synthetic-sciences/openscience/brainstorming

Brainstorming

Good research ideas are not found by asking for good ideas. They come from a clear map of what is known, a deliberate set of moves that produce many candidates, and a cold ranking that throws most of them away. Generation and selection are separate steps; mixing them kills the unusual candidates first.

Rules

1. Map before you generate. Ten minutes on what is established, what is contested and

what is assumed produces better candidates than an hour of free association. Use the user's notes and, when the field is unfamiliar, one research-lookup or a shallow literature-review round.

1. Generate wide, by named moves. Aim for fifteen to thirty candidates across the moves

below. Write each as one line: the change and the outcome it would show. Do not judge while generating.

1. Rank cold. Score each candidate on value if it works, probability it works,

cost to find out, and whether the user can do it with what they have. Prefer the candidate that is cheap to test and informative either way.

1. Commit to a shortlist of three, each with the first experiment that would test it

and the result that would kill it.

1. Say what is already done. A candidate that exists in the literature is a lead to

read, not an idea to pursue; mark it.

Moves

references/methods.md expands these into structured exercises (SCAMPER, morphological analysis, assumption reversal, analogical transfer) for a longer session.

Workflow

• [ ] Map: established / contested / assumed / open, in a short table with sources.
• [ ] Generate: fifteen to thirty one-line candidates tagged by move.
• [ ] Filter: remove duplicates, the already-published, the untestable.
• [ ] Rank: value × probability ÷ cost, plus feasibility with the user's resources.
• [ ] Shortlist three with first experiment and kill criterion; hand to hypotheses.

Selection criteria

• Value: who changes what they do if this is true? A result nobody would act on is low

value however elegant.

• Probability: is there a mechanism or prior evidence that makes it plausible, or is it

a hope?

• Cost: the cheapest experiment that would move the probability substantially; hours

versus months.

• Informativeness: does a negative result teach something? Prefer questions where

both answers matter.

• Fit: the user's data, compute, skills and deadline. A perfect idea for someone else

is not on the shortlist.

Deliverable

## Map
| Established | Contested | Assumed | Open |
 
## Candidates (by move)
1.[gap] ...   2. [transfer] ...   ...
 
## Shortlist
1.<idea>. First experiment: <...>. Killed if: <...>. Value / probability / cost: <...>.
2....
3....
 
## Already done (read these)
-<candidate> → <paper, link>

Before you hand it over

• The map cites its sources or says it comes from the user's notes.
• Candidates span at least four moves; none is a restatement of another.
• Each shortlisted idea has a concrete first experiment and a kill criterion.