The goal of this paper is to study organized flocking behavior and systemic risk in heterogeneous mean-field interacting diffusions. We illustrate in a number of case studies the effect of heterogeneity in the behavior of systemic risk in the system, i.e., the risk that several agents default simultaneously as a result…
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48 results for “heterogeneous behavior”
Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Taskscs.LG
Modeling student behaviors and multiple predictions for early intervention.
problem Predicting student outcomes and interactions among multiple tasks.
method Proposes a variant of LSTM and soft-attention mechanism for heterogeneous behaviors, and co-attention mechanism for task interactions.
result Demonstrated effectiveness in predicting student outcomes and interactions.
Recent years have witnessed an increased focus on interpretability and the use of machine learning to inform policy analysis and decision making. This paper applies machine learning to examine travel behavior and, in particular, on modeling changes in travel modes when individuals are presented with a novel (on-demand)…
The paper develops personalized DAG models for web user behavior.
problem Understanding user behavior transitions between websites with user heterogeneity and network dependency.
method Personalized Binomial DAG models with network-structured covariates, embedding network structure into a dimension-reduced covariate, learning node neighborhoods, and exploring variance-mean relation.
result The proposed algorithm outperforms state-of-the-art competitors in heterogeneous data.
Investor attention is an important concept in behavioral finance. Many articles have conducted cross-disciplinary research leading by this concept. In this paper, we use data extraction technology to collect a large number of Baidu Index keyword search volume data. After analyzing the data, we draw a conclusion that ha…
The paper models social networks with varying levels of reciprocity.
problem Understanding diverse reciprocal behavior in social networks.
method Developed a preferential attachment model with heterogeneous reciprocity.
result Captures the heavy-tailed nature of empirical degree distributions and identifies multiple user groups.
New test detects differences in heterogeneous datasets.
problem Detecting differences between two samples with unknown heterogeneity.
method Developed a nonparametric testing procedure that handles latent heterogeneity through a composite null.
result The test accurately detects differences in the presence of unknown heterogeneity.
Revisits behavioral finance option pricing model to align with rational asset pricing theory.
problem Inconsistency between behavioral finance and rational asset pricing models in option pricing.
method Introduces arbitrage transaction costs to modify the behavioral finance option pricing formula.
result Modifies behavioral finance option pricing formula to be consistent with rational asset pricing theory.
Enhances Ponzi scheme detection on Ethereum using time-aware metapaths.
problem Lack of temporal information in heterogeneous transaction graphs.
method Time-aware Metapath Feature Augmentation (TMFAug) module.
result Significant performance improvements in Ponzi scheme detection.
The paper explores strong identifiability and parameter learning in regression models with heterogeneous responses.
problem Understanding heterogeneity in data populations through conditional distributions of a response variable.
method Investigation of strong identifiability, convergence rates, and posterior contraction behavior in finite mixture of regression models.
result Theoretical findings on conditions for strong identifiability and rates of convergence in regression mixture models.
We develop a behavioral asset pricing model in which agents trade in a market with information friction. Profit-maximizing agents switch between trading strategies in response to dynamic market conditions. Due to noisy private information about the fundamental value, the agents form different evaluations about heteroge…
Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user's behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging…
Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while behavior-based approaches highly rely on the malware training samples and incur prohi…
In market modeling, one often treats buyers as a homogeneous group. In this paper we consider buyers with heterogeneous preferences and products available in many variants. Such a framework allows us to successfully model various market phenomena. In particular, we investigate how is the vendor's behavior influenced by…
LESS combines local predictors for subsets to learn from heterogeneous input-output pairs.
problem Learning from heterogeneous input-output pairs in populations with varied behavior.
method LESS algorithm: generates subsets, trains local predictors, combines them.
result LESS is highly competitive compared to state-of-the-art methods.
Program or process is an integral part of almost every IT/OT system. Can we trust the identity/ID (e.g., executable name) of the program? To avoid detection, malware may disguise itself using the ID of a legitimate program, and a system tool (e.g., PowerShell) used by the attackers may have the fake ID of another commo…
Study on self-consuming generative models with diverse human curation, focusing on convergence and stability.
problem Analyzing self-consuming generative models with heterogeneous human curation.
method Investigates the asymptotic behavior of retraining dynamics using nonlinear Perron--Frobenius theory and Banach contraction mapping.
result Improves convergence results and provides stability and non-stability analyses for the model.
We study the market selection hypothesis in complete financial markets, populated by heterogeneous agents. We allow for a rich structure of heterogeneity: individuals may differ in their beliefs concerning the economy, information and learning mechanism, risk aversion, impatience and 'catching up with Joneses' preferen…
We use the theory of large deviations to study the pricing of investment-grade tranches of synthetic CDO's. In this paper, we consider a heterogeneous pool of names. Our main tool is a large-deviations analysis which allows us to precisely study the behavior of a large amount of idiosyncratic randomness. Our calculatio…
Paper models market dynamics using bull and bear forces.
problem Complex market dynamics influenced by biases and narratives.
method Bias to Behavior from Bull-Bear Dynamics (B4) model.
result Model predicts market trends with superior performance and interpretable insights.
The paper introduces a new volatility model for state heterogeneous financial markets using high-frequency data.
problem State heterogeneity in financial volatility processes.
method Developed a state heterogeneous GARCH-Ito (SG-Ito) model based on continuous Ito diffusion process.
result Empirical studies reveal various state heterogeneities in S&P 500 index volatility.
Paper proposes a method to optimize policies for diverse individuals using heterogeneous data.
problem Learning optimal policies for a heterogeneous population from pre-collected data.
method Individualized offline policy optimization framework for heterogeneous MDPs.
result The proposed P4L algorithm achieves a fast rate of average regret.
Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems heterogeneity), and (2) non-identically distributed data across the ne…
Bayesian method learns causal orderings from heterogeneous data.
problem Learning causal structure from heterogeneous data.
method Order-based Bayesian framework for Gaussian DAG models.
result Causal ordering is identifiable up to two permutations.
This study generalizes an econophysics model to account for trader heterogeneity, finding robust power-law exponents but sensitive prefactors.
problem The original Lillo-Mike-Farmer model assumed homogeneity in traders' order-splitting strategies, which this study generalizes.
method The study proposes a generalised Lillo-Mike-Farmer model and solves it exactly without heuristic assumptions.
result The power-law exponent in the order-sign ACF is robust for arbitrary heterogeneous intensity distributions, but the prefactor is sensitive to heterogeneity.
Fisher et al. extend multi-VAR for better modeling of heterogeneous time series.
problem Modeling structurally heterogeneous processes in social, health, and behavioral sciences.
method Adaptive weighting schemes for penalized estimation of multiple-subject multivariate time series.
result Improved estimation performance compared to alternative estimators.
Spectral clustering is one of the most popular, yet still incompletely understood, methods for community detection on graphs. This article studies spectral clustering based on the Bethe-Hessian matrix for sparse heterogeneous graphs (following the degree-corrected stochastic block model) in a …
Model explains capital allocation and wealth distribution dynamics in a frictional economy.
problem Understanding capital allocation and wealth distribution dynamics in a frictional economy.
method Mean-field game approach to model interactions between expert and household groups.
result Experts accumulate capital during booms and quickly reverse behavior in busts, even without macro-shocks.
Inspired by the recent literature on aggregation theory, we aim at relating the long range correlation of the stocks return volatility to the heterogeneity of the investors' expectations about the level of the future volatility. Based on a semi-parametric model of investors' anticipations, we make the connection betwee…
The paper investigates how dataset quality and heterogeneity affect model confidence in machine learning.
problem Understanding how dataset quality and heterogeneity impact model confidence in machine learning.
method The study uses theoretical explanations and experimental demonstrations to investigate the effects of dataset size, label noise, and class heterogeneity on model confidence.
result Label noise reduces model confidence, while reduced dataset size increases it, and class heterogeneity leads to inconsistent confidence across classes.
Improved forecasting of suicide attempts using LSGPs for patients with little data.
problem Challenges in predicting suicide attempts due to their rarity and patient heterogeneity.
method Introduced Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity.
result LSGPs outperform baseline models, even without kernel-design, and offer new insights into patient similarity.
The dynamics of many socioeconomic systems is determined by the decision making process of agents. The decision process depends on agent's characteristics, such as preferences, risk aversion, behavioral biases, etc.. In addition, in some systems the size of agents can be highly heterogeneous leading to very different i…
Evolutionary dynamics in financial markets with heterogeneities in strategies and risk toleranceq-fin.GN
Model shows how heterogeneity in strategies and risk tolerance affects financial market stability.
problem Understanding how heterogeneity impacts financial market dynamics.
method Agent-based model incorporating heterogeneous investment strategies and risk tolerance.
result Heterogeneity in strategies and risk tolerance suppresses price fluctuations.
A method for inferring motility models and heterogeneity from particle trajectories.
problem Understanding motility patterns from discrete trajectory data of biological agents.
method Maximum likelihood approach for second-order Langevin models with population heterogeneity.
result The proposed method outperforms alternative approaches for short trajectories.
Split-Session Cluster GARCH for Overnight and Intraday Returns: The Role of Tail Heterogeneityecon.EM
The Split-Session Cluster GARCH model captures tail heterogeneity in overnight and intraday returns.
problem Capturing tail behavior and dependence in multivariate asset returns.
method Convolution- distributions, session and sector clustering, block-structured correlation matrices.
result Session-specific and sector-level tail parameters improve model fit and out-of-sample performance.
Identifying behavior that is relatively invariant under different conditions is a challenging task in far-from-equilibrium complex systems. As an example of how the existence of a semi-invariant signature can be masked by the heterogeneity in the properties of the components comprising such systems, we consider the exc…
A novel framework interprets driving patterns using Action phases clustering.
problem Challenges in comprehending driving heterogeneity from underlying behavior mechanisms.
method Resampling and Downsampling Method (RDM) followed by iterative clustering calibration.
result Six driving patterns identified in real-world datasets, revealing dynamic nature of driving.
Study improves choice model accuracy and heterogeneity representation using mixture models.
problem Improving prediction accuracy and heterogeneity representation in choice models.
method Semi-nonparametric Latent Class Choice Model with mixture models and EM algorithm.
result Mixture models enhance prediction accuracy and heterogeneity representation without sacrificing interpretability.
A Cultural Market Modelq-fin.GN
Social interactions and personal tastes shape our consumption behavior of cultural products. In this study, we present a computational model of a cultural market and we aim to analyze the behavior of the consumer population as an emergent phenomena. Our results suggest that the final market shares of cultural products …
The use of kinetic modelling based on partial differential equations for the dynamics of stock price formation in financial markets is briefly reviewed. The importance of behavioral aspects in market booms and crashes and the role of agents' heterogeneity in emerging power laws for price distributions is emphasized and…
Adaptive optimizers improve federated learning performance.
problem Inadequate optimization methods in federated learning.
method Federated versions of adaptive optimizers (Adagrad, Adam, Yogi).
result Adaptive optimizers enhance federated learning performance.
This paper addresses the problem of change-point detection on sequences of high-dimensional and heterogeneous observations, which also possess a periodic temporal structure. Due to the dimensionality problem, when the time between change-points is on the order of the dimension of the model parameters, drifts in the und…
NFT Bubblesq-fin.PR
Study reveals investor behavior in NFT bubbles.
problem Understanding retail investor behavior in asset bubbles.
method Systematic study of NFTs using public blockchain data.
result Sophisticated investors outperform others in NFT bubbles.
Study on evolving interfaces with complex curvature and density effects.
problem Understanding the dynamics of evolving heterogeneous elastic interfaces.
method Modeling an evolving curve with a density function, analyzing the associated gradient flow evolution.
result Analysis of the preservation and asymptotic behavior of geometric properties in the evolving system.
Study shows how diverse investors' learning and preferences shape financial markets.
problem Understanding how diverse investor behaviors and preferences affect market dynamics.
method Developed a multi-agent reinforcement learning framework with heterogeneous preferences and learning mechanisms.
result Diverse investors develop differentiated strategies through interaction, leading to realistic market dynamics.
Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data providing temporal characterization of an individual's behaviors. Is it possible to efficiently couple such rich sensor data with predictive modeling techniques to provide contextual, and…
We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs. We assume that each output has its own likelihood function and use a vector-valued Gaussian process prior to jointly model the parameters in all likelihoods as latent functions. Our multi-output Gaussian process uses a c…
We discuss the behavior of two magnitudes, physical complexity and mutual information function of the outcome of a model of heterogeneous, inductive rational agents inspired in the El Farol Bar problem and the Minority Game. The first is a measure rooted in Kolmogorov-Chaitin theory and the second one a measure related…