Paper Title
Tourist Information in Asymmetric Situation, A New Way to Enhance a Tourist Experience

The idea is centered on an individual who does not know the city and who could benefit from an application that could improve his experience. Many applications exist but are spread over a supply of services and unstructured on the need of a tourist experience. We considered that the essential link is the professional guide and which is an important element of the correction of information asymmetry and that we must propose to the tourist who has often unknown this offer of service. We see that this model where the need is a need for information (transport, hospitality, ...) and not a need for knowledge, will correct the bias of the tourist who arrives with a certain belief in the city to visit. We considered that the agent is based on a Bayesian reasoning commonly accepted as a model of learning and we postulate that a tourist who arrives in a city must learn to improve his experience. In the same way the smart city makes it possible today the use of the data to know if the model is efficient, and if it is able to anticipate the behavior of the user and / or to guide it. At first, I briefly recall Bayesian reasoning, which is our ability to anticipate our actions based on our beliefs, and which often leads to biases. Bayesian inference is a simple mathematical theory that characterizes our "reasoning" in a state of uncertainty because our brain sometimes receives incomplete information and we tend to supplement it according to our beliefs and therefore our brain, most likely. We can think of going to a place believing that it is good because we have heard such and such a story, and we have plausible reasoning in the presence of uncertainty. These reflections lead us to establish a method for implementing a mobile application that will improve the tourism experience. The idea of all this is how to apply it in the situation of an asymmetric information agent who has only a priori probability and who can miss opportunities for visits and the data in his possession can correct all bias.