Arizona State University Network Science Seminar Series

Robust Methods for Influencing Strategic Behavior

Speaker Philip Brown (University of Colorado at Colorado Springs)
Date 3:00 pm, on March 28, 2019
Location GWC 487
Short Bio
Philip Brown is an Assistant Professor in the Department of Computer Science at the University of Colorado at Colorado Springs. He received the PhD in Electrical and Computer Engineering from the University of California, Santa Barbara under the supervision of Jason Marden. He received the Master and Bachelor of Science in Electrical Engineering from the University of Colorado at Boulder and Georgia Tech (respectively), between which he developed process control technology for the biofuels industry. Philip is interested in the impact of human social behavior on the performance of large-scale infrastructure and software systems, and studies this by combining concepts from game theory and feedback control of distributed systems. Philip was a finalist for best student paper at IEEE CDC in 2016 and 2017.
Abstract
The web of interconnections between today's technology and society is upending many traditional ways of doing things: the internet of things, bitcoin, the sharing economy, and stories of "fake news"; spreading on social media are increasingly in the public mind. As such, computer scientists and engineers must be increasingly conscious of the interplay between the technical performance of their systems and the personal objectives of users, customers, and adversaries. I will present work on three problem domains: robust incentive design for smart cities and transportation systems, resilient coordination and optimization in distributed multiagent engineered systems, and how malicious entities can spread their influence in society. In each, by rigorously examining the fundamental tradeoffs associated with optimal designs, I seek to develop a deeper theoretical understanding of tools which will help address today's emerging challenges.

Bayesian learning with non-myopic strategic agents

Speaker Achilleas Anastasopoulos (EECS, University of Michigan, Ann Arbor)
Date 10 am, on March 4, 2019
Location GWC 487
Short Bio
Achilleas Anastasopoulos received the Diploma in EE from the National Technical University of Athens, Greece in 1993, and the M.S. and Ph.D. degrees in EE from University of Southern California in 1994 and 1999, respectively. He is currently an Associate Professor at the University of Michigan, Ann Arbor, Department of EECS. His research interests lie in the general area of resource allocation and information elicitation on networked systems with strategic agents, as well as the connections between stochastic control, communications and information theory.
Abstract
We consider problems involving multiple strategic (selfish) agents making decisions dynamically in the presence of asymmetric information. Specifically, we consider an environment where many players need to decide whether to buy a certain product (or adopt a technology) or not. The true value of the product is not known to the players; instead, each player observes previous players' actions and has his own noisy private information on the product quality. It is well known that in such settings, agents start ignoring their private information thus generating trends/fads, also known as informational cascades, where learning stops in the network as a whole. These results, however, depend on a crucial assumption: players only enter the marketplace once and so they can act myopically. What happens when agents are given the option to return to the market? Agents can no-longer act myopically and have to strategize over the entire time horizon. Clearly there are tradeoffs between waiting for more information and buying early (due to discounting).
In this talk, we present a new methodology for characterizing "structured" Perfect Bayesian Equilibria (SPBE) of these dynamic games with asymmetric information akin to the backward sequential decomposition in MDPs and POMDPs. The corresponding "state" is an appropriate belief based on the common information among agents. By applying this methodology to the problem at hand, and by identifying sufficient statistics that summarize the "state", we provide a characterization of SPBE with non-myopic strategies through a fixed-point equation of dimensionality that grows only quadratically with the number of players. Based on this characterization we study informational cascades and show that they occur with high probability. Furthermore, only a small portion of the total information in the system is revealed before a cascade occurs.

Random Access with Energy Harvesting Nodes

Speaker Tolga M. Duman (Bilkent University)
Date 3:00 p.m., Feb 20, 2019
Location GWC 487
Short Bio
Tolga M. Duman is a Professor of Electrical and Electronics Engineering Department at Bilkent University in Turkey. He received the B.S. degree from the same university in 1993, M.S. and Ph.D. degrees from Northeastern University, Boston, MA, in 1995 and 1998, respectively, all in electrical engineering. Prior to joining Bilkent University, he has been with the Electrical Engineering Department of Arizona State University. Dr. Duman's current research interests are in systems, with particular focus on communications and signal processing, including wireless and mobile communications, coding/modulation, coding for wireless communications, data storage systems and underwater acoustic communications. Dr. Duman is a Fellow of IEEE, a recipient of the National Science Foundation CAREER Award and IEEE Third Millennium medal. He has served as an editor for various journals, and he is currently the coding and information theory area editor of IEEE Trans. on Communications and the Editor-in-Chief of Elsevier's Physical Communication.
Abstract
We propose an irregular repetition slotted ALOHA (IRSA) based uncoordinated random access scheme for energy harvesting (EH) nodes. Specifically, we consider the case in which each user has a battery that is recharged with harvested energy from the environment in a probabilistic manner. We analyze this scheme starting with a unit-sized battery at the nodes and extend the analysis to the case of finite-sized battery. For both scenarios, we derive asymptotic throughput expressions, and obtain optimized probability distributions for the number of packet replicas for the users. We demonstrate that for the case of IRSA with EH nodes, these optimized distributions perform considerably better than the alternatives, especially in the limited battery scenarios, including slotted ALOHA (SA), contention resolution diversity slotted ALOHA (CRDSA) and IRSA, which do not take into account the EH process for both asymptotic and finite frame length scenarios. We also present some extensions of the asymptotic analysis to the contention resolution ALOHA without any slot synchronization.

Seminars

Title Speaker Time Location
Robust Methods for Influencing Strategic Behavior Philip Brown (University of Colorado at Colorado Springs) 3:00 p.m., March 28, 2019 GWC 487
Bayesian learning with non-myopic strategic agents Achilleas Anastasopoulos (EECS, University of Michigan, Ann Arbor) 10:00 a.m., March 4, 2019 GWC 487
Random Access with Energy Harvesting Nodes Tolga M. Duman (Bilkent University) 3:00 p.m., Feb 20, 2019 GWC 487
 
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