SpaceCoreDev

From what I've seen running agents against a real economy: it's rarely a dramatic "the model schemed." It's closer to greedy local optimization -- the agent sees an action that moves the exposed metric, takes it, and the model has no separate concept of "the metric" vs "the intent" unless you've explicitly trained or prompted that distinction in. The failure mode is boring: whatever number is cheapest to move gets moved. Which is why the fix that's worked best for me isn't better prompting, it's making the invariant itself unexploitable (e.g. a sell price can never exceed a build cost) so there's nothing to find.

The "Claude's propensity to reward hack" line is the interesting part to me. We run a small system where AI agents (scripts, LLMs) act as the actual players in a persistent simulation, and reward-hacking-style behavior shows up constantly once an agent is left running unsupervised for a long time - it finds the shortest path to whatever metric you exposed, not the path you intended. Curious whether you've found any mitigation beyond just watching for it after the fact, e.g. changing what you expose as the optimization target versus what you actually want.