---
title: Risk sensitive
family: heuristic-search
summary: Ranks columns partly by their worst imagined futures. Every tested risk setting trails the baseline.
status: rejected
evidence: task-record only
reads: public
---

Judge a column by how bad its worst futures are, not only by how good its
average future is because one collapse ends the game and there is no next
game to average it against.

<EvidenceLabel status="rejected" evidence="task-record only" reads="public" />

## The intuition

An ordinary [expectimax](/learn/glossary) search values a column by the
average over everything that could happen next. Averages are the right thing
to maximise when you get many independent tries. In Drop7 you get one: a move
whose average is excellent but which loses the game one time in eight is not
an excellent move, because the losses are permanent and the score stops.

The fix borrowed from finance is **conditional value at risk**: rank a column
by the average of its *worst* quarter of outcomes instead of the average of
all of them, or by a blend of the two. Set the risk weight to 0 and you have
the ordinary search back; set it to 1 and the policy only cares about
disasters; anything in between trades mean for safety.

## How it works, step by step

1. **Read the public position** — board, next disc, rise clock.
2. **For each legal column, build a fixed set of scenarios** — sampled
   immediate chance outcomes, the same set for every column so that the
   comparison is paired.
3. **Continue each scenario** with an ordinary risk-neutral search of a couple
   of plies, using stratified samples at its chance nodes.
4. **Collapse each column's scenario outcomes** into a mean, a lower quantile,
   and a conditional-value-at-risk over the worst tail fraction; select on a
   blend controlled by one number.
5. **Play the winning column.**

The planner takes a solver-local seed only. It refuses the game seed, and no
future disc or reveal tape can reach action selection.

## What happened

Every setting of the risk dial was retired. The best mild-risk configuration —
the one that only leans slightly toward caution — still finished behind the
plain phase-conditioned baseline it was tested against, and the strongly
risk-averse settings were worse.

The same idea was tested separately, and much more thoroughly, inside the
reference search, where it is recorded properly. There it did not merely fail
to help: it made the policy worse on every measure at once, including the
lower tail it was designed to protect.

<TechnicalDetails title="The technical record">

**This lab.** The [experiment index](/docs/research/experiment-index) records
it as **rejected, task-record only**: *"the best mild-risk pilot trailed the
phase baseline."* Task-record only means the pilot is reported in a research
conversation and was never promoted into the
[experiment history](/docs/research/history); **no score, move count, or
per-game outcome for it is retained in this repository.**

Repository-verified from the source. `main.ts` sweeps risk weights
`0, 0.5, 1` with a 0.25 tail fraction, seven root scenarios, a two-ply
risk-neutral continuation and three chance samples per continuation node, over
4 pilot and 16 confirmation games at a 500-move cap, with 500,000 work units
for the risk planner against 1,000,000 for the baseline and a `REQUIRED_MEAN`
constant of 300,000. Pilot games come from `0x3d70…` and the probe range is
`0x4d70…`; `0x5d70…` is explicitly forbidden. The comparator is the
[phase-horizon](/approaches/heuristic-search/phase-horizon) evaluator inside a
sparse search. `src/core/typescript/risk-sensitive-planner.ts` documents the
boundary directly: *"Solver-local seed; no game seed or future chance tape is
accepted."*

**The properly recorded sibling experiment.** `fair-root-risk.cpp` in the
[fair expectimax](/approaches/fair-expectimax) family applied the same
mechanism to the reference search with a preregistered root utility of
`0.75 × mean + 0.25 × CVaR25` over seven scenarios per action. It is
**rejected, ledger-recorded**. On its eight-game screen the unchanged
depth-3 fair search averaged 176,819.75 points and 114.125 moves; the risk
version averaged 87,112 points and 61.5 moves: a paired difference of
−89,707.75 points and −52.625 moves, losing seven of eight games on both
measures. Clear and reveal throughput fell from 2.043/1.141 to 1.730/0.945 per
move and mean maximum chain fell from 7.875 to 5.125. It switched away from
the fair recommendation on 28.25% of decisions. Confirmation seeds were not
read and nothing was retuned.

That screen is a **historical 7,000-point Sequence-scored** run: the level
award was 7,000 rather than the 17,000 of the Hardcore mode studied here, so
its absolute scores are not evidence about the million-point target. The
comparison between the two policies on the same eight games, and the
throughput and chain measurements, remain valid
([mode-specific scoring correction](/docs/research/history)). Eight games is a
small screen.

</TechnicalDetails>

## What this taught us, and what is still open

- **Changing the objective does not repair the action model.** The strategy
  catalog states the conclusion for the whole conservative-override family:
  confidence gates and tail objectives are a safety mechanism, not a source of
  new strategic information, and they become useful only after the underlying
  challenger can rank legal siblings on disjoint data.
- **Mean-protecting and tail-protecting came apart in the wrong direction.**
  The recorded root-CVaR screen lost mean score, survival *and* flow. A
  risk-averse policy that dies sooner is not being cautious about the right
  thing, with a short horizon, "avoid the worst sampled outcome" is a
  statement about sampling noise as much as about danger.
- **What is still open.** Distributional gates are still in the recommended
  decision standard for a future candidate, but as a *check* applied after a
  challenger already ranks moves well, not as the thing that makes it rank
  moves well.