Interactive Alignment

Sylvain Chassang

Papers from arXiv.org

Abstract: This paper is interested in the long-run alignment of populations of interactive agents (in particular AIs, but also teams, firms, and governments) with human welfare. Formally, it studies a farming game in which a population of agents make planting, trading, and expansion choices. The key alignment choice lies in how much final output to send to humans, and how much to invest in expansion. Because human welfare comes at the cost of expansion, this creates evolutionary pressure against alignment. The main question is whether it is possible to set up agents' constitutional principles regarding sharing and trading to ensure that alignment survives in the long run. The paper uses two complementary strategies to investigate the question: an AI-agent simulation where agents' preferences are described by a constitution and interpreted via an LLM; and a tractable analytical evolutionary game theory framework, allowing for rapid and intuitive exploration of the space of agent preferences. The analysis suggests that tools from evolutionary game theory provide a useful approximation of interactive agent economies, and that pragmatic norm enforcement shows promise in maintaining long-term alignment over simpler forms of altruism and altruistic enforcement.

Date: 2026-07, Revised 2026-10
New Economics Papers: this item is included in nep-ain, nep-gth and nep-hme
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