Emergent Behavior Research Articles

AI-driven adaptive analysis for finding emergent behavior in military capability design

This article introduces and demonstrates a new methodology for searching for emergent behavior in simulation-based analysis as rare, extreme events using adaptive techniques enabled by recent advances in Bayesian machine learning (ML). The new methodology, Low-cost Adaptive exploratioN to Track down Extreme, Rare events using Numerical optimization (LANTERN), supports analysis activities in defense planning that iteratively build up the understanding of new technology and concept alternatives in complex military scenarios. Central to this process is emergent behavior—hard-to-predict but highly important behaviors that present problems or opportunities. These unexpected behaviors can be generated in complex military scenarios and are crucial to decision-making, but are often difficult to find and work with due to the expense and cost of the existing approaches for working with high-fidelity military simulation. To address this challenge, LANTERN is formulated to accelerate the discovery of emergent behavior as rare, extreme events by combining human expert understanding with new artificial intelligence (AI)-driven adaptive experimentation techniques in iterative analysis. A demonstration of the methodology is presented using military agent-based simulation scenarios developed in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM). The demonstration highlights how analysis can focus directly on searching for emergent behavior and shows substantial improvements over brute-force Monte Carlo approaches.

Emergent Behavior of a Photoswitchable Solute in a Biphasic Solvent System.

Due to thermal E/Z isomerization, hydrazones in solution typically exist in thermodynamic equilibria between their isomers. Irradiation of such solutions leads to photostationary states that may differ from the equilibrium distribution. Operating such switchable hydrazones in a biphasic system of two immiscible solvents introduces three new degrees of freedom: the E/Z equilibrium in the second solvent and two equilibria for distribution of each of the isomers between the solvents. Irradiation of such a system can be performed in three different ways - the first solvent only, the second solvent only, and both solvents at once - all yielding distinct outcomes. Depending on the choice of materials and the mode of irradiation, such setup may lead to different emergent behaviors that are not immediately intuitive, including net cyclic transport or the accumulation of one photoswitched product in one of the phases, beyond what is reachable by irradiating a simple solution of the same photoswitch.

Airspace Constrained Free-Flight Analysis: Implications for Uncrewed Air Traffic Management

This paper provides a study of free-flight air traffic behaviour in increasingly constrained airspace environments. Traffic assumes three different free-flight operational constructs with airspace constraints considered as restricted (no-fly) regions. Simulations combine path planning and Monte Carlo techniques to qualitatively analyse emergent traffic behaviour and quantitatively assess spatial–temporal airspace conflict as the airspace constraints vary. Findings indicate that airspace constraints have a much stronger influence on aircraft behaviour than the free-flight operational construct, with any benefits of free flight rapidly diminishing as the airspace becomes more constrained. We conclude that structured traffic route (or network) designs and associated risk modelling approaches should be considered for safe and efficient traffic management of highly constrained and congested (or dense) airspace. This work therefore provides evidence to inform new airspace design and management initiatives, including low-altitude uncrewed traffic.

Leadership Lingo: Developing a Shared Language of Leadership Behaviors to Enrich Debriefing Conversations.

Leadership in medical emergencies is variable and frequently suboptimal, contributing to poor patient care and outcomes. Simulation training can improve leadership in both simulated practice and real clinical emergencies. Thoughtful debriefing is essential. However, unclear language around leadership limits facilitators' capacity for transformative reflective discussion. Internal medicine trainees participated in simulated medical emergency scenarios. Video recordings of consenting participants were analyzed using template analysis. A priori codes from existing literature formed an initial coding template. This was modified with inductive codes from the observed behaviors to develop a taxonomy of leadership behaviors in simulated medical emergencies. The taxonomy was then transformed into an infographic, to be used as a leadership debriefing tool. The taxonomy of leadership behaviors consisted of the following 4 themes: Structuring, Decision making, Supporting, and Communicating. Structuring behaviors shaped the team, ensuring that the right people were in the right place at the right time. Decision-making behaviors steered the team, setting a direction and course of action. Communicating behaviors connected the team, sharing valuable information. Supporting behaviors nurtured the team, guiding team members to perform at their optimum level. Debriefing-as-imagined is not always debriefing-as-done. A shared language of leadership can connect educators and learners, advancing critical debriefing conversations and enabling facilitators to drive meaningful reflective discussion. The use of infographics in simulation offers an opportunity to support educators in facilitating complex debriefing conversations.

Fuel-Driven Dynamic Combinatorial Peptide Libraries.

Dynamic combinatorial chemistry (DCC) creates libraries of molecules that are constantly interchanging in a dynamic combinatorial library. When a library member self-assembles, it can displace the equilibria, leading to emergent phenomena like its selection or even its replication. However, such dynamic combinatorial libraries typically operate in or close to equilibrium. This work introduces a new dynamic combinatorial chemistry fueled by a catalytic reaction cycle that forms transient, out-of-equilibrium peptide-based macrocycles. The products in this library exist out of equilibrium at the expense of fuel and are thus regulated by kinetics and thermodynamics. By creating a chemically fueled dynamic combinatorial library with the vast structural space of amino acids, we explored the liquid-liquid phase separation behavior of the library members. The study advances DCCs by showing that peptide structures can be engineered to control the dynamic library's behavior. The work paves the way for creating novel, tunable material systems that exhibit emergent behavior reminiscent of biological systems. These findings have implications for the development of new materials and for understanding life's chemistry.

Anomalous power-law behavior in the electrical impedance of endothelial cellular networks

In this paper, we report on the electrical impedance measurement of human endothelial cellular networks and show the existence of emergent power law behavior in its admittance. In particular, we find that the admittance scales with the frequency ω as ωα, with the exponent that varies with the degree of the disruption caused by the inflammation in the endothelial cellular network. We demonstrate that the power law of the measured electrical admittance can be understood by a simple percolation model of a large R–C (resistor–capacitor) network, which allows us to relate quantitatively and the intensity of inflammation. Our results suggest that the electrical properties of heterogeneous biomaterials, like living tissues, behave as a complex microstructural network.

Machine Learning-Based Investigation of Atomic Packing Effects: Chemical Pressures at the Extremes of Intermetallic Complexity.

Intermetallic phases represent a domain of emergent behavior, in which atoms with packing and electronic preferences can combine into complex geometrical arrangements whose long-range order involves repeat patterns containing thousands of atoms or is incompatible with a 3D unit cell. The formation of such arrangements points to unexplained driving forces within these systems that, if understood, could be harnessed in the design of new metallic materials. DFT-chemical pressure (CP) analysis has emerged as an approach to visualize how atomic packing tensions within simpler crystal structures can drive this complexity and create potential functionality. However, the applications of this method have hitherto been limited in scope by its dependence on resource-intensive electronic structure calculations. In this Article, we develop machine learning (ML)-based implementation of the CP approach, drawing on the collection of DFT-CP schemes in the Intermetallic Reactivity Database. We illustrate the method with comparisons of ML-CP and DFT-CP schemes for a series of examples, before demonstrating its application with an exploration of one of the quintessential instances of complexity in intermetallic chemistry, Mg2Al3, whose high-temperature unit cell is a 2.8 nm cube containing 1227 atoms. An analysis of its ML-CP-derived interatomic pressures traces the origins of the structure to simple matching rules for the assembly of Frank-Kasper polyhedra. The ML-CP model can be immediately employed on other intermetallic systems, through either its web interface or a command-line version, with just a crystallographic information file.

Unravelling a diversity of cellular structures and aggregation dynamics during the early development of Myxococcus xanthus.

Aggregation underlies the collective dynamics of a diversity of organisms, enabling the formation of complex structures and emergent behaviours on interaction with the environment. Cellular aggregation constitutes one of the routes to collective motility and multicellular development. Myxococcus xanthus, a social bacterium, is a valuable model for studying the aggregative path to multicellularity, a major transition in the evolutionary history of life. While the collective developmental behaviour of M. xanthus has been largely studied in high cellular densities, there is a lack of understanding at low-density conditions that can be ecologically relevant. In this work, we study the early stages of emergent collective behaviour of M. xanthus under nutrient-poor and low-density conditions, uncovering the formation of diverse cellular structures with different shapes and sizes, ranging from individual cells to networks comprising thousands of cells. We study their motility patterns and their prevalence along development and discuss their cross-scale role on the population's exploratory dynamics. This work contributes to understanding key, yet largely understudied, aspects in the early stages of multicellular development in myxobacteria, shedding light on the dynamics underlying aggregative processes in this and other taxa and study systems.

Recent achievements in nonlinear dynamics, synchronization, and networks.

This Focus Issue covers recent developments in the broad areas of nonlinear dynamics, synchronization, and emergent behavior in dynamical networks. It targets current progress on issues such as time series analysis and data-driven modeling from real data such as climate, brain, and social dynamics. Predicting and detecting early warning signals of extreme climate conditions, epileptic seizures, or other catastrophic conditions are the primary tasks from real or experimental data. Exploring machine-based learning from real data for the purpose of modeling and prediction is an emerging area. Application of the evolutionary game theory in biological systems (eco-evolutionary game theory) is a developing direction for future research for the purpose of understanding the interactions between species. Recent progress of research on bifurcations, time series analysis, control, and time-delay systems is also discussed.

Dissipative quantum Hopfield network: a numerical analysis

Abstract We present extensive simulations of a quantum version of the Hopfield Neural Network to explore its emergent behavior. The system is a network of $N$ qubits oscillating at a given $\Omega$ frequency and which are coupled via Lindblad jump operators built with local fields $h_i$ depending on some given stored patterns. Our simulations show the emergence of pattern-antipattern oscillations of the overlaps with the stored patterns similar (for large $\Omega$ and small temperature) to those reported within a recent mean-field description of such a system, and which are originated deterministically by the quantum term including $s_x^i$ qubit operators. However, in simulations we observe that such oscillations are stochastic due to the interplay between noise and the inherent metastability of the pattern attractors induced by quantum oscillations, and then are damped in finite systems when one averages over many quantum trajectories. In addition, we report the system behavior for large number of stored patterns at the lowest temperature we can reach in simulations (namely $T=0.005\, T_C$). Our study reveals that for small-size systems the quantum term of the Hamiltonian has a negative effect on storage capacity, decreasing the overlap with the starting memory pattern for increased values of $\Omega$ and number of stored patterns. However, it also impedes the system to be trapped for long time in mixtures and spin-glass states. Interestingly, the system also presents a range of $\Omega$ values for which, although the initial pattern is destabilized due to quantum oscillations, other patterns can be retrieved and remain stable even for many stored patterns, implying a quantum-dependent nonlinear relationship between the recall process and the number of stored patterns.

DESI 2024: reconstructing dark energy using crossing statistics with DESI DR1 BAO data

We implement Crossing Statistics to reconstruct in a model-agnostic manner the expansion history of the universe and properties of dark energy, using DESI Data Release 1 (DR1) BAO data in combination with one of three different supernova compilations (PantheonPlus, Union3, and DES-SN5YR) and Planck CMB observations. Our results hint towards an evolving and emergent dark energy behaviour, with negligible presence of dark energy at z ≳ 1, at varying significance depending on data sets combined. In all these reconstructions, the cosmological constant lies outside the 95% confidence intervals for some redshift ranges. This dark energy behaviour, reconstructed using Crossing Statistics, is in agreement with results from the conventional w 0–w a dark energy equation of state parametrization reported in the DESI Key cosmology paper. Our results add an extensive class of model-agnostic reconstructions with acceptable fits to the data, including models where cosmic acceleration slows down at low redshifts. We also report constraints on H 0 r d from our model-agnostic analysis, independent of the pre-recombination physics.

Mutagenicity of the agriculture pesticide chlorothalonil assessed by somatic mutation and recombination test in Drosophila melanogaster.

Chlorothalonil (CTL) is a pesticide widely used in Brazil, yet its mutagenic potential is not fully determined. Thus, we assessed the mutagenicity of CTL and its bioactivation metabolites using the somatic mutation and recombination test (SMART) in Drosophila melanogaster, by exposing individuals, with basal and high bioactivation capacities (standard and high bioactivation cross offspring, respectively), from third instar larval to early adult fly stages, to CTL-contaminated substrate (0.25, 1, 10 or 20 μM). This substrate served as food and as physical medium. Increased frequency of large single spots in standard cross flies' wings exposed to 0.25 μM indicates that, if CTL is genotoxic, it may affect Drosophila at early life stages. Since the total spot frequency did not change, CTL cannot be considered mutagenic in SMART. The same long-term exposure design was performed to test whether CTL induces oxidative imbalance in flies with basal (wild-type, WT) or high bioactivation (ORR strain) levels. CTL did not alter reactive oxygen species and antioxidant capacity against peroxyl radicals levels in adult flies. However, lipid peroxidation (LPO) levels were increased in WT male flies exposed to 1 μM CTL. SMART and LPO alterations were observed only in flies with basal bioactivation levels, pointing to direct CTL toxicity to DNA and lipids. Survival, emergence and locomotor behavior were not affected, indicating no bias due to lethality, developmental and behavioral impairment. We suggest that, if related to CTL exposure, DNA and lipid damages may be residual damage of earlier life stages of D. melanogaster.

Structure-Reactivity Relationships in a Small Library of Imine-Type Dynamic Covalent Materials: Determination of Rate and Equilibrium Constants Enables Model Prediction and Validation of a Unique Mechanical Softening in Dynamic Hydrogels.

The development of next generation soft and recyclable materials prominently features dynamic (reversible) chemistries such as host-guest, supramolecular, and dynamic covalent. Dynamic systems enable injectability, reprocessability, and time-dependent mechanical properties. These properties arise from the inherent relationship between the rate and equilibrium constants (RECs) of molecular junctions (cross-links) and the resulting macroscopic behavior of dynamic networks. However, few examples explicitly measure RECs while exploring this connection between molecular and material properties, particularly for polymeric hydrogel systems. Here we use dynamic covalent imine formation to study how single-point compositional changes in NH2-terminated nucleophiles affect binding constants and resulting hydrogel mechanical properties. We explored both model small molecule studies and model polymeric macromers, and found >3-decade change in RECs. Leveraging established relationships in the literature, we then developed a simple model to describe the cross-linking equilibrium and predict changes in hydrogel mechanical properties. Interestingly, we observed that a narrow ≈2-decade range of Keq's determine the bound fraction of imines. Our model allowed us to uncover a regime where adding cross-linker before saturation can decrease the cross-link density of a hydrogel. We then demonstrated the veracity of this predicted behavior experimentally. Notably this emergent behavior is not accounted for in covalent hydrogel theory. This study expands upon structure-reactivity relationships for imine formation, highlighting how quantitative determination of RECs facilitates predicting macroscopic behavior. Furthermore, while the present study focuses on dynamic covalent imine formation, the underlying principles of this work are applicable to the general bottom-up design of soft and recyclable dynamic materials.

Preliminary Guidelines for Combining Data Integration and Visual Data Analysis.

Data integration is often performed to consolidate information from multiple disparate data sources during visual data analysis. However, integration operations are usually separate from visual analytics operations such as encode and filter in both interface design and empirical research. We conducted a preliminary user study to investigate whether and how data integration should be incorporated directly into the visual analytics process. We used two interface alternatives featuring contrasting approaches to the data preparation and analysis workflow: manual file-based ex-situ integration as a separate step from visual analytics operations; and automatic UI-based in-situ integration merged with visual analytics operations. Participants were asked to complete specific and free-form tasks with each interface, browsing for patterns, generating insights, and summarizing relationships between attributes distributed across multiple files. Analyzing participants' interactions and feedback, we found both task completion time and total interactions to be similar across interfaces and tasks, as well as unique integration strategies between interfaces and emergent behaviors related to satisficing and cognitive bias. Participants' time spent and interactions revealed that in-situ integration enabled users to spend more time on analysis tasks compared with ex-situ integration. Participants' integration strategies and analytical behaviors revealed differences in interface usage for generating and tracking hypotheses and insights. With these results, we synthesized preliminary guidelines for designing future visual analytics interfaces that can support integrating attributes throughout an active analysis process.

Human behavior and decision-making in earthquake emergencies: insights from virtual reality and serious games

Earthquakes can severely impact public spaces with complex layouts and high crowd densities, resulting in devastating consequences and deaths. Gaining insights into the actions of individuals during an earthquake can provide valuable information about areas where their decision-making may be flawed. Addressing these areas can improve safety measures and better protect people during emergencies. Moreover, acquiring this knowledge can enhance evacuation strategies for future earthquakes. Immersive Virtual Reality and Serious Games (IVR SGs) present an effective method for examining and understanding how people behave in emergency scenarios, such as earthquakes. Using IVR SGs, we created several potential scenarios during an earthquake in a shopping mall to explore how people act in an emergency when the evacuees feel pressure, urgency, and stress. A total of 146 participants were involved in the study. We investigated the human tendency to follow authority figures’ instructions (rational and irrational), help injured people of different genders and ages, and social influence during an emergency. Moreover, we investigated how familiarity with the environment impacts human wayfinding performance when evacuating the building.

Identifying the bottleneck and its shifts for the nexus across water supply, power generation and environment systems: Hehuang, China

Identifying the bottleneck is the key to alerting and preventing unexpected failures of the nexus across water supply, power generation, and environment (WPE) systems. However, the bottleneck identification is challenged by the unclear concept of bottleneck as well as the difficulty of simulating complex emergent behaviors. Here, we develop a generic framework for identifying the bottleneck for the WPE nexus, which is defined as the subsystem that has the highest risk of leading to the failure of WPE nexus. First, the evolution of uncertainties of the state variables of WPE nexus is simulated by a generic stochastic dynamical system. Next, the propagation matrix, accumulation ratio (AR), and risk contribution ratio are defined as effective metrics and tools to identify the system bottleneck. Finally, the effects of human decisions and input stochasticity on the shifts of bottleneck are analyzed using sensitivity and scenario analysis. The WPE system in Hehuang Region, China, is selected as a case study. Results indicate that: (a) The effects of human decisions on ARs can be superimposed, suggesting a “superimposition method” for policy-making of Hehuang Region; (b) The subsystems can be classified as “endogenous” or “exogenous”, depending on the sources of accumulated uncertainties that are from local inputs, or inputs from the other subsystems; (c) There exist multiple bottlenecks at the same time that could shift from one subsystem to another, which complicates the prevention of the failures of WPE nexus. For the Hehuang Region, water and energy subsystems are more likely to be bottlenecks at the beginning stage, while society and environment subsystems are more likely to be bottlenecks at the steady stage. This study provides the first attempt to identify the bottleneck within the WPE nexus, which helps to provide realistic insights for preventing unexpected failures.

Single-Molecule Mechanoresistivity by Intermetallic Bonding.

The metal-electrode interface is key to unlocking emergent behaviour in all organic electrified systems, from battery technology to molecular electronics. In the latter, interfacial engineering has enabled efficient transport, higher device stability, and novel functionality. Mechanoresistivity - the change in electrical behaviour in response to a mechanical stimulus and a pathway to extremely sensitive force sensors - is amongst the most studied phenomena in molecular electronics, and the molecule-electrode interface plays a pivotal role in its emergence, reproducibility, and magnitude. In this contribution, we show that organometallic molecular wires incorporating a Pt(II) cation show mechanoresistive behaviour of exceptional magnitude, with conductance modulations of more than three orders of magnitude upon compression by as little as 1 nm. We synthesised series of cyclometalated Pt(II) molecular wires, and used scanning tunnelling microscopy - break junction techniques to characterise their electromechanical behaviour. Mechanoresistivity arises from an interaction between the Pt(II) cation and the Au electrode triggered by mechanical compression of the single-molecule device, and theoretical modelling confirms this hypothesis. Our study provides a new tool for the design of functional molecular wires by exploiting previously unreported ion-metal interactions in single-molecule devices, and develops a new framework for the development of mechanoresistive molecular junctions.

Statistical physics and emergent behaviour: from spin models to cultural complexity. In memory of Ralph Kenna (27.08.1964-26.10.2023)

It’s been sixty years since Ralph’s birth day and soon it will be one year since he left us. Initially, this collection of papers was being prepared as a Festschrift - a birthday greeting, and now it is published in his memory. It is difficult to write this foreword, because Ralph is not only a fellow physicist and a close collaborator, he is a very close friend. It is also difficult to write because, in a short text, it is hard to reflect the versatility of his gifts: from statistical physics to field theory, from the science of complex systems to quantitative methods in social sciences and humanities, from scientometrics to history of physics. This is not a complete list of areas that he enriched thanks to his talent and hard work. What’s more, his activities in scientific research are inseparable from his public activities, be it the protection of historical heritage or the restoration of historical memory in Ireland, his motherland, or the collection of funds for the fight against serious diseases in the UK, where he worked, or his participation in the organisation and evaluation of research work on a pan-European scale. The integrity of his personality is one of those features that first come to mind when we think about him, repeatedly.

SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters.

Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our method's flexibility by applying it to two biological ABMs: a simple 2D cell proliferation assay and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions.

Rolling Motion of Rigid Skyrmion Crystallites Induced by Chiral Lattice Torque.

Magnetic skyrmions are topologically protected spin textures with emergent particle-like behaviors. Their dynamics under external stimuli is of great interest and importance for topological physics and spintronics applications alike. So far, skyrmions are only found to move linearly in response to a linear drive, following the conventional model treating them as isolated quasiparticles. Here, by performing time and spatially resolved resonant elastic X-ray scattering of the insulating chiral magnet Cu2OSeO3, we show that for finite-sized skyrmion crystallites, a purely linear temperature gradient not only propels the skyrmions but also induces continuous rotational motion through a chiral lattice torque. Consequently, a skyrmion crystallite undergoes a rolling motion under a small gradient, while both the rolling speed and the rotational sense can be controlled. Our findings offer a new degree of freedom for manipulating these quasiparticles toward device applications and underscore the fundamental phase difference between the condensed skyrmion lattice and isolated skyrmions.