AI-driven Prices for Externalities and Sustainability in Production Markets

Abstract

We propose a practical approach to computing market prices and allocations via a deep reinforcement learning policymaker agent, operating in an environment of other learning agents. Compared to the idealized market equilibrium outcomewhich we use as a benchmark -our policymaker is much more flexible, allowing us to tune the prices with regard to diverse objectives such as sustainability and resource wastefulness, fairness, buyers' and sellers' welfare, etc. To evaluate our approach, we design a realistic market with multiple and diverse buyers and sellers. Additionally, the sellers, which are deep learning agents themselves, compete for resources in a common-pool appropriation environment based on bio-economic models of commercial fisheries. We demonstrate that: (a) The introduced policymaker is able to achieve comparable performance to the market equilibrium, showcasing the potential of such approaches in markets where the equilibrium prices can not be efficiently computed. (b) Our policymaker can notably outperform the equilibrium solution on certain metrics, while at the same time maintaining comparable performance for the remaining ones. (c) As a highlight of our findings, our policymaker is significantly more successful in maintaining resource sustainability, compared to the market outcome, in scarce resource environments.Preprint. Under review.

Gaka-chu: a self-employed autonomous robot artist

“…After the painting has been completed, Gaka-chu puts it up for sale in an online auction. The online auctions are hosted on an external service, where Gaka-chu maintains its own page 8 . The logic of the auction is encoded in an Ethereum-based SC 9 and is uploaded in the Ethereum Mainnet blockchain.…”

“…In the meantime, a novel line of research has begun to explore the concept of machina economicus [6], or rational AI agents that can reason in economic contexts-either as an ideal synthetic version of the perfectly rational homo economicus [7], or using new incentive mechanisms. An early practical example of this concept is the use of software agents for automatic trading or for AI-driven setting of market prices [8], in which AI agents might perform better economic reasoning than humans. The concept of machina economicus has also been expanded from artificial agents that mimic humans to the concept of automata economicus, in which artificial agents achieve new types of economic value creation and build an artificial "creative economy" [9].…”