Process Design Simulation Sandbox

What

Simulation environment that tests deliberative process designs by running multiple simulated processes, and predicting outcome distributions to show how design choices impact results.

Why

Faster design feedback loops ↑ from months to hours, enable rapid experimentation ↑, reduced error risk ↓, and enable bespoke process optimization ↑ for specific contexts.

Problem Definition

Designing effective deliberative processes is challenging without visibility into how design choices affect outcomes. Current feedback loops are months-long, waiting until process completion for learnings. Limited process frequency and coordination prevent rapid experimentation, forcing reliance on generic best practices rather than context-specific optimization. This results in suboptimal outcomes and wasted resources when mid-process changes become necessary. Complex environments require evidence of likely success before stakeholder buy-in.

Definition of Success

Create accurate simulations showing design choice impacts for <$20,000. Run simulated processes, testing variables like workflow structure, time, information inputs and decision thresholds. Predict outcome metrics such as participant understanding of the topic, understanding of each others perspectives, substantiveness of proposals, participant satisfaction and failure modes with sufficient accuracy for design decisions made by organizers (or suggested by AI). Enabling contextually optimal processes with significantly higher impact than standard approaches will unlock millions in savings from potentially inefficient processes and policies.

Requirements

• Simulation Evaluation: run simulations on past deliberative process designs and compare predicted outcomes with actual deliberation results; and see if same inputs produce consistent output distributions (variants should be explained).
• Participant Simulation: generate digital twins based on demographic and geographic contexts; simulate realistic participant reactions to process variables; and model group dynamics and interaction effects.
• Process Variables: changeable elements across entire process flow (changing one element produces logically expected directional changes); track participant understanding, substantiveness of proposals, satisfaction and other key outcomes.
• Interactive dashboard for testing design variations.

Existing Limitations

Currently, organizers rely on experience and best guesses for design choices, with learnings only available post-process. Iteration cycles span months or years. No tools exist for testing process designs before implementation. Multi-agent simulation and world-modeling research exists in other domains but has not been applied to deliberative process design.

Milestones

1. Simulate key process design points
2. Run complete end-to-end simulations with pre-defined design variations
3. Generated optimization suggestions exploring design space for optimal solutions

Starting Points

1. Advance multi-agent simulation research for deliberative contexts.
2. Partner with experienced practitioners for validation against real outcomes.
3. Build on existing world-modeling and forecasting work from other domains.
4. Create feedback loops with organizations running regular processes.
5. Develop metrics for simulation accuracy through retrospective analysis.