Collective Behavior Research Articles

TS-GRU: A Stock Gated Recurrent Unit Model Driven via Neuro-Inspired Computation

Existing risk measurement methods often fail to fully consider the impact of climatic conditions on stock market risk, making it difficult to capture dynamic patterns and long-term dependencies. To address these issues, we propose the TS-GRU method: this approach utilizes a temporal convolutional network (TCN) to extract underlying features from historical data, capturing key characteristics of time series data. Subsequently, a gated recurrent unit (GRU) model is employed to capture dynamic patterns and long-term dependencies within the stock market. Finally, the TS-GRU model is optimized using the Sparrow algorithm based on collective behavior, iteratively evaluating and refining model parameters to obtain improved solutions. Experimental results demonstrate the effectiveness of the TS-GRU method in providing accurate risk assessment and forecasting. This comprehensive approach takes into account carbon finance, climate change, and environmental factors, offering valuable insights to investors to help them to understand and manage investment risks in the ever-changing stock market.

SwaRmverse: An R package for the comparative analysis of collective motion

Abstract Collective motion, that is the coordinated spatial and temporal organisation of individuals, is a core element in the study of collective animal behaviour. The self‐organised properties of how a group moves influence its various behavioural and ecological processes, such as predator–prey dynamics, social foraging and migration. However, little is known about the inter‐ and intra‐specific variation in collective motion. Despite the significant advancement in high‐resolution tracking of multiple individuals within groups, providing collective motion data for animals in the laboratory and the field, a framework to perform quantitative comparisons across species and contexts is lacking. Here, we present the swaRmverse package. Building on two existing R packages, trackdf and swaRm, swaRmverse enables the identification and analysis of collective motion ‘events’, as presented in Papadopoulou et al. (2023), creating a unit of comparison across datasets. We describe the package's structure and showcase its functionality using existing datasets from several species and simulated trajectories from an agent‐based model. From positional time‐series data for multiple individuals (x‐y‐t‐id), swaRmverse identifies events of collective motion based on the distribution of polarisation and group speed. For each event, a suite of validated biologically meaningful metrics are calculated, and events are placed into a ‘swarm space’ through dimensional reduction techniques. Our package provides the first automated pipeline enabling the analysis of data on collective behaviour. The package allows the calculation and use of complex metrics for users without a strong quantitative background and will promote communication and data‐sharing across disciplines, standardising the quantification of collective motion across species and promoting comparative investigations.

Exploring Cognitive Bias Effects on Budget Judgment Behavior: Scoping Review and Research Agenda

Behavior is a critical aspect of public budgeting research. However, much of the scholarly focus has been on collective budgeting behavior, leaving a gap in understanding individuals’ pre-negotiation judgment behavior. This oversight is concerning given evidence from adjacent fields that highlights predictable information-processing irrationalities due to human cognitive limitations. While it is plausible that cognitive biases also influence individuals’ judgments of budget proposals, behavior-informed insights from Economics, Psychology, and Public Administration have yet to be fully integrated into public budgeting research. In this article, we differentiate between individual budget judgment and collective budget negotiation, advocating for renewed attention to budgeting behavior at the micro-level. Through a scoping review, we gather empirically verified effects of cognitive biases on private financial decision behavior and discuss these findings within the context of public sector decision-making. Our theoretical contribution includes the development of an advanced behavioral model of Public Budgeting and hypotheses to test cognitive bias effects on individual budgeting action. Amid recent criticisms of behavioral public administration, we are developing research agenda aimed not only at testing the existence of bias but also at identifying solutions to change judgment behavior, thereby better equipping individuals for collective budgeting action.

A Mathematical Model for Collective Behaviors and Emergent Patterns Driven by Multiple Distinct Stimuli Produced by Multiple Species

Collective migration underlies key developmental and disease processes in vertebrates. Mathematical models describing collective migration can shed light on emergent patterns arising from simple mechanisms. In this paper, a mathematical model for collective migration is given for arbitrary numbers and types of individuals using principles outlined as a set of assumptions, such as the assumed preference for individuals to be “close but not too close" to others. The model is then specified to the case of two species with arbitrary numbers of individuals in each species. A particular form of signal response is used that may be parameterized based on experiments involving two or three agents. In its simplest form, the model describes two species of individuals that emit distinct signals, distinguishes between them, and exhibits responses unique to the type by moving according to signal gradients in various planar regions, a situation described as "mixotaxis". Beyond this simple form, initial conditions and boundary conditions are altered to simulate specific, additional in vitro as well as in vivo dynamics. The behaviors that were specifically accounted for include motility, directed migration, and a functional response to a signal. Ultimately, the paper’s results highlight the ability of a single framework for signal and response to account for patterns seen in multi-species systems, in particular the emergent self-organization seen in the embryonic development of placodal cells, which display chase-and-run behavior, flocking behavior, herding behavior, and the splitting of a herd, depending on initial conditions. Numerical experiments focus around the primary example of neural crest and placodal cell “chase-and-run” and “flocking” behaviors; the model reproduces the separation of placodal cells into distinct clumps, as described in the literature for neural crest and placodal cell development. This model was developed to describe a heterogeneous environment and can be expanded to capture other biological systems with one or more distinct species.

Learning First Aid Knowledge Not Only for the Self But Also for Others: Toward a Collective Protection Motivation Theory

ABSTRACT The practice of learning first aid knowledge on social media has gained considerable attention as a strategy for improving public health. Yet despite this recognition, the practice has not drawn commensurate academic attention. Different from individual health protection behavior, first aid knowledge learning is a collective preventive health behavior due to its collective attributes of protecting the health of others. To address the behavior, we extend the protection motivation theory (PMT) with a mixed-methods approach. Specifically, our qualitative study allows us to clarify the motivations for the behavior, confirm the applicability of PMT in this new context, and identify self- and collective cognition as well as other contextualized factors. The subsequent quantitative study validates the effect of these motivations, with the results indicating that collective cognition shows stronger effects than self-cognition in such a collective behavior context, and emotions (i.e. anticipated regret) play a significant mediating role between cognitive appraisals and protective behaviors. This study extends the health behavior literature and expands PMT by validating self- and collective dimensions. It also offers practical guidelines to practitioners on how to motivate individuals to learn first aid knowledge on social media.

Multiple strategies improved spider wasp optimization for engineering optimization problem solving

The Spider Wasp Optimization (SWO) algorithm is a swarm intelligence optimization technique inspired by the collective behaviors of social animals. This algorithm, designed to address optimization challenges, emulates the unique hunting, nesting, and mating behaviors of female spider wasps. It offers several advantages, including rapid search speed and high solution accuracy. However, when tackling complex optimization problems, it can encounter issues such as getting trapped in local optima, slow early convergence, and the need for manual adjustment of the “Trade-off Rate” (TR) parameter for different problems.To improve the performance and versatility of the SWO algorithm, a Multi-strategy Improved Spider Wasp Optimizer (MISWO) is proposed. Firstly, the Grey Wolf Algorithm is integrated into the initialization phase to enhance early convergence and improve the fitness of the initial population, thereby boosting the algorithm’s global optimization capabilities.Secondly, an adaptive step size operator and Gaussian mutation are introduced during the search phase to automatically adjust the search range at different optimization stages. This enhancement increases both the optimization accuracy and the algorithm’s ability to avoid local optima. The Trade-off Rate (TR) is dynamically selected to better accommodate a variety of problems. Finally, a dynamic lens imaging reverse learning strategy is employed to update optimal individuals, further improving the algorithm’s capacity to escape local optima. To validate the effectiveness of MISWO, it was tested on 23 benchmark functions and 7 engineering optimization problems, and compared with several state-of-the-art algorithms. Experimental results show that MISWO outperforms other algorithms in terms of optimization capability, stability, and adaptability across diverse problems.

Emergent collective behavior evolves more rapidly than individual behavior among acorn ant species

Emergence is a fundamental concept in biology and other disciplines, but whether emergent phenotypes evolve similarly to nonemergent phenotypes is unclear. The hypothesized process of emergent evolution posits that evolutionary change in at least some collective behaviors will differ from evolutionary change in the corresponding intrinsic behaviors of isolated individuals. As a result, collective behavior might evolve more rapidly and diversify more between populations compared to individual behavior. To test whether collective behavior evolves emergently, we conducted a large comparative study using 22 ant species and gathered over 1,500 behavioral rhythm time series from hundreds of colonies and isolated individuals, totaling over 1.5 y of behavioral data. We show that analogous traits measured at individual and collective levels exhibit distinct evolutionary patterns. The estimated rates of phenotypic evolution for the rhythmicity of activity in ant colonies were faster than the evolutionary rates of the same behavior measured in isolated individual ants, and total variation across species in collective behavior was higher than variation in individual behavior. We hypothesize that more rapid evolution and higher variation is a widespread feature of emergent phenotypes relative to lower-level phenotypes across complex biological systems.

Directed motion of cognitive active agents in a crowded three-way intersection

Understanding the navigation through semi-dense crowds at intersections poses a significant challenge in pedestrian dynamics, with implications for facility design and insights into emergent collective behavior. To tackle this problem, a system of cognitive active agents at a crowded three-way intersection is studied using Langevin simulations of intelligent active Brownian particles (iABPs) with directed visual perception (resulting in non-reciprocal interactions) and self-steering avoidance—without volume exclusion. We find that the emergent self-organization depends on agent maneuverability, goal fixation, and vision angle, and identify several forms of collective behavior, including localized flocking, jamming and percolation, and self-organized rotational flows. Additionally, we demonstrate that the motion of individual agents can be characterized by fractional Brownian motion and Lévy walk models across different parameter regimes. Moreover, despite the rich variety of collective behavior, the fundamental flow diagram shows a universal curve for different vision angles. Our research highlights the impact of collision avoidance, goal following, and vision angle on the individual and collective dynamics of interacting pedestrians.

An EpCAM/Trop2 mechanostat differentially regulates collective behaviour of human carcinoma cells.

EpCAM and its close relative Trop2 are well-known cell surface markers of carcinoma, but their potential role in cancer metastasis remains unclear. They are known, however, to downregulate myosin-dependent contractility, a key parameter involved in adhesion and migration. We investigate here the morphogenetic impact of the high EpCAM and Trop2 levels typically found in epithelial breast cancer cells, using spheroids of MCF7 cells as an in vitro model. Intriguingly, EpCAM depletion stimulated spheroid cohesive spreading, while Trop2 depletion had the opposite effect. Combining cell biological and biophysical approaches, we demonstrate that while EpCAM and Trop2 both contribute to moderate cell contractility, their depletions differentially impact on the process of "wetting" a substrate, here both matrix and neighboring cells, by affecting the balance of cortical tension at cell and tissue interfaces. These distinct phenotypes can be explained by partial enrichment at specific interfaces. Our data are consistent with the EpCAM-Trop2 pair acting as a mechanostat that tunes adhesive and migratory behaviours.

CBIL: Collective Behavior Imitation Learning for Fish from Real Videos

Reproducing realistic collective behaviors presents a captivating yet formidable challenge. Traditional rule-based methods rely on hand-crafted principles, limiting motion diversity and realism in generated collective behaviors. Recent imitation learning methods learn from data but often require ground-truth motion trajectories and struggle with authenticity, especially in high-density groups with erratic movements. In this paper, we present a scalable approach, Collective Behavior Imitation Learning (CBIL), for learning fish schooling behavior directly from videos , without relying on captured motion trajectories. Our method first leverages Video Representation Learning, in which a Masked Video AutoEncoder (MVAE) extracts implicit states from video inputs in a self-supervised manner. The MVAE effectively maps 2D observations to implicit states that are compact and expressive for following the imitation learning stage. Then, we propose a novel adversarial imitation learning method to effectively capture complex movements of the schools of fish, enabling efficient imitation of the distribution of motion patterns measured in the latent space. It also incorporates bio-inspired rewards alongside priors to regularize and stabilize training. Once trained, CBIL can be used for various animation tasks with the learned collective motion priors. We further show its effectiveness across different species. Finally, we demonstrate the application of our system in detecting abnormal fish behavior from in-the-wild videos.

Dynamics and Collective Behavior of Chemically Propelled Janus Sphere Dimers in Complex Solvents.

The propulsion mechanisms and collective dynamics of chemically powered Janus sphere dimers at the micro- and nanoscales, confined in a quasi-two-dimensional geometry, are investigated using a coarse-grained microscopic dynamical model. These active Janus dimers consist of two identical Janus spheres, featuring a catalytic cap on one hemisphere. The chemical reaction taking place on the catalytic surface generates asymmetric concentration gradients of product molecules around the Janus sphere, leading to the self-propulsion of the dimers. Depending on the dimer configuration, they exhibit various motion behaviors such as forward propulsion, rotation, and restricted stochastic motion. Due to chemotactic effects and self-diffusiophoretic forces, ensembles of dimers spontaneously form diverse structures, such as transient clusters, stable rotational ensembles, and antiparallel aligned doublets. This study demonstrates that the configurations of Janus sphere dimers significantly influence their self-propulsion and collective behaviors, providing crucial insights for the design and control of active micro- and nanoscale systems.

Platform Pragmatics

This article proposes platform pragmatics as a framework for understanding collective behaviour and forms of labour within platform ecosystems. It contributes to the field of platform criticism by problematising a certain view of users as passive victims of surveillance and algorithmic governmentality. The main argument is developed by thinking through the production of content and forms by users, and their circulation through computational logic and aective contagion. Through some illustrative cases and analyses of cultural habits, the article addresses the political and aesthetic configuration of these forms of production — not only of content/forms, but also of culture and subjectivity. This is explored by thinking through three themes: the subsumption of creativity and opportunism in platform economies; the mobilisation of speculative temporalities not only in computation but also across user practices; and the generalisation of self-reflexivity as a feminised cultural behaviour and aesthetic mode. Finally, I propose to understand platform pragmatics as a mode of subaltern power, that might be alien to traditional political reason, but precisely because of this needs to be grappled with through inventive cultural and social criticism.

Mechanisms of Control and Media Protagonism in Management

Objective: The objective of this study is to investigate the mechanisms of control and media protagonism in the management of epidemics in order to demonstrate the influence that the media in association with the State and large private corporations exert on human behavior. Theoretical Framework: In this topic, the main concepts and theories that underlie the research are presented. The issue of fear arising from contagion in a scenario of disease outbreaks, aspects of collective behavior and the use of institutional and media control methods are highlighted, providing a solid basis for understanding the context of the investigation. Method: The methodology adopted for this research comprises the survey and selection of journalistic texts containing national and international coverage of outbreaks of contagious diseases to organize the corpus of comparative analysis of the influence on the process of legitimization of prophylaxis, surveillance and control measures. Results and Discussion: The results obtained revealed that the media is established with other possibilities besides surveillance, in which control is something even more present, and its role will be that of control through discourse. In the discussion section, these results are contextualized in the light of the theoretical framework, highlighting the implications and relationships identified. Possible discrepancies and limitations of the study are also considered in this section. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the fields of communication and health, in relation to the construction of the media narrative about public health with possible developments in relation to the vaccination campaigns that followed the epidemic and the pandemic that are the objects of this study. Originality/Value: This study contributes to the literature by demonstrating, in different scenarios separated from each other by a decade, but characterized by the worldwide dissemination of contagious diseases, the performance of the same logic of control and media protagonism. The relevance and value of this research are evidenced by the demonstration of the need for the journalist to perceive in his professional practice the asphyxiating implications arising from the control by the discourse imposed by the media on society.

A light-temperature neuron and its adaptive regulation

Abstract The appropriate firing modes for a neuron can be excited under the external stimulus. From the viewpoint of physical, the intrinsic biophysical effects, functional encoding, and the mechanism for the transcription of external signals play an extremely important role in building reliable neuron models. In this paper, a light-temperature neuron model is proposed by connecting a phototube and a thermistor into a nonlinear circuit for investigating the information encoding and responses of neurons under the external optical signals and temperature signals. In this neuron model, a phototube is used to encode external light signals, similar to artificial eyes, and a thermistor can encode temperature intensity. Furthermore, the Hamilton energy (HE) function of neurons is calculated based on the Helmholtz’s theorem, and a self-regulation method is designed by applying the ratio of electric field energy to magnetic field energy to estimate the self-regulation of neurons. The results show that the proposed neuron can reproduce the main characteristics of biological neurons by adjusting the external stimulus. The double coherence resonance is induced under noise temperature. These results could be helpful for researching the collective behaviors in functional neural networks.

A novel high applicability link prediction based on biased random walks

Complex systems are prevalent in nature, and link prediction is crucial for analyzing these systems. This method has gained attention for its ability to uncover various irregular movements with underlying similarities. While individual particle behavior is unpredictable, the collective behavior of large groups can be forecasted more accurately. The variability at lower scales vs universal similarities at higher scales is often overlooked. To address this, we investigate the random walk process on directed networks at a global scale. We propose an improved link prediction method using biased random walks to enhance accuracy and applicability. We first define out-degree neighbors as valid transmission options to prevent conflicts with in-degree paths. We then analyze how out-degrees and in-degrees affect particle transition probabilities, guiding particles toward high out-degrees and low in-degrees for a sustainable process. Finally, we use particle stabilization probabilities at different nodes as a similarity measure for predicting potential connections. Our method improves prediction precision and practicality, as validated by experiments on nine real-world networks, showing significant gains in accuracy and AUC scores.

Swarm Intelligence and Unmanned Systems: The Potentıal Impact of The Principles of Swarm Intelligence and Collective Behaviour in Nature On Unmanned Systems and Autonomous Organizational Structures

This paper examines the potential implications of the principles of swarm intelligence and collective behavior in nature for unmanned systems and autonomous organizational structures. Swarm intelligence is inspired by natural systems in which individual units interact according to simple rules to form a complex and organized whole. These principles can be observed in a wide range of situations, from the synchronized flight of flocks of birds to the harmonized swimming behavior of schools of fish. The study emphasizes that swarm intelligence principles have the potential to create more flexible, resilient and efficient systems with decentralized control mechanisms and autonomous decision-making processes. Furthermore, it is suggested that these approaches can find applications in many fields, from military operations to agricultural and environmental monitoring, from disaster response to urban planning. The study provides a detailed analysis of swarm behavior in nature and discusses how these behaviors can be emulated and optimized in unmanned systems. In this context, the potential impacts of swarm intelligence and collective behavior principles on unmanned systems are evaluated in terms of increasing their adaptability, optimizing energy efficiency and maximizing mission success. It is also argued that these principles can contribute to making unmanned systems more resilient to contingencies and changing environmental conditions. This theoretical study suggests that swarm intelligence principles can provide innovative solutions for future unmanned systems and autonomous organizational structures.

Spectroscopic properties under vibrational strong coupling in disordered matter from path-integral Monte Carlo simulations.

Vibrational strong coupling (VSC), the strong coupling between a Fabry-Perrot cavity and molecular vibrations at mid-infrared frequencies, has received important attention in the last years due to its capacity of modifying both vibrational spectra and chemical reactivity. VSC is a collective effect, and in this work, we introduce Path Integral Monte Carlo (PIMC) simulations that not only take into account the quantum character of the molecular vibrations and of the optical resonance of the cavity but also reproduce this collective behavior by considering multiple replicas of the molecular system. Moreover, we show that it is possible to extract from the PIMC simulations the decomposition of the hybrid optical and molecular states in terms of the bare molecular modes. On a model system of an ensemble of disordered Morse oscillators coupled to a single cavity through the Pauli-Fierz Hamiltonian, PIMC can retrieve known features obtained from analytical modes such as the Tavis-Cummings model and obtain a very close agreement with exact diagonalization for a small number of Morse oscillators. We also find that notwithstanding the anhamonic character of the Morse oscillators, the collective mode coupled to the cavity behaves as a harmonic oscillator, following the quantum central limit theorem.

Adversarial imitation learning with deep attention network for swarm systems

Swarm systems consist of a large number of interacting individuals, which exhibit complex behavior despite having simple interaction rules. However, crafting individual motion policies that can manifest desired collective behaviors poses a significant challenge due to the intricate relationship between individual policies and swarm dynamics. This paper addresses this issue by proposing an imitation learning method, which derives individual policies from collective behavior data. The approach leverages an adversarial imitation learning framework, with a deep attention network serving as the individual policy network. Our method successfully imitates three distinct collective behaviors. Utilizing the ease of analysis provided by the deep attention network, we have verified that the individual policies underlying a certain collective behavior are not unique. Additionally, we have analyzed the different individual policies discovered. Lastly, we validate the applicability of the proposed method in designing policies for swarm robots through practical implementation on swarm robots.

Self-Generated Ions Modify the Pair Interaction and the Phase Separation of Chemically Active Colloids.

Chemically active colloids that release/consume ions are an important class of active matter, and exhibit interesting collective behaviors such as phase separation, swarming, and waves. Key to these behaviors is the pair-wise interactions mediated by the concentration gradient of self-generated ions. This interaction is often simplified as a pair-wise force decaying at 1/r2, where r is the interparticle distance. Here, we show that this simplification fails for isotropic and immotile active colloids with net ion production, such as Ag colloids in H2O2. Specifically, the production of ions on the surface of the Ag colloids increases the local ion concentration, c, and attenuates the pair-wise interaction force that scales with ∇c/c. As a result, the attractive force between an Ag colloid and its neighbor (active or passive) decays at 1/r or 1/r2 for small or large r, respectively. In a population, the attraction of a colloid by a growing cluster also scales with ∇c/c, so that medium-sized clusters grow fastest, and that the cluster coarsening slows with time. These results, supported by finite element and Brownian dynamic simulations, highlight the important role of self-generated ions in shaping the collective behavior of chemically active colloids.

Collective behavior of an adapting synapse-based neuronal network with memristive effect and randomness

Collective behavior of an adapting synapse-based neuronal network with memristive effect and randomness