This article describes a theory of reasoning about uncertainty, baaed on a representation of states of certainty called endorsements The theory of endorsements is an alternative to numerical methods for reasoning about uncertainty, such as subjective Bayesian methods (Shortliffe and Buchanan, 1975; Duda, Hart, and Nilsson, 1976) and the Shafer-Dempster theory (Shafer, 1976). The fundamental con...

We propose that people may gain certain "offensive" and "defensive" advantages for their cherished belief systems (e.g., religious and political views) by including aspects of unfalsifiability in those belief systems, such that some aspects of the beliefs cannot be tested empirically and conclusively refuted. This may seem peculiar, irrational, or at least undesirable to many people because it ...

Decision tree induction systems are being used for knowledge acquisition. Yet they have been developed without proper regard for the subjective Bayesian theory of inductive inference. This paper examines the problem tackled by these systems from the Bayesian view in order to interpret the systems and the heuristic methods they use. It is shown that decision tree systems depart from the usual Ba...

Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a rapid prototyping approach to network construction. We describe criteria for identifying network modules and the use of 'stubs' within a belief network. We propose an object oriented representation for belief networks ...

The lack of consensus on how to characterize humans’ capacity for belief reasoning has been brought into sharp focus by recent research. Children fail critical tests of belief reasoning before 3 to 4 years (Wellman, Cross, & Watson, 2001; Wimmer & Perner, 1983), yet infants apparently pass false belief tasks at 13 or 15 months (Onishi & Baillargeon, 2005; Surian, Caldi, & Sperber, 2007). Non-hu...

The belief network framework for reasoning with uncertainty in knowledgebased systems has been around for some time now. As more and more practical applications employing the framework are being developed, it becomes apparent that the framework lacks with regard to explicit means for exerting control over reasoning. In this paper, we extend the belief network framework with a method for selecti...

A standard assumption underlying traditional accounts of belief change is the principle of minimal change, that an agent’s belief state should be modified minimally to incorporate new information. In this paper we introduce a novel account of belief change in which the agent’s belief state is modified minimally to incorporate exactly the new information. Thus a revision by p∨q will result in a ...

This paper extends Harsanyi’s Impartial Observer Theorem by introducing Knightian Uncertainty in the form of individual belief systems. It features an axiomatic framework societal decision-making presence of uncertainty. The model allows analysis scenarios where individuals agree on ranking but not likelihood social outcomes. The preferences impartial observer are represented by a weighted...

The aim of this paper is to evaluate the predicted reliability of mechatronic systems, by taking into account the epistemic uncertainties. The work reported here presents a new methodology based on integrating the belief functions in the Petri net (PN) model, in order to create a belief network, and to show how to propagate the parametric uncertainties in reliability models. Some notions of unc...

We explore the developmental paradox of false belief understanding. This paradox follows from the claim that young infants already have an understanding of false belief, despite the fact that they consistently fail the elicited-response false belief task. First, we argue that recent proposals to solve this paradox are unsatisfactory because they (i) try to give a full explanation of false belie...