Search

We often rely on others' testimony when learning about new topics, such as health benefits of a novel food. However, the sources are not always knowledgeable, helpful, or unbiased, necessitating an assessment of their credibility. Here, we present a Bayesian model of source credibility, where a listener simultaneously infers the expertise and intention of the source while trying to discern the truth. A key prediction is that rational inference of credibility requires anchoring it on some kernel of shared knowledge. We consider a scenario where both parties have noisy access to the ground truth of familiar topics (e.g., is broccoli healthy?), which serves as a basis for reasoning about a source's credibility on novel topics (e.g., is avocado healthy?). This approach provides a computational framework for understanding how people respond to information in domains like science communication and media consumption.

Carbon finance, as a financial tool for adapting to and mitigating global climate change, can guide enterprises to help develop digital green technological innovation and ultimately realize the transition to a low-carbon economy. This paper selects relevant control variables and applies the spatial econometric model to analyze the impact of carbon financial trading policy on enterprises’ green technological innovation, and examines the different impacts of the policy on the number of green invention-based patent applications and the number of green utility model patent applications, respectively. To examine the heterogeneity of regions and enterprise size, the samples were further grouped. Finally, a gray correlation model is used to study the main drivers of the intrinsic interactions among the three systems of digital technology innovation, carbon finance, and industrial policy. Most of the gray correlations between technological innovation and carbon finance indicators are located between 0.5 and 0.7, and the average value of the correlations of all the indicators is higher than 0.60, with only four lower than 0.65, and the highest indicator is 0.666. It indicates that the correlation between carbon finance and digital green technological innovation among enterprises is high.