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This article argues that the Situation theory and the Channel theory can be used as a general framework for Imperfect Information Management. Different kinds of imperfections are uncertainty, imprecision, vagueness, incompleteness, inconsistency, and context-dependency which can be handled pretty well by our brain. Basic approaches like probability theory and standard logic are intrinsically inefficient in modeling fallacious minds. The generalized probability and nonstandard logic theories have epistemological motivations to provide better models for information integration in cognitive agents. Among many models of them, possibility theory and probabilistic logic theory are the best approaches. I argue, based on a review of different approaches to Imperfect Information Management, that a good framework for it is the Situation theory of Barwise and the Channel theory of Barwise-Seligman. These theories have relied on a powerful and unique epistemological-based notion of information to refer to partiality. These frameworks have a proper approach for context modeling to handle common knowledge and incomplete information. Also, they distinguish belief from knowledge clearly to model the non-monotonic and dynamic nature of knowledge. They discern the logic of the world from information flow in the mind. The objectification process in these theories reveals to us the nature of default or probabilistic rules in perceptions. The concept of the channel can be used to represent those types of reasoning mechanisms that move from one model or logic to another one. The imprecision in our perceptions causes fuzziness in reasoning and vagueness in communication that can be represented by some suitable classifications connected by some channels. This new framework like a network framework can provide a scalable and open framework to cover different models based on a relativistic notion of truth.