Decentralized Token Economy Theory (DeTEcT)
Rem Sadykhov, Geoffrey Goodell, Denis de Montigny, Martin Schoernig, Philip Treleaven
TL;DR
DeTEcT introduces a formal analytical framework for decentralized token economies, combining a tokenomic taxonomy with a bilinear, discrete-time dynamical system to model wealth distribution across agent categories. It provides forward-propagation and inverse-propagation methods to design policy parameters and prices, respectively, and defines a price-manifold/hyperplane structure that ties transaction demands to pricing under a fixed circulation $M$. By embedding a governance-like control mechanism that mints, distributes, and burns tokens, the framework enables systematic stability analysis and objective-driven wealth redistribution, with demonstrations on a toy economy and discussion of extensions to dynamic money supply and broader economic metrics. The approach connects to existing wealth-distribution literature and tokenomics models while offering a practical, data-driven pricing mechanism for policy guidance in token ecosystems like Web3/DeFi. Overall, DeTEcT provides a versatile, theory-grounded platform for algorithmic policy in token economies, with implications for stability, decentralization, and governance.
Abstract
This paper presents a pioneering approach for simulation of economic activity, policy implementation, and pricing of goods in token economies. The paper proposes a formal analysis framework for wealth distribution analysis and simulation of interactions between economic participants in an economy. Using this framework, we define a mechanism for identifying prices that achieve the desired wealth distribution according to some metric, and stability of economic dynamics. The motivation to study tokenomics theory is the increasing use of tokenization, specifically in financial infrastructures, where designing token economies is in the forefront. Tokenomics theory establishes a quantitative framework for wealth distribution amongst economic participants and implements the algorithmic regulatory controls mechanism that reacts to changes in economic conditions. In our framework, we introduce a concept of tokenomic taxonomy where agents in the economy are categorized into agent types and interactions between them. This novel approach is motivated by having a generalized model of the macroeconomy with controls being implemented through interactions and policies. The existence of such controls allows us to measure and readjust the wealth dynamics in the economy to suit the desired objectives.