Evaluating social choice techniques into intelligent environments by agent based social simulation

Computational Model of Recommender System Intervention

A recommender system is an information selection system that offers preferences to users and enhances their decision-making. This system is commonly implemented in human-computer-interaction (HCI) intervention because of its information filtering and personalization. However, its success rate in decision-making intervention is considered low and the rationale for this is associated with users’ psychological reactance which is causing unsuccessful recommender system interventions. This paper employs a computational model to depict factors that lead to recommender system rejection by users and how these factors can be enhanced to achieve successful recommender system interventions. The study made use of design science research methodology by executing a computational analysis based on an agent-based simulation approach for the model development and implementation. A total of sixteen model concepts were identified and formalized which were implemented in a Matlab environment using three major case conditions as suggested in previous studies. The result of the study provides an explicit comprehension on interplaying of recommender system that generate psychological reactance which is of great importance to recommender system developers and designers to depict how successful recommender system interventions can be achieved without users experiencing reactance and rejection on the system.

Social choice functions: A tool for ranking variables involved in action plans against road noise

Traffic noise is gaining importance in planning and operation of roads in developing countries, and particularly in Europe and Latin America. Many variables with different degrees of importance influence the perception of noise from roads. Thus, the problem of prioritizing road stretches for action against such noise is an important issue in environmental noise management. For example, it can be addressed using multicriteria methods. However, these methodologies require criteria or suitable variables to be ranked according to their relative importance. In the present study, for this ranking, a list of nine variables involved in the decision-making process (called "road stretch priority variables") was presented in the form of questionnaires to high-level experts from Andalusia, southern Spain. These experts ranked the variables by relevance. Using the same data, seven social choice functions (Plurality, Raynaud, Kemeny-Young, Copeland, Simpson, Schulze, and Borda) were used in order to rank the variables. The results indicate that the most important variables were those that take into account the parameters of greatest exposure for the citizens, followed by variables related to the intensity of the problem analyzed. The results show that a combination of the use of social choice functions on aggregated information from expert panels can provide a consensus for ranking priority variables related to road stretches.