Adversarial Collaboration Decision-Making: An Overview of Social Quantum Information Processing

摘要

While believing that cooperation is the most efficient form of social behavior (e.g., Nowak et al., 2000), collaborative decision-making (CDM) to solve problems is an aspect of human behavior least yielding to rational predictions. To reduce the complexity of CDM, early game theorists assumed that cooperation and conflict could be represented by static configurations of the choices confronted by humans in a game interaction, leading to the first stable solution of mutual competition (Nash equilibrium), followed by the evolution in repeated games of a second stable solution of mutual cooperation (Axelrod, 1984). But logic under determines reality, R; cooperation in the field to solve ill-defined problems produces suboptimal solutions; and a rigorous logical map from multiple individual preferences to a single group preference is not possible (Arrow, 1951). More problematic for multiple agent systems or computational autonomy, as information (I) uncertainty is reduced to produce knowledge (K), as the number of interactants approach an N of 100 or more, or, ironically, as the number of agents participating in cooperative behavior increases, computability decreases significantly. In contrast, adapting quantum logic to adversarial collaboration produces a robust model of decision-making even as N increases. Implications for C2 decision-making are discussed.