We study a continuous-time opinion model in which agents interact in pairs and each agent has a fixed anchor. At each interaction epoch, both opinions are updated according to a possibly nonlinear rule with random inputs. Under suitable stability and moment assumptions, we establish uniform-in-time propagation of chaos...
We investigate long-time learning in ergodic, potential, monotone mean-field games (MFGs) via a self-fictitious-play (SFP) dynamics coupling an optimally controlled diffusion with a slowly evolving belief. At each time, the state follows the optimal feedback associated with the current belief, while the belief is updat...
Yupeng Bai, Mathieu Laurière, Zhen-Jie Ren et al.· 0 citations
This paper studies the problem of steering collective beliefs in social networks when only a small fraction of nodes can be directly influenced. We propose a sparse optimal control framework built on the Network Drift-Diffusion Model (NDDM). Two intervention mechanisms are considered: direct control and latent (indire...
Bo Wang· Frontiers in Computing and I...· 0 citations
Learning dynamics in zero-sum games are typically analyzed under algorithmic symmetry: both agents use the same update rule, or methods from a common algorithmic family. This is at odds with the nature of zero-sum games; competing agents need not coordinate on algorithm selection. This paper studies algorithmic asymmet...
Network common learning is introduced, a network analogue of common learning, and it is shown that it is attained when neighboring agents'observations differ by many signals, as on the two-dimensional grid, but fails on networks with informational bottlenecks, such as the line.
Olga Rospuskova, Omer Tamuz, Jake Zhang· 0 citations
A novel framework for modeling binary opinions of individuals connected through a weighted directed network, where edge weights quantify interpersonal influence is proposed, which allows individuals to update their biases using structured memory sets that capture limited and delayed information exchange.