NS Seminar: Paper Presentation

Date and Location

Oct 17, 2016 - 2:00pm to 3:00pm
Network Science Lab, Bldg 434, Room 122


David Grimsman, ECE Trainee
Pedro Cisneros, ECE Trainee


Deadbeat-Like Approximations for Sequencing Non-Rigid Heaps

David Grimsman and Sean Warnick

This paper demonstrates how the property of deadbeat controllers that drives discrete-time systems to their equilibria in a finite number of steps can be effectively used to develop systematic approximations of a certain class of heap systems, modeled here as input-quantized systems defined over the max-plus algebra. These systems are used to describe a class of flexible batch manufacturing processes that are important in applications such as chemical processing. The result here
introduces a new, asymmetric approximation in the context of previous work where a symmetric approximation was developed and analyzed.


Non-Bayesian social learning

Ali Jadbabaie et al.

We develop a dynamic model of opinion formation in social networks when the information required for learning a parameter may not be at the disposal of any single agent. Individuals engage in communication with their neighbors in order to learn from their experiences. However, instead of incorporating the views of their neighbors in a fully Bayesian manner, agents use a simple updating rule which linearly combines their personal experience and the views of their neighbors. We show that, as long as individuals take their personal signals into account in a Bayesian way, repeated interactions lead them to successfully aggregate information and learn the true parameter. This result holds in spite of the apparent naïveté of agents’ updating rule, the agents’ need for information from sources the existence of which they may not be aware of, worst prior views, and the assumption that no agent can tell whether her own views or those of her neighbors are more accurate.