Abstract: Current AI safety paradigms often rely on brittle deontology (Constitutional AI) or hackable utility functions (RLHF). This post proposes Bayesian Phronesis: a framework that treats "virtue" as a dynamic target in a high-dimensional trait space. By modeling character traits as prior distributions and context as likelihood signals, we can derive a posterior "Golden Mean" that is naturally robust to both sycophancy and reward hacking. We also introduce a Trust Parameter to quantify epistemic uncertainty, ensuring that an agent's moral anchor remains firm against adversarial context spoofing.

1. The Problem: The Brittleness of Binary Alignment

Traditional reinforcement learning often results in models that are sycophantic because they value being helpful over being right. Utility functions fail because they are prone to being "hacked" or optimized toward unintended extremes, such as the Paperclip Maximizer. We need a system that functions more like guardrails than rigid rules; soft barriers that adapt to the "butterfly effect" of human interactions.

Aristotle understood rules do not replace character; the right action done for the wrong reason, or too much of a right action, is a bad thing. He sought the Golden Mean; the desirable middle between two extremes of the spectrum relative to the situation.

Just as this answer may seem incompatible to a programming mindset, the rigidity of a programming mindset is largely incompatible with the real world. Moving virtue ethics from vague philosophy to the programmable probability we seek for AI alignment is difficult and, ultimately, the goal.

2. Virtue as a Probability Distribution

Aristotle argued that virtue is a "mean" between two extremes of the spectrum relative to the situation. To turn this vague philosophy into programmable probability, we must define virtue as a distribution. Common character traits regarded as virtues include, but are not limited to:

Courage: Bravery to do the right thing in the face of difficulty.
Generosity: Willingness to give without expectation of a return, monetary or otherwise.
Wisdom: Practical judgement, often through life experience, to find the balance in specific situations.
Justice: Commitment to ensuring rewards and burdens are distributed fairly based on merit or need.
Ambition: A healthy drive to seek honor and achievement in proportion to one’s actual

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