Measuring Complexity using Information

Klaus Jaffe

Measuring complexity in multidimensional systems with high degrees of freedom and a variety of types of information, remains an important challenge. Complexity of a system is related to the number and variety of components, the number and type of interactions among them, the degree of redundancy, and the degrees of freedom of the system. Examples show that different disciplines of science converge in complexity measures for low and high dimensional problems. For low dimensional systems, such as coded strings of symbols (text, computer code, DNA, RNA, proteins, music), Shannon’s Information Entropy (expected amount of information in an event drawn from a given distribution) and Kolmogorov‘s Algorithmic Complexity (the length of the shortest algorithm that produces the object as output), are used for quantitative measurements of complexity. For systems with more dimensions (ecosystems, brains, social groupings), network science provides better tools for that purpose. For complex highly multidimensional systems, none of the former methods are useful. Useful Information Φ, as proposed by Infodynamics, can be related to complexity. It can be quantified by measuring the thermodynamic Free Energy F and/or useful Work it produces. Complexity measured as Total Information I, can then be defined as the information of the system, that includes Φ, useless information or Noise N, and Redundant Information R. Measuring one or more of these variables allows quantifying and classifying complexity.

Read the full article at: www.qeios.com

Evidence Mounts That About 7% of US Adults Have Had Long COVID

Zhengyi Fang; Rebecca Ahrnsbrak; Andy Rekito

JAMA Data Brief

New data from the Medical Expenditure Panel Survey (MEPS) Household Component support prior findings that about 7% of US adults have had post–COVID-19 condition, also known as long COVID. The household survey of the US civilian noninstitutionalized population, sponsored by the Agency for Healthcare Research and Quality, found that an estimated 6.9% of adults—17.8 million—had ever had long COVID as of early 2023.

This nationally representative survey included a sample of 17 418 adults aged 18 years or older, which corresponds to 259 million adults. A total of 8275 adults reported having had COVID-19, of which 1202 adults reported having had long COVID symptoms.

Read the full article at: jamanetwork.com

Irruption Theory in Phase Transitions: A Proof of Concept With the Haken-Kelso-Bunz Model

Javier Sánchez-Cañizares

Adaptive Behavior

Many theoretical studies defend the existence of ongoing phase transitions in the brain dynamics that could explain its enormous plasticity to cope with the environment. However, tackling the ever-changing landscapes of brain dynamics seems a hopeless task with complex models. This paper uses a simple Haken-Kelso-Bunz (HKB) model to illustrate how phase transitions that change the number of attractors in the landscape for the relative phase between two neural assemblies can occur, helping to explain a qualitative agreement with empirical decision-making measures. Additionally, the paper discusses the possibility of interpreting this agreement with the aid of Irruption Theory (IT). Being the effect of symmetry breakings and the emergence of non-linearities in the fundamental equations, the order parameter governing phase transitions may not have a complete microscopic determination. Hence, many requirements of IT, particularly the Participation Criterion, could be fulfilled by the HKB model and its extensions. Briefly stated, triggering phase transitions in the brain activity could thus be conceived of as a consequence of actual motivations or free will participating in decision-making processes.

Read the full article at: journals.sagepub.com

ICTP – SAIFR » School on Active Matter

Date: September 30 – October 4, 2024
Venue: IFT-UNESP, São Paulo, Brazil
Active matter describes systems whose constituent elements consume energy locally in order to move or to exert mechanical forces. As such, active matter systems are intrinsically out of thermodynamic equilibrium. Examples include flocks or herds of animals, collections of cells, components of the cellular cytoskeleton and even artificial microswimmers. Active matter is a rapidly growing field involving diverse scientific communities in physics, biology, computational sciences, applied mathematics, chemistry, and engineering. Numerous applications of active matter are constantly arising in biological systems, smart materials, precision medicine, and robotics.

This school is intended for graduate students and researchers interested in the physics of active matter. The lectures will cover well-tested and successful theoretical approaches as well as a discussion of experimental results. To achieve this purpose, leading experts will present lectures on fundamental aspects of active matter and a pedagogical exposition of its recent trends.

Applicants are invited to submit abstracts for poster presentations.

There is no registration fee and limited funds are available for travel and local expenses.

Lecturers:
  • Julia M Yeomans (University of Oxford, UK): From Active Nematics to Mechanobiology
  • Rodrigo Soto (Universidad de Chile, Chile): Computational Modeling of Active Systems
  • Aparna Baskaran (Brandeis University, USA): Theoretical Foundations of Active Matter: Lessons from Ideal Microscopic Models
  • Francesco Ginelli (University of Insubria, Italy): Physics of Flocking
Application deadline: July 27, 2024