Sadykhov, Rem;
Goodell, Geoffrey;
Treleaven, Philip;
(2024)
DeTEcT: Dynamic and Probabilistic Parameters Extension.
Social Science Research Network (SSRN): Amsterdam, The Netherlands.
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Abstract
This paper presents a theoretical extension of the DeTEcT framework proposed by Sadykhov et al., 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.
| Type: | Working / discussion paper |
|---|---|
| Title: | DeTEcT: Dynamic and Probabilistic Parameters Extension |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.2139/ssrn.4845446 |
| Publisher version: | http://dx.doi.org/10.2139/ssrn.4845446 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | DeTEcT, Wealth Distribution, Tokenomics, Token Economy, Simulation Engine, Blockchain Economy |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10200370 |
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