Rahul Chandan

I am a PhD student in the Department of Electrical and Computer Engineering, University of California, Santa Barbara. My supervisor is Jason Marden. Previously, I completed my BASc in Engineering Science (Electrical and Computer Engineering Option) with a minor in Robotics and Mechatronics at the University of Toronto.

Research Overview: My research work focuses on the applications of game theory and optimization to problems at the intersection of economics, engineering, computer science and operations research. I have recently been investigating the design of coordination mechanisms and the manipulation of information in multiagent systems (for example, traffic routing, political campaigns, flocks of drones, etc.).

• 

  1) Coordination mechanisms for the common good: In systems with limited resources, the uncoordinated actions of self-interested decision makers have been observed to significantly deviate from joint behaviour that would otherwise maximize the system welfare (see, for example, the tragedy of the commons and Price of Anarchy in economics). The study of coordination mechanisms aims to identify interventions that mitigate such losses in the system welfare. Examples of coordination mechanisms are monetary tolls and rebates in transportation networks. Though inefficiencies under self-interested decision making are inevitable, appropriately designed coordination mechanisms significantly improve the system welfare of the emergent behaviour.
  Within this research area, I have pursued the following directions:

  • Efficient and exact techniques for computing optimal coordination mechanisms [W1,J2,C6];
  • Robustness of coordination mechanisms to information inconsistencies [C7]; and,
  • Inherent tradeoffs in the joint optimization of the worst- and best-case efficiency [W3], and the short- and long-term efficiency [W4].
• 

  2) The strategic role of information in competitive environments: Real-world competitive resource allocation problems (for example, political campaigns, competitive markets, military strategy, etc.) can be represented as vast interaction graphs with self-interested decision makers at the nodes, and competitions over valuable prizes on the edges. Beyond the characterization of strategic behaviour under established models (for example, positional games, contests, Colonel Blotto games, etc.), it is important to determine whether exogenous mechanisms exist whereby a decision maker can improve her competitive position. My recent work in this area centres on identifying the benefits that withholding, revealing, and otherwise manipulating information can have in competitive environments.
  Within this research area, I have pursued the following directions:

  • Value of budgetary and system-level information in competitive environments [W2,C4];
  • Revealing strategic information to improve one's competitive position (for example, pre-commitments, concessions) [C5,C9]; and,
  • Delegation of budgetary resources in teams [W2,C8].