Understanding human preferences is a fundamental challenge across fields like psychology, AI, and economics. Traditional methods—like surveys or behavioral tracking—often miss the reasoning behind choices or are skewed by biases. Inverse planning, a cognitive science framework, could offer a solution by treating actions as outcomes of goal-driven decision-making and working backward to uncover the preferences that likely caused them.

The Power of Inverse Planning

One way to approach preference inference is by modeling how a rational agent would act to achieve certain goals, then reversing the process to deduce those goals from real behavior. For instance, if someone frequently picks scenic detours over shorter routes, inverse planning might infer they prioritize enjoyment over speed. This method could reveal preferences that direct questioning or surface-level data analysis would miss, especially in ambiguous scenarios.

Potential applications include:

• AI systems: Chatbots or recommenders that adapt to nuanced user intentions.
• Market research: Extracting consumer preferences from shopping patterns without intrusive surveys.
• Policy design: Gauging public priorities from behavioral data like transportation or energy usage.

Building and Validating the Tool

A minimal version could start with a computational model tested on small datasets (e.g., lab experiments where ground-truth preferences are known). Early tools might focus on clear-cut cases—like inferring travel preferences from GPS logs—before tackling noisier real-world data. The model could then be packaged as an open-source library or API, with documentation and case studies to help researchers and developers integrate it into their work.

Key advantages over existing methods:

• Interpretability: Unlike black-box machine learning, it traces actions to specific inferred goals.
• Flexibility: Adaptable to domains where traditional surveys or correlational analysis fall short.

Challenges and Next Steps

Inverse planning can be computationally heavy for complex preferences, so early efforts might focus on domains where efficiency is less critical (e.g., academic research). Combining it with lightweight surveys could help fill data gaps. To test real-world viability, a pilot with behavioral scientists or tech companies could compare its inferences against traditional methods, refining the model based on feedback.

While monetization isn’t the immediate focus, possible paths include licensing for commercial use or offering consulting to tailor the model for specific industries like healthcare or retail.