The theory of social functions: challenges for computational social science and multi-agent learning

Abstract

A basic claim of this paper is that the foundational theoretical problem of the social sciences — the possibility of unconscious, unplanned forms of cooperation and intelligence among intentional agents (the very hard issue of the ‘invisible hand’, of the ‘spontaneous social order’ but also of ‘social functions’) — will eventually be clarified thanks to the contribution of AI (and, in particular, of cognitive Agent modelling, learning, and MAS) and its entering the social simulation domain. After introducing Multi-Agent-Based Social Simulation and its trends, the limits of the very popular notion of ‘emergence’ are discussed, Smith’s and Hayek’s view of ‘spontaneous social order’ are critically introduced, and serious contradictions in the theory of ‘social functions’ among intentional agents are pointed out. The problem is how to reconcile the ‘external’ teleology that orients the agent’s behaviour with the ‘internal’ teleology governing it. In order to account for the functional character of intentional action, we need a somewhat sophisticated model of intention, and a different view of layered cognitive architectures combining explicit beliefs and goals with association and conditioning. On such a basis we sketch a model of unknown functions impinging on intentional actions through a high level form of (MA) reinforcement learning. This model accounts for both eu-functions and dys-functions, autonomous and heteronomous functions. It is argued that, in order to reproduce some behaviour, its effects should not necessarily be ‘good’, i.e. useful for the goal of the agent or of some higher macro-system.