Distributed Algorithms for Aggregative Games on Graphs

By Jayash Koshal, Angelia Nedić, Uday V. Shanbhag. Operational Research, 2016

328 citations recorded by the source corpus.

We consider a class of Nash games, termed as aggregative games, being played over a networked system. In an aggregative game, a player’s objective is a function of the aggregate of all the players’ decisions. Every player maintains an estimate of this aggregate, and the players exchange this information with their local neighbors over a connected network. We study distributed synchronous and asynchronous algorithms for information exchange and equilibrium computation over such a network. Under standard conditions, we establish the almost-sure convergence of the obtained sequences to the equilibrium point. We also consider extensions of our schemes to aggregative games where the players’ objectives are coupled through a more general form of aggregate function. Finally, we present numerical results that demonstrate the performance of the proposed schemes.

Subfields: Computer Networks and Communications, Public Health, Environmental and Occupational Health, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0d32f8bb4a9cba9b8528bcfb5a77fc16ba6b3a13

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Angelia Nedić
2. Wei Shi
3. Alexander Olshevsky
4. Ali H. Sayed
5. Yiguang Hong
6. Michael G. Rabbat
7. Wotao Yin
8. Peng Yi

In Public Health, Environmental and Occupational Health

1. Wei Shi
2. Alexander Olshevsky
3. Yiguang Hong
4. Shu Liang
5. Peng Yi
6. Angelia Nedić

In Statistical and Nonlinear Physics

1. Shu Liang
2. Wei Shi
3. Peng Yi
4. Alexander Olshevsky
5. Yiguang Hong
6. Lacra Pavel
7. Angelia Nedić

Citations in the corpus

• Distributed Nash equilibrium seeking for aggregative games with coupled constraints (2016, methodology): “• Our formulation is quite similar to that in (Koshal et al. 2016), but we further consider linear coupled constraints and study its distributed continuous-time GNE seeking algorithm.”
• A Passivity-Based Approach to Nash Equilibrium Seeking Over Networks (2017, background): “As in [7], [11], and [20], we assume that players are truthful and do not manipulate the information they send for strategic gain.”
• Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs (2016, methodology): “A similar technique has been introduced in [77] for dynamically tracking the average state of a multi-agent system, and for tracking some network-wide aggregate quantities in [28,57].”