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This paper builds on a long-lasting research program on the micro-foundations of innovative decision making, founded on a development of a neglected epistemic aspect of Simon's work, and on contributions in epistemology, in which heuristics are not procedures that are uncertaintyavoiding, economizing on cognitive and search effort, and problem-space reducing, but procedures that are uncertainty-modeling, investing in research effort, and problem-expanding. The paper offers a summary of the main effective heuristics of that kind so far identified, as applied to real processes of innovative decision making under epistemic uncertainty, such as judging and investing in novel entrepreneurial projects. It argues and shows that, in contrast to the common view, a wide range of those procedures, usually thought to belong to different and rival models, can be fruitfully combined.

The purpose of this paper is to offer a dynamic meta-heuristic model of effecting organizational change which informs smooth directing and routinizing change according to the specific situation relevant to every change attempt.

Because of the dynamic nature of variables and their interaction, developing a static model cannot be tenable. This study, therefore, attempts to generate a meta-heuristic method for constructing a dynamic organizational change model by combining qualitative methods (content analysis and Delphi Technique) and Artificial Neural Networks (Fuzzy Theory and Genetic Algorithm).

Each change program requires its unique method of implementation as change attempts are context specific. Hence, static models should give way to some dynamic ones. Whereas such static models abound, this paper stands out as offering a dynamic model for organizational change by using a rather unconventional method.

This can be regarded as a road map informing higher echelons of the complexity and leadership of change, while at the same time helping change agents have access to acceptable amount of variables that can make their change attempts more promising.

This model contains more flexible variables which reflect the incumbent organizations’ situations. While almost all previous models of change attempt take into account a few/handful variables which are seen to impact on change solidly and independently. But such an analysis with the usual statistical and mathematical methods is not justified. This challenge is met here using metaheuristics and artificial intelligence methods. The model formulated, thus, is dynamic, non-linear and multi-dimensional. Entering the data related to any specific field turns it to a customized model suitable for use in a given field; and this is not only a contribution to the theory but also can allegedly increase the chance of the success of the change agent managing to utilize the optimal amount of variables suggested in this paper.