arXiv:2606. 29457v1 Announce Type: new Abstract: When two companies bid to buy the same target, no one knows exactly what the target is worth.
arXiv:2604.22230v2 Announce Type: replace-cross Abstract: Performance manipulation arises when agents exploit easily measurable, routine tasks to inflate observable outcomes without contributing genu...
arXiv:2503. 01701v2 Announce Type: replace-cross Abstract: Most microeconomic models of interest involve optimizing a piecewise linear function.
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:2608. 08407v1 Announce Type: cross Abstract: A bidder can quietly buy a stake in a company before making an offer for it.
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
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:2606. 05363v1 Announce Type: cross Abstract: On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand?
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.
The paper introduces Regret-Weighted Payoff Sampling (RWPS), a budgeted estimator that selectively simulates only payoff-matrix cells relevant to a Nash equilibrium and uses a surrogate model for the remaining entries. RWPS provides an instance-dependent error bound weighted by the opponent’s equilibrium mixture and a coverage result guaranteeing that, once the deviation-relevant set is simulated, surrogate error does not affect either player’s regret. Experiments on three 21×21 general-sum games, including an asymmetric Colonel Blotto, show that RWPS achieves four to six times tighter bounds than previous methods and outperforms other sampling strategies on the CyGym and ANSG cyber simulators at low budgets.
arXiv:2607. 06854v1 Announce Type: cross Abstract: Reinforcement learning agents for imperfect-information card games are only as strong as the opponents they train against, and they are hard to grade, since they beat a random opponent over 99 percent of the time and only tie copies of themselves.
The paper investigates whether pricing algorithms on multi‑seller platforms should incorporate competitors’ prices when learning demand. It compares two strategies: informed sellers that use competitor prices in their learning models, and oblivious sellers that ignore them. The study finds that oblivious sellers must explore prices more aggressively to offset missing competitor information; when all sellers are oblivious, prices eventually converge to the competitive outcome, but insufficient exploration can create many pseudo‑equilibria. In mixed markets, informed sellers earn more, and the unique Nash equilibrium is a fully informed market where prices efficiently converge to the competitive outcome, showing that oblivious modeling does not reliably produce collusion.
arXiv:2606. 29252v1 Announce Type: new Abstract: We study repeated bidding in multi-unit discriminatory (pay-as-bid) auctions for a single bidder with per-round utility equal to value minus $\alpha$ times payment, where $\alpha\in[0,1]$ is a cost-of-capital parameter.