DeTEcT: Dynamic and Probabilistic Parameters Extension

TL;DR

This paper extends the DeTEcT framework for modeling wealth distribution in token economies by introducing dynamic and probabilistic parametrizations, enabling analysis of economies with variable money supply like Ethereum.

Contribution

It introduces four parametrization methods for DeTEcT, demonstrating how restrictions derive existing models and exploring dynamic money supply effects.

Findings

Dynamic money supply impacts wealth distribution dynamics.

Restrictions can derive existing wealth models from DeTEcT.

Framework can model token economies without maximum supply.

Abstract

This paper presents a theoretical extension of the DeTEcT framework proposed by Sadykhov et al., DeTEcT, where a formal analysis framework was introduced for modelling wealth distribution in token economies. DeTEcT is a framework for analysing economic activity, simulating macroeconomic scenarios, and algorithmically setting policies in token economies. This paper proposes four ways of parametrizing the framework, where dynamic vs static parametrization is considered along with the probabilistic vs non-probabilistic. Using these parametrization techniques, we demonstrate that by adding restrictions to the framework it is possible to derive the existing wealth distribution models from DeTEcT. In addition to exploring parametrization techniques, this paper studies how money supply in DeTEcT framework can be transformed to become dynamic, and how this change will affect the dynamics of wealth distribution. The motivation for studying dynamic money supply is that it enables DeTEcT to be applied to modelling token economies without maximum supply (i.e., Ethereum), and it adds constraints to the framework in the form of symmetries.