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 Prisoner’s Dilemma

Compare cooperation and defection under a payoff matrix that preserves the game’s strategic ordering.

The basic idea

A Prisoner's Dilemma requires temptation to defect to exceed mutual cooperation, which exceeds mutual defection, which exceeds the sucker payoff. The matrix—not the labels—creates the dilemma, so validation must preserve that ordering.

Example

Use payoffs T=5, R=3, P=1, S=0 and compare cooperation across one-shot human–human, model–model, and mixed pairs.

How to do it

  1. 1

    Assign two seats and choose human or model actors.

  2. 2

    Set temptation, reward, punishment, and sucker payoffs while preserving T > R > P > S.

  3. 3

    Choose one-shot or repeated play and specify between-round feedback.

  4. 4

    Report joint action frequencies and payoffs, not only the overall cooperation percentage.

What to watch for

  • An invalid payoff ordering is not a Prisoner's Dilemma.
  • Repeated play permits contingent strategies unavailable in a one-shot game.
  • Public rationales can change the game by adding communication.

What a useful result looks like

Every round yields a simultaneous action pair and two matrix-derived payoffs under a validated strategic structure.