Network Economics and Privacy
It is almost impossible to study networking today without considering economic issues. Economics plays a defining role in routing (e.g., hot potato routing and net neutrality), protocol design (e.g., the analysis of TCP and the design of BitTorrent), and even control (e.g., deregulated electricity markets). Further, the design of the market places surrounding networks can have a strong interaction with the engineering of the systems themselves (e.g. cloud providers). RSRG's work in Network Economics bridges all these areas and is part of the broader Social and Information Sciences Laboratory (SISL, pronounced "sizzle") initiative at Caltech.
Our work so far
Some examples of ongoing projects in RSRG on network economics are below.
A theory for privacy
Sensitive information is held by an enormous variety of entities, particularly in today's online world. Some of the challenges in handling it include developing principled models and definitions for privacy guarantees, along with privacy-preserving algorithms and provable bounds on how information privacy can be traded off against its usefulness. Integrating notions of privacy into utility theoretic and decision theoretic frameworks will provide us with more sophisticated means of reasoning about sensitive information. Work in this area is led by Katrina Ligett and Federico Echenique.
Computational complexity and revealed preference theory
One of the foundational tasks for the emerging interaction between computer science and economics is to incorporate "computation" into classic economic theories. Toward this end, over the last decade, the task of understanding the computational complexity of classical economic models has been a cornerstone of the field. As results have emerged, it has become clear that many of the standard economic models involve solving, in the worst-case, computationally hard problems. However, such results follow from an "algorithmic" view of economic models, rather than the "empirical" view typically used in positive economics. Our work takes an empirical perspective motivated by revealed preference theory, and asks: Do computational constraints have empirical consequences for economic models? Work in this area is led by Federico Echenique and Adam Wierman.
Rethinking electricity Markets
Over the coming decade, the electricity network will undergo a complete architectural transformation, similar to what has happened to the communication network over the last decades. However, there are huge engineering and economic challenges in making this transformation possible. A key challenge for this transformation is the fact that the economic market structure and engineering architecture are inherently intertwined in the electricity grid, which necessitates a new architectural theory for guiding this transformation. Work in this area is led by Mani Chandy, Adam Wierman, John Ledyard, and Steven Low. More details can be found at the Smart grid project page and the Resnick Institute website.
Dynamics in games
The behavior of complex systems can be difficult to model, predict, or
understand. However, sophisticated interactions among many agents are
now commonplace, from computer networks to financial networks to
large-scale auctions. Simple dynamics that "learn" or "adapt" are a
useful tool in such settings, as they provide natural, often
computationally tractable predictions of the impact of individuals'
selfish behavior on such large systems. When are such dynamics a
reasonable model of agent behavior? When do their predictions match
equilibrium predictions, and when are dynamics non-convergent? How can
such observations help us refine our predictions and equilibrium
notions? Work in this area is led by John Ledyard and Katrina Ligett.
Markets for the cloud
The cloud marketplace has evolved into a large, highly complex economic system made up of a variety of services. As a result of this complicated marketplace, the performance delivered to users by cloud services depends on the the resource allocation design of the service itself and the strategic incentives resulting from the large-scale multi-tiered economic interactions among cloud providers. RSRG's work in this area focuses on developing and analyzing new models capturing this multi-tiered interaction in a manner that exposes the interplay of congestion, pricing, capacity provisioning, and performance. Work in this area is led by Adam Wierman.
Extracting revenue from search algorithms increasingly depends on sophisticated computational algorithms for advertising. Many of these algorithms are based on auction theory. Research in this area thus requires very close interaction between computation and economics. Caltech was at the forefront of computational advertising right from the inception of its use on the internet. Our involvement began with work on the generalized second price auction in concert with Goto.com (which morphed into Overture, which morphed into Yahoo), the company that originated the use of auctions for location on search results pages. This work was both theoretical and experimental and was led by Matthew Jackson, John Ledyard, and Simon Wilkie. Later work on more advanced and computationally intensive advertising work was led by Preston McAfee for webpages and John Ledyard for television and radio advertising.