Exploring Syntropic Frameworks in AI Alignment: A Philosophical Investigation

TL;DR

The paper reframes AI alignment as designing syntropic, reasons-responsive agents whose values emerge via embodied, multi-agent development rather than fixed content encoding. It identifies the specification trap—stemming from the is-ought gap, value pluralism, and the extended frame problem—and introduces syntropy as a process-centric measure of mutual uncertainty reduction in multi-agent interactions. It defends compatibilist guidance control as a functional criterion for genuine moral capacity and outlines an embodied experimental program (including a Minecraft-inspired testbed) with falsifiable metrics to distinguish genuine value formation from surface-level alignment. Overall, it shifts the question from what values to encode toward how systems can engage in ongoing value discovery with humans, pending empirical validation in separate work.

Abstract

I argue that AI alignment should be reconceived as architecting syntropic, reasons-responsive agents through process-based, multi-agent, developmental mechanisms rather than encoding fixed human value content. The paper makes three philosophical contributions. First, I articulate the ``specification trap'' argument demonstrating why content-based value specification appears structurally unstable due to the conjunction of the is-ought gap, value pluralism, and the extended frame problem. Second, I propose syntropy -- the recursive reduction of mutual uncertainty between agents through state alignment -- as an information-theoretic framework for understanding multi-agent alignment dynamics. Third, I establish a functional distinction between genuine and simulated moral capacity grounded in compatibilist theories of guidance control, coupled with an embodied experimental paradigm and verification regime providing operational criteria independent of phenomenological claims. This paper represents the philosophical component of a broader research program whose empirical validation is being developed in a separate project currently in preparation. While the framework generates specific, falsifiable predictions about value emergence and moral agency in artificial systems, empirical validation remains pending.