A Control Theoretic Approach to Infrastructure-Centric Blockchain Tokenomics

• URL: http://arxiv.org/abs/2210.12881v1
• Date: Sun, 23 Oct 2022 23:23:13 GMT
• Title: A Control Theoretic Approach to Infrastructure-Centric Blockchain Tokenomics
• Authors: Oguzhan Akcin, Robert P. Streit, Benjamin Oommen, Sriram Vishwanath, Sandeep Chinchali
• Abstract summary: This paper argues that token economies for infrastructure networks should be structured differently. New suppliers should continually join the network to provide services and support to the ecosystem. To achieve such an equilibrium, the decentralized token economy should be adaptable and controllable.
• Score: 7.353066706896901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There are a multitude of Blockchain-based physical infrastructure systems, operating on a crypto-currency enabled token economy, where infrastructure suppliers are rewarded with tokens for enabling, validating, managing and/or securing the system. However, today's token economies are largely designed without infrastructure systems in mind, and often operate with a fixed token supply (e.g., Bitcoin). This paper argues that token economies for infrastructure networks should be structured differently - they should continually incentivize new suppliers to join the network to provide services and support to the ecosystem. As such, the associated token rewards should gracefully scale with the size of the decentralized system, but should be carefully balanced with consumer demand to manage inflation and be designed to ultimately reach an equilibrium. To achieve such an equilibrium, the decentralized token economy should be adaptable and controllable so that it maximizes the total utility of all users, such as achieving stable (overall non-inflationary) token economies. Our main contribution is to model infrastructure token economies as dynamical systems - the circulating token supply, price, and consumer demand change as a function of the payment to nodes and costs to consumers for infrastructure services. Crucially, this dynamical systems view enables us to leverage tools from mathematical control theory to optimize the overall decentralized network's performance. Moreover, our model extends easily to a Stackelberg game between the controller and the nodes, which we use for robust, strategic pricing. In short, we develop predictive, optimization-based controllers that outperform traditional algorithmic stablecoin heuristics by up to $2.4 \times$ in simulations based on real demand data from existing decentralized wireless networks.

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