Open research threads.

When investors receive the same information, why do entire markets still move together? This research studies induced herding, how signals, incentives, and uncertainty influence collective financial behavior, and explores whether AI can distinguish rational consensus from irrational imitation.

Every time a planner overrides an algorithm, valuable information is created. This thread explores how operational overrides can become high-quality training data for future AI systems instead of disappearing once today's decision is made.

The best planners rarely follow a dashboard blindly. They combine mathematics, intuition, experience, and context. This research explores whether those decision patterns can be modeled, supported, and eventually reproduced without removing the human from the process.

Organizations generate enormous amounts of qualitative knowledge: meeting notes, emails, interviews, audit findings, and conversations, which rarely influence analytical models. This research investigates whether large language models can transform qualitative evidence into measurable variables while preserving nuance and context.

Most organizations measure whether AI improves productivity. Few measure whether people actually make better decisions because of it. This thread studies how decision-support systems should be evaluated when the output is not speed, but judgment quality.