In general, the "duality of ambiguity"—comprising randomness and fuzziness inherent in human information processing—persistently underlies organizational communication processes. Consequently, analyzing the characteristics of organizational information processing and communication necessitates a research perspective that incorporates this duality. Building on this perspective, the authors have previously developed analytical models for information allocation ratios in Communication Networks (CN) based on fuzzy entropy, which captures the combined nature of randomness and fuzziness. In this study, we extend this line of research to construct an estimation model for information allocation ratios. This model incorporates not only the formal CN structure—specifically, the average path length and load density derived from formal connections—but also informal factors absent from the formal structure: the information processing capability and trustworthiness of each member. The proposed approach aims to estimate allocation ratios that maximize fuzzy entropy while minimizing the constraints imposed by these multi-factor variables. Thereby, we seek to establish a more versatile theoretical framework and verify the model's validity through analyses of simple CN structures representing hypothetical small groups.

Keywords

duality of ambiguity, human information processing, fuzzy entropy, information allocation ratios