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Phase 16 · Lesson 21Learn1.3 h25 lessons in phase

Agent Economies, Token Incentives, Reputation

Long-horizon autonomous agents (METR's 1-hour to 8-hour work-curve) need economic agency. The emerging **5-layer stack** is: **DePIN** (physical compute) → **Identity** (W3C DIDs + reputation capital) → **Cognition** (RAG + MCP) → **Settlement** (account abstraction) → **Governance** (Agentic DAOs). Production agent-incentive networks include **Bittensor** (TAO subnets reward task-specific models), **Fetch.ai / ASI Alliance** (ASI-1 Mini LLM + FET token), and **Gonka** (transformer-based PoW that reallocates compute to productive AI tasks). Academic work: AAMAS 2025's decentralized LaMAS uses **Shapley-value credit attribution** to fairly reward contributing agents; Google Research "Mechanism design for large language models" proposes **token auctions** with second-price payment under monotone aggregation. This lesson builds a minimal agent marketplace, applies Shapley-value credit attribution to a multi-agent pipeline, and runs a second-price token auction so the game-theory machinery lands concretely.

Visual edition planned

This lesson isn’t interactive yet.

It is part of the curriculum and will get the same treatment as Phase 1 — a visual cover, hands-on labs, derivations with numeric checks and a quiz. Until then, the original lesson is the best place to read it:

Part of Phase 16Multi-Agent & Swarms. Use the previous / next cards below to keep browsing the phase.