However, to deal with ambiguity and partial information, new approache s have emerged - examples of which are fuzzy logic, probabilistic modal logic, Bayesian networks and belief-based systems. Even though progress has been made, these approaches genera lly have a limitation: the probability or degree of belief, in general, being kept out of the l ogical semantics, remaining at another level of interpretation on a deterministic model. In other w ords, maintaining the binary characteristic of truth - true or false, with uncertainty being treate d as associated with models, rather than a property of logical language in itself. The proposed logic will be used to solve the problem of tackling certain types of uncertainty and imprecision with Bayesian Networks. The aim is to take advantage of the conceptual and practical benefits of this sy stem in practical situations that have not yet been adequately explored.

In this paper, we provide a theoretical framework to analyze an agent who misinterprets or misperceives the true decision problem she faces. Within this framework, we show that a wide range of behavior observed in experimental settings manifest as failures to perceive implications, in other words, to properly account for the logical relationships between various payoff relevant contingencies. We present behavioral characterizations corresponding to several benchmarks of logical sophistication and show how it is possible to identify which implications the agent fails to perceive. Thus, our framework delivers both a methodology for assessing an agent's level of contingent thinking and a strategy for identifying her beliefs in the absence full rationality.