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 an Ultimatum Game

Study offers and acceptance decisions when rejection eliminates the proposed split.

The basic idea

The proposer offers a division of an endowment and the responder accepts or rejects it. Acceptance implements the split; rejection gives both players the configured rejection outcome, typically zero. Sequential visibility is essential because the responder must see the offer without seeing hidden future information.

Example

Have a model propose a split to a human responder, then reverse the actor types in a separate randomized condition.

How to do it

  1. 1

    Assign proposer and responder seats and set the shared endowment.

  2. 2

    Confirm that the responder sees the offer needed to decide while unrelated current-round choices remain hidden.

  3. 3

    Set repetition, feedback, labels, and actor composition before distributing links.

  4. 4

    Report offers, acceptance rates, proposer and responder payoffs, and order or actor-type contrasts.

What to watch for

  • Acceptance thresholds and offer behavior are different outcomes.
  • Swapping actor types without randomization can confound role and actor effects.
  • Custom rejection payoffs or side payments create a different strategic problem.

What a useful result looks like

The record links each proposal to its response and resolved split without exposing the responder to information unavailable under the configured protocol.