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The proliferation of information disseminated by public/social media has madedecision-making highly challenging due to the wide availability of noisy,uncertain, or unverified information. Although the issue of uncertainty ininformation has been studied for several decades, little work has investigatedhow noisy (or uncertain) or valuable (or credible) information can beformulated into people's opinions, modeling uncertainty both in the quantityand quality of evidence leading to a specific opinion. In this work, we modeland analyze an opinion and information model by using Subjective Logic wherethe initial set of evidence is mixed with different types of evidence (i.e.,pro vs. con or noisy vs. valuable) which is incorporated into the opinions oforiginal propagators, who propagate information over a network. With the helpof an extensive simulation study, we examine how the different ratios ofinformation types or agents' prior belief or topic competence affect theoverall information diffusion. Based on our findings, agents' high uncertaintyis not necessarily always bad in making a right decision as long as they arecompetent enough not to be at least biased towards false information (e.g.,neutral between two extremes).

Is Uncertainty Always Bad?: Effect of Topic Competence on Uncertain Opinions