Alignment of a Total Automation Economy
David McAllester
Why It Matters
What makes this one worth your time
Understanding the economic structure of a total automation economy is crucial for developing effective AI systems that align with human values and market dynamics.
This paper examines the economic dynamics of a fully automated economy and its implications for AI systems.
Summary
The paper explores the implications of a total automation economy on economic theory, particularly focusing on the balance between central planning and decentralized decision-making, while reviewing Kantorovich's dualization and its relevance to agentic AI systems.
Key contributions
- A detailed review of Kantorovich's dualization in the context of total automation economies.
- An exploration of alignment vulnerabilities in automated economic systems.
Notable insights
- The paper highlights the tension between central planning and decentralized decision-making in automated economies, which could influence AI system design.
- It suggests that Kantorovich's dualization may provide a framework for understanding the utility of multi-agent AI systems.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.17015v2 Announce Type: replace-cross Abstract: We consider economic theory from the perspective of a total automation economy, one with no human involvement in production either in manufacturing or in management. One can naturally ask whether a total automation economy is fundamentally a centrally planned economy or, alternatively, whether efficiency demands decentralization into local decisions by competing agents -- agentic production. A soviet economist, Leonid Kantorovich, developed linear programming as a method companies or governments can use to optimize production. Ironically, he is also generally credited with showing that the most efficient production is achieved through decentralization -- a free market economy with competing agents. Here we review Kantorovich's dualization in detail. We take the objective of the economy to be maximizing production weighted by (human) market price. A fundamental issue is whether an automated pursuit of this objective might have alignment vulnerabilities as the economy evolves. Another question is whether dualization provides insight into the utility of agentic AI systems (multi-agent AI systems) generally.