Tree of Knowledge: an Online Platform for Learning the Behaviour of Complex Systems

TL;DR

The paper introduces TreeOfKnowledge, an online platform employing agent-based behaviour learning to analyze complex systems, overcoming limitations of traditional statistical methods by integrating diverse datasets for more accurate insights.

Contribution

It presents a novel methodology and platform for learning complex agent behaviours from heterogeneous data, enhancing robustness over traditional statistical approaches.

Findings

Enables learning from diverse datasets without isolating phenomena.

Improves robustness and accuracy of social system insights.

Leverages internet and computational advances for better models.

Abstract

Many social sciences such as psychology and economics try to learn the behaviour of complex agents such as humans, organisations and countries. The current statistical methods used for learning this behaviour try to infer generally valid behaviour, but can only learn from one type of study at a time. Furthermore, only data from carefully designed studies can be used, as the phenomenon of interest has to be isolated and confounding factors accounted for. These restrictions limit the robustness and accuracy of insights that can be gained from social/economic systems. Here we present the online platform TreeOfKnowledge which implements a new methodology specifically designed for learning complex behaviours from complex systems: agent-based behaviour learning. With agent-based behaviour learning it is possible to gain more accurate and robust insights as it does not have the restriction of conventional statistics. It learns agent behaviour from many heterogenous datasets and can learn from these datasets even if the phenomenon of interest is not directly observed, but appears deep within complex systems. This new methodology shows how the internet and advances in computational power allow for more accurate and powerful mathematical models.