Models Of Collective Behavior Research Articles

Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images.

With the ever-increasing quality and quantity of imaging data in biomedical research comes the demand for computational methodologies that enable efficient and reliable automated extraction of the quantitative information contained within these images. One of the challenges in providing such methodology is the need for tailoring algorithms to the specifics of the data, limiting their areas of application. Here we present a broadly applicable approach to quantification and classification of complex shapes and patterns in biological or other multi-component formations. This approach integrates the mapping of all shape boundaries within an image onto a global information-rich graph and machine learning on the multidimensional measures of the graph. We demonstrated the power of this method by (1) extracting subtle structural differences from visually indistinguishable images in our phenotype rescue experiments using the endothelial tube formations assay, (2) training the algorithm to identify biophysical parameters underlying the formation of different multicellular networks in our simulation model of collective cell behavior, and (3) analyzing the response of U2OS cell cultures to a broad array of small molecule perturbations.

Local well-posedness of the topological Euler alignment models of collective behavior

In this paper we address the problem of well-posedness of multi-dimensional topological Euler-alignment models introduced by Roman and Tadmor (2018 arXiv:1806.01371). The main result demonstrates local existence and uniqueness of classical solutions in class (ρ, u) ∈ Hm+α × Hm+1 on the periodic domain , where 0 < α < 2 is the order of singularity of the topological communication kernel ϕ(x, y), and m = m(n, α) is large. Our approach is based on new sharp coercivity estimates for the topological alignment operator which render proper a priori estimates and help stabilize viscous approximation of the system.

Game-theory models of binary collective behavior

Game-theoretic models were investigated not from the point of view of the maxima of the players' utility functions, as is usually done, but by solving algebraic equations that characterize the Nash equilibrium. This characterization is obtained for models of binary collective behavior, in which players choose one of two possible strategies. Based on the results for the general model, game-theoretic models of conformal threshold Binary Collective Behavior (BCB) are studied, provided the collective is divided into L groups. The conditions for the existence of Nash equilibria is proved. For each Nash equilibrium, its structure is defined. The results obtained are illustrated by two examples of conformal threshold BCB when the group coincides with the whole team and when the latter is divided into two groups. It is shown that the Nash equilibria in the first and second examples are analogues of the equilibria in the dynamic models of M. Granovetter and T. Schelling, respectively.

Noise-induced effects in collective dynamics and inferring local interactions from data.

In animal groups, individual decisions are best characterized by probabilistic rules. Furthermore, animals of many species live in small groups. Probabilistic interactions among small numbers of individuals lead to a so-called intrinsic noise at the group level. Theory predicts that the strength of intrinsic noise is not a constant but often depends on the collective state of the group; hence, it is also called a state-dependent noise or a multiplicative noise. Surprisingly, such noise may produce collective order. However, only a few empirical studies on collective behaviour have paid attention to such effects owing to the lack of methods that enable us to connect data with theory. Here, we demonstrate a method to characterize the role of stochasticity directly from high-resolution time-series data of collective dynamics. We do this by employing two well-studied individual-based toy models of collective behaviour. We argue that the group-level noise may encode important information about the underlying processes at the individual scale. In summary, we describe a method that enables us to establish connections between empirical data of animal (or cellular) collectives and the phenomenon of noise-induced states, a field that is otherwise largely limited to the theoretical literature. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

A network-based microfoundation of Granovetter\u2019s threshold model for social tipping

Social tipping, where minorities trigger larger populations to engage in collective action, has been suggested as one key aspect in addressing contemporary global challenges. Here, we refine Granovetter’s widely acknowledged theoretical threshold model of collective behavior as a numerical modelling tool for understanding social tipping processes and resolve issues that so far have hindered such applications. Based on real-world observations and social movement theory, we group the population into certain or potential actors, such that – in contrast to its original formulation – the model predicts non-trivial final shares of acting individuals. Then, we use a network cascade model to explain and analytically derive that previously hypothesized broad threshold distributions emerge if individuals become active via social interaction. Thus, through intuitive parameters and low dimensionality our refined model is adaptable to explain the likelihood of engaging in collective behavior where social-tipping-like processes emerge as saddle-node bifurcations and hysteresis.

Information integration and collective motility in phototactic cyanobacteria.

Cells in microbial colonies integrate information across multiple spatial and temporal scales while sensing environmental cues. A number of photosynthetic cyanobacteria respond in a directional manner to incident light, resulting in the phototaxis of individual cells. Colonies of such bacteria exhibit large-scale changes in morphology, arising from cell-cell interactions, during phototaxis. These interactions occur through type IV pili-mediated physical contacts between cells, as well as through the secretion of complex polysaccharides (‘slime’) that facilitates cell motion. Here, we describe a computational model for such collective behaviour in colonies of the cyanobacterium Synechocystis. The model is designed to replicate observations from recent experiments on the emergent response of the colonies to varied light regimes. It predicts the complex colony morphologies that arise as a result. We ask if changes in colony morphology during phototaxis can be used to infer if cells integrate information from multiple light sources simultaneously, or respond to these light sources separately at each instant of time. We find that these two scenarios cannot be distinguished from the shapes of colonies alone. However, we show that tracking the trajectories of individual cyanobacteria provides a way of determining their mode of response. Our model allows us to address the emergent nature of this class of collective bacterial motion, linking individual cell response to the dynamics of colony shape.

Asymptotic behavior and control of a “guidance by repulsion” model

We model and analyze a guiding problem, where the drivers try to steer the evaders’ positions toward a target region while the evaders always try to escape from drivers. This problem is motivated by the guidance-by-repulsion model [R. Escobedo, A. Ibañez and E. Zuazua, Optimal strategies for driving a mobile agent in a “guidance by repulsion” model, Commun. Nonlinear Sci. Numer. Simul. 39 (2016) 58–72] where the authors answer how to control the evader’s position and what is the optimal maneuver of the driver. First, we analyze well posedness and behavior of the one-driver and one-evader model, assuming of the same friction coefficients. From the long-time behavior, the exact controllability is proved in a long enough time horizon. Then, we extend the model to the multi-driver and multi-evader case. We assumed three interaction rules in the context of collective behavior models: flocking between evaders, collision avoidance between drivers and repulsive forces between drivers and evaders. These interactions depend on the relative distances, and each agent is assumed to be undistinguishable and obtained an averaged effect from the other individuals. In this model, we develop numerical simulations to systematically explore the nature of controlled dynamics in various scenarios. The optimal strategies turn out to share a common pattern to the one-driver and one-evader case: the drivers rapidly occupy the position behind the target, and want to pursuit evaders in a straight line for most of the time. Inspired by this, we build a feedback strategy which stabilizes the direction of evaders.

Comparative Analysis of Iterative Processes of Equilibrium Calculation in Oligopoly Models

The problem of choosing the optimal behavior of agents in the classic one-product model of the competitive market under linear functions of demand and costs of agents is considered. The dynamic decision-making processes under conditions of uncertainty of decision making by competitors, performed as repeatable static games within a range of admissible answers, are investigated. The analysis targets processes with rational agents using approaches of multi-step reflexive games and models of collective behavior to refine the solutions while observing the current market prices of goods. The processes are distinguished by choosing the current targets: in one case, the agents choose their current output as the current targets when refining the solutions; in another case, the current targets are perceptions of agents about the current equilibrium marginal cost of other agents. It is shown that for oligopolies with Cournot and/ or Stackelberg competition, processes with agents focused on the expected optimum output are more preferable than processes with agents focused on their perceptions of the equilibrium marginal costs of competitors because there are greater opportunities for finding equilibrium states.

Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter

Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users’ temporal activity. Time series of mentions made by individual users to each company’s Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company’s and other users’ actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.

A model of collective behavior based purely on vision.

Classical models of collective behavior often take a "bird's-eye perspective," assuming that individuals have access to social information that is not directly available (e.g., the behavior of individuals outside of their field of view). Despite the explanatory success of those models, it is now thought that a better understanding needs to incorporate the perception of the individual, i.e., how internal and external information are acquired and processed. In particular, vision has appeared to be a central feature to gather external information and influence the collective organization of the group. Here, we show that a vision-based model of collective behavior is sufficient to generate organized collective behavior in the absence of spatial representation and collision. Our work suggests a different approach for the development of purely vision-based autonomous swarm robotic systems and formulates a mathematical framework for exploration of perception-based interactions and how they differ from physical ones.

Digitalization of management: collective behavior modeling on the base of cellular emotional automate

The paper considers the author's approach to digitalization of management of the emotional state of collectives of individuals. Collective models are built on the basis of the «cellular emotional automate» proposed by the authors. The possibility of using the standard rules for cellular automate in modeling the emotional behavior of collectives is shown. The proposed approach can be attributed to artificial intelligence technologies.

Threshold Models of Collective Behavior II: The Predictability Paradox and Spontaneous Instigation

Collective behavior can be notoriously hard to predict. We revisited a possible explanation suggested by Granovetter’s classic threshold model: collective behavior can unexpectedly fail, despite a group’s strong interest in the outcome, because of the sensitivity of cascades to small random perturbations in group composition and the distribution of thresholds. Paradoxically, we found that a small amount of randomness in individual behavior can make collective behavior less sensitive to these perturbations and therefore more predictable. We also examined conditions in which collective behavior unexpectedly succeeds despite the group’s weak interest in the outcome. In groups with an otherwise intractable start-up problem, individual randomness can lead to spontaneous instigation, making outcomes more sensitive to the strength of collective interests and therefore more predictable. These effects of chance behavior become much more pronounced as group size increases. Although randomness is often assumed to be a theoretically unimportant residual category, our findings point to the need to bring individual idiosyncrasy back into the study of collective behavior.

ИНФОРМАЦИОННОАНАЛИТИЧЕСКАЯ СИСТЕМА ПОДДЕРЖКИ ПРИНЯТИЯ РЕШЕНИЙ ДЛЯ СТРАТЕГИЧЕСКОГО ПЛАНИРОВАНИЯ РАЗВИТИЯ РЕГИОНОВ И ГОРОДОВ

In practical applications, the tasks of strategic planning are complex and require many variant calculations when studying various scenarios, which are characterized by significant uncertainty in the forecast estimates. In this regard, the use of a complex of various means of information and analytical support for the activities of analysts and experts is relevant. Within the framework of this question, an information and analytical decision support system is proposed, focused on automating computational procedures in strategic planning. The system is organized on the basis of the Statistica computing platform and includes the platform's software tools, developed macros for describing and presenting statistical and analytical models, as well as decision support modules. The capabilities of the system are shown by specific examples of establishing patterns of development of Russian regions and cities in the real sector of the economy. The corresponding generalized models characterizing the state and development of regions by seven specific indicators in the period from 2012 to 2018 and by four specific indicators characterizing the development of cities in the period from 2003 to 2017 are obtained. It is established that the obtained models of collective behavior, presented in the form of equations of state, do not depend on time, but are determined only by the values of the achieved indicators. The proposed system allows you to increase the efficiency of the decisionmaking process, reduce the time and cost of developing strategic planning documents

Entropy Hierarchies for Equations of Compressible Fluids and Self-Organized Dynamics

We develop a method of obtaining a hierarchy of new higher-order entropies in the context of compressible models with local and nonlocal diffusion and isentropic pressure. The local viscosity is allowed to degenerate as the density approaches vacuum. The method provides a tool to propagate initial regularity of classical solutions provided no vacuum has formed and serves as an alternative to the classical energy method. We obtain a series of global well-posedness results for state laws in previously uncovered cases, including $p(\rho) = c_p \rho$. As an application we prove global well-posedness of collective behavior models with pressure arising from an agent-based Cucker--Smale system.

Anti-conformism in the Threshold Model of Collective Behavior

We provide a detailed study of the threshold model, where both conformist and anti-conformist agents coexist. Our study bears essentially on the convergence of the opinion dynamics in the society of agents, i.e., finding absorbing classes, cycles, etc. Also, we are interested in the existence of cascade effects, as this may constitute an undesirable phenomenon in collective behavior. We divide our study into two parts. In the first one, we basically study the threshold model supposing a fixed complete network, where every one is connected to every one, like in the seminal work of Granovetter. We study the case of a uniform distribution of the threshold, of a Gaussian distribution, and finally give a result for arbitrary distributions, supposing there is one type of anti-conformist. In a second part, we suppose that the neighborhood of an agent is random, drawn at each time step from a distribution. We distinguish the case where the degree (number of links) of an agent is fixed, and where there is an arbitrary degree distribution. We show the existence of cascades and that for most societies, the opinion converges to a chaotic situation.

A Multiscale Review of Behavioral Variation in Collective Foraging Behavior in Honey Bees.

The emergence of collective behavior from local interactions is a widespread phenomenon in social groups. Previous models of collective behavior have largely overlooked the impact of variation among individuals within the group on collective dynamics. Honey bees (Apis mellifera) provide an excellent model system for exploring the role of individual differences in collective behavior due to their high levels of individual variation and experimental tractability. In this review, we explore the causes and consequences of individual variation in behavior for honey bee foraging across multiple scales of organization. We summarize what is currently known about the genetic, developmental, and neurophysiological causes of individual differences in learning and memory among honey bees, as well as the consequences of this variation for collective foraging behavior and colony fitness. We conclude with suggesting promising future directions for exploration of the genetic and physiological underpinnings of individual differences in behavior in this model system.

DLITE Uses Cell-Cell Interface Movement to Better Infer Cell-Cell Tensions

Cell shapes and connectivities evolve over time as the colony changes shape or embryos develop. Shapes of intercellular interfaces are closely coupled with the forces resulting from actomyosin interactions, membrane tension, or cell-cell adhesions. Although it is possible to computationally infer cell-cell forces from a mechanical model of collective cell behavior, doing so for temporally evolving forces in a manner robust to digitization difficulties is challenging. Here, we introduce a method for dynamic local intercellular tension estimation (DLITE) that infers such evolution in temporal force with less sensitivity to digitization ambiguities or errors. This method builds upon previous work on single time points (cellular force-inference toolkit). We validate our method using synthetic geometries. DLITE’s inferred cell colony tension evolutions correlate better with ground truth for these synthetic geometries as compared to tension values inferred from methods that consider each time point in isolation. We introduce cell connectivity errors, angle estimate errors, connection mislocalization, and connection topological changes to synthetic data and show that DLITE has reduced sensitivity to these conditions. Finally, we apply DLITE to time series of human-induced pluripotent stem cell colonies with endogenously expressed GFP-tagged zonulae occludentes-1. We show that DLITE offers improved stability in the inference of cell-cell tensions and supports a correlation between the dynamics of cell-cell forces and colony rearrangement.

Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths

Collective behaviour is of fundamental importance in the life sciences, where it appears at levels of biological complexity from single cells to superorganisms, in demography and the social sciences, where it describes the behaviour of populations, and in the physical and engineering sciences, where it describes physical phenomena and can be used to design distributed systems. Reasoning about collective behaviour is inherently difficult, as the non-linear interactions between individuals give rise to complex emergent dynamics. Mathematical techniques have been developed to analyse systematically collective behaviour in such systems, yet these frequently require extensive formal training and technical ability to apply. Even for those with the requisite training and ability, analysis using these techniques can be laborious, time-consuming and error-prone. Together these difficulties raise a barrier-to-entry for practitioners wishing to analyse models of collective behaviour. However, rigorous modelling of collective behaviour is required to make progress in understanding and applying it. Here we present an accessible tool which aims to automate the process of modelling and analysing collective behaviour, as far as possible. We focus our attention on the general class of systems described by reaction kinetics, involving interactions between components that change state as a result, as these are easily understood and extracted from data by natural, physical and social scientists, and correspond to algorithms for component-level controllers in engineering applications. By providing simple automated access to advanced mathematical techniques from statistical physics, nonlinear dynamical systems analysis, and computational simulation, we hope to advance standards in modelling collective behaviour. At the same time, by providing expert users with access to the results of automated analyses, sophisticated investigations that could take significant effort are substantially facilitated. Our tool can be accessed online without installing software, uses a simple programmatic interface, and provides interactive graphical plots for users to develop understanding of their models.

Small-world networks and synchronisation in an agent-based model of civil violence

ABSTRACTThe rapid evolution and current ubiquity of social media as a form of communication calls for a revision of many models of collective behaviour. In this paper, we modify a classic agent-based model of civil violence by Epstein (2002) consisting of citizen and law-enforcement agents by integrating a Watts-Strogatz small-world network (SWN). The SWN simulates non-local connections between citizens, enabling influence by both local and distant neighbours and providing an analogue to social media. The objective was to examine the influence of non-local connections on civil violence dynamics for varied law-enforcement concentration and network density. For lower law-enforcement concentrations, the SWN influence leads to more frequent large-scale violent outbursts, while for higher law-enforcement concentrations, outcomes depended most strongly on the number of local neighbours. The long-range coupling across the lattice due to the SWN provides a new mechanism for non-trivial dynamics and leads to a synchronisation effect.

Deep attention networks reveal the rules of collective motion in zebrafish.

A variety of simple models has been proposed to understand the collective motion of animals. These models can be insightful but may lack important elements necessary to predict the motion of each individual in the collective. Adding more detail increases predictability but can make models too complex to be insightful. Here we report that deep attention networks can obtain a model of collective behavior that is simultaneously predictive and insightful thanks to an organization in modules. When using simulated trajectories, the model recovers the ground-truth interaction rule used to generate them, as well as the number of interacting neighbours. For experimental trajectories of large groups of 60-100 zebrafish, Danio rerio, the model obtains that interactions between pairs can approximately be described as repulsive, attractive or as alignment, but only when moving slowly. At high velocities, interactions correspond only to alignment or alignment mixed with repulsion at close distances. The model also shows that each zebrafish decides where to move by aggregating information from the group as a weighted average over neighbours. Weights are higher for neighbours that are close, in a collision path or moving faster in frontal and lateral locations. The network also extracts that the number of interacting individuals is dynamical and typically in the range 8–22, with 1–10 more important ones. Our results suggest that each animal decides by dynamically selecting information from the collective.