Add a game simulator for generating synthetic games

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 #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.

## Scope sketch

- Rules engine: territory placement, reinforcement/bonus calculation, attack resolution, fortify,
card sets, elimination, turn order
- 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.

Start with issue #1 to understand the log schema, map/graph representation, and position-strength metrics this simulator must consume. Define completion against the listed validation goals: games terminate, agent strength ordering is stable, distributions are sane, and simulated logs work with the existing analysis stack.

Written by the indexing model from the issue text.