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.

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.

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.

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.

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.

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.

An examination of force maps targeted at orientation interactions in moving groups.

Force mapping is an established method for inferring the underlying interaction rules thought to govern collective motion from trajectory data. Here we examine the ability of force maps to reconstruct interactions that govern individual's tendency to orient, or align, their heading within a moving group, one of the primary factors thought to drive collective motion, using data from three established general collective motion models. Specifically, our force maps extract how individuals adjust their direction of motion on average as a function of the distance to neighbours and relative alignment in heading with these neighbours, or in more detail as a function of the relative coordinates and relative headings of neighbours. We also examine the association between plots of local alignment and underlying alignment rules. We find that the simpler force maps that examined changes in heading as a function of neighbour distances and differences in heading can qualitatively reconstruct the form of orientation interactions, but also overestimate the spatial range over which these interactions apply. More complex force maps that examine heading changes as a function of the relative coordinates of neighbours (in two spatial dimensions), can also reveal underlying orientation interactions in some cases, but are relatively harder to interpret. Responses to neighbours in both the simpler and more complex force maps are affected by group-level patterns of motion. We also find a correlation between the sizes of regions of high alignment in local alignment plots and the size of the region over which alignment rules apply when only an alignment interaction rule is in action. However, when data derived from more complex models is analysed, the shapes of regions of high alignment are clearly influenced by emergent patterns of motion, and these regions of high alignment can appear even when there is no explicit direct mechanism that governs alignment.

The Large-Scale Cultivation of Nematodes to Study Their Collective Behaviors.

Animals exhibit dynamic collective behaviors, as observed in flocks of birds, schools of fish, and crowds of humans. The collective behaviors of animals have been investigated in the fields of both biology and physics. In the laboratory, researchers have used various model animals such as the fruit fly and zebrafish for approximately a century, but it has remained a major challenge to study large-scale complex collective behavior orchestrated by these genetically tractable model animals. This paper presents a protocol to create an experimental system of collective behaviors in Caenorhabditis elegans. The propagated worms climb on the lid of the Petri plate and show collective swarming behavior. The system also controls worm interactions and behaviors by changing the humidity and light stimulation. This system allows us to examine the mechanisms underlying collective behaviors by changing environmental conditions and examining the effects of individual-level locomotion on collective behaviors using mutants. Thus, the system is useful for future research in both physics and biology.

Dynamical behavior mechanism in the network of interaction between group behavior and virus propagation.

This paper introduces a complex network of interaction between human behavior and virus transmission, in which group synchronous behavior influences cure rates. The study examines the influence of individual group behavior on virus transmission, the reciprocal influence of virus transmission on individual group behavior, and the effects of evolving network structures on cluster synchronization. It also analyzes the conditions necessary for virus extinction or the occurrence of a pandemic, as well as the conditions for achieving individual group synchronization. The paper provides discriminant conditions to distinguish between aggregation behavior and virus extinction. The proposed model effectively captures the phenomenon of resurgence observed in many viruses. The conclusions drawn are rigorously validated through simulations conducted under various conditions, confirming the validity and reliability of the findings.

Collective dynamics of a coupled Hindmarsh-Rose neurons with locally active memristor.

A Locally active memristors can mimic neural synapses, resulting in rich neuro-morphological dynamics in biological neurons. To illustrate the impact of a local active memristive synapse, we consider coupled Hindmarsh-Rose (HR) neurons. Firstly, the dynamical transitions of the proposed system are investigated using bifurcation analysis and Lyapunov exponents, and we find that the transition between periodic and chaotic states depends on the input currents and memristive coupling strength. By performing the two-parameter analysis, the existence of periodic and chaotic regions is revealed. The collective behavior is then examined by expanding the network to include memristive coupled HR neurons under different network connectivities. We show that the system achieves synchronization behavior for all network connectivities, including regular, random, and small-world, when the strength of the memristive coupling is increased.