Equilibria and Learning in Modular Marketplaces

TL;DR

This paper explores the design and equilibrium analysis of a modular marketplace where API providers set prices strategically, and a platform aggregates outputs, demonstrating that efficient approximate equilibria exist and can be learned through decentralized algorithms.

Contribution

It introduces a market model with strategic API pricing, proves the existence of near-optimal equilibria under a specific algorithm, and shows these equilibria can be learned via no-regret algorithms.

Findings

Existence of ε-approximate equilibria under the bang-per-buck algorithm.

Equilibria provide constant-factor approximation to the optimal buyer value.

Decentralized no-regret learning algorithms can find these equilibria.

Abstract

We envision a marketplace where diverse entities offer specialized "modules" through APIs, allowing users to compose the outputs of these modules for complex tasks within a given budget. This paper studies the market design problem in such an ecosystem, where module owners strategically set prices for their APIs (to maximize their profit) and a central platform orchestrates the aggregation of module outputs at query-time. One can also think about this as a first-price procurement auction with budgets. The first observation is that if the platform's algorithm is to find the optimal set of modules then this could result in a poor outcome, in the sense that there are price equilibria which provide arbitrarily low value for the user. We show that under a suitable version of the "bang-per-buck" algorithm for the knapsack problem, an $\varepsilon$-approximate equilibrium always exists, for any arbitrary $\varepsilon > 0$. Further, our first main result shows that with this algorithm any such equilibrium provides a constant approximation to the optimal value that the buyer could get under various constraints including (i) a budget constraint and (ii) a budget and a matroid constraint. Finally, we demonstrate that these efficient equilibria can be learned through decentralized price adjustments by module owners using no-regret learning algorithms.