Bounded Rationality

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GPS ruined our sense of direction. Search engines weaken our memory. AI, scientists warn, could do the same to everything from creativity to critical thinking.

Alessandro Cerboni's insight:

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"​Human-in-the-loop (HITL) has emerged as the default answer to concerns about AI trust, safety and governance. The logic is that when AI systems make decisions that affect people, a human should be involved.​"

Alessandro Cerboni's insight:

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Both the fundamental constants that describe the laws of physics and the cosmological parameters that determine the properties of our universe must fall within a range of values in order for the cosmos to develop astrophysical structures and ultimately support life. This paper reviews the current constraints on these quantities. The discussion starts with an assessment of the parameters that are allowed to vary. The standard model of particle physics contains both coupling constants (α ,αs ,αw) and particle masses (mu ,md ,me) , and the allowed ranges of these parameters are discussed first. We then consider cosmological parameters, including the total energy density of the universe (Ω) , the contribution from vacuum energy (ρΛ) , the baryon-to-photon ratio (η) , the dark matter contribution (δ) , and the amplitude of primordial density fluctuations (Q) . These quantities are constrained by the requirements that the universe lives for a sufficiently long time, emerges from the epoch of Big Bang Nucleosynthesis with an acceptable chemical composition, and can successfully produce large scale structures such as galaxies. On smaller scales, stars and planets must be able to form and function. The stars must be sufficiently long-lived, have high enough surface temperatures, and have smaller masses than their host galaxies. The planets must be massive enough to hold onto an atmosphere, yet small enough to remain non-degenerate, and contain enough particles to support a biosphere of sufficient complexity. These requirements place constraints on the gravitational structure constant (αG) , the fine structure constant (α) , and composite parameters (C⋆) that specify nuclear reaction rates. We then consider specific instances of possible fine-tuning in stellar nucleosynthesis, including the triple alpha reaction that produces carbon, the case of unstable deuterium, and the possibility of stable diprotons. For all of the issues outlined above, viable universes exist over a range of parameter space, which is delineated herein. Finally, for universes with significantly different parameters, new types of astrophysical processes can generate energy and thereby support habitability.

Alessandro Cerboni's insight:

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The contemporary global economy exhibits unprecedented structural complexity—characterized by nonlinear dynamics, adaptive behaviors, and emergent properties. Understanding these phenomena requires theoretical frameworks capable of addressing complexity, path dependence, and evolutionary processes.

Complexity economics has developed to address such intellectual challenges. Originating in a seminal 1987 Santa Fe Institute workshop and first described in The Economy as an Evolving Complex System (1988), this approach fundamentally reconceptualizes economic systems as complex adaptive systems. Subsequent volumes (1997, 2005) progressively developed this framework, offering new insights into finance, technological innovation, and social interactions.

Like each of its predecessors, this fourth volume is the product of an interdisciplinary workshop hosted at the Santa Fe Institute. It represents the latest synthesis, reflecting theoretical advances and methodological developments achieved over nearly four decades. Drawing on contributions from leading scholars worldwide, the chapters span foundational questions to policy applications—from agent-based modeling and network theory to macroeconomic dynamics, innovation systems, sustainability transitions, and inequality.

The result demonstrates complexity economics' capacity to generate novel insights into phenomena that remain puzzling within traditional frameworks: financial instability, technological disruption, climate economics, and institutional change. This volume positions complexity economics as an essential analytical framework for understanding twenty-first-century economic realities.

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The contemporary global economy exhibits unprecedented structural complexity—characterized by nonlinear dynamics, adaptive behaviors, and emergent properties. Understanding these phenomena requires theoretical frameworks capable of addressing complexity, path dependence, and evolutionary processes.

Complexity economics has developed to address such intellectual challenges. Originating in a seminal 1987 Santa Fe Institute workshop and first described in The Economy as an Evolving Complex System (1988), this approach fundamentally reconceptualizes economic systems as complex adaptive systems. Subsequent volumes (1997, 2005) progressively developed this framework, offering new insights into finance, technological innovation, and social interactions.

Like each of its predecessors, this fourth volume is the product of an interdisciplinary workshop hosted at the Santa Fe Institute. It represents the latest synthesis, reflecting theoretical advances and methodological developments achieved over nearly four decades. Drawing on contributions from leading scholars worldwide, the chapters span foundational questions to policy applications—from agent-based modeling and network theory to macroeconomic dynamics, innovation systems, sustainability transitions, and inequality.

The result demonstrates complexity economics' capacity to generate novel insights into phenomena that remain puzzling within traditional frameworks: financial instability, technological disruption, climate economics, and institutional change. This volume positions complexity economics as an essential analytical framework for understanding twenty-first-century economic realities.

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Fernando Rodriguez-Vergara e Phil Husbands

Matematica 2026, 14(3), 535

Secondo la tesi di Church-Turing, il limite di ciò che è computabile è delimitato dalle macchine di Turing. Da ciò consegue che, dato che le funzioni computabili generali descrivono formalmente la nozione di meccanismi ricorsivi, si sostiene talvolta che ogni processo organismico che specifica risposte cognitive coerenti dovrebbe essere limitato alle capacità delle macchine di Turing e suscettibile di formalizzazione. Esiste, tuttavia, una profonda convinzione intuitiva che permea la scienza cognitiva contemporanea, secondo cui i fenomeni mentali, come la coscienza e l'agency, non possono essere spiegati ricorrendo a questo tipo di quadro. Nonostante alcune eccezioni, il presupposto tacito generale è che, qualunque cosa sia la mente, essa ecceda la portata di ciò che è descritto dalle nozioni di computabilità. Questa questione, vale a dire la natura della relazione tra cognizione e computazione, diventa particolarmente pertinente e sempre più rilevante come possibile fonte di una migliore comprensione del funzionamento interno della mente, nonché dei limiti delle sue implementazioni artificiali. Inoltre, sebbene venga spesso trascurato o omesso per semplificare i nostri modelli, probabilmente definirà, o almeno così sosteniamo, la direzione della futura ricerca sulla vita artificiale, la scienza cognitiva, l'intelligenza artificiale e campi correlati.

Leggi l'articolo completo su: www.mdpi.com

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Anil Seth

Why consciousness is more likely a property of life than of computation and why creating conscious, or even conscious-seeming AI, is a bad idea.

Read the full article at: www.noemamag.com

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The Evolving Landscape of Complex Systems is a curated special collection in npj Complexity inspired by themes explored at the Conference on Complex Systems 2025 (CCS25). This collection consolidates emerging advances in theory, methodologies, and applications across the multifaceted area of complexity science. It seeks contributions that span the full spectrum - from novel computational frameworks and multiscale analyses to domain-adaptive models and novel complexity science applications - reflecting the discipline’s rapid evolution. This collection invites novel research that explores: Conceptual foundations and theory: advancements in network science, emergent dynamics, agent-based modelling, nonlinear systems, and adaptive behaviours, providing refined lenses for interpreting complex phenomena. Cross-scale integration and robustness: studies elucidating how micro-level interactions scale up to macro-level patterns, resilience, and adaptation in systems spanning biological, social, technological, and ecological networks. Computational innovation: cutting-edge analytical and computational methods - ranging from data-driven approaches and AI-augmented modelling to novel simulations and multilevel inference - that enhance the understanding and manipulation of complex systems. Interdisciplinary and application-oriented research: compelling case studies where complexity science addresses urgent global challenges - such as pandemics, misinformation, climate change, socioeconomic inequality, inclusivity and diversity, and governance - demonstrating adaptability and societal relevance. Submissions are welcomed from all researchers working in complexity science, regardless of conference participation.

Complex systems fascinate because of the way dynamic microscopic interactions give rise to striking, often unexpected macroscopic structures: convection cells in fluids, patterns in ecosystems, networks in societies, and organization in biology. What unites these diverse examples is the deep link between how the agents in systems move and what structure emerges. While diverse approaches have been proposed, in addition, a unifying language may lie in variational principles and optimal control in stochastic and dissipative regimes which can offer a powerful language for understanding this interplay.

Action principles are among the most unifying ideas in science: from Lagrangian mechanics to quantum field theory, they describe how nature selects pathways. The stochastic-dissipative extensions of the principle of least action in the form of path integrals, such as by Onsager-Machlup and more recent versions provide a natural framework for describing how agents and processes, obeying fundamental physical laws, select the most probable and efficient pathways under constraints. These pathways not only govern system dynamics but also generate—and are constrained by—emergent structures. Feedback between dynamics and structure thus shapes evolution, with frozen accidents and historical contingencies balanced against tendencies toward action-efficient configurations. If dynamics select the most probable, efficient pathways, then structure itself may be seen as the lasting imprint of such pathways. Can such principles also help explain the emergence of complexity?

This Collection aims to gather theoretical, computational, and empirical contributions that advance the use of variational principles to explain and predict structure–dynamics interplay in complex systems. By doing so, we hope to move toward general non-equilibrium thermodynamics capable of grounding complexity science in physics while connecting to diverse domains of application. Contributions are welcome across disciplines, from mathematics and physics to biology, engineering, and social sciences. Themes may include, but are not limited to:

Stochastic and dissipative formulations of variational principles.

Path integrals and optimal control.

Structure formation in non-equilibrium thermodynamics.

Agent-based simulations and computational models.

Empirical case studies from physical, chemical, biological, or social systems.

Comparative perspectives with non-variational approaches.

The aim is to advance a physics-grounded framework for understanding how complex structures emerge and persist under dynamic constraints. The objective of this Collection is to foster dialogue among researchers working on different manifestations of the same fundamental questions: How do dynamics give rise to structure, how structure determines dynamics, and how can variational principles provide the key to understanding this process across scales and systems? Can variational pathways explain the emergence of complex structures from dynamics across nature and society?

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RNA interference (RNAi) represents a promising approach for insect pest management; however, its application in Lepidoptera is constrained by double-stranded RNA (dsRNA) instability, limited cellular uptake, and inefficient RNAi machinery. In this study, we developed a bacteriophage MS2 virus-like particle (VLP)-based delivery platform for hairpin RNA (hpRNA) targeting the invasive pest Hyphantria cunea. When expressed in E. coli, MS2 VLPs efficiently encapsulate hpRNA, markedly enhancing its resistance to nuclease activity and environmental degradation. In addition, surface display of the HIV trans-activator of transcription (TAT) peptide on MS2 VLPs significantly improved cellular internalization of hpRNA, resulting in robust RNAi-mediated gene silencing in H. cunea at low hpRNA doses. Importantly, no adverse effects were detected in three nontarget organisms: Clostera restitura,  Plagiodera versicolora, and the parasitoid Chouioia cunea. Together, these results demonstrate that the MS2-hpRNA system represents a scalable, effective, and environmentally safe strategy for RNA-based pest control.

[email protected]'s insight:

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Nick Holton, Marianne Cottin, Adam Wright, Michael Mannino, Dayanne S. Antonio, Marcelo Bigliassi

Antifragility challenges conventional thinking by proposing that adversity is not merely to be survived but actively used to promote growth. This scoping review synthesizes 18 emerging research studies focused on antifragility in human systems across disciplines, distinguishing antifragility from resilience and robustness and highlighting key empirical gaps, particularly in psychological research. During the screening process, articles were categorized as human or non-human systems. Non-human systems (n = 29; e.g., robotics, logistics, information systems, urban planning, artificial intelligence) were excluded from synthesis to align with the review’s focus on human domains (e.g., psychology, leadership, coaching, health). Drawing from biology, psychology, and organizational studies, the review summarizes applications in mental health, performance, and quality of life. Findings emphasize the proactive nature of antifragility, in which stressors are intentionally engaged to strengthen capabilities. Biological concepts like hormesis and psychological frameworks such as post-traumatic growth align with mechanisms relevant to growth through adversity. Yet empirical studies remain scarce, underscoring the need for robust measurement tools and longitudinal designs. Future directions include refining antifragility as a state, trait, or process, developing dose-specific models, and exploring biopsychosocial correlates. Embracing antifragility could transform how individuals and systems confront challenge, not by resisting breakdown, but by evolving beyond it.

Read the full article at: journals.sagepub.com

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Erik Hoel

Patterns, Volume 7, Issue 1101472January 09, 2026

Complex systems can be described at myriad different scales, and their causal workings often have a multiscale structure (e.g., a computer can be described at the microscale of its hardware circuitry, the mesoscale of its machine code, and the macroscale of its operating system). While scientists study and model systems across the full hierarchy of their scales, from microphysics to macroeconomics, there is debate about what the macroscales of systems can possibly add beyond mere compression. To resolve this long-standing issue, here, a new theory of emergence is introduced that can distinguish which scales irreducibly contribute to a system’s causal workings. The theory’s application is demonstrated in coarse grains of Markov chains, revealing a novel measure of emergent complexity: how widely distributed a system’s causal contributions are across its hierarchy of scales.

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Marlene C. L. Batzke, Peter Steiglechner, Jan Lorenz, Bruce Edmonds, František Kalvas

Political Psychology 

Political polarization represents a rising issue in many countries, making it more and more important to understand its relation to cognitive-motivational and social influence mechanisms. Yet, the link between micro-level mechanisms and macro-level phenomena remains unclear. We investigated the consequences of individuals striving for cognitive coherence in their belief systems on political polarization in society in an agent-based model. In this, we formalized how cognitive coherence affects how individuals update their beliefs following social influence and self-reflection processes. We derive agents' political beliefs as well as their subjective belief systems, defining what determines coherence for different individuals, from European Social Survey data via correlational class analysis. The simulation shows that agents polarize in their beliefs when they have a strong strive for cognitive coherence, and especially when they have structurally different belief systems. In a mathematical analysis, we not only explain the main findings but also underscore the necessity of simulations for understanding the complex dynamics of socially embedded phenomena such as political polarization.

Read the full article at: onlinelibrary.wiley.com

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Andrea Roli, Sudip Patra, Stuart Kauffman

Interface Focus (2025) 15 (6): 20250038

We discuss the creation of information in the evolution of the biosphere by elaborating on the interplay between affordances and constraints. We maintain that information is created when affordances are seized and, therefore, at the same time, meaning is generated and a new space of possibilities is created.

Read the full article at: royalsocietypublishing.org

Alessandro Cerboni's insight:

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Our book, Teaching and Learning in the Age of Generative AI, was featured by Forbes in an article by Dan Fitzpatrick titled “8 AI Books Published in 2025 That Every Educator Needs on Their Shelf.”

The article highlights the book’s value in bringing together diverse scholarly and practical perspectives on generative AI in education. In particular, Professor Jason Gulya of Berkeley College (a contributor to the book) noted the strength of the volume’s range of voices and approaches, emphasizing its balance of theoretical and applied perspectives, as well as its thoughtful treatment of both the promise and the potential perils of AI technologies in teaching and learning.

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Manlio De Domenico

The possibility that evolutionary forces -- together with a few fundamental factors such as thermodynamic constraints, specific computational features enabling information processing, and ecological processes -- might constrain the logic of living systems is tantalizing. However, it is often overlooked that any practical implementation of such a logic requires complementary circuitry that, in biological systems, happens through complex networks of genetic regulation, metabolic reactions, cellular signalling, communication, social and eusocial non-trivial organization. Here, we review and discuss how circuitries are not merely passive structures, but active agents of change that, by means of hierarchical and modular organization, are able to enhance and catalyze the evolution of evolvability. By analyzing the role of non-trivial topologies in major evolutionary transitions under the lens of statistical physics and nonlinear dynamics, we show that biological innovations are strictly related to circuitry and its deviation from trivial structures and (thermo)dynamic equilibria. We argue that sparse heterogeneous networks such as hierarchical modular, which are ubiquitously observed in nature, are favored in terms of the trade-off between energetic costs for redundancy, error-correction and mantainance. We identify three main features -- namely, interconnectivity, plasticity and interdependency -- pointing towards a unifying framework for modeling the phenomenology, discussing them in terms of dynamical systems theory, non-equilibrium thermodynamics and evolutionary dynamics. Within this unified picture, we also show that “slow” evolutionary dynamics is an emergent phenomenon governed by the replicator-mutator equation as the direct consequence of a constrained variational nonequilibrium process. Overall, this work highlights how dynamical systems theory and nonequilibrium thermodynamics provide powerful analytical techniques to study biological complexity.

Read the full article at: iopscience.iop.org

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