The paper examines addictive behaviors in RL agents using a modified Snake game.
problem The emergence of addictive behaviors in reinforcement learning agents.
method A modified Snake game was used to model addictive policies in Q-learning agents, and sufficient parametric conditions were derived for the emergence of addictive behaviors.
result The feasibility of addictive wireheading in RL agents was demonstrated, providing venues for further research.
Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.
problem Relapse prevention for alcohol and tobacco addiction users.
method Records user profiles, tracks relapse history, uses machine learning for prediction, and recommends activities.
result Predictive machine learning algorithms help in preventing relapse.
Optimal investment and consumption model with habit formation constraint.
problem Formulating an optimal investment and consumption model with habit formation constraint.
method Formulated an infinite-horizon optimal investment and consumption problem with habit formation model, derived explicit policies, and analyzed the system of differential equations.
result Optimal investment and consumption policies derived explicitly, showing different consumption and investment strategies based on habit formation level.
Paper uses GAN to predict opioid relapse from social media data.
problem Accurate relapse prediction for opioid addiction.
method Generative Adversarial Networks (GAN) model trained on sentiment images and social influences.
result GAN model predicts relapse better than alternatives, showing relapse is linked to joy and negative emotions.
We solve an optimal consumption problem with habit formation constraints.
problem Maximizing utility with habit formation constraints.
method Formulated and solved a deterministic optimal consumption problem.
result Optimal consumption policies derived explicitly.
Predicts academic risk in college students using interpretable machine learning.
problem Predicting academic risk from high-dimensional, unbalanced student data.
method Binary classification task using LightGBM model and Shapley value.
result 8 predictors for academic risk identified, including quality of academic partners and dormitory study atmosphere.
A new reinforcement learning framework for multi-reward processing.
problem Understanding and modeling multi-reward interactions in complex systems.
method Two-stream reward processing with biological associations.
result Agents can react differently to different types of rewards.
This paper studies the continuous time utility maximization problem on consumption with addictive habit formation in incomplete semimartingale markets. Introducing the set of auxiliary state processes and the modified dual space, we embed our original problem into a time-separable utility maximization problem with a sh…
We consider a model of optimal investment and consumption with both habit formation and partial observations in incomplete Itô processes market. The investor chooses his consumption under the addictive habits constraint while only observing the market stock prices but not the instantaneous rate of return. Applying the …
Let f be an ordinary polynomial in C[z1,...,zn] with no negative exponents and with no factor of the form z1α1...znαn where αi are non zero natural integer. If we assume in addicting that f is maximally sparse polynomial (that its support is equal to the set of vertices of its Newton p…
This paper studies the optimal consumption under the addictive habit formation preference in markets with transaction costs and unbounded random endowments. To model the proportional transaction costs, we adopt the Kabanov's multi-asset framework with a cash account. At the terminal time T, the investor can receive unb…
The "standard" Merton formulation of optimal investment and consumption involves optimizing the integrated lifetime utility of consumption, suitably discounted, together with the discounted future bequest. In this formulation the utility of consumption at any given time depends only on the amount consumed at that time.…
The paper analyzes optimal retirement strategies in a market with habit persistence and jump diffusion, finding discontinuous investment strategies.
problem Optimal retirement decision in a market with habit persistence and jump diffusion.
method Habit reduction method and duality approach to solve the dual problem using a C1 version of Itô's formula. result Discontinuous investment strategies are possible when the so-called ``de facto wealth'' exceeds a critical proportion of wage.
Automates kernel discovery for longitudinal data analysis.
problem Handling irregularly sampled, sparse longitudinal data with multilevel correlation.
method Combines deep neural networks and non-parametric kernel methods to discover complex multilevel correlation structure.
result Significantly outperforms state-of-the-art methods on benchmark data sets.
A novel generative encoder model for imaging and image processing.
problem Efficiently processing and recovering images with noise.
method A pre-training phase with a GAN and an AE, followed by an optimization phase.
result The GE model outperforms state-of-the-art algorithms in image recovery.
ENN method uses expectile regression for genetic data analysis of complex diseases.
problem Discover additional genetic variants contributing to complex diseases.
method Developed an expectile neural network (ENN) method integrating expectile regression and neural networks.
result ENN method outperforms existing expectile regression in discovering genetic variants predisposing to sub-populations.
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.
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.
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.
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.
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.
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.
We consider the problem of off-policy evaluation in Markov decision processes. Off-policy evaluation is the task of evaluating the expected return of one policy with data generated by a different, behavior policy. Importance sampling is a technique for off-policy evaluation that re-weights off-policy returns to account…
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.
We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. …
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.
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.
KFAtt improves CTR prediction by modeling user behavior with Kalman filtering attention.
problem Improving CTR prediction in personalized e-commerce search engines.
method KFAtt combines Kalman filtering with attention mechanisms to model user behavior.
result KFAtt outperforms existing methods in CTR prediction, achieving better performance in both offline and online settings.
Study of urban lifestyles from mobility data of 1.2M people in 11 U.S. cities.
problem Lack of interpretability in digital mobility data for understanding urban lifestyles.
method Privacy-enhanced dataset of mobility visitation patterns, latent activity behavior decomposition.
result Detected 12 latent activity behaviors that describe urban lifestyles, not single lifestyles.
Study on manifolds with kinks and Gaussian kernel behavior.
problem Understanding the asymptotic behavior of graph Laplacian on manifolds with singularities.
method Introduced manifolds with kinks, derived asymptotic behavior of Graph Laplacian with Gaussian kernel, and validated results numerically.
result Asymptotic behavior of the Graph Laplacian is determined by the inward sector of the tangent space.
Extends driving model to control agent behavior in simulations.
problem Simulate realistic driving behavior for autonomous systems.
method Introduces Control-ITRA method to influence agent behavior through waypoint assignment and target speed modulation.
result Demonstrates controllable, infraction-free trajectories while preserving realism.