AI and the Mitigation of Error: A Thermodynamics of Teams

Traditional theories of social models conceptualize teams as distributed processors, disregarding the interdependence necessary to multi-task. Yet, interdependence characterizes social behavior. Instead, traditional theory favor cooperation, a state of least entropy production (LEP), without understanding the causes, limits or consequences of cooperation. As a simple example of interdependence, foraging prey overgraze forests free of predators. In our model, interdependence creates uncertainty, tradeoffs and signals (e.g., prices, coordination, innovation). Unlike individuals, the ability of teams to multitask reflects a quantum-like entanglement that represents maximum entropy production (MEP) when solving the problems signaled by society to improve its welfare. Our model supports findings that evolution in nature is driven by the MEP from making intelligent choices. Exploiting interdependence improves team intelligence, improves performance and reduces the risk of human error; forced cooperation disorganizes it by increasing the risk of error; e.g., if team cooperation improves teamwork, widespread forced cooperation in an autocracy or bureaucracy reduces social intelligence by adding unnecessary noise to signals. In our model, competition between teams self-organizes outsiders willing to sort through the noise for signals of the choices that improve social welfare (e.g., teams in courtrooms; science; entertainment; sports; businesses). Social systems organized around competition (e.g., stronger signals from robust checks and balances) better control a society by more correctly sizing teams to solve problems with fewer errors compared to autocracies or bureaucracies. Overall, we predict, the density of MEP directed at solving problems in a society with the constraints imposed from strong checks and balances, yet able to freely self-organize its labor and capital within those constraints, is denser.