Modeling Responsibility Attribution in a Group

<div>Attributing responsibilities to a group of agents who contribute to a collective cause</div><div>is common in our daily life. While we as humans could relatively efficiently assign</div><div>responsibility to others using our intuition, the wide range of possible scenarios and</div><div>complexity of our cognitive mechanisms should not be overlooked. Understanding</div><div>of how rational responsibility judgments are made could expand our knowledge</div><div>about human causal and moral reasoning. Over recent years, a few theories have</div><div>been developed to account for responsibility attribution, but they have various theoretical</div><div>and empirical limitations. In this thesis, an extended computational model</div><div>is proposed in an effort to expand the explanatory power of previous models. We</div><div>devised two sets of experiments to test the performance of this newmodel, especially</div><div>with regards to aspects including (1) epistemic state of rater, (2) perceived autonomy</div><div>of agents, and (3) value range of contributions. Our results suggest that the new</div><div>model demonstrated reasonable power in predicting responsibility attribution, albeit</div><div>with several unexpected inaccuracies. The close relationship among responsibility,</div><div>criticality, and pivotality is nonetheless supported.</div>