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

Configure game payoffs, incentives, and feedback

Set experimental units, dollar displays, payoff parameters, repeated rounds, and information visibility without changing the intended mechanism accidentally.

The basic idea

Payoffs and feedback define the decision environment. Experimental tokens, a displayed conversion rate, and real compensation are distinct. Between-round visibility can show all resolved actions and balances, only a player's own balance, or no feedback until completion.

Example

Use 100 experimental points per round, display a researcher-defined conversion for comprehension, show only each player's cumulative balance, and handle actual incentive payment outside the app under the study protocol.

How to do it

  1. 1

    Choose a neutral payoff-unit label and set the optional display conversion separately.

  2. 2

    Review game-specific endowments, multipliers, and matrices using the payoff preview.

  3. 3

    Select full, own-balance-only, or no between-round feedback based on the hypothesis.

  4. 4

    State how experimental payoffs translate into actual compensation, including caps, bonuses, and payment timing, in participant materials.

What to watch for

  • AI Deliberator records payoffs but does not disburse participant money.
  • A displayed dollar equivalent is not a payment promise unless the consent and compensation process makes it one.
  • Feedback policies must be compared as experimental treatments if they differ across arms.

What a useful result looks like

Participants receive understandable rules, the server enforces the selected information boundary, and the export preserves both experimental payoffs and their configured display conversion.