A quantum approach to multi-agent systems (MAS), organizations, and control

In some rapidly approaching future, on a battlefield, deep-space or planetary mission, teams of agents will be confronted with a problem beyond their computational capability, putting missions at risk. This risk arises from a lack of social theory based on first principles for decision-making in the face of ill-defined problems (idp’s). Also, no first principles exist to address the downside of cooperation (e.g., terrorist cells; corruption; and, regarding agents, reductions in computational power from communication costs when an increasing number of agents cooperates interactively). These problems make traditional social models impractical for a multiple-agent system to solve idp’s. In contrast to logical positivist models, such as command or consensus decision models, quantizing the pro-con positions in decision-making may produce a robust model that increases in computational power with N. Previously, optimum solutions of idp’s were found to occur when incommensurable beliefs interacting before neutral decision makers generated sufficient emotion to process information, I, but insufficient to impair the interaction, producing more trust compared to cooperation. This model has been extended to the first quantum information density functional theory of groups, especially mergers between organizations; we begin now to integrate our model with Markovian models.