A general approach to information correction and fusion for belief functions is proposed, where not only may the information items be irrelevant, but sources may lie as well. We introduce a new correction scheme, which takes into account uncertain metaknowledge on the source’s relevance and truthfulness and that generalizes Shafer’s discounting operation. We then show how to reinterpret all con...

The study aimed to assess whether showing emotion in an organizational inquiry into failure affects perceptions of truthfulness as a function of the match between the explanation of what caused the failure and the emotion expressed. Two web-based studies were conducted. Participants with work experience saw videos of an inquiry and rated the protagonist's truthfulness. In both studies protagoni...

In the design of complex systems, models can be used to predict a future system’s performance as well as approximate stakeholder preferences on performance. This paper examines the problem of model truthfulness and the challenge of trusting models, with a focus on value models and how they are used to predict stakeholder preferences. A framework is proposed for the analysis of these issues (tru...

The problem of peer prediction is to elicit information from agents in settings without any objective ground truth against which to score reports. Peer prediction mechanisms seek to exploit correlations between signals to align incentives with truthful reports. A long-standing concern has been the possibility of uninformative equilibria. For binary signals, a multi-task output agreement (OA) me...

We present general techniques for proving inapproximability results for several paradigmatic truthful multidimensional mechanism design problems. In particular, we demonstrate the strength of our techniques by exhibiting a lower bound of 2 − 1 m for the scheduling problem with m unrelated machines (formulated as a mechanism design problem in the seminal paper of Nisan and Ronen on Algorithmic M...