Complexity Digest

Birds flock. Locusts swarm. Fish school. In these chaotic assemblies, order somehow emerges. Collective behaviors differ in their details from one species to another, but they largely adhere to principles of collective motion that physicists have worked out over centuries. Now, using technologies that only recently became available, researchers have been able to study these patterns of collective animal behavior more closely than ever before. These new insights are unlocking some of the secret fitness advantages of living as part of a group rather than as an individual. The improved understanding of swarming pests such as locusts could also help to protect global food security.

In this episode, co-host Steven Strogatz interviews the evolutionary ecologist Iain Couzin about  how and why animals exhibit collective behaviors, and the secret advantages that arise from them.

Listen at: play.prx.org

Patrick McMillen & Michael Levin 

Communications Biology volume 7, Article number: 378 (2024)

A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.

Read the full article at: www.nature.com

Alan Dorin, Susan Stepney

Artificial Life (2024) 30 (1): 1–15.

The field called Artificial Life (ALife) coalesced following a workshop organized by Chris Langton in September 1987 (Langton, 1988a). That meeting drew together work that had been largely carried out from the 1950s through to the 1980s. A few years later, Langton became the founding editor of this journal, Artificial Life, which started its life with Volume 1, Issue 1_2 in the (northern) winter of 1993/1994.1 This current issue therefore begins the 30th volume and 30th year of Artificial Life. We think this is a milestone worth celebrating!
In the proceedings of that first workshop, Langton famously defined ALife as the study of “life as it could be,” of “possible life,” in contrast to biology’s study of “life as we know it to be” (on Earth). His stated aim was to derive “a truly general theoretical biology capable of making universal statements about life wherever it may be found and whatever it may be made of ” (Langton, 1988b, p. xvi).

Read the full article at: direct.mit.edu

How computers simulate cities and how they are also being embedded in cities, changing our behavior and the way in which cities evolve.

At every stage in the history of computers and communications, it is safe to say we have been unable to predict what happens next. When computers first appeared nearly seventy-five years ago, primitive computer models were used to help understand and plan cities, but as computers became faster, smaller, more powerful, and ever more ubiquitous, cities themselves began to embrace them. As a result, the smart city emerged. In The Computable City, Michael Batty investigates the circularity of this peculiar evolution: how computers and communications changed the very nature of our city models, which, in turn, are used to simulate systems composed of those same computers.

Batty first charts the origins of computers and examines how our computational urban models have developed and how they have been enriched by computer graphics. He then explores the sequence of digital revolutions and how they are converging, focusing on continual changes in new technologies, as well as the twenty-first-century surge in social media, platform economies, and the planning of the smart city. He concludes by revisiting the digital transformation as it continues to confound us, with the understanding that the city, now a high-frequency twenty-four-hour version of itself, changes our understanding of what is possible.

More at: mitpress.mit.edu

Tom Froese

Entropy 2024, 26(4), 288

Cognitive science is confronted by several fundamental anomalies deriving from the mind–body problem. Most prominent is the problem of mental causation and the hard problem of consciousness, which can be generalized into the hard problem of agential efficacy and the hard problem of mental content. Here, it is proposed to accept these explanatory gaps at face value and to take them as positive indications of a complex relation: mind and matter are one, but they are not the same. They are related in an efficacious yet non-reducible, non-observable, and even non-intelligible manner. Natural science is well equipped to handle the effects of non-observables, and so the mind is treated as equivalent to a hidden ‘black box’ coupled to the body. Two concepts are introduced given that there are two directions of coupling influence: (1) irruption denotes the unobservable mind hiddenly making a difference to observable matter, and (2) absorption denotes observable matter hiddenly making a difference to the unobservable mind. The concepts of irruption and absorption are methodologically compatible with existing information-theoretic approaches to neuroscience, such as measuring cognitive activity and subjective qualia in terms of entropy and compression, respectively. By offering novel responses to otherwise intractable theoretical problems from first principles, and by doing so in a way that is closely connected with empirical advances, irruption theory is poised to set the agenda for the future of the mind sciences.

Read the full article at: www.mdpi.com

This June, discover the science that teaches us about collected patterns of behavior, helps us understand the fluctuations of global finance, and can help us meet societal, organization and global challenges. 

This course provides an introduction to essential concepts of complex systems and related mathematical methods and simulation strategies with application to physical, biological and social systems.

Concepts to be covered include: emergence, complexity, networks, self-organization, pattern formation, evolution, adaptation, fractals, chaos, cooperation, competition, attractors, interdependence, scaling, dynamic response, information and function.

Methods to be covered include: statistical methods, cellular automata, agent-based modeling, pattern recognition, system representation and informatics.

More  at: necsi.edu

In this episode, we examine how the course of human history has shaped our scientific knowledge, why the physics community prioritizes some questions over others, and why progress in complex systems research is especially difficult. Academia continues to operate within set boundaries and students are taught certain concepts as fundamental and to skirt others completely. However, the history of science demonstrates that such concepts aren’t always set in stone. It’s possible that blowing open the “shackles of reality,” such as redefining the concept of life itself, and reprioritizing the problems that scientists want to tackle, might help scientists make more progress in this very difficult world of complexity research.

Listen at: complexity.simplecast.com

An ode to systems engineers―whose invisible work undergirds our life―and an exploration of the wicked problems they tackle.

Our world is filled with pernicious problems. How, for example, did novice pilots learn to fly without taking to the air and risking their lives? How should cities process mountains of waste without polluting the environment? Challenges that tangle personal, public, and planetary aspects―often occurring in health care, infrastructure, business, and policy―are known as wicked problems, and they are not going away anytime soon.

In linked chapters focusing on key facets of systems engineering―efficiency, vagueness, vulnerability, safety, maintenance, and resilience―engineer Guru Madhavan illuminates how wicked problems have emerged throughout history and how best to address them in the future. He examines best-known tragedies and lesser-known tales, from the efficient design of battleships to a volcano eruption that curtailed global commerce, and how maintenance of our sanitation systems constitutes tikkun olam, or repair of our world. Braided throughout is the uplifting tale of Edwin Link, an unsung hero who revolutionized aviation with his flight trainer. In Link’s story, Madhavan uncovers a model mindset to engage with wickedness.

An homage to society’s innovators and maintainers, Wicked Problems offers a refreshing vision for readers of all backgrounds to build a better future and demonstrates how engineering is a cultural choice―one that requires us to restlessly find ways to transform society, but perhaps more critically, to care for the creations that already exist.

More at: www.amazon.com

There are many images of science and the activities of scientists. Some would imply that science will eventually reach the limits of knowledge while others create an expectation of endless horizons. Some people would believe that science has or will provide the answers to key open questions that lie ahead, while others experience fear regarding its development. In this book, we will look at all these aspects, going from particles, via atoms, cells, stars, galaxies, our place in the universe, to explore what makes us, human beings, really unique in nature: our ability to imagine and shape the future by making use of the scientific method. The book is an EPS action designed to address the social dimension of science and the grand challenges in physics that will bring radical change to developed societies, raise standards of living at the global scale, and provide basic understanding of nature on the horizon 2050.

Read the full book at: iopscience.iop.org

NERCCS 2025: The Eighth Northeast Regional Conference on Complex Systems will follow the success of the previous NERCCS conferences to promote the emerging venue of interdisciplinary scholarly exchange for complex systems researchers in the Northeast U.S. region (and beyond) to share their research outcomes through presentations and online publications, network with their peers, and promote interdisciplinary collaboration and the growth of the research community.

NERCCS will particularly focus on facilitating the professional growth of early career faculty, postdocs, and students in the region who will likely play a leading role in the field of complex systems science and engineering in the coming years.

The 2025 conference will be held primarily in person in the Innovative Technologies Complex at Binghamton University, with an online participation option via Zoom.

More at: nerccs2025.github.io

This book provides a unified, theory-driven introduction to key mathematical and agent-based models of social dynamics and cultural evolution, teaching readers how to build their own models, analyze them, and integrate them with empirical research programs. It covers a variety of modeling topics, each exemplified by one or more archetypal models, and helps readers to develop strong theoretical foundations for understanding social behavior. Modeling Social Behavior equips social, behavioral, and cognitive scientists with an essential tool kit for thinking about and studying complex social systems using mathematical and computational models.

More at: press.princeton.edu

Michele Avalle, Niccolò Di Marco, Gabriele Etta, Emanuele Sangiorgio, Shayan Alipour, Anita Bonetti, Lorenzo Alvisi, Antonio Scala, Andrea Baronchelli, Matteo Cinelli & Walter Quattrociocchi 
Nature (2024)

Growing concern surrounds the impact of social media platforms on public discourse1,2,3,4 and their influence on social dynamics5,6,7,8,9, especially in the context of toxicity10,11,12. Here, to better understand these phenomena, we use a comparative approach to isolate human behavioural patterns across multiple social media platforms. In particular, we analyse conversations in different online communities, focusing on identifying consistent patterns of toxic content. Drawing from an extensive dataset that spans eight platforms over 34 years—from Usenet to contemporary social media—our findings show consistent conversation patterns and user behaviour, irrespective of the platform, topic or time. Notably, although long conversations consistently exhibit higher toxicity, toxic language does not invariably discourage people from participating in a conversation, and toxicity does not necessarily escalate as discussions evolve. Our analysis suggests that debates and contrasting sentiments among users significantly contribute to more intense and hostile discussions. Moreover, the persistence of these patterns across three decades, despite changes in platforms and societal norms, underscores the pivotal role of human behaviour in shaping online discourse.

Read the full article at: www.nature.com

Rafael Prieto-Curiel, Juan P. Ospina

Environment International

Volume 185, March 2024, 108541

The use of cars in cities has many negative impacts, including pollution, noise and the use of space. Yet, detecting factors that reduce the use of cars is a serious challenge, particularly across different regions. Here, we model the use of various modes of transport in a city by aggregating Active mobility (A), Public Transport (B) and Cars (C), expressing the modal share of a city by its ABC triplet. Data for nearly 800 cities across 61 countries is used to model car use and its relationship with city size and income. Our findings suggest that with longer distances and the congestion experienced in large cities, Active mobility and journeys by Car are less frequent, but Public Transport is more prominent. Further, income is strongly related to the use of cars. Results show that a city with twice the income has 37% more journeys by Car. Yet, there are significant differences across regions. For cities in Asia, Public Transport contributes to a substantial share of their journeys. For cities in the US, Canada, Australia, and New Zealand, most of their mobility depends on Cars, regardless of city size. In Europe, there are vast heterogeneities in their modal share, from cities with mostly Active mobility (like Utrecht) to cities where Public Transport is crucial (like Paris or London) and cities where more than two out of three of their journeys are by Car (like Rome and Manchester).

Read the full article at: www.sciencedirect.com

Felipe S. Abrahão, Santiago Hernández-Orozco, Narsis A. Kiani, Jesper Tegnér, Hector Zenil

We demonstrate that Assembly Theory, pathway complexity, the assembly index, and the assembly number are subsumed and constitute a weak version of algorithmic (Kolmogorov-Solomonoff-Chaitin) complexity reliant on an approximation method based upon statistical compression, their results obtained due to the use of methods strictly equivalent to the LZ family of compression algorithms used in compressing algorithms such as ZIP, GZIP, or JPEG. Such popular algorithms have been shown to empirically reproduce the results of AT’s assembly index and their use had already been reported in successful application to separating organic from non-organic molecules, and the study of selection and evolution. Here we exhibit and prove the connections and full equivalence of Assembly Theory to Shannon Entropy and statistical compression, and AT’s disconnection as a statistical approach from causality. We demonstrate that formulating a traditional statistically compressed description of molecules, or the theory underlying it, does not imply an explanation or quantification of biases in generative (physical or biological) processes, including those brought about by selection and evolution, when lacking in logical consistency and empirical evidence. We argue that in their basic arguments, the authors of AT conflate how objects may assemble with causal directionality, and conclude that Assembly Theory does nothing to explain selection or evolution beyond known and previously established connections, some of which are reviewed here, based on sounder theory and better experimental evidence.

Read the full article at: arxiv.org

See Also:

Assembly Theory: What It Does and What It Does Not Do

Molecular assembly indices of mineral heteropolyanions: some abiotic molecules are as complex as large biomolecules

Xiao Yang, Réka Albert, Lauren Molloy Elreda, & Nilam Ram

Social influence processes can induce desired or undesired behavior change in individual members of a group. Empirical modeling of group processes and the design of network-based interventions meant to promote desired behavior change is somewhat limited be-cause the models often assume that the social influence is assimilative only and that the networks are not fully connected. We introduce a Boolean network method that addresses these two limitations. In line with dynamical systems principles, temporal changes in group members’ behavior are modeled as a Boolean network that also allows for application of control theory design of group management strategies that might direct the groups to-wards desired behavior. To illustrate the utility of the method for psychology, we apply the Boolean network method to empirical data of individuals’ self-disclosure behavior in multi-week therapy groups (N = 135, 18 groups, T = 10 ∼ 16 weeks). Empirical results provide descrip-tion of each group member’s pattern of self-disclosure and social influence and identification of group-specific network control strategies that would elicit self-disclosure from the majori-ty of the group. Of the 18 group models, 16 included both assimilative and repulsive social in-fluence. Useful control strategies were not needed for 10 already well-functioning groups, were identified for 6 groups, and were not available for 2 groups. The findings illustrate the utility of the Boolean network method for modeling the simultaneous existence of assimila-tive and repulsive social influence processes in small groups, and developing strategies that may direct groups toward desired states without manipulating social ties.

Read the full article at: advances.in

HONGZHONG DENG, JI LI, HONGQIAN WU, and BINGFENG GE

Advances in Complex SystemsVol. 26, No. 07n08, 2350011

System structure can affect or decide the system function. Many pioneers have analyzed the impact of system’s macro-statistical characteristics, such as degree distribution and giant component, on system performance. But only few research works were conducted on the relation of mesoscopic structure and agent property with system task performance. In this paper, we designed a scenario that, in a multiagent system, agents will try their best to form a qualified team to fulfill more system tasks under the requirements from agent property, structure and task. The theoretical and simulation results show that the agent link network, agent properties and task requirement will co-affect the dynamic team formation and at last have serious effects on a system’s task completion ratio and performance. Some factors such as network density and task introduction period have positive influence. Task execution time and team size have negative influence. Some factors show a counter-intuitive influence. The clustering coefficient has not much influence as people expected and the task publicity time isn’t bigger the better. Notably, system performance is affected by the coupling effect, instead of the independent effects of all factors. The effect of system structure on system function conditionally relies on the support from agent ability and task requirement.

Read the full article at: www.worldscientific.com

Josh C. Bongard

Communications of the ACM

The automated design, construction, and deployment of autonomous and adaptive machines is an open problem. Industrial robots are an example of autonomous yet nonadaptive machines: they execute the same sequence of actions repeatedly. Conversely, unmanned drones are an example of adaptive yet non-autonomous machines: they exhibit the adaptive capabilities of their remote human operators. To date, the only force known to be capable of producing fully autonomous as well as adaptive machines is biological evolution. In the field of evolutionary robotics,9 one class of population-based metaheuristics—evolutionary algorithms—are used to optimize some or all aspects of an autonomous robot. The use of metaheuristics sets this subfield of robotics apart from the mainstream of robotics research, in which machine learning algorithms are used to optimize the control policya of a robot. As in other branches of computer science the use of a metaheuristic algorithm has a cost and a benefit. The cost is that it is not possible to guarantee if (or when) an optimal control policy will be found for a given robot. The benefit is few assumptions must be made about the problem: evolutionary algorithms can improve both the parameters and the architecture of the robot’s control policy, and even the shape of the robot itself.

Read the full article at: cacm.acm.org

Cory Glover, Albert-László Barabási
The links of a physical network cannot cross, which often forces the network layout into non-optimal entangled states. Here we define a network fabric as a two-dimensional projection of a network and propose the average crossing number as a measure of network entanglement. We analytically derive the dependence of the crossing number on network density, average link length, degree heterogeneity, and community structure and show that the predictions accurately estimate the entanglement of both network models and of real physical networks.

Read the full article at: arxiv.org

Jordi Piñero, Ricard Solé, and Artemy Kolchinsky
Phys. Rev. Research 6, 013275

Harvesting free energy from the environment is essential for the operation of many biological and artificial systems. We use techniques from stochastic thermodynamics to investigate the maximum rate of harvesting achievable by optimizing a set of reactions in a Markovian system, possibly under various kinds of topological, kinetic, and thermodynamic constraints. This question is relevant for the optimal design of new harvesting devices as well as for quantifying the efficiency of existing systems. We first demonstrate that the maximum harvesting rate can be expressed as a constrained convex optimization problem. We illustrate it on bacteriorhodopsin, a light-driven proton pump from Archaea, which we find is close to optimal under realistic conditions. In our second result, we solve the optimization problem in closed-form in three physically meaningful limiting regimes. These closed-form solutions are illustrated on two idealized models of unicyclic harvesting systems.

Read the full article at: link.aps.org

Giona Casiraghi, Georges Andres
Changes in the timescales at which complex systems evolve are essential to predicting critical transitions and catastrophic failures. Disentangling the timescales of the dynamics governing complex systems remains a key challenge. With this study, we introduce an integrated Bayesian framework based on temporal network models to address this challenge. We focus on two methodologies: change point detection for identifying shifts in system dynamics, and a spectrum analysis for inferring the distribution of timescales. Applied to synthetic and empirical datasets, these methologies robustly identify critical transitions and comprehensively map the dominant and subsidiaries timescales in complex systems. This dual approach offers a powerful tool for analyzing temporal networks, significantly enhancing our understanding of dynamic behaviors in complex systems.

Read the full article at: arxiv.org