Farita Tasnim and David H. Wolpert

Entropy 2023, 25(7), 1078

Many dynamical systems consist of multiple, co-evolving subsystems (i.e., they have multiple degrees of freedom). Often, the dynamics of one or more of these subsystems will not directly depend on the state of some other subsystems, resulting in a network of dependencies governing the dynamics. How does this dependency network affect the full system’s thermodynamics? Prior studies on the stochastic thermodynamics of multipartite processes have addressed this question by assuming that, in addition to the constraints of the dependency network, only one subsystem is allowed to change state at a time. However, in many real systems, such as chemical reaction networks or electronic circuits, multiple subsystems can—or must—change state together. Here, we investigate the thermodynamics of such composite processes, in which multiple subsystems are allowed to change state simultaneously. We first present new, strictly positive lower bounds on entropy production in composite processes. We then present thermodynamic uncertainty relations for information flows in composite processes. We end with strengthened speed limits for composite processes.

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Daniele Carlesso, Justin M. McNab, Christopher J. Lustri, Simon Garnier, and Chris R. Reid

PNAS 120 (29) e2216217120

Weaver ants link their bodies together to form chains over gaps and reach unexplored territories. The decision to join or leave a chain is made by individuals, but has cost implications at the colony level, as longer chains sequester more ants, which cannot perform other tasks. Furthermore, the payoff of a chain remains unknown until it is complete and the new area is explored. We demonstrate that individual ants modulate the time they spend in the chain based on their proximity to the ground and that this local behavioral rule caps the colony-level investment into chains. Our theoretical model offers insights into collective decision-making in the absence of payoff information, and could prove useful in the engineering of multiagent systems.

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Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. MacLaren, Sriniwas Pandey, Shelley D. Dionne, Francis J. Yammarino, Hiroki Sayama

Human collectives, e.g., teams and organizations, increasingly require participation of members with diverse backgrounds working in networked social environments. However, little is known about how network structure and the diversity of member backgrounds would affect collective processes. Here we conducted three sets of human-subject experiments which involved 617 participants who collaborated anonymously in a collective ideation task on a custom-made online social network platform. We found that spatially clustered collectives with clustered background distribution tended to explore more diverse ideas than in other conditions, whereas collectives with random background distribution consistently generated ideas with the highest utility. We also found that higher network connectivity may improve individuals’ overall experience but may not improve the collective performance regarding idea generation, idea diversity, and final idea quality.

Read the full article at: arxiv.org

Marc Barthelemy

Reports on Progress in Physics

Stochastic equations constitute a major ingredient in many branches of science, from physics to biology and engineering. Not surprisingly, they appear in many quantitative studies of complex systems. In particular, this type of equation is useful for understanding the dynamics of urban population. Empirically, the population of cities follows a seemingly universal law – called Zipf’s law – which was discovered about a century ago and states that when sorted in decreasing order, the population of a city varies as the inverse of its rank. Recent data however showed that this law is only approximate and in some cases not even verified. In addition, the ranks of cities follow a turbulent dynamics: some cities rise while other fall and disappear. Both these aspects – Zipf’s law (and deviations around it), and the turbulent dynamics of ranks – need to be explained by the same theoretical framework and it is natural to look for the equation that governs the evolution of urban populations. We will review here the main theoretical attempts based on stochastic equations to describe these empirical facts. We start with the simple Gibrat model that introduces random growth rates, and we will then discuss the Gabaix model that adds friction for allowing the existence of a stationary distribution. Concerning the dynamics of ranks, we will discuss a phenomenological stochastic equation that describes rank variations in many systems – including cities – and displays a noise-induced transition. We then illustrate the importance of exchanges between the constituents of the system with the diffusion with noise equation. We will explicit this in the case of cities where a stochastic equation for populations can be derived from first principles and confirms the crucial importance of inter-urban migrations shocks for explaining the statistics and the dynamics of the population of cities.

Read the full article at: iopscience.iop.org

Klaus Jaffe

Emergence, information and energy are fundamental properties of nature. We know that it takes free energy to acquire information, and it takes information to increment free energy. Energy obeys all laws of thermodynamics, while information does not. Emergence occurs in dynamic complex systems: when more than one dimension of reality interacts and novel properties of energy and information emerge. Information can be either useful or not in producing free energy. Information can reveal itself in different forms (as entropy, physically encoded, mechanical, biological, structural, in neural or social networks, etc.). Information may increase free energy by reducing entropy in the system, or by capturing free energy from the surroundings. The interaction between information and energy has been studied mostly in physical-chemistry and engineering. Now we find it everywhere, including in computer sciences, genetics, biotechnology, experimental social sciences, and experimental law. In emergent systems new possibilities of increasing free energy and useful information appear. Emergent complexity is visible in the transitions from subatomic particles to atoms, from atoms to molecules, to cells, to organisms, to societies and ecosystems. General and simple concepts are presented to help untangle the forces behind evolutionary processes leading to ever more complexity with more free energy and useful information, giving birth to life. We need to quantify changes in energy and information to better understand the dynamics of emergence in complex far-from-equilibrium systems.

Read the full article at: www.qeios.com