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

Design a behavioral economics game

Configure established strategic-interaction games for human, model, and mixed groups with auditable payoffs.

The basic idea

A behavioral game converts choices into outcomes under explicit rules. The scientific object is the configured mechanism, actor composition, information structure, repetition, and incentive system—not merely the familiar name of the game.

Example

Create a repeated Trust Game with one human and one model, private between-round feedback, a three-times investment multiplier, and a clearly labeled experimental-token payoff.

How to do it

  1. 1

    Choose the game that represents the behavior or strategic tension you want to study.

  2. 2

    Set seat types, model identities, roles, rounds, endowments, multipliers, payoff matrix, and feedback policy.

  3. 3

    Read the plain-language payoff preview and check every possible action against the intended mechanism.

  4. 4

    Launch a small test, use each human seat link, and verify moves, ledger totals, model costs, and exports.

What to watch for

  • Changing standard payoffs can change the game's strategic structure.
  • Displayed experimental currency is not automatically real participant compensation.
  • Repeated play, identity visibility, and feedback can materially change behavior.

What a useful result looks like

The launched session has an immutable configuration, assigned seats, private human links, recorded actions, round-level ledger entries, and a reproducible export.