Zobrazeno 1 - 10
of 343
pro vyhledávání: '"Chazelle, Bernard"'
Averaging dynamics drives countless processes in physics, biology, engineering, and the social sciences. In recent years, the $s$-energy has emerged as a useful tool for bounding the convergence rates of time-varying averaging systems. We derive new
Externí odkaz:
http://arxiv.org/abs/2410.22341
We investigate the emergence of periodic behavior in opinion dynamics and its underlying geometry. For this, we use a bounded-confidence model with contrarian agents in a convolution social network. This means that agents adapt their opinions by inte
Externí odkaz:
http://arxiv.org/abs/2403.06376
We establish sufficient conditions for the quick relaxation to kinetic equilibrium in the classic Vicsek-Cucker-Smale model of bird flocking. The convergence time is polynomial in the number of birds as long as the number of flocks remains bounded. T
Externí odkaz:
http://arxiv.org/abs/2207.00213
A major challenge in biomedical data science is to identify the causal genes underlying complex genetic diseases. Despite the massive influx of genome sequencing data, identifying disease-relevant genes remains difficult as individuals with the same
Externí odkaz:
http://arxiv.org/abs/2001.06135
Autor:
Chazelle, Bernard
The {\em $s$-energy} is a generating function of wide applicability in network-based dynamics. We derive an (essentially) optimal bound of $(3/\rho s)^{n-1}$ on the $s$-energy of an $n$-agent symmetric averaging system, for any positive real $s\leq 1
Externí odkaz:
http://arxiv.org/abs/1802.01207
Autor:
Chazelle, Bernard
We introduce the concept of a Markov influence system (MIS) and analyze its dynamics. An MIS models a random walk in a graph whose edges and transition probabilities change endogenously as a function of the current distribution. This article consists
Externí odkaz:
http://arxiv.org/abs/1802.01208
Autor:
Wang, Chu, Chazelle, Bernard
We analyze the dynamics of the Learning-Without-Recall model with Gaussian priors in a dynamic social network. Agents seeking to learn the state of the world, the "truth", exchange signals about their current beliefs across a changing network and upd
Externí odkaz:
http://arxiv.org/abs/1609.05990
Autor:
Chazelle, Bernard, Wang, Chu
An important result from psycholinguistics (Griffiths & Kalish, 2005) states that no language can be learned iteratively by rational agents in a self-sustaining manner. We show how to modify the learning process slightly in order to achieve self-sust
Externí odkaz:
http://arxiv.org/abs/1609.03960
The classic Hegselmann-Krause (HK) model for opinion dynam- ics consists of a set of agents on the real line, each one instructed to move, at every time step, to the mass center of all the agents within a fixed distance R. In this work, we investigat
Externí odkaz:
http://arxiv.org/abs/1511.02975