arXiv research

Study on stock price formation on trees with multi-population and non-rational agents.

Toric quasifolds extend toric geometry to non-rational polytopes.

Optimizes bounds for multiple T-singularities on surfaces.

For linear actions of real reductive Lie groups we prove the Kempf-Ness Theorem about closed orbits and the Kirwan-Ness Stratification Theorem of the null cone. Since our completely self-contained proof focuses strongly on geometric and analytic methods, essentially avoiding any deep algebraic result, it applies also t…

Proves a new inequality for certain complex surfaces.

Customer behavior is often assumed to follow weak rationality, which implies that adding a product to an assortment will not increase the choice probability of another product in that assortment. However, an increasing amount of research has revealed that customers are not necessarily rational when making decisions. In…

In this article we consider a generalization of manifolds and orbifolds which we call quasifolds; quasifolds of dimension k are locally isomorphic to the quotient of R^k by the action of a discrete group - tipically they are not Hausdorff topological spaces. The analogue of a torus in this geometry is a quasitorus. We …

New proof for weak mixing in polygonal billiards.

Montgomery-Yang problem predicts that every pseudofree differentiable circle action on the 5-dimensional sphere has at most 3 non-free orbits. Using a certain one-to-one correspondence, Kollár formulated the algebraic version of the Montgomery-Yang problem: every projective surface $S$ with the second Betti number $b_2…

Sharp estimate for 2-systole on Kähler surfaces with positive scalar curvature.

We call complex quasifold of dimension k a space that is locally isomorphic to the quotient of an open subset of the space C^k by the holomorphic action of a discrete group; the analogue of a complex torus in this setting is called a complex quasitorus. We associate to each simple polytope, rational or not, a family of…

Monopole invariant studies contact structures on 3-manifolds.

Associated with a smooth, $d$-closed $(1, 1)$-form $α$ of possibly non-rational De Rham cohomology class on a compact complex manifold $X$ is a sequence of asymptotically holomorphic complex line bundles $L_k$ on $X$ equipped with $(0, 1)$-connections $\bar\partial_k$ for which $\bar\partial_k^2\neq 0$. Their study was…

Analyzes new economic paradigm for non-independent consumer choices.

Study extends complex sections on non-holomorphic objects on Kähler manifolds.

Method models other agents' behaviors without requiring direct observation.

Agent-to-agent finance aims to manage payments and trust for AI agents.

AI agents manage portfolios, improving on human oversight.

New algorithm reduces learning regret in multi-agent systems with unknown dynamics.

RL agents outperform baselines in asset allocation.

Agents learn to give rewards to others in a shared learning environment.

New algorithm reduces regret in multi-agent bandits with malicious agents.

We formulate and analyze a multi-agent model for the evolution of individual and systemic risk in which the local agents interact with each other through a central agent who, in turn, is influenced by the mean field of the local agents. The central agent is stabilized by a bistable potential, the only stabilizing force…

Algorithm maximizes total reward in multi-agent bandits with adversarial corruptions.

I2C enables agents to learn efficient communication without redundancy.

We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use Multi Agent Reinforcement Learning to train both agents simultaneously as they negotiate with each other in the training environment. We al…

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

In mix-game which is an extension of minority game, there are two groups of agents; group1 plays the majority game, but the group2 plays the minority game. This paper studies the change of the average winnings of agents and volatilities vs. the change of mixture of agents in mix-game model. It finds that the correlatio…

We propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. We model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected actions of the group to render the model independent of the number of age…

Many learning agents impact a financial market model, showing complex dynamics.

Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples. That is, RL agents can learn, as we learn. Given the importance of learning in our intelligence, RL has been thought to be one of key compone…

Agents collaborate to reduce regret in a multi-agent linear bandit problem with side information.

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

A novel framework uses goal-conditioned reinforcement learning to generate diverse samples.

Agents need world models to generalize multi-step tasks.

ODC protocol improves learning in asynchronous multi-agent bandits.

The ability of modeling the other agents, such as understanding their intentions and skills, is essential to an agent's interactions with other agents. Conventional agent modeling relies on passive observation from demonstrations. In this work, we propose an interactive agent modeling scheme enabled by encouraging an a…

EPC curriculum improves MARL performance as agent population grows.

The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predic…

Agents learn social skills from each other, improving performance.

This paper presents an analytical treatment of economic systems with an arbitrary number of agents that keeps track of the systems' interactions and agents' complexity. This formalism does not seek to aggregate agents. It rather replaces the standard optimization approach by a probabilistic description of both the enti…

Study uses AI agents to improve equity portfolio management.

Study improves online learning with adaptable agents in various settings.

Kernel method improves cooperative decision-making among agents.

This work formalizes and extends parameter sharing in multi-agent reinforcement learning.

We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions t…

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

A simple learning agent learns to trade in an agent-based market model.