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

机译:是不确定性总是坏吗?:主题能力对不确定的意见的影响

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The proliferation of information disseminated by public/social media has made decision-making highly challenging due to the wide availability of noisy, uncertain, or unverified information. Although the issue of uncertainty in information has been studied for several decades, little work has investigated how noisy (or uncertain) or valuable (or credible) information can be formulated into people's opinions, modeling uncertainty both in the quantity and quality of evidence leading to a specific opinion. In this work, we model and analyze an opinion and information model by using Subjective Logic where the 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 of original propagators, who propagate information over a network. With the help of an extensive simulation study, we examine how the different ratios of information types or agents' prior belief or topic competence affect the overall information diffusion. Based on our findings, agents' high uncertainty is not necessarily always bad in making a right decision as long as they are competent enough not to be at least biased towards false information (e.g., neutral between two extremes).
机译:由于嘈杂,不确定或未验证的信息的广泛可用性,公共/社交媒体传播的信息的扩散使决策具有高度挑战性。虽然已经研究了一些数十年的信息的不确定性问题,但很少的工作已经调查了如何将(或不确定)或有价值的(或可信)信息制定在人们的意见中,在导致的数量和证据质量中建模不确定性具体意见。在这项工作中,我们使用主观逻辑来模拟和分析意见和信息模型,其中初始证据与不同类型的证据混合(即Pro与Con或Noisy Vs.有价值)混合在一起原始传播者,谁通过网络传播信息。在广泛的仿真研究的帮助下,我们研究了信息类型或代理人的现有信念或主题能力的不同比例如何影响整体信息扩散。根据我们的研究结果,代理商的高不确定性在做出正确的决定方面并不一定是糟糕的决定,只要它们足够竞争力,不能至少偏向虚假信息(例如,两个极端之间的中性)。

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