Kimon Drakopoulos

On Learning With Finite Memory

We consider an infinite collection of agents who make decisions, sequentially, about an unknown underlying binary state of the world. Each agent, prior to making a decision, receives an independent private signal whose distribution depends on the state of the world. Moreover, each agent also observes the decisions of its last K immediate predecessors. We study conditions under which the agent decisions converge to the correct value of the underlying state. We focus on the case where the private signals have bounded information content and investigate whether learning is possible, that is, whether there exist decision rules for the different agents that result in the convergence of their sequence of individual decisions to the correct state of the world. We first consider learning in the almost sure sense and show that it is impossible, for any value of K. We then explore the possibility of convergence in probability of the decisions to the correct state. Here, a distinction arises: if K=1, learning in probability is impossible under any decision rule, while for K ≥ 2, we design a decision rule that achieves it. We finally consider a new model, involving forward looking strategic agents, each of which maximizes the discounted sum (over all agents) of the probabilities of a correct decision. (The case, studied in the previous literature, of myopic agents who maximize the probability of their own decision being correct is an extreme special case.) We show that for any value of K, for any equilibrium of the associated Bayesian game, and under the assumption that each private signal has bounded information content, learning in probability fails to obtain.

An Efficient Curing Policy for Epidemics on Graphs

We provide a dynamic policy for the rapid containment of a contagion process modeled as an SIS epidemic on a bounded degree undirected graph with

When Is a Network Epidemic Hard to Eliminate

n

A lower bound on the performance of dynamic curing policies for epidemics on graphs

nodes. We show that if the budget

Estimating Social Network Structure and Propagation Dynamics for an Infectious Disease

r

An efficient curing policy for epidemics on graphs

of curing resources available at each time is

Active Contours on Graphs: Multiscale Morphology and Graphcuts

\Omega (W)

Optimal Signaling of Content Accuracy: Engagement vs. Misinformation

, where

Theoretical Analysis of Active Contours on Graphs

W

Segmentation and Skeletonization on Arbitrary Graphs Using Multiscale Morphology and Active Contours

is the CutWidth of the graph, and also of order