This paper examines how heterogeneity affects flocking behavior and systemic risk in interacting agents.
problem The impact of heterogeneity on flocking behavior and systemic risk in interacting agents.
method Mean-field interacting diffusions and Laplace asymptotics.
result An asymptotic formula characterizes the tail loss probability and the effect of heterogeneity on systemic risk.
New method learns fractional order of PDEs from flocking particle simulations.
problem Deriving effective nonlocal influence functions from discrete agent-based models.
method Agent-based model, fractional PDEs, Gaussian process regression, Bayesian optimization.
result Learned Euler equations accurately predict flocking behavior.
Characterizes knot-theoretic flocks up to 64 elements.
problem Classifying ternary quasigroups for knot theory.
method Group action on flock colorings to improve knot-theoretic invariant.
result Enumerated and characterized knot-theoretic flocks up to 64 elements.
Transformers approximate mean-field dynamics of indistinguishable particles.
problem Approximating the dynamics of indistinguishable particles in complex systems.
method Using transformers to model the mean-field dynamics of interacting particle systems.
result Theoretical bounds on the distance between true and transformer-obtained mean-field dynamics.
Model assesses systemic risk in crude oil and gasoline futures markets.
problem Systemic risk in high-frequency crude oil and gasoline futures markets.
method Hawkes flocking model examining endogeneity and interactivity.
result Significantly higher endogenous systemic risk in WTI crude oil compared to gasoline, with gasoline having a higher influence on WTI.
Recognizes collective sheep movement activities online.
problem Recognizing collective animal movement activities.
method Discriminative framework for tracking flock positions and velocities online.
result Good accuracy in learning skewed collective activities.
A networked learning method for correlated data outperforms federated learning in precision.
problem Estimating models from correlated data distributed across a network.
method Local linear model estimation with network regularization and information exchange.
result The weighted ensemble average estimate converges faster and more precisely than federated learning.
The paper proposes a GP-based method for discovering second-order particle dynamics models.
problem Discovering a general second-order particle-based model for agent interactions.
method Gaussian Process-based approach integrating two independent GP priors on latent interaction kernels.
result The method learns effective nonlinear dynamics representations from small data sets.
Graph neural networks learn decentralized controllers from data.
problem Finding optimal decentralized controllers for autonomous agents is challenging.
method Adapting graph neural networks to handle delayed communications and ensure scalability and transferability.
result Graph neural networks can learn decentralized controllers from data, addressing the scalability and practical implementation issues of centralized controllers.
Enhances particle filters with neural augmentation for multi-sub-state tracking.
problem Particle filters struggle with complex or approximated models and low latency requirements.
method Learning Flock (LF) uses a neural network to correct particle weights based on sub-particle relationships.
result LF improves performance, robustness, and latency in radar multi-target tracking.
Estimates interaction laws from agent trajectories without assuming their form.
problem Inferring interaction laws from observational data in complex systems.
method Non-parametric statistical learning approach from trajectory data.
result Effectiveness demonstrated in various disciplines, including physics, opinion dynamics, and biology.
New method learns SDEs with structured noise from data.
problem Learning SDEs with structured noise from data.
method Nonparametric framework for drift and diffusion terms.
result Accurately infers low-dimensional interaction kernels.
Estimates interaction kernels from agent-based dynamics data.
problem Inferring interaction kernels from agent-based dynamical systems.
method Nonparametric estimators based on regularized least squares.
result Accurate predictions of emergent behaviors for new initial conditions.
New tool for summarizing time-varying data shapes.
problem Understanding dynamic data shapes.
method Introducing crocker stacks for time-varying metric spaces.
result Demonstrated utility in parameter identification task.
Model shows how adding liquidity in banking system can lead to defaults.
problem Systemic risk and interbank lending in banking systems.
method Simple model using Feller diffusions and Markov Nash equilibrium.
result Adding liquidity leads to defaults in banking system.
JAX MD enables differentiable physics simulations for molecular dynamics.
problem Performing efficient and differentiable physics simulations for molecular dynamics.
method Differentiable physics simulation environments, interaction potentials, neural networks, flexible primitives.
result Differentiable physics simulations can be used for meta-optimization and scaling to large particle systems.
New method learns population dynamics from snapshots, outperforming existing models.
problem Capturing periodic and other dynamical properties of population dynamics.
method Wasserstein Lagrangian Mechanics (WLM) for learning second-order dynamics from observed marginals.
result WLM outperforms existing methods across various dynamics, including vortex dynamics, embryonic development, and flocking.
New method for online learning in interacting particle systems.
problem Parameter estimation in stochastic interacting particle systems.
method Stochastic approximation of gradient of asymptotic log likelihood using continuous observations.
result Convergence to stationary points of asymptotic log-likelihood under suitable assumptions.
Wide and Deep GNN learns from distributed graphs and retrain online.
problem Decentralized graph support changes over time, causing mismatch between training and testing graphs.
method Wide and Deep GNN architecture with distributed online learning.
result Convergence guarantees for online retraining of the wide part of the GNN.
Model captures neural activity related to behavior while separating internal computations.
problem Capturing neural activity related to behavior from complex brain recordings.
method Behavior-decomposed linear dynamical systems (b-dLDS) model.
result Improves over state-of-the-art models in disentangling behavior-related dynamics.
Behavior modification improves prediction accuracy by nudging user behavior.
problem Improving prediction accuracy using behavior modification techniques.
method Combining prediction and behavior modification with reinforcement learning algorithms.
result Behavior modification can make predictions more certain but may not generalize.
Paper presents a new time-series segmentation technique for mobile phone user behavior.
problem Current segmentation techniques do not accurately capture individual user behavior over time.
method Behavior-Oriented Time Segmentation (BOTS) technique that considers temporal coverage and number of incidences.
result BOTS technique better captures user behavior at various times of day and week.
LISBET automates social behavior analysis using machine learning.
problem Manual annotation of social behaviors is time-consuming, biased, and misses subtle interactions.
method Self-supervised learning on body tracking data.
result Automated detection and segmentation of social interactions.
The paper introduces new metrics for evaluating generative models of behavior.
problem Lack of quantitative evaluation criteria for unsupervised behavior discovery.
method Proposed and investigated several metrics for generative models of behavior.
result The proposed metrics correspond with biologists' intuitions and allow for model evaluation and bias understanding.
Proposes a graph-based system for personalized news recommendation considering multiple user behaviors.
problem Lack of considering multiple user behaviors in news recommendation systems.
method Builds an interaction behavior graph, applies DeepWalk and G-CNN for news and behavior sequence representations, introduces core and coritivity features.
result Achieves personalized news recommendation considering user's concentration degree of interests.
Study reveals LLM personality patterns but lacks behavioral consistency.
problem Understanding and validating personality traits in LLMs.
method Characterized LLM personality across three dimensions: training dynamics, self-report validity, and intervention effects.
result Self-reported traits do not reliably predict behavior, and instructional alignment affects trait expression but not behavior.
Paper improves off-policy evaluation by estimating behavior policy.
problem Evaluating policies with data from a different behavior policy.
method Importance sampling with an estimated behavior policy.
result Estimating behavior policy reduces mean squared error.
Herd behavior is an important economic phenomenon, especially in the context of the recent financial crises. In this paper, herd behavior in global stock markets is investigated with a focus on intercontinental comparison. Since most existing herd behavior indices do not provide a comparative method, we propose a new h…
New approach uses Wasserstein distances to score and optimize policy behaviors.
problem Comparing reinforcement learning policies and guiding policy optimization.
method Dual formulation of Wasserstein distances in latent behavioral space, learning score functions, smoothed WDs, stochastic gradient descent, on-policy algorithms.
result Demonstrated improved performance over existing methods in various environments.
Method learns behavioral states from wearable sensor data.
problem Understanding behavioral patterns from sensor data.
method Non-parametric Bayesian approach to model sensor data.
result Learned behavioral states cluster participants into meaningful groups and predict psychological states.
Turing Learning infers system behavior without metrics, outperforming traditional methods.
problem Inferring complex behaviors of systems without predefined metrics.
method Simultaneously optimizes model and classifier populations using behavior data.
result Successfully infers behaviors of physical robot swarms and simulated systems.
Detects illegal stock market trading behaviors using graph ranking methods.
problem Detecting irregular trade behaviors in the stock market.
method Three graph Laplacian based semi-supervised ranking methods.
result Un-normalized and symmetric normalized graph Laplacian based methods outperform the random walk Laplacian method.
Study examines if LLMs' trading styles match real market behavior.
problem Lack of behavioral consistency in LLMs' trading strategies.
method Year-long simulations with LLMs, operationalizing behavioral finance drivers, and comparing with financial theory.
result LLMs' strategy switching is only partially consistent with behavioral finance theories.
Predicts entity-wise topical behavior from network logs.
problem Predicting entity-wise topical behavior from network logs.
method Combines RNN and CNN learning architectures with reduction steps to form homogeneous topical metrics.
result Improves prediction accuracy with both temporal and spatial gains compared to MLP.
A test measures artificial agents' human-like behavior in video games.
problem Measuring the believability of artificial agents' human-like behavior.
method Developed a non-parametric two-sample hypothesis test.
result The p-value correlates with human judgment of human-like behavior. Method uses DNNs to approximate functions with specific asymptotic behavior.
problem Approximating functions with given asymptotic behavior.
method Specifically constructed terms combined with unconstrained DNN.
result Enforcing asymptotic behavior leads to better approximation and faster convergence.
Extracts behavioral features from smartphone and wearable data.
problem Processing raw data streams from smartphones and wearables for human behavior analysis.
method Generic framework for processing raw data streams and extracting behavioral features.
result Extracts useful features related to non-verbal human behavior from raw data streams.
Model predicts human food choices based on demographics.
problem Predicting human food choices from demographic data.
method Non-deterministic model based on NHANES dataset and behavioral studies.
result Generates synthetic data similar to original dataset.
New method interprets object representations from human behavior.
problem Understanding how mental object representations relate to human behavior.
method Sparse, non-negative representations of objects estimated from behavioral judgments.
result Representations predict latent object similarity and are interpretable.
Study models Ricci flow on complex surfaces, showing mixed behavior.
problem Understanding long-time behavior of Ricci flow on complex surfaces.
method BiLipschitz models for 4-manifolds (minimal surfaces of general type).
result Exhibits a combination of expanding and static behavior.
Paper develops a framework for learning interpretable representations of sequential decision behavior.
problem Obtaining a transparent description of existing behavior.
method Inverse decision modeling framework, formalizing both forward and inverse problems.
result Learning interpretable representations of behavior, including suboptimal actions, biased beliefs, and imperfect knowledge.
Survey visual analytics methods for detecting anomalous user behaviors.
problem Understanding and detecting anomalous user behaviors in various domains.
method Survey and classification of visual analytics methods in four categories.
result Discussion of findings and potential research directions.
Study uses contrastive learning to analyze market order behavior.
problem Understanding diverse market order behaviors.
method Self-supervised learning with triplet loss for order representation.
result Identified distinct behavior types using K-means clustering.
Dual-view mixture models cluster users with features and latent behaviors inferred from actions.
problem Clustering users based on features and latent behavioral functions inferred from indirect observations.
method Dual-view mixture models with non-parametric Dirichlet Process for automatic cluster number inference.
result Dual-view models outperform single-view models when one view lacks information.
Collective behavior of the complex socio-economic systems is heavily influenced by the herding, group, behavior of individuals. The importance of the herding behavior may enable the control of the collective behavior of the individuals. In this contribution we consider a simple agent-based herding model modified to inc…
Study asymptotic behaviors of solutions near singular boundaries for the Yamabe problem.
problem Boundary behavior of the singular Yamabe problem near singular boundaries.
method Analysis of asymptotic behaviors and derivation of optimal estimates for background metrics.
result Solutions are well approximated by solutions in tangent cones at singular points.
Method identifies multi-scale behavioral motifs in rat locomotion.
problem Challenging to identify behavioral motifs across different time scales.
method Preprocessing and parallel HMM training for different scales.
result Rat locomotion is composed of distinct motifs from second to minute scale.
Study uses ML to analyze financial behavior in big data.
problem Challenges in analyzing financial big data.
method Applied machine learning to financial behavioral data.
result ML models can effectively estimate performance in financial markets.