We study a continuous-time opinion model in which agents interact in pairs and each agent has a fixed anchor. At each interaction, 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: as t...
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...
Yu-Peng Bai, Mathieu Laurière, Zhen-Jie Ren et al.· 0 citations
A sparse optimal control framework built on the Network Drift-Diffusion Model (NDDM) is proposed, which shows that the best centrality choice depends strongly on the network topology, and the system undergoes phase transitions as control parameters vary.
Bo Wang· Frontiers in Computing and I...· 0 citations
The results show that fast convergence need not require coordinated algorithm selection: one agent can compensate for a slower opponent and algorithmic asymmetry is highlighted as a useful lens for understanding cross-class interactions in multiagent optimization.
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.