Decision-makers often rely on diverse sources of evidence—such as models, expert opinions, and heuristic reasoning—to make informed choices. However, combining these different types of evidence is challenging due to varying reliability, susceptibility to bias, and lack of transparency in how conclusions are reached. This can lead to poorly calibrated decisions, wasted resources, and missed opportunities for robust insights. A tool that systematically evaluates and aggregates evidence could help address these issues.

How the Idea Works

The proposed tool would function as an interactive platform where users input different types of evidence—such as expert opinions, data models, or heuristic rules—and adjust parameters like reliability, counterintuitiveness, or discoverability. The tool would then simulate how these sources interact under different conditions, allowing users to explore "what-if" scenarios (e.g., "What if expert biases were 20% higher?"). Outputs could include confidence scores, bias susceptibility estimates, and accuracy metrics, visualized in an intuitive dashboard.

Key features might include:

• An epistemic playground where users tweak parameters to see how conclusions change.
• A comparison mode to contrast different aggregation methods (e.g., Bayesian vs. simple averaging).
• Interactive visualizations showing how evidence reliability shifts under different assumptions.

Potential Applications and Stakeholders

This tool could benefit a wide range of users:

• Researchers in interdisciplinary fields (e.g., climate science, economics) where conflicting evidence types are common.
• Policymakers who need to weigh expert opinions against model predictions.
• Business strategists evaluating forecasts that blend qualitative and quantitative data.

Potential revenue streams could include a freemium model (basic features free, advanced simulations paywalled), enterprise licensing for governments or corporations, or grant funding due to its societal value.

Execution and Differentiation

One way to execute this idea would be to start with a minimal viable product (MVP) focused on aggregating expert testimony, allowing users to adjust bias and discoverability sliders. Later phases could add model integration, heuristic reasoning modules, and collaboration tools.

Unlike existing tools—such as meta-analysis software or expert prediction aggregators—this idea stands out by offering real-time "what-if" testing, accommodating both qualitative and quantitative evidence, and letting users define their own parameters rather than relying on fixed frameworks.

By blending simulation, customization, and cross-evidence analysis, this tool could help decision-makers navigate the complexities of weighing heterogeneous evidence more effectively.