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 Public Goods Game

Study contributions to a shared pool among two to eight human, model, or mixed participants.

The basic idea

Each player contributes from an endowment; pooled contributions are multiplied and redistributed across the group. The multiplier and group size determine whether contributing benefits the group while remaining individually costly.

Example

Four participants each receive 100 tokens, independently contribute to a common pool, and share a doubled pool equally after all choices are locked.

How to do it

  1. 1

    Choose two to eight seats and assign actor types and model identities.

  2. 2

    Set endowment, public-goods multiplier, rounds, and feedback visibility.

  3. 3

    Confirm that current-round contributions remain hidden until resolution.

  4. 4

    Analyze contribution levels, group totals, individual payoffs, and adaptation across rounds with group clustering.

What to watch for

  • Multiplier and group size jointly determine incentives.
  • Players within one group are not statistically independent.
  • Showing rationales or prior contributions can create communication and reputation effects.

What a useful result looks like

The ledger records each contribution, the multiplied group pool, and every participant's resolved payoff for each round.