Emergent Behavior Research Articles

Defensive behavior is linked to altered surface chemistry following infection in a termite society

The care-kill response determines whether a sick individual will be treated or eliminated from an insect society, but little is known about the physiological underpinnings of this process. We exploited the stepwise infection dynamics of an entomopathogenic fungus in a termite to explore how care-kill transitions occur, and identify the chemical cues behind these shifts. We found collective responses towards pathogen-injected individuals to vary according to severity and timing of pathogen challenge, with elimination, via cannibalism, occurring sooner in response to a severe active infection. However, injection with inactivated fungal blastospores also resulted in increased albeit delayed cannibalism, even though it did not universally cause host death. This indicates that the decision to eliminate an individual is triggered before pathogen viability or terminal disease status has been established. We then compared the surface chemistry of differently challenged individuals, finding increased amounts of long-chained methyl-branched alkanes with similar branching patterns in individuals injected with both dead and viable fungal blastospores, with the latter showing the largest increase. This coincided with the highest amounts of observed cannibalism as well as signs of severe moribundity. Our study provides new mechanistic insight into the emergent collective behaviors involved in the disease defense of a termite society.

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.

The Atmospheric Boundary Layer and the Initiation of the MJO

Abstract The Indian Ocean is a frequent site for the initiation of the Madden–Julian oscillation (MJO). The evolution of convection during MJO initiation is intimately linked to the subcloud atmospheric mixed layer (ML). Much of the air entering developing cumulus clouds passes through the cloud base; hence, the properties of the ML are critical in determining the nature of cloud development. The properties and depth of the ML are influenced by horizontal advection, precipitation-driven cold pools, and vertical motion. To address ML behavior during the initiation of the MJO, data from the 2011/12 Dynamics of the MJO Experiment (DYNAMO) are utilized. Observations from the research vessel Revelle are used to document the ML and its modification during the time leading up to the onset phase of the October MJO. The mixed layer depth increased from ∼500 to ∼700 m during the 1–12 October suppressed period, allowing a greater proportion of boundary layer thermals to reach the lifting condensation level and hence promote cloud growth. The ML heat budget defines an equilibrium mixed layer depth that accurately diagnoses the mixed layer depth over the DYNAMO convectively suppressed period, provided that horizontal advection is included. The advection at the Revelle is significantly influenced by low-level convective outflows from the southern ITCZ. The findings also demonstrate a connection between cirrus clouds and their remote impact on ML depth and convective development through a reduction in the ML radiative cooling rate. The emergent behavior of the equilibrium mixed layer has implications for simulating the MJO with models with parameterized cloud and turbulent-scale motions.

Leader affective presence, psychological safety and team proactive problem prevention

AbstractLeader affective presence, the tendency of leaders to consistently evoke feelings in team members, has gained prominence in the context of leadership and teamwork. However, prior research lacks a comprehensive theoretical framework, focuses on limited team processes, and relies mainly on cross sectional designs to study this construct. Building upon theories of the social functions of affect, this study examines the relationship between leaders' affective presence and team member behaviour, specifically focusing on team proactive problem prevention and examining whether this relationship is mediated by team psychological safety. Using a two‐wave panel model with 504 professionals in 134 teams, our findings revealed that positive leader affective presence, characterized by instilling enthusiasm, joy and inspiration among team members, was positively associated with perceptions of team psychological safety. In turn, team psychological safety was positively related to leaders' assessments of proactive problem prevention. Interestingly, negative leader affective presence, which triggers worry, tension and stress, was not related to team psychological safety but was positively associated with team proactive problem prevention. These results highlight the importance of leader affective presence in shaping emergent team states and team member behaviour. Researchers and practitioners should thus consider these insights when assessing and intervening in leadership and teamwork processes in organizations.

Reinforcement Learning-Based Formation Pinning and Shape Transformation for Swarms

Swarm models hold significant importance as they provide the collective behavior of self-organized systems. Boids model is a fundamental framework for studying emergent behavior in swarms systems. It addresses problems related to simulating the emergent behavior of autonomous agents, such as alignment, cohesion, and repulsion, to imitate natural flocking movements. However, traditional models of Boids often lack pinning and the adaptability to quickly adapt to the dynamic environment. To address this limitation, we introduce reinforcement learning into the framework of Boids to solve the problem of disorder and the lack of pinning. The aim of this approach is to enable drone swarms to quickly and effectively adapt to dynamic external environments. We propose a method based on the Q-learning network to improve the cohesion and repulsion parameters in the Boids model to achieve continuous obstacle avoidance and maximize spatial coverage in the simulation scenario. Additionally, we introduce a virtual leader to provide pinning and coordination stability, reflecting the leadership and coordination seen in drone swarms. To validate the effectiveness of this method, we demonstrate the model’s capabilities through empirical experiments with drone swarms, and show the practicality of the RL-Boids framework.

System confluence and the reinvention of automobility.

The aim of the paper is to provide a longitudinal account of the emergence and stabilization of the automobility system and to assess the contemporary state of the system in the early stages of an ongoing sustainability transition. The production, use, and disposal of cars, in a pervasive global automobility system, are examined to reveal and explain the growing sustainability significance of overlaps with other systems. System-to-system confluence to varying degrees is ongoing with electricity, housing, aerospace, and information, software, and communications systems. The interfaces between multiple systems are evidenced by contestation for legitimacy via technological innovation and organizational experimentation. The result is uncertainty among key actors and stakeholders, institutional reforms, diverse corporate strategies, and emergent societal practices and behaviors. The paper thereby provides a contextualized account of the tension between barriers to change that may preserve the coherence of the automobility system and differential boundary effects arising from the impact of other production-consumption systems that may result in regime fragmentation. Confluence with other systems may resolve some sustainability contradictions but will also create new ones. Appeal to sustainability science will be key to evaluating how far existing sustainability problems will be resolved, and how far new ones will emerge in the automobility transition.

Design, Selection, and Evaluation of Reinforcement Learning Single Agents for Ground Target Tracking

Previous approaches for small fixed-wing unmanned air systems that carry strapdown rather than gimbaled cameras achieved satisfactory ground target tracking performance using both standard and deep reinforcement learning algorithms. However, these approaches have significant restrictions and abstractions to the dynamics of the vehicle, such as constant airspeed and constant altitude, because the number of states and actions was necessarily limited. Thus, extensive tuning was required to obtain good tracking performance. The expansion from 4 state–action degrees of freedom to 15 enabled the agent to exploit previous reward functions that produced novel yet undesirable emergent behavior. This paper investigates the causes of and various potential solutions to undesirable emergent behavior in the ground target tracking problem. A combination of changes to the environment, reward structure, action space simplification, command rate, and controller implementation provides insight into obtaining stable tracking results. Consideration is given to reward structure selection and refinement to mitigate undesirable emergent behavior. Results presented in the paper for a simulated environment of a single unmanned air system tracking a randomly moving single ground target show that a soft actor–critic algorithm can produce feasible tracking trajectories without limiting the state space and action space, provided that the environment is properly posed.

The validation of pedestrian trajectories during turning and obstacle avoidance in virtual environments

Virtual Reality (VR) enables to study pedestrian evacuation behavior in emergencies. Yet, studies are lacking in assessing the similarity of pedestrian behaviors like avoiding and turning in virtual and real environments. In this work, we test pedestrians' avoiding and turning behavior in virtual environments from the first-person and third-person perspectives and compare the results in real environments based on the pedestrian trajectories. It is shown that the differences between the real and virtual environments in many indicators were significant in statistics, and the trajectories of participants from the third-person perspective in the virtual environment were much more similar to those from the real environment under our experimental settings. Meanwhile, most participants (22 out of 23) think that their avoiding and turning behaviors in the virtual environment from the third-person perspective were more in line with those in the real environment. The study shows that the third-person perspective is better than the first-person perspective in terms of trajectory indicators and controlling experience. The study could be used as a basis in the future for the perspective choosing while carrying out similar virtual experiments, so as to ensure the similarity of pedestrian trajectories in virtual and real environments.

Modelling flocks of birds and colonies of ants from the bottom up

This paper advocates the use of compositional specifications based on formal languages as a means of modelling and analysing sophisticated collective behaviour in natural systems. With the use of appropriate linguistic constructs, models can be developed that are both compact and intuitive, and can be easily refined and extended in small steps. Automated workflows can be implemented on top of this methodology to provide quick feedback, enabling rapid design iterations. To support our argument, we present three examples from the natural world, focusing on flocks of birds and colonies of ants, which feature well-known examples of emergent behaviour in collective adaptive systems. We use an agent-based language to develop simple models that aim at capturing these collective phenomena, and discuss the specific language constructs that we use in the process. Then, we adapt an existing verification tool for the language to simulate our models, and show that our simulations do display emergent behaviour.

Emergent behavior of conjugate-coupled Stuart–Landau oscillators in directed star networks

Emergent behavior of conjugate-coupled Stuart–Landau oscillators in directed star networks

Bridging the data gap between children and large language models.

Large language models (LLMs) show intriguing emergent behaviors, yet they receive around four or five orders of magnitude more language data than human children. What accounts for this vast difference in sample efficiency? Candidate explanations include children's pre-existing conceptual knowledge, their use of multimodal grounding, and the interactive, social nature of their input.

The excitable nature of polymerizing actin and the Belousov-Zhabotinsky reaction.

The intricate regulatory processes behind actin polymerization play a crucial role in cellular biology, including essential mechanisms such as cell migration or cell division. However, the self-organizing principles governing actin polymerization are still poorly understood. In this perspective article, we compare the Belousov-Zhabotinsky (BZ) reaction, a classic and well understood chemical oscillator known for its self-organizing spatiotemporal dynamics, with the excitable dynamics of polymerizing actin. While the BZ reaction originates from the domain of inorganic chemistry, it shares remarkable similarities with actin polymerization, including the characteristic propagating waves, which are influenced by geometry and external fields, and the emergent collective behavior. Starting with a general description of emerging patterns, we elaborate on single droplets or cell-level dynamics, the influence of geometric confinements and conclude with collective interactions. Comparing these two systems sheds light on the universal nature of self-organization principles in both living and inanimate systems.

Learning by Autonomous Manifold Deformation with an Intrinsic Deforming Field

A self-organized geometric model is proposed for data dimension reduction to improve the robustness of manifold learning. In the model, a novel mechanism for dimension reduction is presented by the autonomous deforming of data manifolds. The autonomous deforming vector field is proposed to guide the deformation of the data manifold. The flattening of the data manifold is achieved as an emergent behavior under the virtual elastic and repulsive interaction between the data points. The manifold’s topological structure is preserved when it evolves to the shape of lower dimension. The soft neighborhood is proposed to overcome the uneven sampling and neighbor point misjudging problems. The simulation experiment results of data sets prove its effectiveness and also indicate that implicit features of data sets can be revealed. In the comparison experiments, the proposed method shows its advantage in robustness.

Cluster-Spin-Glass Magnetic Behavior and Morphology in the Coordination Polymer Alloys FeyCo1-yBTT.

We report the synthesis of a series of pseudo-1D coordination polymer (CP) materials with the formula FeyCo1-yBTT (BTT = 1,3,5-benzenetrithiolate). These materials were structurally characterized by PXRD Rietveld, EXAFS, and PDF analyses, revealing that the CP superstructure enables a continuous and isomorphous alloy between the two homometallic compounds. Lower Fe loadings exhibit emergent spin glass magnetic behavior, such as memory effects and composition-dependent spin glass response time constants ranging from 6.9 × 10-9 s to 1.8 × 10-6 s. These data are consistent with the formation of spin clusters within the lattice. The magnetic behavior in these materials was modeled via replica exchange Monte Carlo simulation, which provides a good match for the experimentally measured spin glassing and magnetic phase transitions. These findings underscore how the rigid superstructure of CP and MOF scaffolds can enable the systematic tuning of physical properties, such as the spin glass behavior described here.

Applications of discrete computation in paleoclimate systems

Applications of discrete computation in paleoclimate systems In this earth climate system focus, Michael R. Gipp, Acting President from Marine Mining Corp, details the applications of discrete computation in paleoclimate systems. This paper introduces some concepts of the application of discrete computation in characterizing paleoclimatic systems. Such systems have chaotic inputs at different time and spatial scales. The methodology presented in this article was influenced by Crutchfield’s (1994) e-machine constructions, which were devised to characterize emergent behaviour in complex systems and appeared in Gipp (2022).

A scoping review of empirical research on emergent intraverbal behavior

AbstractFor decades, Skinner's analysis of verbal behavior has been applied in a variety of contexts. One critically important topic in this area is the extent to which some verbal operants may be learned indirectly, as a result of learning other verbal operants. This phenomenon is often referred to as emergent verbal behavior, and is critical to our understanding of how language is learned in a generative fashion across the lifespan. Emergent intraverbal repertoires are especially important because responses under some degree of intraverbal control may constitute a majority of responses in a fully formed verbal repertoire. Recent literature reviews suggest that there are many published studies on emergent intraverbals, but that this body of literature is highly heterogeneous. To provide an overview of this literature and map out the various tactics used across studies, we conducted a scoping review. We identified 99 total experiments on emergent intraverbals contained in 79 total articles. Findings are summarized in terms of populations and independent variables studied, procedural variations, and recommendations for future research.

Emergent Self-Assembly of Sustainable Plastics Based on Amino Acid Nanocrystals.

Development of biodegradable plastic materials is of primary importance in view of acute environmental and health problems associated with the accumulation of plastic waste. We fabricated a biodegradable composite material based on hydroxyethyl cellulose polymer and tyrosine nanocrystals, which demonstrates enhanced strength and ductility (typically mutually excluding properties), superior to most biodegradable plastics. This emergent behavior results from an assembly pattern that leads to a uniform nanoscale morphology and strong interactions between the components. Water-resistant biodegradable composites encapsulated with hydrophobic polycaprolactone as a protection layer were also fabricated. Self-assembly of robust sustainable plastics with emergent properties by using readily available building blocks provides a valuable toolbox for creating sustainable materials.

Acoustically Powered Nano- and Microswimmers: From Individual to Collective Behavior.

Micro- and nanoscopic particles that swim autonomously and self-assemble under the influence of chemical fuels and external fields show promise for realizing systems capable of carrying out large-scale, predetermined tasks. Different behaviors can be realized by tuning swimmer interactions at the individual level in a manner analogous to the emergent collective behavior of bacteria and mammalian cells. However, the limited toolbox of weak forces with which to drive these systems has made it difficult to achieve useful collective functions. Here, we review recent research on driving swimming and particle self-organization using acoustic fields, which offers capabilities complementary to those of the other methods used to power microswimmers. With either chemical or acoustic propulsion (or a combination of the two), understanding individual swimming mechanisms and the forces that arise between individual particles is a prerequisite to harnessing their interactions to realize collective phenomena and macroscopic functionality. We discuss here the ingredients necessary to drive the motion of microscopic particles using ultrasound, the theory that describes that behavior, and the gaps in our understanding. We then cover the combination of acoustically powered systems with other cross-compatible driving forces and the use of ultrasound in generating collective behavior. Finally, we highlight the demonstrated applications of acoustically powered microswimmers, and we offer a perspective on the state of the field, open questions, and opportunities. We hope that this review will serve as a guide to students beginning their work in this area and motivate others to consider research in microswimmers and acoustic fields.

Robotic Swarms: Decentralized Control and Emergent Behaviour

This study explores the world of robotic swarms, looking at their distributed control systems and the formation of intelligent behaviours. The paper demonstrates how decentralized control allows robotic swarms to independently decide locally while encouraging behaviours like flocking, exploration, and task distribution. These behaviours reveal how swarm systems are flexible and scalable, particularly in dynamic environments. The effects are wide-ranging and include uses in agriculture, disaster readiness, surveillance, and environmental monitoring. Robotic swarms led by decentralized control have a lot of potential to improve automation and robustness in practical settings. The study does, however, highlight the need for continued research in algorithm design to strike a balance between individual autonomy and group collaboration. When using robotic swarms, security measures and ethical considerations are crucial factors. By providing options for additional investigation and experimentation with robotic swarms as a platform for teaching and developing the subject, this research makes a positive contribution to the robotics research. Overall, the results highlight the potential and difficulties of distributed control in robotic swarms, as well as its broad impact on a variety of fields of study.

The mechanics of embedded fiber networks

Fiber networks underlie the mechanical behavior of a wide range of natural and engineered materials. Interestingly, these networks are often embedded within amorphous matrices rather than appearing in isolation. However, despite their frequent occurrence as embedded rather than isolated networks, few prior studies have focused on investigating the role of embedding on the emergent mechanical behavior of these systems. To address this, we adopt a mortar-type embedding approach within the finite element framework and perform simulations to systematically fill this knowledge gap. Within this study, we focus on soft tissues as an exemplary class of materials where embedded fiber networks are essential to mechanical function. Specifically, we investigate the role of embedding on the strain energy distribution within the networks across the bending, stretching, torsional, and shear fiber-level loading modes. Therein, we specifically focus on semi-flexible fiber networks. In addition to revealing the role of embedding on the networks themselves, we also investigate how the networks affect the mechanics of the matrix material. Together, we find that embedding fundamentally alters the mechanics of semi-flexible fiber networks and the surrounding matrices. Most importantly, we find that embedding semi-flexible fiber networks leads to strain-stiffening and negative Poynting effect of the resulting composite material. Furthermore, semi-flexible fiber networks induce stress heterogeneity in their host material and increase its resistance to compression. Overall, our work improves our fundamental understanding of an important class of materials. By making our implementation openly available, we also hope to help others learn more about embedded semi-flexible fiber networks in the context of materials other than soft tissues.