Source code is at github: email@example.com:worden-lee/consensus-simulation.git and firstname.lastname@example.org:worden-lee/adap-dyn.git.
I did a very preliminary "experiment": a "consensus process" with one person is able to find a local fitness peak. Actually, this is an important baseline: I'll probably want to know how often it finds an acceptable solution, how good it is, etc.
What are some initial experiments to do?
- people working independently vs.
- people each generating a proposal and then voting vs.
- people searching together in some simple way:
Compare mean fitness of outcome, how satisfied each person is.
- replicate Page's result?
- generate something similar but different to Page's - usefulness of people with somewhat different valuations, as opposed to the same valuation but different search heuristics
Revision 1e0b42d of consensus-simulation does the following:
- initial proposal is 0
- each individual does steepest-ascent search to a local peak on their landscape, offers that as a next proposal.
- each individual evaluates each next proposal, blocks it if they consider it worse than the existing proposal
- if any new proposals have no blocks, the first of them is accepted
- until no more progress is made. note this is a deterministic process.
Preliminary runs suggest that for completely uncorrelated landscapes (with themselves and with each other) larger group size implies no progress, due to too many blocks.
Higher-dimensional landscapes may support more progress with somewhat larger groups than smaller landscapes.
This model protocol is somewhat unforgiving: one person's best offer may be crappy for others, while a nearby point nearly as good may be much better received, but we don't give it a chance. We might have much better success if we put those intermediates on the table as well.