Quantum-inspired modeling of distributed intelligence systems with artificial intelligent agents self-organization

Distributed intelligence systems (DIS) containing natural (NIA) and artificial (AIA) intelligence agents of decision making (DM) belong to interdisciplinary areas of promising studies aimed at digitalization of routine processes in industry, economy, management, and everyday life. In this work, we suggest a novel quantum-inspired approach to study crucial features of DIS consisting of NIAs (users) and AIAs (digital assistants) that possess self-organization within a complex network. We propose a mapping procedure for Russell’s valence-arousal circumplex model onto an effective quantum-like two-level system to characterize a NIA cognitive system in a simple DM process. We demonstrate that conditions for opinion formation and social impact in a DIS are relevant to the second-order (laser-like) phase transition that establishes a nonvanishing average information field in the DIS, which is reminiscent of the social laser phenomenon that may appear in social networks. We demonstrate the complex behavior of social impact in the DIS that implies the hierarchical formation of opinion leaders in the network, which we elucidated using the eigenvector centrality criterion. We introduce a set of cooperativity parameters for the users, which play a significant role in DIS features. Cooperativity parameters reveal how frequently AIAs and NIAs communicate with each other accounting for their facilities in information exchange. We show that at weak AIA -- NIA coupling many uncertainties in the DIS complicate a certain (non-zero) opinion formation for DM agents. We offer an adaptive approach to increase the users' impact by establishing a network-dependent coupling rate with their digital assistants. In this case, the mechanism of AIA control helps resolve the DM process in the presence of some uncertainties evoked by the variety of users' preferences. Our findings open new prospectives in different areas where AIAs represent useful teammates in solving common problems with humans in the framework of network organizations.