Abstract: At present, the increasingly complex and dynamic situation and environment put forward higher requirements for multi-individual learning from past events, accumulating coping experience, and quickly realizing the iteration of solutions. However, in the process of multi-individual learning from experience, different individuals often do not have enough knowledge of each other’s information and analytic methods, resulting in disagreement, delayed discussion, and failing to reach conclusions in time. The phenomena of not knowing what information other individuals have and/or what analytical methods other individuals will use as“asymmetry” is summarized in this paper. Using theory building method, focusing on the process of multi-individual learning from experience, taking “information” and “analytic method” as two dimensions, a two-dimensional matrix based on the asymmetry of information and the asymmetry of analytic method is established, and then four conditions are put forward. These four conditions include: information asymmetry and analytic method asymmetry, information asymmetry and analytic method symmetry, information symmetry and analytic method asymmetry, and information symmetry and analytic method symmetry. It is pointed out that the asymmetry of information and/or analytic methods will reduce learning efficiency, and the process mechanism model of multi-individual learning from experience based on asymmetric perspective. Then, the practical methods are put forward to reduce asymmetry from the perspective of “willingness” and “capability”, and the practical method base for reducing asymmetry in multi-individual learning from experience is constructed. Approaches are explored to solve the problems caused by the asymmetry of information and/or analytic method in multi-individual learning from experience, and theoretical framework and practical guidance for improving learning efficiency are provided.

Key words: multi-individual learning from experience; time-space theory; information asymmetry; analytic method asymmetry; learning efficiency