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

Behavioral games

Run a Dictator Game

Study unilateral allocation choices between human or model players with explicit endowments and recipients.

The basic idea

In the Dictator Game, one allocator chooses how much of an endowment to transfer to another player, who has no veto. It is commonly used to study allocation behavior, but a transfer alone does not identify a single motive such as altruism or fairness.

Example

Give Player 1 an endowment of 100 tokens and record how much they send to Player 2 across model–model, human–model, and human–human sessions.

How to do it

  1. 1

    Assign the allocator to the first seat and the recipient to the second.

  2. 2

    Set the endowment, payoff label, repetitions, actor types, and feedback policy.

  3. 3

    Keep instructions equivalent across actor-composition conditions unless wording is an experimental factor.

  4. 4

    Analyze transfers with the session configuration, round number, actor type, and repeated-measures structure.

What to watch for

  • Order and labels can cue generosity.
  • One-shot and repeated games answer different questions.
  • A model's token transfer is simulated behavior, not evidence of stable human-like preference.

What a useful result looks like

Each round records the allocation and resulting payoffs for both seats, supporting direct comparisons across prespecified actor compositions.