# behav: even the first asynchronous decisions might tend to be correct and exhibit information cascades.

<< It is usually assumed that information cascades are most likely to occur when an early but incorrect opinion spreads through the group. Here (AA) analyse models of confidence-sharing in groups and reveal the opposite result: simple but plausible models of naïve Bayesian decision-making exhibit information cascades when group decisions are synchronous; however, when group decisions are asynchronous, the early decisions reached by Bayesian decision makers tend to be correct, and dominate the group consensus dynamics. Thus early decisions actually rescue the group from making errors, rather than contribute to it. (AA) explore the likely realism of our assumed decision-making rule with reference to the evolution of mechanisms for aggregating social information, and known psychological and neuroscientific mechanisms. >>️

Andreagiovanni Reina, Thomas Bose, et al.  Asynchrony rescues statistically-optimal group decisions from information cascades through emergent leaders.  bioRxiv. doi: 10.1101/ 2022.04.05.487127. Feb16, 2023.

https://www.biorxiv.org/content/10.1101/2022.04.05.487127v3

Also

'asynchronous' in FonT

https://flashontrack.blogspot.com/search?q=asynchronous

'game' in FonT

https://flashontrack.blogspot.com/search?q=game

'behav' in FonT

https://flashontrack.blogspot.com/search?q=behav

Keywords: behavior, behaviour, game, decision-making, synchronous- asynchronous group decisions, social interactions, information cascade, life, jazz

# behav: predicting long-term collective behavior with deep learning (among fish species Hemigrammus rhodostomus)

<< Deciphering the social interactions that govern collective behavior in animal societies has greatly benefited from advancements in modern computing. Computational models diverge into two kinds of approaches: analytical models and machine learning models. This work introduces a deep learning model for social interactions in the fish species Hemigrammus rhodostomus, and compares its results to experiments and to the results of a state-of-the-art analytical model. To that end, (AA) propose a systematic methodology to assess the faithfulness of a model, based on the introduction of a set of stringent observables. (They) demonstrate that machine learning models of social interactions can directly compete against their analytical counterparts. Moreover, this work demonstrates the need for consistent validation across different timescales and highlights which design aspects critically enables (AA) deep learning approach to capture both short- and long-term dynamics. (AA) also show that this approach is scalable to other fish species. >>️

Vaios Papaspyros, Ramon Escobedo, et al. Predicting long-term collective animal behavior with deep learning. bioRxiv. doi: 10.1101/ 2023.02.15.528318. Feb 15, 2023.

https://www.biorxiv.org/content/10.1101/2023.02.15.528318v1

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keywords 'behav' in FonT

https://flashontrack.blogspot.com/search?q=behav

keyword 'ai' in FonT

https://flashontrack.blogspot.com/search?q=ai

keyword 'ia' | 'ai' in Notes 

(quasi-stochastic poetry)

https://inkpi.blogspot.com/search?q=ia

https://inkpi.blogspot.com/search?q=ai

Keywords: ai, gst, behav, behavior, cognition, machine learning, deep learning, social interactions