Institutional Metaphors for Designing Large-Scale Distributed AI versus AI Techniques for Running Institutions

TL;DR

This paper explores the use of institutional metaphors in designing large-scale distributed AI systems and examines AI techniques for managing institutional structures, highlighting conceptual and modeling challenges.

Contribution

It introduces a novel perspective by applying institutional metaphors to large-scale distributed AI design and discusses AI methods for operating institutional systems.

Findings

Institutional metaphors aid in designing scalable distributed AI.

Agent-based modeling helps understand social and legal institutions.

Challenges in modeling complex institutional behaviors are identified.

Abstract

Artificial Intelligence (AI) started out with an ambition to reproduce the human mind, but, as the sheer scale of that ambition became manifest, it quickly retreated into either studying specialized intelligent behaviours, or proposing over-arching architectural concepts for interfacing specialized intelligent behaviour components, conceived of as agents in a kind of organization. This agent-based modeling paradigm, in turn, proves to have interesting applications in understanding, simulating, and predicting the behaviour of social and legal structures on an aggregate level. For these reasons, this chapter examines a number of relevant cross-cutting concerns, conceptualizations, modeling problems and design challenges in large-scale distributed Artificial Intelligence, as well as in institutional systems, and identifies potential grounds for novel advances.