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.
Abstract
We study global games in which agents coordinate locally, with their social network neighbors, contingent on a favorable state. Before acting, agents learn the private signals of all agents within network distance $r$. As $r$ grows, every agent learns the state, but efficient coordination depends on higher-order beliefs, which are shaped by the geometry of the network. We introduce network common learning, a network analogue of common learning, and show 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, where only the safe action survives in equilibrium.
Kearns, Roth, and Ryu show that the output agent's error approaches the global predictor's error along sufficiently deep paths with suitable feature coverage, while insufficient depth can prevent aggregation even in large networks.
M. Bateni, Z. Hadizadeh, Mohammadtaghi Hajiaghayi et al.· 0 citations
Networked decentralized optimal equilibrium learning dynamics in which agents generate randomized semantic content/discontent signals from local payoff comparisons and exchange time-stamped time-stacked tables rather than raw actions, payoff information or local estimates/parameters are proposed.
Seref Taha Kiremitci, Muhammed O. Sayin· 0 citations
Results show that a modest amount of predicted information is enough to accelerate decentralized routing well below Kleinberg's classical bound, and that even when nodes reveal no coordinates at all, reliable delivery remains achievable.
Ladan Kian, M. Tan, Dariusz R. Kowalski· 0 citations
It is known that somebody’s behavior (decision) in a stochastic social network may be influenced by that of his (or her) friends. In this paper, we
consider two stochastic social network game models (a) and (b) which can
be defined respectively by two different utility functions. Some sufficient conditions for the exis...
Min Xia· Annals of Applied Mathematic...· 0 citations
We study a network formation game in which agents sponsor links at a linear cost in order to maximize centrality, defined as a weighted sum of walk counts with positive and weakly decreasing weights. This class includes Katz Bonacich centrality and total communicability and captures environments in which access decays...
Itai Arieli, João Correia-da-Silva, Wade Hann-Caruthers et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
