NOTES · SOURCES
True Causes is an online implementation of Structured Democratic Dialogue, the method that grew out of John Warfield’s Interactive Management and was developed by Alexander Christakis and Yiannis Laouris. It is not new: fifty years of peer-reviewed work, hundreds of documented dialogues. Laouris and Christakis called it harnessing collective wisdom, and named scale as its hardest limit; lifting that limit is what True Causes is for. A few deliberate departures. One of them: the method keeps every idea attached to its author's name throughout, and its practitioners argue anonymity weakens accountability. Here nobody is named inside a dialogue, because in a workplace or a divided community the name is what stops people writing the true card. We think the trade is right; it is untested. See the research. The mechanism science around it, the rules tested against bloc voting, and the implementation are what we add.
The wider literature: Structured Democratic Dialogue · Interpretive Structural Modelling · Interactive Management. Our own simulations and their results ship with the software and are described in the notes bundled with it.
What else is out there. Polis finds where a crowd already agrees and shows you the clusters. Remesh and tools like it scale that agreement-finding to thousands of people. The Habermas Machine writes a statement most of a group can sign. All three are good at one question: what do we agree on? This asks a different one — what causes what? — settles it pair by pair, and keeps the disagreements it cannot settle instead of smoothing them over. The nearest tool is the method's own: IdeaPrism, from Laouris's team, runs clustering and mapping asynchronously and sends disagreements back for justification.
Not the wisdom of crowds. Galton's ox and every prediction market since work by averaging independent guesses: the errors cancel because nobody talked. That is the right tool for a number — a weight, a date, a price. It has no answer for does A cause B, where a hundred uninformed guesses average to an uninformed guess. This works the other way round: people are made to talk first, so that what they judge is informed; and their judgements are not averaged but counted against a bar they must clear together. Independence is what a crowd needs; shared meaning is what a group needs.
In other fields' words. To a computer scientist this is human-based computation: people answer each pair, and an algorithm, interpretive structural modelling, assembles the map. To an economist it works on dispersed knowledge, in Hayek's sense: what a problem needs is spread across many people, and no one holds it all. On a public question it is civic intelligence. And it is collective problem solving, not collective decision-making: it informs a decision and leaves the decision to the people who must answer for it.
Run a Structured Democratic Dialogue online, on your own question.