A reinforcement learning approach to optimizing preference on a social network

Matthew Reyes casts consumer decision making within the framework of random utility and outlines a simplified scenario of optimizing preference on a social network to illustrate the steps in a companys allocation decision, from learning parameters from data to evaluating the consequences of different marketing allocations.

The problem of influencing preference toward products on social networks has attracted considerable attention over the past couple of decades. Previous approaches have suffered from two subtle yet significant drawbacks. The first is that they model consumer decision making as best-response, deterministic maximization of some numerical utility. The second is that their decomposition of utility does not include influence by marketers for the respective companies. Matthew Reyes casts consumer decision making within the framework of random utility. Random utility theory views so-called utility as a parametrization of observed frequencies of choice. The decomposition of utility corresponds to variables that are either observable through data collection or under the control of an external agent, in this case a company. The decomposition of utility that Matthew presents explicitly includes influence by marketers from two competing companies. Incorporating the marketer into the model of consumer decision making allows a company to evaluate the effect of different marketing allocations on the evolution of preferences on the network. The combination of a random choice model and the inclusion of marketers into the model allow this important problem to be cast in the reinforcement learning paradigm. Matthew outlines a simplified scenario illustrating the steps in a company’s allocation decision, from learning parameters from data to evaluating the consequences of different marketing allocations.