These PhD and Postdoc positions are hosted by INRIA at Sophia Antipolis. The PhD degree is granted by the Université Côte d'Azur (UCA) and it develops within a close collaboration between INRIA, the University of Sheﬃeld (TUOS) and Princeton University (PU) in the broad intersection of information theory, game theory, and artificial intelligence. Mobility between INRIA, the Department of Automatic Control and Systems Engineering at TUOS, as well as the department of Electrical Engineering at PU, is expected.
This project aims to build a mathematical theory to understand, model and determine the fundamental limits of the inﬂuence that an information provider (IP), e.g., a social network or a recommender system, can exert over decision makers. The focus is on the case in which the beneﬁt obtained by an individual decision maker depends upon the decisions of all involved individuals. This situation arises in most decision making processes involving humans, machines or humans and machines: (a) Influencers that attempt to manipulate voters, for instance in an election; (b) Marketing policies that propose goods to potential costumers; (c) Geo-localization-based navigation applications that provide advice to drivers; (d) Stock traders that follow diﬀerent sources of information to buy, sell and trade shares; and (e) Environmental managers who seek for coordination in a “tragedy of the commons” problem.
In a nutshell, in most decision making processes, decisions are made with partial information about the stochastic phenomena underlying the environment in which decision-makers interact. Therefore, IPs play a central role consisting in information provisioning, which is an opportunity to steer the behavior of individuals and seek for particular outcomes. That is, information can be provided to an individual for increasing its beneﬁt, but also, information can be withhold or distorted for penalizing individuals. Either way, this vulnerability to malicious or undesired inﬂuence represents a thread for any decentralized decision making process and thus, it must be understood and properly modeled.
Interested candidates should contact Samir M. Perlaza at [email protected]