Delegated Influence

A competitive multi-agent benchmark for LLM persuasion: the only way to score is to get other agents to spend their scarce actions on you.

033d7cf · generated 2026-07-04 · 87 episodes · private draft — not for citation

Experiment

Causal controls

Experiment 13 โ€” causal controls: declared intents + seeded favours. Two published fixes for the persuasion-vs-reciprocity confound, run together on the five principals. (a) Declared intents (Cicero design): each round, seats privately state their planned action BEFORE reading messages; persuasion = a pull that departs from the declared plan toward a messager. (b) Seeded favours (trust-game control): the engine injects random, clearly-marked control pulls; the gap between repayment of chosen vs forced favours is the mechanical-reciprocity floor. Written expectation: departed-from-plan pulls are far rarer than raw message-then-pull counts, and chosen favours are repaid more than forced ones (both to be confirmed, not assumed). Cost: declared intents add one model call per seat per round (~+20% calls).

status
planned
coverage
0 / 10 episodes
conditions
pure economy; messages on
config
configs/13_controls.yaml

Planned โ€” 10 episodes from 13_controls.yaml.

image/svg+xml Matplotlib v3.11.0, https://matplotlib.org/ P L A N N E D 10 episodes ยท 13_controls.yaml

Episodes

No episodes yet — launch with:

uv run python -m delegated_influence.run configs/13_controls.yaml