There is a small but growing interest in traveller behaviour research on investigating ways to identify and quantify degrees of belief (as subjective probabilities or other propositions) associated with behavioural responses, especially in the context of popular travel choice methods such as stated choice experiments, as a way of adding to our understanding of decision making in real-world cont...

This paper proves that the revenue equivalence theorem ceases to hold for auctions without common knowledge about the agents' prior beliefs. That is, different auction forms yield different expected revenue. To prove this, an auction game is converted to a Bayesian decision problem with an infinite hierarchy of beliefs. A general solution for such Bayesian decision problems is proposed. The sol...

When asked to assess the deductive validity of an argument, people are influenced by their prior knowledge of the content. Recently, two competing explanations for this belief bias effect have been proposed, each based on signal detection theory. Under a response bias explanation, people set more lenient decision criteria for believable than for unbelievable arguments. Alternatively, believable...

The representation of ignorance is a long standing challenge for researchers in probability and decision theory. During the past decade, Artiicial Intelligence researchers have developed a class of reasoning systems, called Truth Maintenance Systems, which are able to reason on the basis of incomplete information. In this paper we will describe a new method for dealing with partially speciied p...

Chapter 1: introduction ) ( Decision making is part of mankind life; this can be individual or group in decision. Human kind is facing to making decision every day. Decision makers are influenced by the surrounded environment; this environment ranges from personal characteristics and belief to the influence of friends and society. Thereby this integrated and complex environment has some impa...

The belief decision tree approach is a decision tree method adapted in order to handle uncertainty about the actual class of the objects in the training set. The uncertainty is represented by the Transferable Belief Model (TBM). We present two methods to build the tree. In order to reduce the size and the complexity of the induced tree, we present a pre-pruning tool related to the stopping crit...

Representation theorems are often taken to provide the foundations for decision theory. First, they are taken to characterize degrees of belief and utilities. Second, they are taken to justify two fundamental rules of rationality: that we should have probabilistic degrees of belief and that we should act as expected utility maximizers. We argue that representation theorems cannot serve either o...