Abstract
Many disciplines are wrought with high levels of uncertainty and many unknowns including transdisciplinary design. Hence, to achieve some advancement within a discipline, we must provide aids in achieving mitigation of complexity and increasing understanding. We achieve this by investigating beyond the boundaries of the existing discipline's design concepts. This means stretching the boundaries of knowledge by traditional observation and analysis known as hard work using "elbow-grease" or reviewing those methods and processes of other disciplines, which might have applicability to our discipline's domain. Therefore, we addresses the challenge of minimizing ambiguity and fuzziness of understanding in large volumes of complex transdisciplinary information content and explore transdisciplinary synthesis via cognition based frameworks, for improving actionable decisions. Specifically, Recombinant Knowledge Assimilation (RNA) & Artificial Cognitive Neural Framework (ACNF) which recombine and assimilate knowledge based in human cognitive processes, formulated and embedded in a neural network of genetic algorithms and stochastic decision making, towards minimizing ambiguity and maximizing clarity. Thus, we introduce trans-disciplinary concepts, for application to specific problem sets, in order to achieve qualitative solutions for the large volumes of complex interconnected data and applications, which exist today.
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