Papers

Alva Noë

From The Entanglement, which was published last month by Princeton University Press.

When she dances, a young child already moves her body with a sensitivity to what is expected of her. Perhaps she has seen videos of Billie Eilish or Taylor Swift; she has danced with her mom; she has a bank of personalities and images that supply her with a sense of what feels right. Remarkably, what feels right has everything to do with what would look right to others—with her sensitivity, however unarticulated, to how others would respond to her. What she actualizes is nothing less than the embodiment of choreographic ideas of which she is not the author. This is a distinctively human form of intelligence at work.

The child’s dancing is the location of what I want to call an entanglement between her native impulse to move and an artistic representation of what movement is supposed to be. We come to embody choreographic ideas when we dance. We do so naturally, and we cannot avoid doing so.

Read the full article at: harpers.org

Sebastian Stengele, David Drexel and Gemma De las Cuevas

Classical spin Hamiltonians are a powerful tool to model complex systems, characterized by a local structure given by the local Hamiltonians. One of the best understood local structures is the grammar of formal languages, which are central in computer science and linguistics, and have a natural complexity measure given by the Chomsky hierarchy. If we see classical spin Hamiltonians as languages, what grammar do the local Hamiltonians correspond to? Here, we cast classical spin Hamiltonians as formal languages, and classify them in the Chomsky hierarchy. We prove that the language of (effectively) zero-dimensional spin Hamiltonians is regular, one-dimensional spin Hamiltonians is deterministic context-free, and higher-dimensional and all-to-all spin Hamiltonians is context-sensitive. This provides a new complexity measure for classical spin Hamiltonians, which captures the hardness of recognizing spin configurations and their energies. We compare it with the computational complexity of the ground state energy problem, and find a different easy-to-hard threshold for the Ising model. We also investigate the dependence on the language of the spin Hamiltonian. Finally, we define the language of the time evolution of a spin Hamiltonian and classify it in the Chomsky hierarchy. Our work suggests that universal spin models are weaker than universal Turing machines.

Read the full article at: royalsocietypublishing.org

IGOR GROSSMANN , MATTHEW FEINBERG, DAWN C. PARKER, NICHOLAS A. CHRISTAKIS, PHILIP E. TETLOCK, AND WILLIAM A. CUNNINGHAM

SCIENCE 15 Jun 2023 Vol 380, Issue 6650

Advances in artificial intelligence (AI), particularly large language models (LLMs), are substantially affecting social science research. These transformer-based machine-learning models pretrained on vast amounts of text data are increasingly capable of simulating human-like responses and behaviors (1, 2), offering opportunities to test theories and hypotheses about human behavior at great scale and speed. This presents urgent challenges: How can social science research practices be adapted, even reinvented, to harness the power of foundational AI? And how can this be done while ensuring transparent and replicable research?

Read the full article at: www.science.org

Tom Froese, Natalya Weber, Ivan Shpurov, Takashi Ikegami

The theory of autopoiesis has been influential in many areas of theoretical biology, especially in the fields of artificial life and origins of life. However, it has not managed to productively connect with mainstream biology, partly for theoretical reasons, but arguably mainly because deriving specific working hypotheses has been challenging. The theory has recently undergone significant conceptual development in the enactive approach to life and mind. Hidden complexity in the original conception of autopoiesis has been explicated in the service of other operationalizable concepts related to self-individuation: precariousness, adaptivity, and agency. Here we advance these developments by highlighting the interplay of these concepts with considerations from thermodynamics: reversibility, irreversibility, and path-dependence. We interpret this interplay in terms of the self-optimization model, and present modeling results that illustrate how these minimal conditions enable a system to re-organize itself such that it tends toward coordinated constraint satisfaction at the system level. Although the model is still very abstract, these results point in a direction where the enactive approach could productively connect with cell biology.

Read the full article at: www.biorxiv.org

The puzzling behavior of black hole interiors has led researchers to propose a new physical law: the second law of quantum complexity.

Read the full article at: www.quantamagazine.org

By treating molecules as geometric tessellations, scientists devised a new way to forecast how 2D materials might self-assemble.

Read the full article at: www.quantamagazine.org

Keith Farnsworth

This is an un-reviewed, unpublished pre-print of a paper written for a special issue of BioSystems on Code Biology. It explains why biological codes are essential for life to be autonomous. The first reason is that autonomy requires freedom from exogenous control (cause-effect) and this is achieved by stripping the force from efficient causes using transducers at the organism boundary (e.g. cell receptors) so they become signal-response: contingent influences, rather than controls. All transducers embody a cypher or a code. The second is that evolvable reproduction requires the maintenance of a separate formal information store (from Von Neumann's replicator theory), which must be causally isolated from the rest of the replicating system. This is achieved by storing the information in a form that does not correlate with the rest of the system, hence it requires code-translation to become causal. These explanations rest on the idea of efficient cause being the result of empowering formal cause with a physical force-field (Farnsworth 2022 - BioSystems). They also provide a new and powerful way of understanding semiotic communications in general.

Read the full article at: www.researchgate.net

Yan Leng , Xiaowen Dong, Esteban Moro, Alex Pentland

Information Systems Research

We use high-resolution mobile phone data with geolocation information and propose a novel technical framework to study how social influence propagates within a phone communication network and affects the offline decision to attend a performance event. Our fine-grained data are based on the universe of phone calls made in a European country between January and July 2016. We isolate social influence from observed and latent homophily by taking advantage of the rich spatial-temporal information and the social interactions available from the longitudinal behavioral data. We find that influence stemming from phone communication is significant and persists up to four degrees of separation in the communication network. Building on this finding, we introduce a new “influence” centrality measure that captures the empirical pattern of influence decay over successive connections. A validation test shows that the average influence centrality of the adopters at the beginning of each observational period can strongly predict the number of eventual adopters and has a stronger predictive power than other prevailing centrality measures such as the eigenvector centrality and state-of-the-art measures such as diffusion centrality. Our centrality measure can be used to improve optimal seeding strategies in contexts with influence over phone calls, such as targeted or viral marketing campaigns. Finally, we quantitatively demonstrate how raising the communication probability over each connection, as well as the number of initial seeds, can significantly amplify the expected adoption in the network and raise net revenue after taking into account the cost of these interventions.

Read the full article at: pubsonline.informs.org

David Matthews, Andrew Spielberg, Daniela Rus, Sam Kriegman, Josh Bongard

Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost every detail of every robot built to date has been manually determined by a human designer after several months or years of iterative ideation, prototyping, and testing. Inspired by evolutionary design in nature, the automated design of robots using evolutionary algorithms has been attempted for two decades, but it too remains inefficient: days of supercomputing are required to design robots in simulation that, when manufactured, exhibit desired behavior. Here we show for the first time de-novo optimization of a robot's structure to exhibit a desired behavior, within seconds on a single consumer-grade computer, and the manufactured robot's retention of that behavior. Unlike other gradient-based robot design methods, this algorithm does not presuppose any particular anatomical form; starting instead from a randomly-generated apodous body plan, it consistently discovers legged locomotion, the most efficient known form of terrestrial movement. If combined with automated fabrication and scaled up to more challenging tasks, this advance promises near instantaneous design, manufacture, and deployment of unique and useful machines for medical, environmental, vehicular, and space-based tasks.

Read the full article at: arxiv.org

Luca Gallo, Lucas Lacasa, Vito Latora, Federico Battiston

Many real-world complex systems are characterized by interactions in groups that change in time. Current temporal network approaches, however, are unable to describe group dynamics, as they are based on pairwise interactions only. Here, we use time-varying hypergraphs to describe such systems, and we introduce a framework based on higher-order correlations to characterize their temporal organization. We analyze various social systems, finding that groups of different sizes have typical patterns of long-range temporal correlations. Moreover, our method reveals the presence of non-trivial temporal interdependencies between different group sizes. We introduce a model of temporal hypergraphs with non-Markovian group interactions, which reveals complex memory as a fundamental mechanism underlying the pattern in the data.

Read the full article at: arxiv.org

Bo Yang

We have discovered a novel transition rule for binary cellular automata (CA) that yields self-replicating structures across two spatial and temporal scales from sparsely populated random initial conditions. Lower-level, shapeshifting clusters frequently follow a transient attractor trajectory, generating new clusters, some of which periodically self-duplicate. When the initial distribution of live cells is sufficiently sparse, these clusters coalesce into larger formations that also self-replicate. These formations may further form the boundaries of an expanding complex on an even larger scale. This rule, dubbed ``Outlier,'' is rotationally symmetric and applies to 2D Moore neighborhoods. It was evolved through Genetic Programming during an extensive automated search for rules that foster open-ended evolution in CA. While self-replicating structures, both crafted and emergent, hav

Read the full article at: arxiv.org