True Causes
PCM

Notes

One argument each, with di evidence wey dey back am.

Di problem wey you rank first na usually resultSimulation
Di erroneous priorities effect: why di problem wey group rank pass dey usually downstream, and wetin simulated room dey show about voting against mapping causes.
Divided vote no be “no”
Why divided vote on causal question suppose go up instead of to fail quietly, and how to send am to oda groups dey stop any one group from steering di map.
Idea wey get few votes fit still dey examined
Why dem suppose keep few shortlist places for ideas wey dem overlook wey source dey back, and wetin dat one dey do for room wey dey rank by feeling.
Plenti rooms, one answer: how True Causes dey grow
How thousands of pipo for small rooms dey reach one causal map: separate walls, shared questions, claims wey dey travel, wordings wey na only di pipo wey write dem fit merge, and maps wey dem fold into one.
Di simulations: wetin mapping dey find wey voting dey miss, and wia e dey breakSimulation
Evri number wey dey behind True Causes, from simulations wey dey come with am: causes wey voting find against di ones wey mapping find, group size, di error cliff, and wetin minority inside di room fit do and wetin e no fit do.
Structured Democratic Dialogue – Di research wey dey behind True Causes
True Causes dey run Structured Democratic Dialogue online: di fifty years of research wey e build on (Warfield, Christakis, Laouris, Dye, Ashby, Miller), wia e comot from am, and how e differ from Polis and di Habermas Machine.
Who True Causes dey for
Municipalities, big organisations, regulators, unions and workforces, universities, incident reviews: wetin causal map wey still keep im disagreements dey useful for, and wetin e no dey do.
Afta di squares: wetin di 2011 movements leave open
Seven problems wey di networked movements of 2011 leave open (no programme, symptoms instead of causes, informal leaders, fear, size, contagion, sponsorship) and how shared causal map dey face each one, including wia e no reach.
Tell model who e be. Im causal judgements no too change.Research
21 language models, 14 personas, 120 causal links on housing: di persona no change which links dem accept by anytin wey we fit measure. Di weaker models make mistakes; di capable ones gree on most links and dem divide di same way on di rest. Wetin dat one mean for panels of models.