Behavioral economics and empirical public policy.

McGeown et al. (2013) wrote a think-piece, not an experiment.

They map how demand curves, price elasticity, and unit price can turn policy questions into numbers.

The goal is to give governments data on which reinforcers people will still work for when costs rise.

The paper finds that behavioral economics already has the math.

BCBAs only need to plug in response-cost data to predict real-world choice at a city-wide scale.

Furrebøe et al. (2017) extend the idea. They say behavior analysis gives the missing lab tools that turn loose "irrational choice" talk into testable contingencies.

Heyman (2023) shows the next step. He uses the same demand-curve logic to explain why clients over-pick the richest reinforcer and how to re-bundle options to stop it.

Lyons (1995) and Szempruch et al. (1993) are the grandparents. They linked unit price to behavioral momentum and rat production functions years earlier, proving the math works in the operant lab before R et al. took it to city hall.

You can now speak policy language. Plot a demand curve for screen time, snacks, or social praise in your functional assessment. Show the team the exact price point where problem behavior drops. Use the same graph to justify funding for denser reinforcement schedules to the school board.