MELANIE MITCHELL

SCIENCE 10 Nov 2023 Vol 382, Issue 6671

Current AI systems seem to be lacking a crucial aspect of human intelligence: rich internal models of the world. A tenet of modern cognitive science is that humans are not simply conditioned-reflex machines; instead, we have inside our heads abstracted models of the physical and social worlds that reflect the causes of events rather than merely correlations among them. We rely on these mental models to simulate and predict the likely results of possible actions, to reason and plan in unfamiliar situations, to imagine counterfactuals (“what would have happened if I hadn’t stopped the car in time?”), and to update our knowledge and beliefs on the basis of experiences. Moreover, we have mental models not only of the external world and other people, but of ourselves, enabling us to assess and explain our reasoning and decision-making processes. How such models are implemented in our brains is much debated, but there is little doubt that they are foundational to our intelligence.

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Marcin Korecki, Damian Dailisan, Joshua Yang, Dirk Helbing

To improve the performance of systems, optimization has been the prevailing approach in the past. However, the approach faces challenges when multiple goals shall be simultaneously achieved. For illustration, we study a multi-agent system, where agents have a plurality of different, and mutually inconsistent goals. We then allowed agents in the system to vote on which traffic signal controllers, which were trained on different goals using deep reinforcement learning, would control the intersection. Taking decisions based on suitable voting procedures turns out to lead to favorable solutions, which perform highly for several goals rather than optimally for one goal and poorly for others. This opens up new opportunities for the management or even self-governance of complex systems that require the consideration and achievement of multiple goals, such as many systems involving humans. Here, we present results for traffic flows in urban street networks, which suggest that “democratizing traffic” would be a promising alternative to centralized control of traffic flows.

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Sean M. Carroll

Effective Field Theory (EFT) is the successful paradigm underlying modern theoretical physics, including the “Core Theory” of the Standard Model of particle physics plus Einstein’s general relativity. I will argue that EFT grants us a unique insight: each EFT model comes with a built-in specification of its domain of applicability. Hence, once a model is tested within some domain (of energies and interaction strengths), we can be confident that it will continue to be accurate within that domain. Currently, the Core Theory has been tested in regimes that include all of the energy scales relevant to the physics of everyday life (biology, chemistry, technology, etc.). Therefore, we have reason to be confident that the laws of physics underlying the phenomena of everyday life are completely known.

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Gerardo Iñiguez, Sara Heydari, János Kertész & Jari Saramäki
Nature Communications volume 14, Article number: 5217 (2023)

Tie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at the network and individual levels. Egocentric networks, networks of relationships around an individual, exhibit few strong ties and more weaker ties, as evidenced by electronic communication records. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and driving mechanisms of social tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets of interactions between millions of people during months to years. We find universality in tie strength distributions and their individual-level variation across communication modes, even in channels not reflecting offline social relationships. Via a simple model of egocentric network evolution, we show that the observed universality arises from the competition between cumulative advantage and random choice, two tie reinforcement mechanisms whose balance determines the diversity of tie strengths. Our results provide insight into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior.

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Fernando E. Rosas, Pedro A.M. Mediano, Christopher Timmermann, Andrea I Luppi, Diego Candia-Rivera, Reza Abbasi-Asl, Adam Gazzaley, Morten L Kringelbach, Suresh Daniel Muthukumaraswamy, Daniel Bor, Sarah Garfinkel, Robin Carhart-Harris

A growing body of work shows that autonomic signals provide a privileged evidence stream to capture various aspects of subjective and neural states. This work investigates the potential for autonomic markers to track the effects of psychedelics – potent psychoactive drugs with important scientific and clinical value. For this purpose, we introduce a novel Bayesian framework to estimate the entropy of heart rate dynamics under psychedelics. We also calculate Bayesian estimates of mean heart rate and heart rate variability, and investigate how these measures relate to subjective reports and neural effects. Results on datasets covering four drugs – lysergic acid diethylamide (LSD), dimethyltryptamine (DMT), psilocybin, and sub-anaesthetic doses of the dissociative agent ketamine – show consistent increases in mean heart rate, high-frequency heart rate variability, and heart rate entropy during the psychedelic experience. Moreover, these effects have predictive power over various dimensions of the psychedelic experience. Changes in heart rate entropy were found to be correlated with increases in brain entropy, while other autonomic markers were not. Overall, our results show that a cost-efficient autonomic measure has the potential to reveal surprising detail about subjective and brain states, opening up a range of new research avenues to explore in both basic and clinical neuroscience.

Read the full article at: www.biorxiv.org