Calvo Martín M, Rodriguez Palacio E, Deneubourg J-L, Nicolis SC

PLoS ONE 18(7): e0287845

The stability of collective decisions-making in social systems is crucial as it can lead to counterintuitive phenomena such as collective memories, where an initial choice is challenged by environmental changes. Many social species face the challenge to perform collective decisions under variable conditions. In this study, we focused on situations where isolated individuals and groups of the American cockroach (Periplaneta americana) had to choose between two shelters with different luminosities that were inverted during the experiment. The darker shelter was initially preferred, but only groups that reached a consensus within that shelter maintain their choice after the light inversion, while isolated individuals and small groups lacked site fidelity. Our mathematical model, incorporating deterministic and probabilistic elements, sheds light on the significance interactions and their stochasticity in the emergence and retention of a collective memory.

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Pratissoli, F., Reina, A., Kaszubowski Lopes, Y. et al.

Nature Communications 14, 4063 (2023).

We investigate how reliable movement can emerge in aggregates of highly error-prone individuals. The individuals—robotic modules—move stochastically using vibration motors. By coupling them via elastic links, soft-bodied aggregates can be created. We present distributed algorithms that enable the aggregates to move and deform reliably. The concept and algorithms are validated through formal analysis of the elastic couplings and experiments with aggregates comprising up to 49 physical modules—among the biggest soft-bodied aggregates to date made of autonomous modules. The experiments show that aggregates with elastic couplings can shrink and stretch their bodies, move with a precision that increases with the number of modules, and outperform aggregates with no, or rigid, couplings. Our findings demonstrate that mechanical couplings can play a vital role in reaching coherent motion among individuals with exceedingly limited and error-prone abilities, and may pave the way for low-power, stretchable robots for high-resolution monitoring and manipulation. In biology, individuals are known to achieve higher navigation accuracy when moving in a group compared to single animals. The authors show that simple self-propelled robotic modules that are incapable of accurate motion as individuals can achieve accurate group navigation once coupled via deformable elastic links.

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Conor Heins, Beren Millidge, Lancelot da Costa, Richard Mann, Karl Friston, Iain Couzin
Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and ‘social forces’ such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modelling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically-observed collective phenomena, including cohesion, milling and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference — without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal non-trivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.

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Rebecca Katz, Kate Toole, Hailey Robertson, Alaina Case, Justin Kerr, Siobhan Robinson-Marshall, Jordan Schermerhorn, Sarah Orsborn, Michael Van Maele, Ryan Zimmerman, Tess Stevens, COVID AMP Coding Team, Alexandra Phelan, Colin Carlson & Ellie Graeden
Scientific Data volume 10, Article number: 491 (2023)

As the COVID-19 pandemic unfolded in the spring of 2020, governments around the world began to implement policies to mitigate and manage the outbreak. Significant research efforts were deployed to track and analyse these policies in real-time to better inform the response. While much of the policy analysis focused narrowly on social distancing measures designed to slow the spread of disease, here, we present a dataset focused on capturing the breadth of policy types implemented by jurisdictions globally across the whole-of-government. COVID Analysis and Mapping of Policies (COVID AMP) includes nearly 50,000 policy measures from 150 countries, 124 intermediate areas, and 235 local areas between January 2020 and June 2022. With up to 40 structured and unstructured characteristics encoded per policy, as well as the original source and policy text, this dataset provides a uniquely broad capture of the governance strategies for pandemic response, serving as a critical data source for future work in legal epidemiology and political science.

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