Abstract
We study how emotions influence norm outcomes
in decision-making contexts. Following the literature, we provide baseline Dynamic Bayesian models to capture an agent’s two perspectives on a directed norm. Unlike the literature, these models
are holistic in that they incorporate not only norm
outcomes and emotions but also trust and goals.
We obtain data from an empirical study involving
game play with respect to the above variables. We
provide a step-wise process to discover two new
Dynamic Bayesian models based on maximizing
log-likelihood scores with respect to the data. We
compare the new models with the baseline models
to discover new insights into the relevant relationships. Our empirically supported models are thus
holistic and characterize how emotions influence
norm outcomes better than previous approaches