Emergent Behavior Research Articles

Homophily in opinion networks affects collective risk perception in heterogeneous populations

Understanding how to accurately inform the population about the risks of disasters is key for the well-functioning of our societies. Risk communication is also an essential feature of the disaster cycle, fundamentally contributing to preparedness but also to management. Here, we use an agent-based model (ABM) to investigate such an important problem. Specifically, we study the emergent behavior of a population of individuals who revise their opinion on therisk of a certain event, based on information received from an institution, processed through individual sensitivity, and discussed with peers. Such a complex process may include several biases, e.g., due to heterogeneous risk perception across the population, and homophily, i.e., tendency of individuals to interact with like-minded people.Our ABM, which encapsulates these crucial features, allows us to perform a campaign of numerical simulations towards gaining mathematically-grounded insights into their impact on the emergent behavior of the population and, ultimately, on how accurately institutional information is received and processed by a population. Such insights can be useful to design empirical studies to test them and, in case of empirical support, to use them to design recommendations for policy decision makers.

Explosive transitions in coupled Lorenz oscillators.

We study the transition to synchronization in an ensemble of chaotic oscillators that are interacting on a star network. These oscillators possess an invariant symmetry and we study emergent behavior by introducing the timescale variations in the dynamics of the nodes and the hub. If the coupling preserves the symmetry, the ensemble exhibits consecutive explosive transitions, each one associated with a hysteresis. The first transition is the explosive synchronization from a desynchronized state to a synchronized state which occurs discontinuously with the formation of intermediate clusters. These clusters appear because of the driving-induced multistability and the resulting attractors exhibit intermittent synchrony (antisynchrony). The second transition is the explosive death that occurs as a result of stabilization of the stable fixed points. However, if the symmetry is not preserved, the system again makes a first-order transition from an oscillatory state to death, namely, an explosive death. These transitions are studied with the help of the master stability functions, Lyapunov exponents, and the stability analysis.

Body and Brain Quality-Diversity in Robot Swarms

In biological societies, complex interactions between the behavior and morphology of evolving organisms and their environment have given rise to a wide range of complex and diverse social structures. Similarly, in artificial counterparts such as swarm-robotics systems, collective behaviors emerge via the interconnected dynamics of robot morphology (sensory-motor configuration), behavior (controller), and environment (task). Various studies have demonstrated morphological and behavioral diversity enables biological groups to exhibit adaptive, robust, and resilient collective behavior across changing environments. However, in artificial (swarm robotic) systems there is little research on the impact of changing environments on morphological and behavioral (body-brain) diversity in emergent collective behavior, and the benefits of such diversity. This study uses evolutionary collective robotics as an experimental platform to investigate the impact of increasing task environment complexity (collective behavior task difficulty) on the evolution and benefits of morphological and behavioral diversity in robotic swarms. Results indicate that body-brain evolution using coupled behavior and morphology diversity maintenance yields higher behavioral and morphological diversity, which is beneficial for collective behavior task performance across task environments. Results also indicate that such behavioral and morphological diversity maintenance coupled with body-brain evolution produces neuro-morpho complexity that does not increase concomitantly with task complexity.

Modeling and analyzing competitive epidemic diseases with partial and waning virus-specific and cross-immunity

In this paper, we consider a novel mathematical modeling framework for the spread of two competitive diseases in a well-mixed population. The proposed framework, which we term a bivirus SIRIS model, encapsulates key real-world features of natural immunity, accounting for different levels of (partial and waning) virus-specific and cross protection acquired after recovery. Formally, the proposed framework consists of a system of coupled nonlinear ordinary differential equations that builds on a classical bivirus susceptible–infected–susceptible model by means of the addition of further states to account for (temporarily) protected individuals. Through the analysis of the proposed framework and of two specializations, we offer analytical insight into how natural immunity can shape a wide range of complex emergent behaviors, including eradication of both diseases, survival of the fittest one, or even steady-state co-existence of the two diseases.

Successive quasienergy collapse and breakdown of photon blockade in the few-emitter limit

The emergent behavior that arises in many-body systems of increasing size follows universal laws that become apparent in order-to-disorder transitions. While this behavior has been traditionally studied for large numbers of emitters, recent progress allows for the exploration of the few-emitter limit, where correlations can be measured and connected to microscopic models to gain further insight into order-to-disorder transitions. We explore this few-body limit in the driven and damped Tavis–Cummings model, which describes a collection of atoms interacting with a driven and damped cavity mode. Our exploration revolves around the dressed states of the atomic ensemble and field, whose energies are shown to collapse as the driving field is increased to mark the onset of a dissipative quantum phase transition. The collapse occurs in stages and is an effect of light-matter correlations that are overlooked for single atoms and neglected in mean-field models. The implications of these correlations over the macroscopic observables of the system are presented. We encounter a shift in the expected transition point and an increased number of parity-broken states to choose from once the ordered phase is reached.

Emergence of inertia in the low-Reynolds regime of self-diffusiophoretic motion.

For isotropic swimming particles driven by self-diffusiophoresis at zero Reynolds number (where particle velocity responds instantaneously to applied force), the diffusive timescale of emitted solute can produce an emergent quasi-inertial behavior. These particles can orbit in a central potential and reorient under second-order dynamics, not the first-order dynamics of classical zero-Reynolds motion. They are described by a simple effective model that embeds their history-dependent behavior as an effective inertia, this being the most primitive expression of memory. The system can be parameterized with dynamic quantities such as particle size and swimming speed, without detailed knowledge of the diffusiophoretic mechanism.

Employing simulation to assess value alignment and fulfillment within construction project settings and team dynamics

Value-based management has been gaining momentum in the Project Management field. Effectively managing value, and any competing objectives, is in fact crucial for achieving successful projects. To do so, project settings and team interactions need to be investigated as they have a major influence on delivering value and aligning stakeholders' perceptions. Therefore, the aim of this study is to explore the impact of project settings and team-related factors on value fulfillment. By doing so, it seeks to develop a tool that assists managers in implementing effective strategies for managing value on their projects. Accordingly, an agent-based simulation model was developed to model the interactions between teams and their effect on emergent behavior with respect to value adoption. By inputting relevant data and configuring different parameters, results from the applied case study demonstrated that the simulation tool is capable of providing valuable insights on value fulfillment levels. Findings revealed that fulfilling the various value propositions differs significantly, contingent upon diverse factors within the team and based on the clarity of the proposition itself. Results also highlighted that projects may experience fluctuations, exposing misalignment, particularly when there is an ongoing clarification of value attributes.

Simultaneous Arrival of Multiple Agonists: How the Endothelium Senses and Responds

The endothelium is the innermost layer of all blood vessels. It controls numerous cardiovascular functions including vascular contractility, haemostasis, inflammation and the exchange of nutrients and waste products between circulating blood and tissue. To control each cardiovascular function, the endothelium processes and responds to endless streams of information that originate from multiple sources (i.e., blood cells, hormones, neighbouring endothelial cells or underlying smooth muscle cells). Although each endothelial cell has only a limited number of interconnected neighbors (on average six) it is able to decipher and respond to multiple streams of information. To process information, specific cells are primed to detect specific activators and these spatially-distinct populations communicate across the endothelial network to coordinate vascular responses. However, how the endothelium integrates and deciphers information when multiple activators are present simultaneously is not understood. Here, by examining the Ca2+ response in thousands of endothelial cells in intact arteries, we show that the interactions of individual endothelial cells give rise to new behaviours that are absent when cells are examined in isolation. In our study, we examined the Ca²⁺ activity in thousands of endothelial cells in intact rat mesenteric arteries exposed to low concentrations (EC25) of acetylcholine, ATP, or both simultaneously. Our findings reveal that cells which respond to acetylcholine but not ATP (‘acetylcholine-sensitive’), exhibit an enhanced Ca²⁺ response when both activators are present together. The same is true for ‘ATP-sensitive’ cells (i.e., cells that do not respond to acetylcholine, EC25) - the Ca2+ response is augmented when both agonists are present. In addition, the number of neighbours that ‘acetylcholine-sensitive’ or ‘ATP-sensitive’ cells have that respond to either agonist, significantly impacts how the Ca2+ response is altered. Interestingly, the augmented response does not conform to a simple linear summation or average of the acetylcholine/ ATP responses. Instead, a new distinct signal is generated. This suggests that cells perform signaling computations and can combine information from multiple sources. New features that appear in a system and which differ from the expectations of the sum of the components are called ‘emergent properties’. Emergent systems arise from non-linear interactions and create new collective behaviors that make the whole (the endothelium) much greater than the sum of the parts (individual endothelial cells). Our data reveals how communication among neighboring endothelial cells results in emergent behavior across the endothelium, and how this novel behavior plays a significant role in influencing cardiovascular function. British Heart Foundation. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Learning spatio-temporal patterns with Neural Cellular Automata.

Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and Partial Differential Equation (PDE) trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as learning rules that capture the dynamics of Turing pattern formation in nonlinear PDEs. We demonstrate that NCA can generalise very well beyond their PDE training data, we show how to constrain NCA to respect given symmetries, and we explore the effects of associated hyperparameters on model performance and stability. Being able to learn arbitrary dynamics gives NCA great potential as a data driven modelling framework, especially for modelling biological pattern formation.

Out-of-equilibrium interactions and collective locomotion of colloidal spheres with squirming of nematoelastic multipoles

Many living and artificial systems show similar emergent behavior and collective motions on different scales, starting from swarms of bacteria to synthetic active particles, herds of mammals, and crowds of people. What all these systems often have in common is that new collective properties like flocking emerge from interactions between individual self-propelled or driven units. Such systems are naturally out-of-equilibrium and propel at the expense of consumed energy. Mimicking nature by making self-propelled or externally driven particles and studying their individual and collective motility may allow for deeper understanding of physical underpinnings behind collective motion of large groups of interacting objects or beings. Here, using a soft matter system of colloids immersed into a liquid crystal, we show that resulting so-called nematoelastic multipoles can be set into a bidirectional locomotion by external oscillating electric fields. Out-of-equilibrium elastic interactions between such colloidal objects lead to collective flock-like behaviors emerging from time-varying elasticity-mediated interactions between externally driven propelling particles. Repulsive elastic interactions in the equilibrium state can be turned into attractive interactions in the out-of-equilibrium state under applied external electric fields. We probe this behavior at different number densities of colloidal particles and show that particles in dense dispersions collectively select the same direction of a coherent motion due to elastic interactions between near neighbors. In our experimentally implemented design, their motion is highly ordered and without clustering or jamming often present in other colloidal transport systems, which is promising for technological and fundamental-science applications, like nano-cargo transport, out-of-equilibrium assembly, and microrobotics.

A general framework and decentralised algorithms for collective computational processes

Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is the aggregate process abstraction: in an asynchronous round-based computational model, it allows to specify how aggregate-level computations are spawned, take form or spread on a domain of devices, and ultimately quit. Previous presentations of aggregate processes, however, are given in the formal framework of the field calculus, requiring knowledge of its syntax and articulated semantics. To provide a more accessible and language-agnostic presentation of such an abstraction, in this paper we introduce a general formal framework of collective computational processes (CCP). Specifically, as key contribution, we model and describe the programming interface (spawn construct) and dynamics of CCPs on event structures. Furthermore, we also propose novel algorithms for efficient propagation and termination of CCPs, based on statistics on the information speed and a notion of progressive wave-like closure. Crucially, thanks to our theoretical framework, we can provide optimality guarantees for the proposed algorithms, whose performance, superior to the state of the art, is assessed by simulation. Finally, to show applicability of CCPs, we provide a case study of situated service discovery in peer-to-peer networks.

Rationally Designing the Supramolecular Interfaces of Nanoparticle Superlattices with Multivalent Polymers.

In supramolecular materials, multiple weak binding groups can act as a single collective unit when confined to a localized volume, thereby producing strong but dynamic bonds between material building blocks. This principle of multivalency provides a versatile means of controlling material assembly, as both the number and the type of supramolecular moieties become design handles to modulate the strength of intermolecular interactions. However, in materials with building blocks significantly larger than individual supramolecular moieties (e.g., polymer or nanoparticle scaffolds), the degree of multivalency is difficult to predict or control, as sufficiently large scaffolds inherently preclude separated supramolecular moieties from interacting. Because molecular models commonly used to examine supramolecular interactions are intrinsically unable to examine any trends or emergent behaviors that arise due to nanoscale scaffold geometry, our understanding of the thermodynamics of these massively multivalent systems remains limited. Here we address this challenge via the coassembly of polymer-grafted nanoparticles and multivalent polymers, systematically examining how multivalent scaffold size, shape, and spacing affect their collective thermodynamics. Investigating the interplay of polymer structure and supramolecular group stoichiometry reveals complicated but rationally describable trends that demonstrate how the supramolecular scaffold design can modulate the strength of multivalent interactions. This approach to self-assembled supramolecular materials thus allows for the manipulation of polymer-nanoparticle composites with controlled thermal stability, nanoparticle organization, and tailored meso- to microscopic structures. The sophisticated control of multivalent thermodynamics through precise modulation of the nanoscale scaffold geometry represents a significant advance in the ability to rationally design complex hierarchically structured materials via self-assembly.

A Compositional Theory of Linearizability

Compositionality is at the core of programming languages research and has become an important goal toward scalable verification of large systems. Despite that, there is no compositional account of linearizability , the gold standard of correctness for concurrent objects. In this article, we develop a compositional semantics for linearizable concurrent objects. We start by showcasing a common issue, which is independent of linearizability, in the construction of compositional models of concurrent computation: interaction with the neutral element for composition can lead to emergent behaviors, a hindrance to compositionality. Category theory provides a solution for the issue in the form of the Karoubi envelope. Surprisingly, and this is the main discovery of our work, this abstract construction is deeply related to linearizability and leads to a novel formulation of it. Notably, this new formulation neither relies on atomicity nor directly upon happens-before ordering and is only possible because of compositionality, revealing that linearizability and compositionality are intrinsically related to each other. We use this new, and compositional, understanding of linearizability to revisit much of the theory of linearizability, providing novel, simple, algebraic proofs of the locality property and of an analogue of the equivalence with observational refinement . We show our techniques can be used in practice by connecting our semantics with a simple program logic that is nonetheless sound concerning this generalized linearizability.

Predisposition in Dynamic Covalent Chemistry: The Role of Non-Covalent Interactions in the Assembly of Tetrahedral Boronate Cages.

Directional bonding strategies guide the design of complex molecular architectures, yet challenges arise due to emergent behavior. Rigid structures face geometric constraints and sensitivity to mismatches, hindering the efficient assembly of molecular organic cages (MOCs). Harnessing intramolecular non-covalent interactions offers a promising solution, broadening geometrical possibilities and enhancing adaptability to boost assembly yields. However, identifying these interactions remains challenging, with their full potential sometimes latent until final assembly. This study explores these challenges by synthesizing boronic acid tripods with varied oxygen positions at the tripodal feet and investigating their role in assembling tetrahedral boronate MOCs. Our results reveal substantial differences in the assembly efficiency among tripods. While the building blocks with oxygen in the benzylic position relative to the central aromatic ring form the MOCs in high yields, those with the oxygen atom directly bound to the central aromatic ring, only yield traces. Through X-ray crystallography and DFT analyses, we elucidate how intramolecular interactions profoundly influence the geometry of the building blocks and cages in a relay-like fashion, highlighting the importance of considering intramolecular interactions in the rational design of (supra)molecular architectures.

Meta predictive learning model of languages in neural circuits.

Large language models based on self-attention mechanisms have achieved astonishing performances, not only in natural language itself, but also in a variety of tasks of different nature. However, regarding processing language, our human brain may not operate using the same principle. Then, a debate is established on the connection between brain computation and artificial self-supervision adopted in large language models. One of most influential hypotheses in brain computation is the predictive coding framework, which proposes to minimize the prediction error by local learning. However, the role of predictive coding and the associated credit assignment in language processing remains unknown. Here, we propose a mean-field learning model within the predictive coding framework, assuming that the synaptic weight of each connection follows a spike and slab distribution, and only the distribution, rather than specific weights, is trained. This meta predictive learning is successfully validated on classifying handwritten digits where pixels are input to the network in sequence, and moreover, on the toy and real language corpus. Our model reveals that most of the connections become deterministic after learning, while the output connections have a higher level of variability. The performance of the resulting network ensemble changes continuously with data load, further improving with more training data, in analogy with the emergent behavior of large language models. Therefore, our model provides a starting point to investigate the connection among brain computation, next-token prediction, and general intelligence.

Formation of recurring transient Ca2+-based intercellular communities during Drosophila hematopoiesis

Tissue development occurs through a complex interplay between many individual cells. Yet, the fundamental question of how collective tissue behavior emerges from heterogeneous and noisy information processing and transfer at the single-cell level remains unknown. Here, we reveal that tissue scale signaling regulation can arise from local gap-junction mediated cell-cell signaling through the spatiotemporal establishment of an intermediate-scale of transient multicellular communication communities over the course of tissue development. We demonstrated this intermediate scale of emergent signaling using Ca2+ signaling in the intact, ex vivo cultured, live developing Drosophila hematopoietic organ, the lymph gland. Recurrent activation of these transient signaling communities defined self-organized signaling "hotspots" that gradually formed over the course of larva development. These hotspots receive and transmit information to facilitate repetitive interactions with nonhotspot neighbors. Overall, this work bridges the scales between single-cell and emergent group behavior providing key mechanistic insight into how cells establish tissue-scale communication networks.

A data-driven agent-based model of occupants’ thermal comfort behaviors for the planning of district-scale flexible work arrangements

In a global context of increasing flexibility in the way workplaces and the districts in which they are located are used, there is a need for occupant-driven approaches to plan urban energy systems. Several authors have suggested the use of agent-based models (ABM) of building occupants in urban building energy simulations due to their ability to reproduce emergent behaviors from individual agents’ actions. However, few works in the literature take full advantage of the ABM paradigm, accounting for both occupant presence and energy-relevant behaviors at the district scale. In this work, we propose a methodology to develop a data-driven, agent-based model of building occupants’ activities and thermal comfort in an urban district. Our methodology combines the use of campus-scale Wi-Fi data to derive feasible occupant activity and location plans, along with thermal preference profiles derived from a longitudinal field study where off-the-shelf, non-intrusive sensors were used to capture the right-here-right-now thermal preference of 35 participants in the same case study district. Our model is then used to explore how different district operation strategies could affect building energy performance in the context of increased workspace flexibility. Our results show that by providing a diversity of building operation conditions, with different buildings having different set point temperatures, occupants’ thermal comfort hours could be improved by an average of about 10% with little effect on district energy performance. Meanwhile, a 6%–15% average decrease in space cooling energy use intensity was observed when implementing occupant-driven ventilation and setpoint controls, regardless of location choice scenario.

Dynamic Inhibition of Calcite Dissolution in Flowing Acidic Pb2+ Solutions.

Reactions of mineral surfaces with dissolved metal ions at far-from-equilibrium conditions can deviate significantly from those in near-equilibrium systems due to steep concentration gradients, ion-surface interactions, and reactant transport effects that can lead to emergent behavior. We explored the effect of dissolved Pb2+ on the dissolution rate and topographic evolution of calcite (104) surfaces under far-from-equilibrium acidic conditions (pH 3.7) in a confined single-pass laminar-flow geometry. Operando measurements by digital holographic microscopy were conducted over a range of Pb2+ concentrations ([Pb2+] = 0 to 5 × 10-2 M) and flow velocities (v = 1.67-53.3 mm s-1). Calcite (104) surface dissolution rates decreased with increasing [Pb2+]. The inhibition of dissolution and the emergence of unique topographic features, including micropyramids, variable etch pit shapes, and larger scale topographic patterns, became increasingly apparent at [Pb2+] ≥ 5 × 10-3 M. A better understanding of such dynamic reactivity could be crucial for constructing accurate models of geochemical transport in aqueous carbonate systems.

Universal slow dynamics of chemical reaction networks.

Understanding the emergent behavior of chemical reaction networks (CRNs) is a fundamental aspect of biology and its origin from inanimate matter. A closed CRN monotonically tends to thermal equilibrium, but when it is opened to external reservoirs, a range of behaviors is possible, including transition to a new equilibrium state, a nonequilibrium state, or indefinite growth. This study shows that slowly driven CRNs are governed by the conserved quantities of the closed system, which are generally far fewer in number than the species. Considering both deterministic and stochastic dynamics, a universal slow-dynamics equationis derived with singular perturbation methods and is shown to be thermodynamically consistent. The slow dynamics is highly robust against microscopic details of the network, which may be unknown in practical situations. In particular, nonequilibrium states of realistic large CRNs can be sought without knowledge of bulk reaction rates. The framework is successfully tested against a suite of networks of increasing complexity and argued to be relevant in the treatment of open CRNs as chemical machines.

Emergence Behavior Versus Physical-Like Behavior

In this paper, we study the dynamical complexity of points with emergence behavior but without weak face behavior, especially for points without physical-like behavior in certain dynamical systems such as transitive Anosov systems. We use the tools of saturated sets to prove that these points show strong dynamical complexity in the sense of entropy, density and distributional chaos. We obtain some observations of those results related to irregular sets and level sets. These results strengthen the previous results of [Catsigeras et al., 2019; Hou et al., 2023].