Papers

INBAL ARNON, SIMON KIRBY, JENNY A. ALLEN, CLAIRE GARRIGUE, EMMA L. CARROLL, AND ELLEN C. GARLAND
SCIENCE 6 Feb 2025 Vol 387, Issue 6734 pp. 649-653

Humpback whale song is a culturally transmitted behavior. Human language, which is also culturally transmitted, has statistically coherent parts whose frequency distribution follows a power law. These properties facilitate learning and may therefore arise because of their contribution to the faithful transmission of language over multiple cultural generations. If so, we would expect to find them in other culturally transmitted systems. In this study, we applied methods based on infant speech segmentation to 8 years of humpback recordings, uncovering in whale song the same statistical structure that is a hallmark of human language. This commonality, in two evolutionarily distant species, points to the role of learning and cultural transmission in the emergence of properties thought to be unique to human language.

Read the full article at: www.science.org

Nicolas Rouleau and Michael Levin

It is commonly assumed that a useful theory of consciousness (ToC) will, among other things, explain why consciousness is associated with brains. However, the findings of evolutionary biology, developmental bioelectricity, and synthetic bioengineering are revealing the ancient pre-neural roots of many mechanisms and algorithms occurring in brains – the implication of which is that minds may have preceded brains. Most of the work in the emerging field of diverse intelligence emphasizes externally observable problem-solving competencies in unconventional media, such as cells, tissues, and life-technology chimeras. Here, we inquire about the implications of these developments for theories that make a claim about what is necessary and/or sufficient for consciousness. Specifically, we analyze popular current ToCs to ask: what features of the theory specifically pick out brains as a privileged substrate of inner perspective, or, do the features emphasized by the theory occur elsewhere. We find that the operations and functional principles described or predicted by most ToCs are remarkably similar, that these similarities are obscured by reference to particular neural substrates, and that the focus on brains is more driven by convention and limitations of imagination than by any specific content of existing ToCs. Encouragingly, several contemporary theorists have made explicit efforts to apply their theories to synthetic systems in light of the recent wave of technological developments in artificial intelligence (AI) and organoid bioengineering. We suggest that the science of consciousness should be significantly open to minds in unconventional embodiments.

Read the full article at: osf.io

Elisa Heinrich Mora, Kaleda K. Denton, Michael E. Palmer, and Marcus W. Feldman

PNAS 122 (3) e2417078122

Conformist and anticonformist biases in acquiring cultural variants have been documented in humans and several nonhuman species. We introduce a framework for quantifying these biases when cultural traits are ordered, with greater and lesser values, and either continuous (e.g., level of a behavior) or discrete (e.g., number of displays of a behavior). Unlike previous models, we do not measure a cultural variant’s popularity by its distance to the population mean, but rather by its distance to other variants. We find that conformity can produce a variety of population distributions that need not center around the initial population’s mean variant. Anticonformity may lead to highly polarized or uniformly distributed populations, depending on its strength and on individuals’ precision when copying others.

Read the full article at: www.pnas.org

Marina Dubova, et al.

PNAS 122 (5) e2401230121

The preference for simple explanations, known as the parsimony principle, has long guided the development of scientific theories, hypotheses, and models. Yet recent years have seen a number of successes in employing highly complex models for scientific inquiry (e.g., for 3D protein folding or climate forecasting). In this paper, we reexamine the parsimony principle in light of these scientific and technological advancements. We review recent developments, including the surprising benefits of modeling with more parameters than data, the increasing appreciation of the context-sensitivity of data and misspecification of scientific models, and the development of new modeling tools. By integrating these insights, we reassess the utility of parsimony as a proxy for desirable model traits, such as predictive accuracy, interpretability, effectiveness in guiding new research, and resource efficiency. We conclude that more complex models are sometimes essential for scientific progress, and discuss the ways in which parsimony and complexity can play complementary roles in scientific modeling practice.

Read the full article at: www.pnas.org

Ana Maria Jaramillo, Mariana Macedo, Marcos Oliveira, Fariba Karimi, Ronaldo Menezes

The participation of women in academia has increased in the last few decades across many fields (e.g., Computer Science, History, Medicine). However, this increase in the participation of women has not been the same at all career stages. Here, we study how gender participation within different fields is related to gender representation in top-ranking positions in productivity (number of papers), research impact (number of citations), and co-authorship networks (degree of connectivity). We analyzed over 80 million papers published from 1975 to 2020 in 19 academic fields. Our findings reveal that women remain a minority in all 19 fields, with physics, geology, and mathematics having the lowest percentage of papers authored by women at 14% and psychology having the largest percentage at 39%. Women are significantly underrepresented in top-ranking positions (top 10% or higher) across all fields and metrics (productivity, citations, and degree), indicating that it remains challenging for early researchers (especially women) to reach top-ranking positions, as our results reveal the rankings to be rigid over time. Finally, we show that in most fields, women and men with comparable productivity levels and career age tend to attain different levels of citations, where women tend to benefit more from co-authorships, while men tend to benefit more from productivity, especially in pSTEMs. Our findings highlight that while the participation of women has risen in some fields, they remain under-represented in top-ranking positions. Greater gender participation at entry levels often helps representation, but stronger interventions are still needed to achieve long-lasting careers for women and their participation in top-ranking positions.

Read the full article at: arxiv.org

Giorgio Kaniadakis, Tiziana Di Matteo, Antonio Maria Scarfone & Giampiero Gervino

The European Physical Journal B Volume 97, article number 203, (2024)

This issue contains peer-reviewed papers based on selected contributions presented at the International Conference on Statistical Physics (SigmaPhi) held in Chania-Crete (Greece) from July 10th to July 14th, 2023 (http://sigmaphisrv.polito.it/). The challenge facing statistical physics today is expanding beyond conventional conceptions of physics, bringing together multiple research streams that were thought to be separate and independent for the majority of the 20th century. In this topical issue, we present a collection of papers that demonstrate the current applications of statistical physics in a variety of different fields, including networks, biophysics, statistical mechanics, kinetic theory, and cosmology.

Read the full article at: link.springer.com

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

Human collective tasks in 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 functional diversity of member backgrounds would interact with each other and affect collective processes. Here we conducted three sets of human-subject experiments which involved 617 university students who collaborated anonymously in a collective ideation task on a custom-made online social network platform. We found that spatially clustered collectives with assortative 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: www.nature.com

Francesco Bullo; Pietro Hiram Guzzi

Recent years have seen a remarkable rise in the number of applications of network science in
the fields of biology and medicine. Networks in biology and medicine aim to represent the
organisation of living systems as a set of interacting elements at different scales, from the
subcellular to the population level. The initial application of network science in medicine
primarily focused on understanding the structure of protein interaction networks and using
these relationships to hypothesize new disease genes or novel therapeutic targets. At the same
time, network science has been widely applied in the context of molecular biology, for example
to model biological processes as gene regulatory networks. Now, with the continual influx
of biomedical big data, which is providing increasingly detailed information about various
aspects of molecular biology and medicine, the scale and scope of the network models used in
biology and medicine have skyrocketed. For example, improvements in medical imaging has
greatly facilitated the study of brain interaction networks.
Moving forward, it is imperative to develop approaches that holistically model the
complexity inherent in biological systems. Network science, in particular, has the potential to
answer critical questions in medicine that cannot be addressed through standard approaches.
By capitalizing on tools designed to quantify the fundamental properties of large-scale complex
systems, network science offers a complementary view to that of systems biology, which
tends to focus on basic mechanisms and small to medium-scale biological models. We believe
there has never been a better opportunity to employ network science to make sense of large,
complex biological systems and tackle some of medicine’s most challenging open questions. In this white paper, we review several key areas where network science has the best opportunity to contribute to medical applications and posit several critical future directions for the field.

Read the full article at: netscisociety.net

Edmonds, B., Hofstede, G. J., Koch, J., le Page, C., Lim, T., Lippe, M., Nöldeke, B., & van Delden, H.

Socio-Environmental Systems Modelling, 6, 18593.

Socio-Ecological System modelling projects are becoming increasingly complicated, with multiple actors and aspects being the norm. Such projects can cause problems for the modellers when this involves different elements, goals, philosophies, etc., all pulling in different directions – we call this “Chimaera Modelling.” Although such situations are common when you talk to modellers, they do not seem to be explicitly discussed in the literature. In this paper, we attempt to turn this perceived “inside” phenomenon into an “outside” phenomenon and to start a debate to increase transparency among the modelling community. We discuss the different aspects which may be relevant to this problem to start this debate, including: the underlying philosophy, modelling goals, extent of choice the modellers have, different stages of modelling, and kinds of actors that are involved. We further map out some of the dimensions with which Chimaera Modelling connects. We briefly discuss these and propose to the community as a whole to work on their methodological development, feasibility, risks and applicability as their resolution is far beyond the scope of this paper. We end with a brief description of the broad possible approaches to such situations. Our main message is a call for recognition of Chimaera Modelling as a likely side-effect of multi-stakeholder, multi-purpose projects, and to take this into account proactively at the project team level and be transparent about the tensions and contradictions that underly such modelling.

Read the full article at: sesmo.org

Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-László Barabási, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, János Kertész, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John Shawe-Taylor, Alessandro Vespignani

Artificial Intelligence Volume 339, February 2025, 104244

Read the full article at: www.sciencedirect.com

Abbas Shoja-Daliklidash, Morteza Nattagh-Najafi, Nasser Sepehri-Javan

In this paper, we address a longstanding challenge in self-organized criticality (SOC) systems: establishing a connection between sandpiles and complex networks. Our approach employs a similarity-based transfer function characterized by two parameters, =(r1,r2). Here, r1 quantifies the similarity of local activities, while r2 governs the filtration process used to convert a weighted network into a binary one. We reveal that the degree centrality distribution of the resulting network follows a generalized Gamma distribution (GGD), which transitions to a power-law distribution under specific conditions. The GGD exponents, estimated numerically, exhibit a dependency on . Notably, while both decreasing r1 and r2 lead to denser networks, r2 plays a more significant role in influencing network density. Furthermore, the Shannon entropy is observed to decrease linearly with increasing r2, whereas its variation with r1 is more gradual. An analytical expression for the Shannon entropy is proposed. To characterize the network structure, we investigate the clustering coefficient (cc), eigenvalue centrality (e), closeness centrality (c), and betweenness centrality (b). The distributions of cc, e, and c exhibit peaked profiles, while b displays a power-law distribution over a finite interval of k. Additionally, we explore correlations between the exponents and identify a specific parameter regime of  and k where the e−k, c−k, and b−k correlations become negative.

Read the full article at: arxiv.org

Leila Hedayatifar, Alfredo J. Morales, Dominic E. Saadi, Rachel A. Rigg, Olha Buchel, Amir Akhavan, Egemen Sert, Aabir Abubaker Kar, Mehrzad Sasanpour, Irving R. Epstein, Yaneer Bar-Yam

Predicting dynamic behaviors is one of the goals of science in general as well as essential to many specific applications of human knowledge to real world systems. Here we introduce an analytic approach using the sigmoid growth curve to model the dynamics of individual entities within complex systems. Despite the challenges posed by nonlinearity and unpredictability in system behaviors, we demonstrate the applicability of the sigmoid curve to capture the acceleration and deceleration of growth, predicting an entitys ultimate state well in advance of reaching it. We show that our analysis can be applied to diverse systems where entities exhibit nonlinear growth using case studies of (1) customer purchasing and (2) U.S. legislation adoption. This showcases the ability to forecast months to years ahead of time, providing valuable insights for business leaders and policymakers. Moreover, our characterization of individual component dynamics offers a framework to reveal the aggregate behavior of the entire system. We introduce a classification of entities based upon similar lifepaths. This study contributes to the understanding of complex system behaviors, offering a practical tool for prediction and system behavior insight that can inform strategic decision making in multiple domains.

Read the full article at: arxiv.org

Chris Fields, Michael Levin

Physics of Life Reviews

• An information-theoretic approach to biology renders “objects” and “processes” interchangable at every scale.
• Morphogenesis is a process of memory construction at every scale.
• Life depends on lateral information flows between its component lineages at every scale.
• Viewing living systems as multi-scale competency architectures forefronts communication via scale-appropriate interfaces, as opposed to manipulation of components, as a strategy for both therapuetic intervention and bioengineering.

Read the full article at: www.sciencedirect.com

Stuart Bartlett, Andrew W. Eckford, Matthew Egbert, Manasvi Lingam, Artemy Kolchinsky, Adam Frank, Gourab Ghoshal

This paper explores the idea that information is an essential and distinctive feature of living systems. Unlike non-living systems, living systems actively acquire, process, and use information about their environments to respond to changing conditions, sustain themselves, and achieve other intrinsic goals. We discuss relevant theoretical frameworks such as ``semantic information'' and ``fitness value of information''. We also highlight the broader implications of our perspective for fields such as origins-of-life research and astrobiology. In particular, we touch on the transition to information-driven systems as a key step in abiogenesis, informational constraints as determinants of planetary habitability, and informational biosignatures for detecting life beyond Earth. We briefly discuss experimental platforms which offer opportunities to investigate these theoretical concepts in controlled environments. By integrating theoretical and experimental approaches, this perspective advances our understanding of life's informational dynamics and its universal principles across diverse scientific domains.

Read the full article at: arxiv.org

Roberto Murcio, Balamurugan Soundararaj

The accurate estimation of human activity in cities is one of the first steps towards understanding the structure of the urban environment. Human activities are highly granular and dynamic in spatial and temporal dimensions. Estimating confidence is crucial for decision-making in numerous applications such as urban management, retail, transport planning and emergency management. Detecting general trends in the flow of people between spatial locations is neither obvious nor easy due to the high cost of capturing these movements without compromising the privacy of those involved. This research intends to address this problem by examining the movement of people in a SmartStreetSensors network at a fine spatial and temporal resolution using a Transfer Entropy approach.

Read the full article at: arxiv.org