emergence as the conversion of information: A unifying theory [166], overcomes the traditional dichotomy between strong and weak emergence and, aiming at finding a formal theory of emergence able to identify the intrinsic scale of function of complex systems, propose a mathematical framework in which emergence is identified with information conversion across scales. They base it on information theory and successfully apply it on a model system of Boolean networks.…”

Reduced emergent character of neural dynamics in patients with a disrupted connectome

“…Thus, we acknowledge that an important avenue for future work will be to extend our approach beyond pairwise interactions, and quantify causal emergence across larger groups of regions, up to the entire brain simultaneously, whether through theoretical developments or computational approximations. Indeed, recent advances in the related but complementary account of emergence proposed by Integrated Information Theory [75][76][77][78] present promising avenues for future investigation. Similarly, "hierarchy" is a protean, multi-faceted concept in neuroscience 7,16 , and different operationalisations may bear different relationships with emergence, which should be borne in mind when interpreting the present results.…”