CE48 - Fondements du numérique: informatique, automatique, traitement du signal

Opinion dynamics in social networks in presence of multiple decision-makers – NICETWEET

Submission summary

In the NICETWEET project, we develop a new, complete, and unified methodology to address an important and generic problem that appears in economics, finance and politics. Several decision-makers are in competition for propagating ideas or selling goods, services, etc. to a large number of agents who are connected through a physical or digital social network. The agents are thus under the (endogenous) influence of their neighbors in the social network graph but also under the exogenous influence of the decision-makers. The latter have a certain knowledge about the social network and the underlined opinion dynamics and they use it for target advertising purposes. To address this problem, we have formed an interdisciplinary team that possesses the required expertise: control theory and more specifically opinion dynamics, game theory, information theory, complex networks, and economics. In contrast with the closest works, we assume that the social network possesses new features that correspond to the economic applications of interest that are analyzed. It is very important to note that we also assume the presence of multiple decision-makers. Indeed, our preliminary knowledge about economic applications indicates that some key features need to be accounted for in a simultaneous manner. Some of these features are: the social network is often large and sparse; agents enter or leave the network randomly at any time; decision-makers may have imperfect knowledge of the social network and opinion dynamics parameters; some agents may exchange not only their opinion but also their reliability; possible presence of extremists. A salient feature with respect to the state-of-the art is that opinion dynamics over the social network can be controlled and several decision-makers try to control it. To address this problem of controlled opinion dynamics in presence of multiple decision-makers who typically have non-aligned utility functions, we will resort to game theory and contribute to bridging the gap between the formal literature of control and the typically non-formal literature of economics and marketing. More specifically, one of our technical goals is to account for the social network and opinion dynamics mathematical models to construct a formal and systematic way of designing efficient and implementable viral marketing strategies for decision-makers. One important issue in the design of viral marketing strategies is the way of allocating a given advertising, campaign, or influence budget among the social network agents and over time (in presence of competition). Efficiency will be measured in terms of using the available information by a given decision-maker and by its way of reacting to the behavior of the other decision-makers. To design viral marketing strategies in the stochastic (repeated) game framework, several approaches will be adopted in parallel to manage the risk aspect. Considering partial information about the social network and opinion dynamics, one approach will be to extract insights from the derived limiting performance characterization theorems namely, theorems that characterize the achievable long-term utilities under partial information. A step further will be to characterize equilibrium utilities and design practical equilibrium space-time budget scheduling strategies i.e., to specify exactly how a decision-maker should allocate his advertising budget among the social network agents and over time. Another approach will be to exploit multi-player learning techniques such as Bayesian learning rules tailored to the problem of interest. To implement the ambitious road-map of NICETWEET and conduct the corresponding research, recent and promising results obtained within the consortium will be exploited as a foundation of the project.

Project coordination

Irinel-Constantin Morarescu (Centre de recherche en automatique de Nancy (CRAN))

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

CRAN - UMR 7039 Centre de recherche en automatique de Nancy (CRAN)
L2S Laboratoire des Signaux et Systèmes
LIA Laboratoire d'Informatique d'Avignon
BETA Bureau d'économie théorique et appliquée (UMR 7522)

Help of the ANR 329,155 euros
Beginning and duration of the scientific project: October 2020 - 48 Months

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