arXiv research

A novel framework for adaptive multi-agent communication in reinforcement learning.

Adaptive clustering and personalization algorithms minimize regret in multi-agent stochastic linear bandits.

Enhances financial analysis with multi-agent collaboration.

When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval. Models of agent responsiveness, termed "strategic manipulation," analyze the interaction between a learner and agents in a world where all agents are equally able …

We consider a heterogeneous agent-based economic model where economic agents have strictly bounded rationality and where income allocation strategies evolve through selective imitation. Income is calculated by a Cobb-Douglas type production function, and selection of strategies for imitation depends on the income growt…

We propose a model for equity trading in a population of agents where each agent acts to achieve his or her target stock-to-bond ratio, and, as a feedback mechanism, follows a market adaptive strategy. In this model only a fraction of agents participates in buying and selling stock during a trading period, while the re…

Adaptive policies for multi-agent RL improve adaptiveness in changing environments.

Develops a generic two-layer framework for adaptive ABMs.

ATLAS uses LLMs to adaptively trade by optimizing prompts and coordinating agents.

We describe a simple model for speculative trading based on adaptive behavior of economic agents.The adaptive behavior is expressed through a feedback mechanism for changing agents' stock-to-bond ratios, depending on the past performance of their portfolios.The stock price is set according to the demand-supply for the …

Adaptive populations such as those in financial markets and distributed control can be modeled by the Minority Game. We consider how their dynamics depends on the agents' initial preferences of strategies, when the agents use linear or quadratic payoff functions to evaluate their strategies. We find that the fluctuatio…

We derive a class of macroscopic differential equations that describe collective adaptation, starting from a discrete-time stochastic microscopic model. The behavior of each agent is a dynamic balance between adaptation that locally achieves the best action and memory loss that leads to randomized behavior. We show tha…

The average economic agent is often used to model the dynamics of simple markets, based on the assumption that the dynamics of many agents can be averaged over in time and space. A popular idea that is based on this seemingly intuitive notion is to dampen electric power fluctuations from fluctuating sources (as e.g. wi…

Adapts agent strategies on-the-fly for better cross-play in cooperative settings.

CARL safely adapts RL agents for safety-critical tasks.

Algorithm for decentralized competition among adaptive agents.

Study coevolutionary trading-agent dynamics in continuous strategies.

EPC curriculum improves MARL performance as agent population grows.

This work proposes ACTC for adaptive distributed learning under communication constraints.

A Kyle-inspired model with adaptive agents explains excess volatility and volatility clustering.

Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time. Given that it is impractical …

A major challenge in cognitive science and AI has been to understand how autonomous agents might acquire and predict behavioral and mental states of other agents in the course of complex social interactions. How does such an agent model the goals, beliefs, and actions of other agents it interacts with? What are the com…

We study the problem of designing AI agents that can robustly cooperate with people in human-machine partnerships. Our work is inspired by real-life scenarios in which an AI agent, e.g., a virtual assistant, has to cooperate with new users after its deployment. We model this problem via a parametric MDP framework where…

Curious Replay improves model-based reinforcement learning agents' adaptability.

Network agents solve adaptive regression problems with compressed signals.

The paper examines how slightly biasing towards under-represented groups in sequential selection processes can lead to long-term fairness.

AI agents improve forecast combination in empirical economics.

This research tackles information design in multi-agent reinforcement learning.

Adaptive Bayesian learning agent for non-stationary bandits.

PEAR dynamically reconfigures agent roles to prevent persistent biases in multi-agent debates.

AI agents improve forecast combination but require transparency.

Multi-agent models have been used in many contexts to study generic collective behavior. Similarly, complex networks have become very popular because of the diversity of growth rules giving rise to scale-free behavior. Here we study adaptive networks where the agents trade ``wealth'' when they are linked together while…

AMSAs adaptively manage crypto-currency trading by selecting multiple strategies based on market conditions.

This paper improves MARL for networked systems through new protocols and discount factors.

A model is developed to study the effectiveness of innovation and its impact on structure creation and structure change on agent-based societies. The abstract model that is developed is easily adapted to any particular field. In any interacting environment, the agents receive something from the environment (the other a…

MASA framework uses RL to balance portfolio returns and risks.

Novel approach models opponent learning dynamics in multi-agent reinforcement learning.

Paper analyzes convergence rates for multi-agent learning in games.

AGENTICAITA uses AI agents to autonomously trade markets without human intervention.

The recent trend for acquiring big data assumes that possessing quantitatively more and qualitatively finer data necessarily provides an advantage that may be critical in competitive situations. Using a model complex adaptive system where agents compete for a limited resource using information coarse-grained to differe…

New algorithm reduces multi-agent bandit regret by sharing data.

FGNNs improve game-playing AI by exploiting symmetries.

LLMs in financial markets show diverse behaviors, from stable to speculative, challenging rational expectations.

We consider a two-agent MDP framework where agents repeatedly solve a task in a collaborative setting. We study the problem of designing a learning algorithm for the first agent (A1) that facilitates a successful collaboration even in cases when the second agent (A2) is adapting its policy in an unknown way. The key ch…

ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.

MIDAS learns to adaptively control other cars in urban driving scenarios.

Agents learn social skills from each other, improving performance.

Study shows how adaptive market agents can lead to persistent overpricing in financial markets.