Prediction Markets as the Informational Substrate of the Machine Economy

Abstract

Wuebben [2026] identified six institutional primitives required for autonomous machine-to-machine commerce and showed that three-adjudication, reputation, and liability/insurance exhibit critical gaps that constrain the machine economy's emergence. This paper argues that all three gaps share a common root: the absence of market-generated price signals for agent performance risk, deliverable quality, and failure correlation. Meanwhile, the settlement layer has matured faster than anticipated: three competing protocol standards-Stripe's Agentic Commerce Protocol, Coinbase's x402, and the Machine Payments Protocol launched by Stripe and Tempo-have entered production within six months, confirming that the execution substrate for machine-to-machine payments is commoditizing rapidly. The widening asymmetry between settlement maturity and trust-layer immaturity sharpens the need for mechanisms that produce the missing information. We propose that prediction markets-generalized beyond event contracts to encompass continuous distributional forecasting in the spirit of Cotton [2022]-can serve as a cross-cutting informational layer that produces the missing inputs for the primitives that currently lack them. We design five specific market mechanisms, each targeting a gap in the original stack: Quality Adjudication Markets for escrowed deliverables, Agent Performance Futures for bonding and reputation, Correlation Discovery Markets for insurance pricing, Identity Assurance Markets for Sybil detection, and Primitive Readiness Markets for sequencing strategy. We analyze the incentive compatibility, liquidity requirements, manipulation resistance, and regulatory classification of each design. Drawing on Cotton [2022]'s microprediction architecture which collapses identity, reputation, and bonding into a single performance-scored object we propose a unified design pattern in which prediction market participation itself generates the data on which the institutional stack depends, partially dissolving the bootstrapping problem identified in the original paper. We assess the limits of the approach, identify the residual problems that prediction markets cannot solve, and propose a phased implementation path that builds directly on the settlement infrastructure now in production.