In this work, we are interested in finding the most efficient use of a budget to promote an opinion by paying agents within a group to supplant their true opinions. We model opinions as continuous scalars ranging from 0 to 1 with 1 (0) representing extremely positive (negative) opinion. We focus on asymmetric confidence between agents. The iterative update of an agent corresponds to the best response to other agents' actions. The resulting confidence matrix can be seen as an equivalent Markov chain. We provide simple and efficient algorithms to solve this problem and we show through an example how to solve the stated problem in practice.
Alonso Silva is currently a Member of Technical Staff at Nokia Bell Labs, in the Department of Mathematics. He is also a permanent member of the Laboratory of Information, Networking and Communication Sciences (LINCS). He received his Ph.D. in Physics from the École Supérieure d'Électricité in June 2010. He did his Ph.D. at INRIA Sophia-Antipolis under the direction of Professor Eitan Altman. He has previously worked as postdoctoral researcher in the Department of EECS at the University of California, Berkeley, and at INRIA Paris Rocquencourt. Prior to his Ph.D., he received his B.Sc. of Mathematical Engineering and his Mathematical Engineering degree from the Department of Mathematical Engineering (DIM) at the Universidad de Chile. He has received the best paper award at UNet'17, the second best paper award at MSN'11 and the best student paper award at Valuetools'08.