On model design for simulation of collective intelligence
The study of collective intelligence (CI) systems is increasingly gaining interest in a variety of research and application domains. Those domains range from existing research areas such as computer networks and collective robotics to upcoming areas of agent-based and insect-based computing; also including applications on the internet and in games and movies. CI systems are complex by nature and (1) are effectively adaptive in uncertain and unknown environments, (2) can organise themselves autonomously, and (3) exhibit emergent behaviour. Among others, multi-agent systems, complex adaptive systems, swarm intelligence and self-organising systems are considered to be such systems. The explosive wild growth of research studies of CI systems has not yet led to a systematic approach for model design of these kinds of systems. Although there have been recent efforts on the issue of system design (the complete design trajectory from identifying system requirements up to implementation), the problem of choosing and specifying a good model of a CI system is often done implicitly and sometimes even completely ignored. The aim of this article is to bring to the attention that model design is an essential as well as an integral part of system design. We present a constructive approach to systematically design, build and test models of CI systems. Because simulation is often used as a way to research CI systems, we particularly focus on models that can be used for simulation. Additionally, we show that it is not necessary to re-invent the wheel: here, we show how existing models and algorithms can be used for CI model design. The approach is illustrated by means of two example studies on a (semi-automated) multi-player game and collaborative robotics.
model design collective intelligence multi-agent systems self-organising systems artificial intelligence