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Inman Harvey

Artificial Life (2024) 30 (1): 48–64.

We survey the general trajectory of artificial intelligence (AI) over the last century, in the context of influences from Artificial Life. With a broad brush, we can divide technical approaches to solving AI problems into two camps: GOFAIstic (or computationally inspired) or cybernetic (or ALife inspired). The latter approach has enabled advances in deep learning and the astonishing AI advances we see today—bringing immense benefits but also societal risks. There is a similar divide, regrettably unrecognized, over the very way that such AI problems have been framed. To date, this has been overwhelmingly GOFAIstic, meaning that tools for humans to use have been developed; they have no agency or motivations of their own. We explore the implications of this for concerns about existential risk for humans of the “robots taking over.” The risks may be blamed exclusively on human users—the robots could not care less.

Read the full article at: direct.mit.edu

Julyan H. E. Cartwright, Jitka Čejková, Elena Fimmel, Simone Giannerini, Diego Luis Gonzalez, Greta Goracci, Clara Grácio, Jeanine Houwing-Duistermaat, Dragan Matić, Nataša Mišić, Frans A. A. Mulder, Oreste Piro

Artificial Life (2024) 30 (1): 16–27.

In the mid-20th century, two new scientific disciplines emerged forcefully: molecular biology and information-communication theory. At the beginning, cross-fertilization was so deep that the term genetic code was universally accepted for describing the meaning of triplets of mRNA (codons) as amino acids. However, today, such synergy has not taken advantage of the vertiginous advances in the two disciplines and presents more challenges than answers. These challenges not only are of great theoretical relevance but also represent unavoidable milestones for next-generation biology: from personalized genetic therapy and diagnosis to Artificial Life to the production of biologically active proteins. Moreover, the matter is intimately connected to a paradigm shift needed in theoretical biology, pioneered a long time ago, that requires combined contributions from disciplines well beyond the biological realm. The use of information as a conceptual metaphor needs to be turned into quantitative and predictive models that can be tested empirically and integrated in a unified view. Successfully achieving these tasks requires a wide multidisciplinary approach, including Artificial Life researchers, to address such an endeavour.

Read the full article at: direct.mit.edu

Francis Heylighen, Shima Beigi, and Tomas Veloz

Systems 2024, 12(4), 111

This paper summarizes and reviews Chemical Organization Theory (COT), a formalism for the analysis of complex, self-organizing systems across multiple disciplines. Its elements are resources and reactions. A reaction maps a set of resources onto another set, thus representing an elementary process that transforms resources into new resources. Reaction networks self-organize into invariant subnetworks, called ‘organizations’, which are attractors of their dynamics. These are characterized by closure (no new resources are added) and self-maintenance (no existing resources are lost). Thus, they provide a simple model of autopoiesis: the organization persistently recreates its own components. The resilience of organizations in the face of perturbations depends on properties such as the size of their basin of attraction and the redundancy of their reaction pathways. Application domains of COT include the origin of life, systems biology, cognition, ecology, Gaia theory, sustainability, consciousness, and social systems.

Read the full article at: www.mdpi.com

AMBRA AMICO, LUCA VERGINER, GIONA CASIRAGHI, GIACOMO VACCARIO, AND FRANK SCHWEITZER
SCIENCE ADVANCES
17 Jan 2024
Vol 10, Issue 3

Supply chain disruptions may cause shortages of essential goods, affecting millions of individuals. We propose a perspective to address this problem via reroute flexibility. This is the ability to substitute and reroute products along existing pathways, hence without requiring the creation of new connections. To showcase the potential of this approach, we examine the US opioid distribution system. We reconstruct over 40 billion distribution routes and quantify the effectiveness of reroute flexibility in mitigating shortages. We demonstrate that flexibility (i) reduces the severity of shortages and (ii) delays the time until they become critical. Moreover, our findings reveal that while increased flexibility alleviates shortages, it comes at the cost of increased complexity: We demonstrate that reroute flexibility increases alternative path usage and slows down the distribution system. Our method enhances decision-makers’ ability to manage the resilience of supply chains.

Read the full article at: www.science.org

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