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 Trust or Investment Game

Measure investment and return behavior after a transferred amount is multiplied.

The basic idea

The first player sends part of an endowment, the mechanism multiplies that transfer, and the second player decides how much to return. The first action reflects investment under strategic uncertainty; the second reflects reciprocity and self-interest under the configured incentives.

Example

Player 1 sends 20 of 100 tokens, the game triples it to 60, and Player 2 chooses a return after seeing the available multiplied amount.

How to do it

  1. 1

    Assign investor and trustee seats and specify endowment and multiplier.

  2. 2

    Verify the payoff preview for zero, partial, and full transfers and returns.

  3. 3

    Decide whether resolved histories or balances are visible between repeated rounds.

  4. 4

    Analyze sent proportions, returned proportions, payoffs, and learning across rounds separately.

What to watch for

  • Raw return amounts should be interpreted relative to the amount available.
  • Repeated feedback can produce reputation and learning effects.
  • Do not interpret model behavior as proof that a model experiences trust.

What a useful result looks like

Each round preserves the amount sent, multiplied opportunity, amount returned, and final payoff for both actors.