Author: cxdig

Mapping foundational contributions in complex systems and network science

This initiative aims to identify and structure the foundational contributions that define complex systems, network science, and related domains, across theory, methods, and applications. The goal is to build a coherent, field-wide reference that reflects how the discipline is actually used and understood across different subdomains.

In recent years, large-scale models and automated systems have made it possible to synthesize vast amounts of scientific information. However, identifying what is foundational — what truly shapes the conceptual and methodological backbone of a field — still requires distributed expert judgment. This effort is designed to complement algorithmic approaches by leveraging collective intelligence: many independent perspectives, aggregated into a structured view.

The objective is not to produce a simple ranking of famous papers, but to build a structured map of the field’s foundations, including works that may be missing from keyword-based or citation-based approaches.

Read the full article at: manliodedomenico.com

Integrated information theory: the good, the bad and the misunderstood

Adam B. Barrett, Borjan Milinkovic, Pedro A. M. Mediano, Fernando E. Rosas, Daniel Bor, Lionel Barnett, Anil K. Seth

The integrated information theory of consciousness (IIT) is uniquely ambitious in proposing a mathematical formula, derived from apparently fundamental properties of conscious experience, to describe the quantity and quality of consciousness for any physical system that possesses it. IIT has generated considerable debate, which has engendered some misunderstandings and misrepresentations. Here we address and hope to remedy this. We begin by concisely summarising the essentials of IIT. Given IIT is supposed to apply universally, we do this with reference to an arbitrary patch of matter, as opposed to the usual system of discrete computational units. Then, after briefly summarising IIT’s theoretical and empirical achievements, we focus on five points which we consider especially important for driving forward new theory and increasing understanding. First, a high value of the measure Φ is not synonymous with `more consciousness’. We describe how Φ might be replaced with a suite of quantities to obtain a multi-dimensional characterisation of states of consciousness. Second, we describe with nuance the distinct flavour of panpsychism implied by IIT — whereby space (and time) are tiled with substrates of (proto-) consciousness — and find this is not problematic for the theory. Third, Φ is not well-defined for real physical systems, and has not been computed on any real physical system. Fourth, so far only proxies for IIT measures have been computed, and not approximations. Fifth, for IIT to fit with current successful theories in fundamental physics, a reformulation in terms of continuous fields would be needed.

Read the full article at: arxiv.org

Informal connections outweigh coauthorship ties in academic impact

Lluís Danús, William Dinneen, Carolina Torreblanca, Guy Grossman, and Sandra González-Bailón

PNAS 123 (18) e2511050123

The term “invisible college” refers to communication networks that help scientists exchange information and advance knowledge. These networks create social capital, granting access to resources like new ideas and support. Measuring those intangible exchanges is an empirical challenge. Here we approximate these ties through the analysis of the “thank you” notes appended to journal articles. Our findings show that scholars disconnected from this layer of academic social capital have lower publication impact. We also show that informal ties provide support not captured by coauthorship ties, which reflect a more rigid form of collaboration. Documenting how informal structures of support operate can help leverage collective resources in the pursuit of shared intellectual goals.

Read the full article at: www.pnas.org

Anna Guerrero | How to Model Science as a Complex System

Tracing the historical dynamics of science can reveal how scientific knowledge emerges and evolves over time. Because scientific knowledge is embedded in increasingly complex systems, comprising shifting relationships among people, the organisms and matter they study, technology, data, publications, and the concepts they utilize, scholars are looking beyond traditional historiographical methods towards quantitative and computational tools. Big data, network analysis, and machine learning enhance the scale and speed of analysis, but these methods often ignore or erase the critical roles that context (like time period, geography, and discipline) and different types of data (like image and audio data) play in the development of new knowledge. In this talk, I present context- and data-sensitive computational methods that extend efforts to model the evolution of science as a complex system. These methods reveal when new knowledge emerges and how the features of old scientific information constrain features of new scientific knowledge.

Read the full article at: www.mivideo.it.umich.edu