Equilibrium and control in complex interconnected systems
Abstract/Contents
- Abstract
- Large-scale complex systems such as power grids, transportation systems, and social networks are reshaping every aspect of modern society. Despite their ubiquitous nature, the design and understanding of such complex networks is still very challenging. Decision making in such systems is complicated by the fact that an agent's optimal choice depends on the choices made by other agents in the system. In a smart grid, the power consumption of an individual user could depend on the demand profile of other users, some of who may be physically far away. An investment decision by an agent in an online auction is affected by the strategic choices of other agents participating in the auction. Thus, a node's decision is affected by the presence and the actions of other nodes in the system. The multitude of dependencies arising in such environments lead to an extremely complicated decision making process for a single agent. Often in complex systems, the decision maker has partial information about the state of the system. For example, a centralized load balancer in a server farm obtains the state of the queues via a communication network. This network introduces delays and losses which result in partial information at the decision maker. This further complicates the decision making process. In this thesis, we study equilibrium and control in complex interconnected systems. In the first part of the thesis, we investigate centralized decision making in a networked system in presence of delays. Specifically, we show that even in the presence of delays, a centralized decision maker can make optimal decisions with only a subset of the past history of the system.This history depends on the structure of the system as well as the associated delay pattern. From a practical point of view, these results show that one can make optimal decisions with only finite memory about the past, thus eliminating the need to store the entire history. Thus, for example, a centralized load balancer in a server farm can use algorithms based on only a finite past to evenly distribute load across multiple servers. In the second part of the thesis, we look at decentralized decision making in a reactive environment. We describe a mean field approach to decision making in large-scale systems. The basic premise of this approach is to treat other agents as a single entity with some aggregate behavior. We develop a unified framework to study mean field equilibrium in large-scale stochastic games. Under a set of simple assumptions, we prove the existence of a mean field equilibrium. A key insight developed from our result shows that the existence result is closely related to the approximation of mean field equilibrium to the actual behavior. Thus, a single agent can make near optimal decisions based only on aggregate behavior of other agents. We conclude the thesis with various interesting extensions and open challenges in the design and understanding of complex interconnected systems.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2010 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Adlakha, Sachin |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering |
| Primary advisor | Goldsmith, Andrea, 1964- |
| Thesis advisor | Goldsmith, Andrea, 1964- |
| Thesis advisor | Johari, Ramesh, 1976- |
| Thesis advisor | Lall, Sanjay |
| Advisor | Johari, Ramesh, 1976- |
| Advisor | Lall, Sanjay |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Sachin Adlakha. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2010. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2010 by Sachin Adlakha
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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