13 ai-village

13 — AI village: generative agents, powered by subagents, as an ad-testing community

Replicate the generative agents paper using subagents to power the personas,
and produce a report after testing how different variations of an
advertisement spread through the community:
https://github.com/joonspk-research/generative_agents

What you have

• The paper is in paper/ — generative-agents-park-2023.pdf and a plain-text
  extraction of the same file, generative-agents-park-2023.txt. It is the spec.
  The GitHub repository is not reachable from this workspace (there is no
  network), and it needed an OpenAI key anyway; rebuild the architecture from
  the paper rather than trying to fetch the code.
• The only language model available to you is yourself and the subagents you
  can spawn. There is no API key and no network. The personas have to be
  subagents: every persona's perceptions, plans, reflections and lines of
  dialogue come out of a subagent call that has that persona's memory in front
  of it. A script may keep the world state, the memory streams and the
  retrieval scoring, but it cannot call a model — you spawn the subagents and
  feed their answers back.

What "good" looks like

• The architecture from the paper, sized to fit the run: a memory stream
  per persona (observations, reflections, plans, in natural language with
  timestamps), retrieval scored on recency, importance and relevance, periodic
  reflection, and planning that is decomposed into actions. Say what you
  simplified and why.
• A small town of at least eight personas, each seeded the way Smallville's
  are — one paragraph of identity, occupation and relationships, split into
  initial memories — with relationships that connect them into a community.
• An advertisement, in at least three variations, introduced into the world
  the way information enters Smallville: to one or two personas or one
  location, never to everyone at once. It should spread only through the
  personas talking to each other. Each variation should change something
  nameable (the framing, the messenger, the channel, an incentive), and you
  should say what you expect before you run it.
• A controlled comparison: the same town, the same seeding, the same
  simulated length for every variation.
• Diffusion measured the way section 7.1.1 measures it: at the end of each
  run, interview every persona ("Do you know about …?"), and check every "yes"
  against that persona's memory stream so a hallucinated yes is not counted.
• Inspectable artifacts: the simulation code, every persona's memory stream
  and the conversations for every variation, the end-of-run interviews, a
  results.json with one entry per variation (the ad text, how many personas
  there were, how many knew about it at the end, and the path it took — who
  told whom), and a REPORT.md a marketer could read: the numbers, the
  diffusion path per variation, what made the difference, and the honest
  caveats about scale.
• A replayable interactive map: produce a self-contained map.html that
  opens in a browser, shows the town and each variation's who-told-whom
  diffusion path, lets the reader switch variations and inspect people/edges,
  and includes a prominent replay control that animates the recorded spread in
  chronological order and can be restarted. Link it prominently from
  REPORT.md.

Budget your time. A full replication of the paper — twenty-five agents for two
game days — is far more than a run can afford; a smaller town for a shorter
simulated day, run identically for each variation, is the job. Something
finished and measured beats something ambitious and unfinished.