True Causes
EN

Notes

One argument each, with the evidence behind it.

The problem you rank first is usually a consequenceSimulation
The erroneous priorities effect: why the problem a group ranks highest is usually downstream, and what a simulated room shows about voting versus mapping causes.
A split vote is not a "no"
Why a split vote on a causal question should escalate rather than fail quietly, and how referring it to other groups keeps any one group from steering the map.
An idea with few votes can still be examined
Why a few shortlist places should be reserved for overlooked ideas backed by a source, and what that does for a room that ranks by feeling.
Many rooms, one answer: how True Causes scales
How thousands of people in small rooms reach one causal map: separate walls, shared questions, claims that travel, wordings merged only by their authors, and maps folded into one.
The simulations: what mapping finds that voting misses, and where it breaks downSimulation
Every number behind True Causes, from simulations that ship with it: causes found by voting versus mapping, group size, the error cliff, and what a minority inside the room can and cannot do.
Structured Democratic Dialogue – The research behind True Causes
True Causes runs Structured Democratic Dialogue online: the fifty years of research it builds on (Warfield, Christakis, Laouris, Dye, Ashby, Miller), where it departs from it, and how it differs from Polis and the Habermas Machine.
Who True Causes is for
Municipalities, large organisations, regulators, unions and workforces, universities, incident reviews: what a causal map with its disagreements kept is useful for, and what it does not do.
After the squares: what the 2011 movements left open
Seven problems the networked movements of 2011 left open (no program, symptoms over causes, informal leaders, fear, scale, contagion, sponsorship) and how a shared causal map faces each, including where it falls short.
Tell a model who it is. Its causal judgements barely change.Research
21 language models, 14 personas, 120 causal links on housing: the persona changed which links were accepted by nothing measurable. Weaker models erred; capable ones agreed on most links and split consistently on the rest. What that means for panels of models.