This paper considers platform competition in a two-sided market that includes buyers and sellers. One of the platforms benefits from a partial belief advantage, in that each side believes that it is more likely that the other side will join the advantaged platform. We find that the degree of the platform’s belief advantage affects its decision regarding the business model (i.e., whether to subs...

In the coherentist approach to belief change, all beliefs are assumed to be ordered according to their epistemic entrenchment. On the other hand, in the foundational approach to belief change, which explicitly distinguishes “basic” beliefs from “inferred” beliefs, basic beliefs are ordered according to their retractability. Both entrenchment and retractability are distinct tie-breaking mechanis...

Bayesianism is a theory of inductive inference that makes use of the mathematical theory of probability. Bayesians usually hold that the relevant probabilities should be interpreted in terms of rational degrees of belief. This still leaves much scope for disagreement, since there is no consensus about what norms govern rational degrees of belief. In this chapter, we first provide an introductio...

We suggest an approach to the aggregation of non-independent belief structures that makes use of a weighted aggregation of the belief structures where the weights are related to the degree of dependence. It is shown that this aggregation is non-commutative, the fused value depends on the sequencing of the evidences. We then consider the problem of how best to sequence the evidence. We investiga...

Schwitzgebel (2001) —henceforth «S»— offers three examples in order to convince us that there are situations in which individuals are neither accurately describable as believing that p or failing to so believe, but are rather in «in-between states of belief». He then argues that there are no «Bayesian» or representational strategies for explicating these, and proposes a dispositional account. I...

A novel task, using a continuous spatial layout, was created to investigate the degree to which (in centimeters) 3-year-old children's (N = 63), 5-year-old children's (N = 60), and adults' (N = 60) own privileged knowledge of the location of an object biased their representation of a protagonist's false belief about the object's location. At all ages, participants' knowledge of the object's act...

Dempster and Shafer introduced the belief function to measure some one’s degree of beliefs or subjective probabilities. In IFSA’99 and NAFIPS’9, we presented two distinct proofs that a belief function is, in fact, an inner probability using the methods of functional analysis and measure theory respectively. In this paper, we continue the study and derive a new Dempster’s rule of combination bas...

How to manage conflict is still an open issue in Dempster-Shafer evidence theory. The correlation coefficient can be used to measure the similarity of evidence in Dempster-Shafer evidence theory. However, existing correlation coefficients of belief functions have some shortcomings. In this paper, a new correlation coefficient is proposed with many desirable properties. One of its applications i...

The Web has become the most important information source for most of us. Unfortunately, there is no guarantee for the correctness of information on the Web. Moreover, different websites often provide conflicting information on a subject. Several truth discovery methods have been proposed for various scenarios, and they have been successfully applied in diverse application domains. In this paper...

TensorFlow Probability is a powerful library for statistical analysis in Python. Using Probability’s implementation of Bayesian methods, modelers can incorporate prior information and obtain parameter estimates quantified degree belief the results. Resampling methods like Markov Chain Monte Carlo also be used to perform analysis. As an alternative, we show how use numerical optimization estim...