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Deferred from the initial scoping of #1, which is analysis-only. Filing so we don't lose it.
Idea
Implement a D12-rules game engine that can play games start to finish with configurable agents,
producing logs in the same schema the scraper emits. Since the rules are the same whether a human
or a bot plays, board-dynamics findings from simulated games transfer to real ones.
Why it's worth doing
Null models. Most of the interesting claims in basic approach #1 are comparative: is this map unbalanced,
is this position-strength trajectory unusual, do these games cluster more than chance? Each of
those needs a generative null to compare against, and a simulator is the principled way to get
one.
Counterfactuals. Replay a real game from turn k with a different move and measure the
swing in position strength. Turns descriptive metrics into causal ones.
Metric validation. A position-strength metric should predict wins in simulation. If it
doesn't, it's wrong, and we'd rather learn that from cheap simulated games than from the paper.
Coverage. Real data will be thin for rare configurations (unusual maps, player counts,
drop-out patterns). Simulation fills gaps.
Agent interface with a few baselines (random-legal, greedy-expansion, bonus-completion,
border-minimizing), plus a hook for learned agents
Emits logs conforming to the same schema as scraped games, so the whole analysis stack runs on
simulated and real games interchangeably
Validation: agent strength ordering is stable, games terminate, distributions are sane
Explicitly out of scope here
Negotiation between agents. LLM agents talking to each other is a different project answering a
different question — see the companion issue on external conversation corpora. Board play only.
Depends on
#1 — needs the log schema, map/graph representation, and position-strength metrics to exist first,
since the whole point is to plug into them.
Deferred from the initial scoping of #1, which is analysis-only. Filing so we don't lose it.
Idea
Implement a D12-rules game engine that can play games start to finish with configurable agents,
producing logs in the same schema the scraper emits. Since the rules are the same whether a human
or a bot plays, board-dynamics findings from simulated games transfer to real ones.
Why it's worth doing
is this position-strength trajectory unusual, do these games cluster more than chance? Each of
those needs a generative null to compare against, and a simulator is the principled way to get
one.
swing in position strength. Turns descriptive metrics into causal ones.
doesn't, it's wrong, and we'd rather learn that from cheap simulated games than from the paper.
drop-out patterns). Simulation fills gaps.
Scope sketch
card sets, elimination, turn order
border-minimizing), plus a hook for learned agents
simulated and real games interchangeably
Explicitly out of scope here
Negotiation between agents. LLM agents talking to each other is a different project answering a
different question — see the companion issue on external conversation corpora. Board play only.
Depends on
#1 — needs the log schema, map/graph representation, and position-strength metrics to exist first,
since the whole point is to plug into them.