You are right about mode-collapse -- and that observation is exactly what makes this interesting.

In my other comment here, I described The Sims' zodiac from 1997: Will Wright computed signs from personality via Euclidean distance to archetypal vectors, displayed them cosmetically, and wrote zero behavioral code. The zodiac affected nothing. Yet testers reported bugs: "The zodiac influence is too strong! Tune it down!"

Your "mode-collapse with stochastic noise" is the same phenomenon measured from the other direction. In The Sims: zero computed difference, perceived personality. In this LLM experiment: minimal computed difference, perceived personality. Same gap.

Will called it the Simulator Effect: players imagine more than you simulate. I would argue mode-collapse IS the Simulator Effect measured from the output side.

But here is where it becomes actionable: one voice is the wrong number of voices.

ChatGPT gives you the statistical center -- mode-collapse to the bland mean. The single answer that offends no one and inspires no one. You can not fix this with better prompting because it is the inevitable result of single-agent inference.

Timothy Leary built MIND MIRROR in 1985 -- psychology software visualizing personality as a circumplex, based on his 1950 PhD dissertation on the Interpersonal Circumplex. The Sims inherited this (neat, outgoing, active, playful, nice). But a personality profile is not an answer. It is a lens.

The wild part: in 1970, Leary took his own test during prison intake, gamed it to get minimum security classification (outdoor work detail), and escaped by climbing a telephone wire over the fence. The system's own tools became instruments of liberation.

https://github.com/SimHacker/moollm/tree/main/skills/mind-mi...

MOOLLM's response: simulate an adversarial committee within the same call. Multiple personas with opposing propensities -- a paranoid realist, an idealist, an evidence prosecutor -- debating via Robert's Rules. Stories that survive cross-examination are more robust than the statistical center.

https://github.com/SimHacker/moollm/tree/main/skills/adversa...

I wrote this up with links into the project:

https://github.com/SimHacker/moollm/blob/main/designs/sims-a...

The bigger project is MOOLLM -- treating the LLM as eval() for a microworld OS. K-lines, prototype-based instantiation, many-voiced deliberation. The question I keep wrestling with: mode-collapse as limitation vs feature. The Sims exploited it. MOOLLM routes around it.

Would value your take on the information-theoretic framing -- particularly whether multi-agent simulation actually increases effective entropy or just redistributes it.

https://github.com/SimHacker/moollm

The MOOLLM Eval Incarnate Framework: Skills are programs. The LLM is eval(). Empathy is the interface. Code. Graphics. Data. One interpreter. Many languages. The Axis of Eval.

https://github.com/SimHacker/moollm/blob/main/designs/MOOLLM...