The paper investigates when information sharing enhances decentralized discovery by separating its effects on pooled estimation and independent rescue actions in finite discovery models. It shows that a registered incremental-sharing protocol improves discovery only when pooled residual error decreases faster than an independent rescue attempt, and that equilibrium selection can determine whether sharing is beneficial. The study uses synthetic, finite models without human or organizational data.
arXiv:2607. 18045v1 Announce Type: new Abstract: Organizations often pool dispersed information into one ranking and then allow many agents to act on that shared view.
Organizations often pool dispersed information into one ranking and then allow many agents to act on that shared view. In a discovery problem, this can improve beliefs while reducing coverage.
arXiv:2607. 18300v1 Announce Type: cross Abstract: We extend Incentive Compatible Exploration beyond the Bayesian full-information setting of Kremer et al.
arXiv:2608. 10529v1 Announce Type: cross Abstract: The multi-armed bandit problem is a central framework in sequential decision-making, extensively studied under sub-Gaussian reward assumptions.
arXiv:2608. 10526v1 Announce Type: cross Abstract: Motivated by decentralized applications, we study cooperative multi-agent bandits in continuous (Lipschitz) action spaces when the Lipschitz constant is unknown.
The paper introduces a decentralized learning framework for finding socially optimal equilibria in finite normal-form games played over dynamic communication networks. Agents only observe their own payoffs, lack prior knowledge of the game, and communicate with time-varying neighbors using low-bandwidth, time-stamped tables instead of raw actions or payoff data. The proposed dynamics combine randomized semantic signals, table fusion, and temporal majority reconstruction to achieve finite-time logarithmic regret guarantees for optimal equilibrium selection under utilitarian and proportional-fair social welfare objectives, as demonstrated by simulations.
arXiv:2602. 16965v2 Announce Type: replace Abstract: We study the decentralized multi-player stochastic bandit problem over a continuous, Lipschitz-structured action space where hard collisions yield zero reward.
The paper introduces a decentralized decision-making framework for teams operating under partial observability and unknown system dynamics. By leveraging low-rank latent dynamics and delayed shared information, each team member learns an approximate Markov decision process using only local private data and delayed common updates. The resulting algorithm achieves near‑optimal team performance without requiring a centralized coordinator or training, and the authors provide finite‑sample guarantees and a sample‑complexity bound.
arXiv:2607. 17924v1 Announce Type: cross Abstract: Multi-agent policy optimization, exemplified by PPO-based methods, is a key branch of cooperative Multi-Agent Reinforcement Learning (MARL).
arXiv:2609.22757v1 Announce Type: cross Abstract: A (coarse) correlated equilibrium (CE) is information-value-free (IVF) if a player can match the payoff obtained from recommendations by committing t...
arXiv:2607. 23029v1 Announce Type: cross Abstract: Federated learning enables collaborative model training across distributed clients without centralising their data, yet privacy remains a persistent concern because the shared model updates can leak information about local datasets.
arXiv:2506. 03802v2 Announce Type: replace Abstract: We introduce a learning problem in a generalized two-sided matching market, where agents select actions to interact with their match.