Post-AGI Economies: Autonomy and the First Fundamental Theorem of Welfare Economics
- URL: http://arxiv.org/abs/2604.21216v1
- Date: Thu, 23 Apr 2026 02:13:29 GMT
- Title: Post-AGI Economies: Autonomy and the First Fundamental Theorem of Welfare Economics
- Abstract summary: We argue that the theorem ought to be subject to an autonomy qualification.<n>We set out conditions for which autonomy-complete competitive equilibrium is autonomy-Pareto efficient.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The First Fundamental Theorem of Welfare Economics assumes that welfare-bearing agents are autonomous and implicitly relies on a binary distinction between autonomy and instrumentality. Welfare subjects are those who have autonomy and therefore the capacity to choose and enter into utility comparisons, while everything else does not. In post-AGI economies this presupposition becomes nontrivial because artificial systems may exhibit varying degrees of autonomy, functioning as tools, delegates, strategic market actors, manipulators of choice environments, or possible welfare subjects. We argue that the theorem ought to be subject to an autonomy qualification where the impact of these changes in autonomy assumptions is incorporated. Using a minimal general-equilibrium model with autonomy-conditioned welfare, welfare-status assignment, delegation accounting, and verification institutions, we set out conditions for which autonomy-complete competitive equilibrium is autonomy-Pareto efficient. The classical theorem is recovered as the low-autonomy limit.
Related papers
- A game theory for foundation models shows new paths to rational cooperation through similarity inference [56.53805518668248]
We show that foundation model agents engaging in optimal planning consistently converge to stable cooperation.<n>By shifting from decoupled to embedded agency, these agents model themselves as part of the universe they inhabit.
arXiv Detail & Related papers (2026-08-04T17:24:33Z) - Post-AGI Economies: Superposition and the Second Fundamental Theorem of Welfare Economics [0.0]
We give an autonomy-qualified Second Welfare Theorem stating the joint conditions convexity, stable moral status, non-fungible rights, welfare selection, non manipulation, governed self modification, and verification under which an autonomy optimum remains certifiably decentralizable, distinguishing economic preference superposition, a hypothesis about context-indexed choice, from neural feature superposition.
arXiv Detail & Related papers (2026-06-06T17:27:38Z) - Autonomy for Older Adult-Agent Interaction [0.0]
As the global population ages, artificial intelligence (AI)-powered agents have emerged as potential tools to support older adults' caregiving.<n>This paper examines four key dimensions of autonomy for older adults: decision-making autonomy, goal-oriented autonomy, control autonomy, and social responsibility autonomy.
arXiv Detail & Related papers (2025-07-17T03:46:13Z) - Towards a Taxonomy for Autonomy in Large-Scale Agile Software Development [1.0957528713294875]
This paper presents our work towards building a framework for autonomy in large-scale agile development.<n>The preliminary version identifies five levels, and 21 categories of autonomy grouped into three areas.<n>These aspects of autonomy can be taken into account when analyzing or defining the limits of autonomy in large-scale agile software development.
arXiv Detail & Related papers (2025-03-04T14:15:16Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - A Quantitative Autonomy Quantification Framework for Fully Autonomous Robotic Systems [0.0]
This paper focuses on the full autonomous mode and proposes a quantitative autonomy assessment framework based on task requirements.
The framework provides not only a tool for quantifying autonomy, but also a regulatory interface and common language for autonomous systems developers and users.
arXiv Detail & Related papers (2023-11-03T14:26:53Z) - Causal Fairness for Outcome Control [68.12191782657437]
We study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable.
In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision.
We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this.
arXiv Detail & Related papers (2023-06-08T09:31:18Z) - Strategyproof and Proportionally Fair Facility Location [77.16035689756859]
We focus on a simple, one-dimensional collective decision problem (often referred to as the facility location problem)
We analyze a hierarchy of proportionality-based fairness axioms of varying strength.
For each axiom, we characterize the family of mechanisms that satisfy the axiom and strategyproofness.
arXiv Detail & Related papers (2021-11-02T12:41:32Z) - The AI Economist: Optimal Economic Policy Design via Two-level Deep
Reinforcement Learning [126.37520136341094]
We show that machine-learning-based economic simulation is a powerful policy and mechanism design framework.
The AI Economist is a two-level, deep RL framework that trains both agents and a social planner who co-adapt.
In simple one-step economies, the AI Economist recovers the optimal tax policy of economic theory.
arXiv Detail & Related papers (2021-08-05T17:42:35Z) - Decentralized Reinforcement Learning: Global Decision-Making via Local
Economic Transactions [80.49176924360499]
We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems.
We derive a class of decentralized reinforcement learning algorithms.
We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
arXiv Detail & Related papers (2020-07-05T16:41:09Z) - The AI Economist: Improving Equality and Productivity with AI-Driven Tax
Policies [119.07163415116686]
We train social planners that discover tax policies that can effectively trade-off economic equality and productivity.
We present an economic simulation environment that features competitive pressures and market dynamics.
We show that AI-driven tax policies improve the trade-off between equality and productivity by 16% over baseline policies.
arXiv Detail & Related papers (2020-04-28T06:57:18Z) - Learning to Optimize Autonomy in Competence-Aware Systems [32.3596917475882]
We propose an introspective model of autonomy that is learned and updated online through experience.
We define a competence-aware system (CAS) that explicitly models its own proficiency at different levels of autonomy and the available human feedback.
We analyze the convergence properties of CAS and provide experimental results for robot delivery and autonomous driving domains.
arXiv Detail & Related papers (2020-03-17T14:31:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.