Deciding between available alternatives, each more or less appealing than others depending on the proportion of people choosing it, is a classical decision and control problem.
However, in today's hyperconnected world, each decision maker is exposed to a wealth of easily available information, with either low reliability or filtered through cultural characteristics. Moreover, the individuals social networks are not spatially constrained, which means individuals capture information through the network from peers that potentially experience a completely different environment.
This project aims at producing a new understanding of the decision dynamics through a multilayer network approach where the perception of the option rewards is perceived and transmitted through direct exposure, through an imperfect information layer and a non spatially embedded social network. The preference for one choice over the others does not directly depends on the individuals experience of that option, nor solely on the experience of one node immediate neighbours. The project will relate the new insights in decision dynamics to the dynamics of information spreading, to the structure of the social network and the characteristic of the information layer.
Aim This project aims at understanding the tipping point in decision making when individuals are immersed in a social network which changes the perception of the rewards associated to the choices.
• Construct explicit social network models with information spreading dynamics
• Construct feedback mechanism, relating to real-world scenarios, which provide rewards for the choices made.
• Construct feedback mechanism from the collective decision onto the reward dynamics, mimicking real world scenarios