An Old-Fashioned Economic Tool Can Tame Pricing Algorithms

Abstraction: Price controls curbing AI pricing algorithm discrimination and collusion

Key points:

• Tsinghua University researchers (2022 preprint) showed price controls can mathematically balance producer/consumer surplus in personalized algorithmic pricing.
• Pricing algorithms at Amazon, Uber, Lyft use consumer data to estimate willingness-to-pay (WTP) and set individualized prices.
• Studies show personalized pricing can unintentionally charge higher rates to racial/ethnic minorities based on geographic and data signals.
• Experimental conditions show pricing algorithms can learn to collude, fixing prices — though present-bias wiring of most current AIs limits this risk.
• Price controls reduce total surplus (producer + consumer combined) even while achieving fairer distribution — a known economic trade-off.
• Renzhe Xu and Peng Cui (Tsinghua) validated controls using 2002 Kiel experiment WTP dataset, showing the seller's informational advantage was erased by a price range constraint.

Connections: Amazon · Uber · Tsinghua University · Algorithmic Pricing · Price Controls · Algorithmic Discrimination

Source: https://www.scientificamerican.com/article/an-old-fashioned-economic-tool-can-tame-pricing-algorithms/