DefaultConfig now describes a more realistic world: a moderately novel fake
(novelty 0.3), some ambient harm awareness (0.2), and a strong but imperfect
education program (programEffect 0.8) so educated students mostly, not always,
refuse. The headline shifts from 99/70/7 (a perfect program) to 100/83/21 out
of 120 (83/69/18 percent): no program >> random >> most-connected still holds,
targeting still wins by ~4x, but the program is no longer a perfect wall.
Golden values re-pinned in the engine and API tests; the preset base matches.
The forward-chance formula test now neutralises its baseline so it pins the
formula, not the tuned defaults.
Config is the single source of truth for parameters (TS types will be
generated from these structs in milestone 2); all randomness flows from
its three seeds, so identical configs give identical results. Golden
test pins the default world: none=99/120 (82%), random=70/120 (58%),
most-connected=7/120 (6%). Same story as the prototype's 85/64/8 with
different dice; the ordering and the collapse are asserted explicitly,
exact Python numbers are out of scope by design.
'go run ./cmd/spreadlab' prints the three-scenario comparison.
This completes milestone 1 (engine ported, parameterised, tested).
