Current Projects

• Dynamic Coordination Processes for Distributed Planning with Limited Communication, NSF (DST-0414727), PI: Stephen D. Patek

  As is clear from the emergence of the Internet, the national electric power grid, and other large-scale network infrastructures, engineering systems today are increasingly reliant on distributed control authority and coordination between subsystems. Typically, coordination is achieved in engineering systems through the specification of ad hoc protocols for relatively well defined (constrained) interactions between distributed systems. As information systems become more integrated into society, however, we find that existing protocols are not adequately tuned for new applications and/or unexpected situations. Though issues of decentralized control and planning are becoming more prevalent in the engineering systems we build today, there unfortunately appears to be little in the way of underlying guiding principles and theory for designing and operating such systems. Notions of game theory and decentralized control go only part way toward revealing the basic problems associated with distributed engineering systems, especially in situations where distributed agents/players/actors all recognize the same performance objective and would work together except for the problem of having little or no opportunities to coordinate their actions because of limited communication. Thus, our goal is to derive an enhanced understanding of coordination without explicit communication by posing a new class of sequential decision processes, known as coordination processes, whose analysis will provide new theoretical insights and new algorithmic approaches in decentralized systems and distributed planning applications.

   

• Security of Supply & Strategic Learning in Restructured Power Markets, NSF (ECS-0224747), PI: Alfredo Garcia

  In this project, we study the long run reliability (or security of supply) of electricity markets under price caps. This (and other forms or market intervention) cast doubts on the market ability to provide new capacity in a socially efficient manner (scale, technology and timing). We propose to develop a dynamic game model of investment. A full characterization of equilibrium investment will shed light on a number of contentious issues in the restructuring debate. Examples include; the possibility of "boom and bust" cycles in equilibrium, a potential technology bias with harmful effects on the environment, and the need to incorporate capacity markets. As a second major activity, we study learning algorithms as potentially powerful computational tools for electricity markets. The complexity of electricity markets calls for the incorporation of some form of bounded rationality in the modeling efforts. However, when players use simple, adaptive (possibly sub-optimal) rules, repeated interaction may induce equilibrium outcomes in the long run. Although, many "agent based" simulation models have been advocated for analyzing electricity markets, they lack solid theoretical support on issues such as convergence and/or the nature of equilibrium. In this proposal we will represent competition in electricity markets under certain congestion management protocols, as games with a special structure, i.e. "potential games." For this class of games, the class of "fictitious play" learning algorithms has been proven to converge with probability one. Computational tests a large-scale model will serve to validate and assess the practicality of the class of strategic learning models proposed.

   Past Projects

• Complex Networks Optimization, NSF (DMI-0217371), PI: Alfredo Garcia
• Solution Concepts for Distributed Decision Making without Coordination, NASA (######), PI: Peter A. Beling