AI Deliberator
Guides
How to use AI DeliberatorView all 58 guides

Getting started

Evaluate and export a Council runHow to ask a better decision questionHow to choose models for an AI councilRun your first AI councilUse the Consideration MapContinue a decision with follow-up questionsEstimate and control LLM costs

Everyday decisions

Use an AI council for a career changeEvaluate a leadership role or promotionThink through a relationship decisionDecide whether to move to a new cityCompare degrees, certificates, and education optionsAnalyze a major purchaseUse live evidence mode

Work and teams

Use deliberation for a business strategy decisionSupport a hiring decision responsiblyPrioritize a product roadmapRun a decision pre-mortemMap a stakeholder disagreementAnalyze a policy proposal

Research design

Duplicate and pilot an experimentDesign an LLM deliberation experimentDefine outcomes and dependent variablesConfigure experimental conditions and independent variablesCompare deliberation with homogeneous reconsiderationBuild an independent model ensembleUse randomization, replications, and compute matching

Participant studies

Build a participant interaction studyDesign pre- and post-interaction questionnairesChoose Likert, slider, and questionnaire formatsAdd attention, manipulation, and exposure checksAnalyze and export participant-study resultsBuild a composable experiment timelineMeasure human–AI advice relianceAdd process tracing and cognitive tasksRun a human deliberation conditionBuild a human–AI hybrid deliberation chainRun a text, image, or video stimulus experimentRun a learning and tutoring experimentPlan a powered participant studyMonitor recruitment, cost, and attrition

Behavioral games

Design a behavioral economics gameRun a Dictator GameRun an Ultimatum GameRun a Trust or Investment GameRun a Prisoner’s DilemmaRun a Public Goods GameRun a Stag Hunt GameConfigure game payoffs, incentives, and feedbackManage private game links, consent, and completion

Benchmarks and results

Select benchmark questions and subsetsRun MMLU-Pro in AI DeliberatorRun PolicyBench for public-policy reasoningRun CogToM for social cognitionCreate and read 95% confidence-interval plotsCollaborate and preserve a reproducible experiment recordAnalyze sentiment and quantitative text metricsCompare two Council runs
Workspace

Participant studies

Build a composable experiment timeline

Arrange consent, baseline measurement, stimuli, assigned interaction, process tasks, final outcomes, and debriefing in a clear participant journey.

The basic idea

A timeline is part of the treatment protocol, not merely page navigation. Stage order can prime participants, expose information, and determine whether a measure is genuinely pre- or post-treatment, so AI Deliberator preserves scientifically necessary boundaries while allowing meaningful ordering choices within them.

Example

Show a policy vignette, collect an initial judgment, assign a solo-AI or council discussion, collect a revised judgment, run five reaction-time trials, and finish with the post-treatment questionnaire.

How to do it

  1. 1

    Enable only the modules the hypothesis requires.

  2. 2

    Use the timeline arrows to order eligible pre-interaction and post-interaction stages.

  3. 3

    Confirm that consent, assignment, the final questionnaire, and completion remain in their protected positions.

  4. 4

    Preview and pilot the complete journey before opening recruitment.

What to watch for

  • Moving a measure across treatment changes its interpretation.
  • Long study journeys increase attrition and satisficing.
  • A future external module such as ConjointSurvey.com should return a signed completion state rather than exposing participant bearer links.

What a useful result looks like

The saved study specification contains one readable, reproducible participant journey with ordering constraints that prevent common treatment-timing mistakes.