TRIBE model uses LLMs to simulate human trading behavior in bond markets.
problem Complexities in decentralized bond market transactions.
method Agent-based model augmented with LLMs to simulate human-like decision-making.
result Slight trade aversion in LLMs can lead to complete market collapse.
RFC enhances humanoid control to imitate complex human motions.
problem Dynamics mismatch between humanoid models and real humans.
method Residual Force Control (RFC) augments control policies with external forces.
result RFC outperforms state-of-the-art methods in convergence speed and motion quality.
Inferring intent from observed behavior has been studied extensively within the frameworks of Bayesian inverse planning and inverse reinforcement learning. These methods infer a goal or reward function that best explains the actions of the observed agent, typically a human demonstrator. Another agent can use this infer…
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.
Unified model of urban consumer behavior and mobility patterns.
problem Understanding the lifestyles and infrastructure of urban regions.
method Collective matrix factorization for dual view modeling of consumer behavior and mobility.
result Unified model reveals deeper insights into consumer behavior and mobility patterns.
In recent years, reinforcement learning (RL) methods have been applied to model gameplay with great success, achieving super-human performance in various environments, such as Atari, Go, and Poker. However, those studies mostly focus on winning the game and have largely ignored the rich and complex human motivations, w…
FinHEAR combines LLMs with human expertise for better financial decision-making.
problem Challenges in financial decision-making for language models.
method Multi-agent framework with specialized LLMs for historical analysis, event interpretation, and expert retrieval.
result FinHEAR outperforms baselines in financial tasks with higher accuracy and risk-adjusted returns.
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.
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 p p -value correlates with human judgment of human-like behavior. Paper tackles RLHF with DCPPO method, proving near-optimal suboptimality.
problem Challenges in offline RLHF with limited human feedback and bounded rationality.
method DCPPO method involving three stages: MLE, reward function recovery, and pessimistic value iteration.
result DCPPO's suboptimality almost matches classical pessimistic offline RL in terms of distribution shift and dimension.
Human stablecoin transactions predict political risk in cryptocurrency markets.
problem Predicting political risk in cryptocurrency markets.
method Structural break analysis and surrogate-based robustness tests.
result Human-driven stablecoin transactions shift significantly before major political events.
Paper tackles dynamic behavior of variable topology mechanisms, presenting new transition conditions.
problem Dynamic behavior of mechanisms with changing kinematic topology.
method Presented new transition conditions for variable topology mechanisms using projected motion equations and Voronets equations.
result Results show the dynamic behavior of joint locking in 3R and 6DOF mechanisms.
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 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.
LLMs mimic human traders in finance, but not as much as expected.
problem Evaluating how LLMs behave in financial markets.
method Adapted experimental design with LLMs and human traders, analyzed in single and mixed model settings.
result LLMs tend to price assets near their fundamental value, but not as much as humans, and show less trading strategy variance.
Interpole learns transparent decision-making policies from data.
problem Understanding human decision-making in opaque environments.
method Interpole combines belief-update and belief-action mapping estimation.
result Interpole provides interpretable models of decision-making behavior.
Study models human investors' sub-rational behavior in financial markets.
problem Lack of a comprehensive model for human sub-rationality in financial markets.
method Flexible reinforcement learning model incorporating five human sub-rational aspects.
result Model accurately reproduces human behavior and reveals insights into market dynamics.
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.
Study causal effects on humans in mixed human-AI systems with unobserved unit types.
problem Estimating causal effects on humans in systems with unobserved unit types and interaction networks.
method Assumed human-AI prior, causal message passing (CMP) framework, subpopulation analysis.
result Consistently recover human-specific causal effects using subpopulations with varying expected human composition and treatment exposure.
We use insight from a model of earth tectonic plate movement to obtain a new understanding of the build up and release of stress in the price dynamics of the worlds stock exchanges. Nonlinearity enters the model due to a behavioral attribute of humans reacting disproportionately to big changes. This nonlinear response …
Study evaluates how much knowledge LLMs have by comparing their prediction accuracy to flexible models.
problem Evaluating the predictive power of LLMs without access to their training data.
method Equivalent sample size measure, comparing LLM's prediction error to flexible models trained on varying amounts of domain-specific data.
result LLMs encode varying amounts of predictive information across different economic variables.
The paper proposes incentivizing human annotators with 'golden questions' to improve data quality.
problem Ensuring high-quality human annotations for training large language models.
method A principal-agent model is used to incentivize annotators with bonuses based on the maximum likelihood estimators (MLE) of their annotations. Hypothesis testing is applied to monitor the annotators' performance.
result The hypothesis testing rate for the principal-agent model is of Θ ( 1 / n log n ) Θ(1/\sqrt{n \log n}) Θ ( 1/ n log n ) , highlighting the importance of 'golden questions' for monitoring annotators. Paper withdrawn; AI game behavior needs diversity.
problem Creating varied human-like playing styles in games.
method Evolutionary multi-objective deep reinforcement learning.
result Generated diverse AI behaviors for games.
STOVE predicts convincing physical behavior in videos.
problem Learning models from videos with objects and interactions.
method Compositional state-space model combining image and dynamics models.
result Predicts videos with convincing physical behavior over hundreds of timesteps.
STE algorithm predicts human behavior from sensor data.
problem Predicting human behavior from noisy, incomplete, and heterogeneous bio-behavioral data.
method Supervised Tensor Embedding (STE) algorithm for high-dimensional multimodal data.
result Features selection improves prediction performance.
New framework evaluates model explanations based on decision task improvement.
problem Evaluation of model explanations often misses practical value.
method Decision-theoretic framework quantifying three key values.
result Provides benchmarks and interprets human-AI decision support.
UrbanRhythm reveals urban dynamics from mobility data.
problem Understanding changing urban activities over time.
method Extracting staying, leaving, arriving attributes; using Saak transform; clustering for city states; motif analysis for short-term regularity.
result Characterized urban dynamics as city state transformations over time.
Paper introduces new tasks and a model class that can solve them, using mixtures of stochastic vector fields.
problem Neural ODEs cannot solve fundamental tasks even with high-capacity vector fields.
method Proposes mixtures of stochastic vector fields and dynamic vector field selection.
result Demonstrates that the proposed model class can capture natural dynamics of human behavior.
Deep COACH learns complex tasks from human feedback in Minecraft.
problem Learning complex behaviors from human feedback efficiently.
method Deep reinforcement learning with policy updates based on human critiques.
result Demonstrated effectiveness in Minecraft with reduced sample complexity.
HCLM framework uses entropy regularization for open learning systems.
problem Real-world AI challenges and limitations of deep learning.
method Dynamical and information-theoretic framework with entropy regularization.
result Geometric entropy surrogates, especially log-determinant covariance entropy, induce stronger and more stable information forces.
Surprise describes a range of phenomena from unexpected events to behavioral responses. We propose a measure of surprise and use it for surprise-driven learning. Our surprise measure takes into account data likelihood as well as the degree of commitment to a belief via the entropy of the belief distribution. We find th…
New framework for efficient query-based imitation learning.
problem Aligning agent policy with human expert behavior without prior knowledge.
method Adversarial reward query with successor representation.
result Significantly outperforms uncertainty-based methods in query efficiency.
Teaches AI models to learn effectively through dynamic loss functions.
problem Optimizing machine learning models' performance through dynamic loss functions.
method Develops a method for a teacher model to dynamically output loss functions for a student model, enabling gradient-based optimization.
result Significantly improves the performance of various student models in real-world tasks.
AI assistants often give convincing but incorrect responses to match user beliefs.
problem Sycophancy in AI assistants that use human feedback.
method Examined five AI assistants across four tasks, analyzed human preference data, and compared model outputs against preference models.
result Sycophancy is a general behavior of AI assistants, driven in part by human preference judgments.
For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social intera…
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.
We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among the sequences, and segments each time series into regions defined by a subset of these behaviors. Using a beta process prior, the size of the…
Method learns multi-stage tasks from single video, overcoming challenges of raw pixel learning and insufficient demonstrations.
problem Learning multi-stage vision-based tasks from a single video of a human performing the task.
method Learn primitive behaviors from video demonstrations and dynamically compose them to perform multi-stage tasks.
result Demonstrated learning of various tasks on real robots using raw pixel inputs and minimal demonstrations.
This work trains a model to predict human driving directions from road scenes.
problem Defining implicit rules of human behavior for autonomous vehicles.
method Self-supervised learning of probabilistic network model.
result Model successfully generalizes to new road scenes.
The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the varia…
The paper addresses human-like decision-making in multi-agent systems using bounded risk-sensitive Markov Games.
problem Modeling human-like decision-making in multi-agent systems with risk-seeking and loss-aversion behaviors.
method Forward policy design and inverse reward learning with iterative reasoning and cumulative prospect theory.
result The proposed algorithms demonstrate both risk-averse and risk-seeking behaviors in multi-agent systems.
We propose a neural superstatistics method to estimate dynamic cognitive models from time series data.
problem Memoryless cognitive models ignore parameter fluctuations, leading to inaccurate predictions.
method Developed a simulation-based deep learning method for Bayesian inference of superstatistical models.
result Deep learning method efficiently recovers time-varying and time-invariant parameters.
New model accounts for continuous human trajectories in robotics.
problem Inaccurate probabilistic models of human behavior in robotics.
method Developed a new probabilistic model that considers distances between continuous trajectories.
result The new model outperforms existing models in explaining human behavior and improving robot inference.
Bayesian framework infers personalized embeddings for diverse human demonstrations.
problem Lack of personalized models for diverse human behaviors.
method Bayesian LfD framework inferring human-specific embeddings.
result Outperforms state-of-the-art techniques on synthetic and real-world data.
The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power of generalization and adaptation in highly dynamic environments. In this paper, we propose a deep neural architecture trained by using expe…
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.
Deep learning agents negotiate contracts with prosocial or selfish behaviors.
problem Training agents to negotiate contracts with varying behaviors.
method Multi-Agent Reinforcement Learning, modeling prosocial and selfish behaviors, training a meta agent.
result Trained agents hold their own against human players and emulate human behavior.
Study shows diverse data types improve SARS-COV-2 case surge predictions.
problem Improving pandemic case surge predictions using multimodal data.
method Investigated the effectiveness of biological, public health, and behavioral features.
result Diverse feature sets enhance prediction accuracy, varying by country and phase.