Collective behavior from surprise minimization.

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 modeling 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 nontrivial 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.

Quantitative Metrics in Mass-Gathering Studies: A Comprehensive Systematic Review.

INTRODUCTION: Mass gatherings are events where many people come together at a specific location for a specific purpose, such as concerts, sports events, or religious gatherings, within a certain period of time. In mass-gathering studies, many rates and ratios are used to assess the demand for medical resources. Understanding such metrics is crucial for effective planning and intervention efforts. Therefore, this systematic review aims to investigate the usage of rates and ratios reported in mass-gathering studies. METHODS: In this systematic review, the PRISMA guidelines were followed. Articles published through December 2023 were searched on Web of Science, Scopus, Cochrane, and PubMed using the specified keywords. Subsequently, articles were screened based on titles, abstracts, and full texts to determine their eligibility for inclusion in the study. Finally, the articles that were related to the study's aim were evaluated. RESULTS: Out of 745 articles screened, 55 were deemed relevant for inclusion in the study. These included 45 original research articles, three special reports, three case presentations, two brief reports, one short paper, and one field report. A total of 15 metrics were identified, which were subsequently classified into three categories: assessment of population density, assessment of in-event health services, and assessment of out-of-event health services. CONCLUSION: The findings of this study revealed notable inconsistencies in the reporting of rates and ratios in mass-gathering studies. To address these inconsistencies and to standardize the information reported in mass-gathering studies, a Metrics and Essential Ratios for Gathering Events (MERGE) table was proposed. Future research should promote consistency in terminology and adopt standardized methods for presenting rates and ratios. This would not only enhance comparability but would also contribute to a more nuanced understanding of the dynamics associated with mass gatherings.

Network science for museums.

This paper introduces network science to museum studies. The spatial structure of the museum and the exhibit display largely determine what visitors see and in which order, thereby shaping their visit experience. Despite the importance of spatial properties in museum studies, few scientific tools have been developed to analyze and compare the results across museums. This paper introduces the six habitually used network science indices and assesses their applicability to museum studies. Network science is an empirical research field that focuses on analyzing the relationships between components in an attempt to understand how individual behaviors can be converted into collective behaviors. By taking the museum and the visitors as the network, this methodology could reveal unknown aspects of museum functions and visitor behavior, which could enhance exhibition knowledge and lead to better methods for creating museum narratives along the routes.

When do stereotypes undermine indirect reciprocity?

Social reputations provide a powerful mechanism to stimulate human cooperation, but observing individual reputations can be cognitively costly. To ease this burden, people may rely on proxies such as stereotypes, or generalized reputations assigned to groups. Such stereotypes are less accurate than individual reputations, and so they could disrupt the positive feedback between altruistic behavior and social standing, undermining cooperation. How do stereotypes impact cooperation by indirect reciprocity? We develop a theoretical model of group-structured populations in which individuals are assigned either individual reputations based on their own actions or stereotyped reputations based on their groups' behavior. We find that using stereotypes can produce either more or less cooperation than using individual reputations, depending on how widely reputations are shared. Deleterious outcomes can arise when individuals adapt their propensity to stereotype. Stereotyping behavior can spread and can be difficult to displace, even when it compromises collective cooperation and even though it makes a population vulnerable to invasion by defectors. We discuss the implications of our results for the prevalence of stereotyping and for reputation-based cooperation in structured populations.

Flock members experience gas pressures higher than lone individuals.

Local interactions between flock members in absence of centralized control generate collective dynamics characterized by coherent large-scale patterns. We investigate whether aggregates of individuals like birds, swarms and fishes behaving in concert with their neighbors may modify the physical properties of the fluid medium in which they are embedded. Using the K-Nearest Neighbors algorithm to simulate collective animal behavior, we showed that the occurrence of collective dynamics can modify the physical parameters of the phase space in which the interacting individuals' trajectories take place. This means that lone individuals experience the nearby fluid medium (i.e., the air in case of birds/insects and the water in case of fishes) differently from flock members. In particular, our framework suggests that a bird belonging to a group and acting collectively with its neighbors perceives the nearby atmosphere as denser, compared with an isolated bird.

Predicting the long-term collective behaviour of fish pairs with deep learning.

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus. We compare the results of our deep learning approach with experiments and with the results of a state-of-the-art analytical model. To that end, we propose a systematic methodology to assess the faithfulness of a collective motion model, exploiting a set of stringent individual and collective spatio-temporal observables. We demonstrate that machine learning (ML) models of social interactions can directly compete with their analytical counterparts in reproducing subtle experimental observables. Moreover, this work emphasizes the need for consistent validation across different timescales, and identifies key design aspects that enable our deep learning approach to capture both short- and long-term dynamics. We also show that our approach can be extended to larger groups without any retraining, and to other fish species, while retaining the same architecture of the deep learning network. Finally, we discuss the added value of ML in the context of the study of collective motion in animal groups and its potential as a complementary approach to analytical models.

Investigating the dynamics of collective behavior among pedestrians crossing roads: A multi-user virtual reality approach.

The utility maximization theory, based on the rationality of human beings, has proven effective in modeling pedestrians' decision-making processes while crossing roads. However, there are still unexplained variations in crossing behavior, and deviations from the rational utility model frequently occur in real-life scenarios. This experimental study sheds new light on the presence of inter-individual interactions among pedestrians and the nature of collective behaviors during road crossings. The present study develops a multi-pedestrian virtual reality simulator specifically designed to investigate the impact of social interaction on pedestrians' eye-scanning patterns, perceived responses, crossing behaviors, and the associated crash risk. Our findings indicate that the collective behavior significantly influences pedestrians' behaviors by diverting their attention from essential eye-scanning patterns that reflect their cognitive processes. Pedestrians in pairs exhibit a higher tendency to fixate on each other, spend less time in the decision phase, walk at a slower pace during the crossing phase, and consequently face a higher degree of exposure to dangerous situations compared to when crossing alone. Encouraged by these findings on the effects of social interaction, we discuss preventive strategies to mitigate the negative impacts of collective behavior and foster pedestrians' safety awareness.

Experimental evidence suggests intergroup relations are, by default, neutral rather than aggressive.

The target article offers a game-theoretical analysis of primitive intergroup aggression (i.e., raiding) and discusses difficulties in achieving peace. We argue the analysis does not capture the actual strategy space, missing out "do-nothing." Experimental evidence robustly shows people prefer doing nothing against out-group members over cooperating with/attacking them. Thus, the target article overestimates the likelihood of intergroup aggression.

Description of the Collective Behavior in Competition of Young Soccer Players Under-16 (U-16), Under-17 (U-17), Under-19 (U-19), and Under-23 (U-23), Considering the Areas of the Pitch and Phases of the Game.

ABSTRACT: Nieto, S, Echeazarra, I, Errekagorri, I, and Castellano, J. Description of the collective behavior in competition of young soccer players under-16 (U-16), under-17 (U-17), under-19 (U-19), and under-23 (U-23), considering the areas of the pitch and phases of the game. J Strength Cond Res 38(4): 714-723, 2024-The objective was to employ an integrated observational-notational record to describe collective behavior (CB) in competition matches of young soccer players from 4 teams, U-16, U-17, U-19, and U-23, playing for a professional soccer club academy. A total of 1,481 possessions were registered. Collective behavior, captured by GPS devices, was described with the variables: depth, surface area, stretch index, length, and width. In addition, from the notational analysis, the behavior depending on the area of the field (Z1, Z2, Z3, and Z4) and ball possession (POS), nonball possession (nPOS), and set pieces phases were contextualized. A 2-tailed analysis of variance was applied to compare the CB of the teams. Results show that: (a) in all ages/categories, CB varied significantly in and without the possession of the ball and was conditioned by the area of the pitch where the ball was; (b) CB showed higher values with the ball and greater variability without it; and (c) depending on the age, CB presented differences with larger effect sizes in depth and length, in and without the possession of the ball, in Z1 and Z2. Integrating positional data to the moment of play and areas of the field could help a more accurate analysis of the competition to better understand the CB evolution of young soccer players.

Conspiracy Theory as Individual and Group Behavior: Observations from the Flat Earth International Conference.

Conspiratorial thinking has been with humanity for a long time but has recently grown as a source of societal concern and as a subject of research in the cognitive and social sciences. We propose a three-tiered framework for the study of conspiracy theories: (1) cognitive processes, (2) the individual, and (3) social processes and communities of knowledge. At the level of cognitive processes, we identify explanatory coherence and faulty belief updating as critical ideas. At the level of the community of knowledge, we explore how conspiracy communities facilitate false belief by promoting a contagious sense of understanding, and how community norms catalyze the biased assimilation of evidence. We review recent research on conspiracy theories and explain how conspiratorial thinking emerges from the interaction of individual and group processes. As a case study, we describe observations the first author made while attending the Flat Earth International Conference, a meeting of conspiracy theorists who believe the Earth is flat. Rather than treating conspiracy belief as pathological, we take the perspective that is an extreme outcome of common cognitive processes.

Use of nominal group technique methods in the virtual setting: A reflective account and recommendations for practice.

Nominal group technique methods involve the use of structured activities within groups comprised of purposefully selected stakeholders (nominal groups), with the broad aim of achieving a level of consensus and prioritising information. In this paper, we will report how we facilitated nominal groups, using Microsoft Teams, to prioritise content for a theory-based behaviour change intervention to improve responses to clinically deteriorating patients. Our methods incorporated development and piloting of research materials, facilitation of online nominal groups with different stakeholders, and a structured approach to ranking behaviour change strategies. Practical suggestions are offered based on our experience of using this method in a virtual context.

Donald Trump e o fascismo: uma análise inspirada na teoria crítica / Donald Trump and fascism: an analysis inspired by critical theory / Donald Trump y el fascismo: un análisis inspirado en la teoría crítica / Donald Trump et le fascisme: une analyse inspirée de la théorie critique

Resumo A atuação de Donald Trump durante o período em que esteve na presidência dos Estados Unidos suscita a investigação de possíveis semelhanças entre ele e líderes fascistas do passado. A proposta deste ensaio é apresentar reflexões sobre a atuação política de Trump, inspiradas pelas discussões sobre a psicologia e a propaganda fascista na teoria crítica. Embora pareça impossível tomar Trump por um líder fascista clássico, principalmente em razão de contextos históricos muito diferentes, também é impossível desconsiderar o nexo entre suas estratégias políticas e o modus operandi de agitadores fascistas no século XX. Além disso, é inegável que sua política mobiliza elementos sociopsicológicos que remontam às análises da emergência do fascismo histórico, como a identificação com uma figura idealizada e transcendente, a submissão a uma autoridade ou causa superior e a agressividade direcionada às ameaças do out-group.

Abstract Donald Trump's actions during his presidency calls for an investigation regarding possible similarities between him and fascist leaders of the past. This essay is reflects on Trump's political actions inspired by discussions on fascist psychology and propaganda within Critical Theory. Although Trump may escape the category of a classic fascist leader, mainly due to the different historical contexts, the similarities between his political strategies and those of 20th-century fascist agitators is undeniable. Moreover, his politics mobilize socio-psychological elements that date back to the emergence of historical fascism, such as identification with an idealized and transcendent identity, submission to a superior authority or cause, and aggressiveness directed to out-group threats.

Resumen La actuación de Donald Trump durante el período en el que fue presidente de los Estados Unidos plantea la posibilidad de investigar posibles similitudes entre los líderes fascistas del pasado y él. El propósito de este ensayo es presentar reflexiones sobre la actuación política de Trump inspiradas en discusiones sobre psicología y propaganda fascista en teoría crítica. Si bien parece imposible ver a Trump como un líder fascista clásico, principalmente debido a contextos históricos muy diferentes, también es imposible ignorar el nexo entre sus estrategias políticas y el modus operandi de los agitadores fascistas en el siglo XX. Además, es innegable que su política moviliza elementos sociopsicológicos que se remontan al análisis del surgimiento del fascismo histórico, como la identificación con una identidad idealizada y trascendente, la sumisión a una autoridad o causa superior, y agresividad dirigida a amenazas del out-group.

Résumé Les actions de Donald Trump au cours de sa présidence appellent une enquête sur les similitudes possibles entre lui et les leaders fascistes du passé. Cet essai réfléchit aux actions politiques de Trump en s'inspirant des discussions sur la psychologie et la propagande fasciste au sein de la Théorie Critique. Bien que Trump puisse échapper à la catégorie de leader fasciste classique, principalement en raison de contextes historiques très différents, les similitudes entre ses stratégies politiques et celles des agitateurs fascistes du XXe siècle sont indéniable. En outre, sa politique mobilise des éléments socio-psychologiques qui remontent à l'émergence du fascisme historique, tels que l'identification à une identité idéalisée et transcendante, la soumission à une autorité ou à une cause supérieure, et l'agressivité dirigées vers les menaces du out-group.

Kin-recognition and predation shape collective behaviors in the cannibalistic nematode Pristionchus pacificus.

Kin-recognition is observed across diverse species forming an important behavioral adaptation influencing organismal interactions. In many species, the molecular mechanisms involved are difficult to characterize, but in the nematode Pristionchus pacificus molecular components regulating its kin-recognition system have been identified. These determine its predatory behaviors towards other con-specifics which prevents the killing and cannibalization of kin. Importantly, their impact on other interactions including collective behaviors is unknown. Here, we explored a high altitude adapted clade of this species which aggregates abundantly under laboratory conditions, to investigate the influence of the kin-recognition system on their group behaviours. By utilizing pairwise aggregation assays between distinct strains of P. pacificus with differing degrees of genetic relatedness, we observe aggregation between kin but not distantly related strains. In assays between distantly related strains, the aggregation ratio is frequently reduced. Furthermore, abolishing predation behaviors through CRISPR/Cas9 induced mutations in Ppa-nhr-40 result in rival strains successfully aggregating together. Finally, as Caenorhabditis elegans are found naturally occurring with P. pacificus, we also explored aggregation events between these species. Here, aggregates were dominated by P. pacificus with the presence of only a small number of predators proving sufficient to disrupt C. elegans aggregation dynamics. Thus, aggregating strains of P. pacificus preferentially group with kin, revealing competition and nepotism as previously unknown components influencing collective behaviors in nematodes.

Os Fenômenos de Massa e as Redes Sociais no Contexto Político Brasileiro: Uma Leitura Psicanalítica / Mass Phenomena and Social Networks in the Brazilian Political Context: A Psychoanalytic Reading / Fenómenos de Masas y Redes Sociales en el Contexto Político Brasileño: Una Lectura Psicoanalítica

O presente artigo tem como objetivo analisar os movimentos de massa contemporâneos presentes no cenário político brasileiro nos anos de 2013 e 2015, assim como seus efeitos no laço social, a partir das contribuições da psicanálise, valendo-se, em especial, dos textos clássicos de Freud que tratam sobre as massas e o mal-estar na cultura e das elaborações lacanianas sobre os discursos que sustentam o laço social. Com isso, busca-se levantar algumas hipóteses e refletir acerca do fenômeno de formação de massas na atualidade, onde se destaca a mais potente ferramenta de mobilização política contemporânea: as redes sociais. Atualmente, avolumam-se os exemplos no Brasil e no mundo de manifestações convocadas pelas redes sociais, com seu poder de propagação e disseminação de informação e desinformação. Nos últimos dez anos, o que colhemos é a intensificação e complexificação desses processos, conforme avança o "poder" das redes sociais e a forma como ela vem se estabelecendo em nossas sociedades. Na copulação entre ciência e capitalismo observada nas redes, os algoritmos respondem e operam de acordo com os interesses do mercado, manipulando as subjetividades.

This article aims to analyze the contemporary mass movements present on the Brazilian political scenario in the years 2013 and 2015, as well as their effects on the social bond, based on the contributions of psychoanalysis, using, in particular, Freud's classic texts that deal with the masses, civilization and its discontents and the lacanian elaborations on the discourses that sustain the social bond. With this, we seek to raise some hypotheses and reflect on the phenomenon of mass formation today, where the most powerful tool of contemporary political mobilization stands out: the social networks. Currently, examples in Brazil and around the world of demonstrations convened by social networks, with their power to propagate and disseminate information and misinformation, are growing. In the last ten years, what we have seen is the intensification and complexity of these processes, as the "power" of social networks advances and the way in which it has been establishing itself in our societies. In the copulation between science and capitalism observed in the networks, the algorithms respond and operate according to the interests of the market, manipulating subjectivities.

Este artículo tiene como objetivo analizar los movimientos de masas contemporáneos presentes en el escenario político brasileño en los años 2013 y 2015, así como sus efectos en el lazo social, a partir de las contribuciones del psicoanálisis, utilizando, en particular, de los textos clásicos de Freud que tratan sobre las masas y el malestar en la cultura y las elaboraciones lacanianas sobre los discursos que sustentan el lazo social. Con ello, buscamos plantear algunas hipótesis y reflexionar sobre el fenómeno de la formación de masas en la actualidad, donde se destaca la herramienta más poderosa de la movilización política contemporánea: las redes sociales. Actualmente, crecen los ejemplos en Brasil y en el mundo de manifestaciones convocadas por las redes sociales, con su poder de propagar y difundir información y desinformación. En los últimos diez años lo que hemos visto es la intensificación y complejidad de estos procesos, a medida que avanza el "poder" de las redes sociales y la forma en que se ha ido instalando en nuestras sociedades. En la cópula entre ciencia y capitalismo observada en las redes, los algoritmos responden y operan de acuerdo a los intereses del mercado, manipulando subjetividades.

Between-group cooperation in bonobos.

Bonobos provide insight into the origins of partner-specific cooperation in human groups.

Estimation of gestating sows' welfare status based on machine learning methods and behavioral data.

Estimating the welfare status at an individual level on the farm is a current issue to improve livestock animal monitoring. New technologies showed opportunities to analyze livestock behavior with machine learning and sensors. The aim of the study was to estimate some components of the welfare status of gestating sows based on machine learning methods and behavioral data. The dataset used was a combination of individual and group measures of behavior (activity, social and feeding behaviors). A clustering method was used to estimate the welfare status of 69 sows (housed in four groups) during different periods (sum of 2 days per week) of gestation (between 6 and 10 periods, depending on the group). Three clusters were identified and labelled (scapegoat, gentle and aggressive). Environmental conditions and the sows' health influenced the proportion of sows in each cluster, contrary to the characteristics of the sow (age, body weight or body condition). The results also confirmed the importance of group behavior on the welfare of each individual. A decision tree was learned and used to classify the sows into the three categories of welfare issued from the clustering step. This classification relied on data obtained from an automatic feeder and automated video analysis, achieving an accuracy rate exceeding 72%. This study showed the potential of an automatic decision support system to categorize welfare based on the behavior of each gestating sow and the group of sows.

Modeling fashion as an emergent collective behavior of bored individuals.

Boredom is an aversive mental state that is typically evoked by monotony and drives individuals to seek novel information. Despite this effect on individual behavior, the consequences of boredom for collective behavior remain elusive. Here, we introduce an agent-based model of collective fashion behavior in which simplified agents interact randomly and repeatedly choose alternatives from a circular space of color variants. Agents are endowed with a memory of past experiences and a boredom parameter, promoting avoidance of monotony. Simulating collective color trends with this model captures aspects of real trends observed in fashion magazines. We manipulate the two parameters and observe that the boredom parameter is essential for perpetuating fashion dynamics in our model. Furthermore, highly bored agents lead future population trends, when acting coherently or being highly popular. Taken together, our study illustrates that highly bored individuals can guide collective dynamics of a population to continuously explore different variants of behavior.

Co-evolution of conditional cooperation and social norm.

The co-evolution of conditional cooperation and social norms has garnered significant attention, yet the underlying mechanisms remain elusive. Social norms result from empirical expectations, individual expectations of group behavior, and normative expectations, the population's expectations of individual behavior. Aligning these expectations aids in norm formation, but diverse individual reactions to observed behavior and their sensitivity to norm conformity can be challenging. In our study, the agents are initially endowed with diverse conditional expectations, which mirror their anticipations regarding group behavior and their inherent inclination to conform to social norms, indicative of their sensitivity to psychic costs. These agents engage in a repeated public goods game, where their decisions to cooperate are shaped by their conditional expectations and the observed levels of cooperation within their group. Concurrently, free riders experience psychic costs determined by the overall level of cooperation, contribution costs, and the individual's inclination to adhere to social norms. Remarkably, our simulations unveil that agents commencing with random conditional expectations and a propensity to conform to norms can adapt to lower conditional expectations and moderate their propensity to conform to norms when initial cooperation levels are high and the contribution cost is reduced. Interestingly, increasing contribution costs intensify the population's response to norm enforcement, but this doesn't always result in a corresponding increase in cooperation. By incorporating population diversity and accounting for empirical and normative expectations within our model, we gain valuable insights into the intricate relationship between conditional cooperation and the emergence of social norms.

Phenomenon of Nightclub Shots: Mass Psychogenic Disease?

BACKGROUND: This article examines the possibility that the "nightclub shots" epidemic is a "mass psychogenic disease" phenomenon, by comparing the various cases of "mass sociogenic diseases" reported in the literature. We carried out a literature review on PubMed. The keywords used were "mass hysteria", "mass sociogenic disease", "mass psychogenic disease" and "epidemic of multiple unexplained symptoms". RESULTS: Our review of the literature revealed several elements common to the various "mass hysterias" we identified. These phenomena generally appear in a climate of anxiety specific to the era in which they occur, in this case the fear of bioterrorism in the 21st century. Symptoms are generally benign and transient, appearing and resolving easily without the identification of an organic cause. They usually occur in a small group of individuals, and more frequently in young people and women. The media can exacerbate the phenomenon. CONCLUSION: The phenomenon of epidemics of nightclub shots seems to fit into the common framework of "mass psychogenic diseases" identified in the literature. This diagnosis could therefore be evoked, in the absence of any other objective somatic explanation.

Recovery of dynamical similarity from lossy representations of collective behavior of midge swarms.

Understanding emergent collective phenomena in biological systems is a complex challenge due to the high dimensionality of state variables and the inability to directly probe agent-based interaction rules. Therefore, if one wants to model a system for which the underpinnings of the collective process are unknown, common approaches such as using mathematical models to validate experimental data may be misguided. Even more so, if one lacks the ability to experimentally measure all the salient state variables that drive the collective phenomena, a modeling approach may not correctly capture the behavior. This problem motivates the need for model-free methods to characterize or classify observed behavior to glean biological insights for meaningful models. Furthermore, such methods must be robust to low dimensional or lossy data, which are often the only feasible measurements for large collectives. In this paper, we show that a model-free and unsupervised clustering of high dimensional swarming behavior in midges (Chironomus riparius), based on dynamical similarity, can be performed using only two-dimensional video data where the animals are not individually tracked. Moreover, the results of the classification are physically meaningful. This work demonstrates that low dimensional video data of collective motion experiments can be equivalently characterized, which has the potential for wide applications to data describing animal group motion acquired in both the laboratory and the field.