Tootfinder

I began posting this around year 2000.... It still seems quite valid.
The First Law of the Internet
Every person shall be free to use the Internet in any way that is privately beneficial without being publicly detrimental.
The burden of demonstrating public detriment shall be on those who wish to prevent the private use.
Such a demonstration shall require clear and convincing evidence of public detriment.
The public detriment must be of such deg…

Multi-agent learning under uncertainty: Recurrence vs. concentration
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose Blanchet
https://arxiv.org/abs/2512.08132 https://arxiv.org/pdf/2512.08132 https://arxiv.org/html/2512.08132
arXiv:2512.08132v1 Announce Type: new
Abstract: In this paper, we examine the convergence landscape of multi-agent learning under uncertainty. Specifically, we analyze two stochastic models of regularized learning in continuous games -- one in continuous and one in discrete time with the aim of characterizing the long-run behavior of the induced sequence of play. In stark contrast to deterministic, full-information models of learning (or models with a vanishing learning rate), we show that the resulting dynamics do not converge in general. In lieu of this, we ask instead which actions are played more often in the long run, and by how much. We show that, in strongly monotone games, the dynamics of regularized learning may wander away from equilibrium infinitely often, but they always return to its vicinity in finite time (which we estimate), and their long-run distribution is sharply concentrated around a neighborhood thereof. We quantify the degree of this concentration, and we show that these favorable properties may all break down if the underlying game is not strongly monotone -- underscoring in this way the limits of regularized learning in the presence of persistent randomness and uncertainty.
toXiv_bot_toot

Approximation of the Range Ambiguity Function in Near-field Sensing Systems
Marcin Wachowiak, Andr\'e Bourdoux, Sofie Pollin
https://arxiv.org/abs/2509.22423 https://…

Approximation of the Range Ambiguity Function in Near-field Sensing Systems
This paper investigates the range ambiguity function of near-field systems where bandwidth and near-field beamfocusing jointly determine the resolution. First, the general matched filter ambiguity function is derived and the near-field array factors of different antenna array geometries are introduced. Next, the near-field ambiguity function is approximated as a product of the range-dependent near-field array factor and the ambiguity function due to the utilized bandwidth and waveform. An appro…