The game theory of how algorithms can drive up prices

Original Article ↗ Hacker News Discussion ↗

Algorithmic Pricing, Stability, and Game Theory

  • Several commenters connect the article’s results to earlier work on learning agents in artificial markets, noting prior findings of instability (bubbles, crashes) versus these “stable but high-price” equilibria.
  • There is curiosity about extension from 2-player to N-player games: some expect more competitors to make collusion-like dynamics harder; others think more players could actually make stable algorithmic pricing easier, especially with humans supervising the systems.
  • The “nudge high” gas-station thought experiment is seen as intuitive: algorithms periodically test high prices to converge on mutually profitable high-high equilibria.

Real-World Dynamic Pricing and Discrimination

  • Many observe that dynamic/algorithmic pricing is already pervasive: Amazon price cycling, Walmart’s store-by-store prices, online vs in-store price differences, and A/B testing to find what “the local market will bear.”
  • Commenters discuss the historical move from haggling to fixed prices (including religious and behavioral-economics motives) and predict a broad return to individualized price discrimination as automation lowers labor costs.

Regulation, Enforcement, and Policy Ideas

  • Some argue regulators can and do control inputs and methods (e.g., insurance rate filings) and could restrict use of competitor data or require justification based on allowed datasets.
  • Others are skeptical: algorithmic collusion is hard to prove, enforcement often hinges on narrow concepts like “nonpublic data,” and fines may be smaller than profits.
  • Proposed solutions include: profit caps (as with utilities), public zero-margin competitors, government entry into high-margin markets, mandatory financial transparency, and stronger antitrust. Critics worry profit caps become de facto targets.

Collusion, “Algorithms,” and Number of Players

  • Multiple commenters stress that the core issue is information and incentives, not “algorithms” as such; the same strategies could be executed with pen and paper.
  • One view: if firms knowingly adopt a shared pricing algorithm, that choice itself is collusion; others see it more as systemic design failure than intentional conspiracy.
  • The number of competitors (n) is repeatedly highlighted: with few players, tacit or explicit collusion is easy; with many, undercutting is more attractive, making sustained high prices harder.

Broader Economic Debates

  • Long subthreads debate how well Econ 101 “free market” models apply in reality, especially under modern concentration, advertising, and overcapacity.
  • Housing and rent algorithms (e.g., RealPage/Greystar–type scenarios) are cited as concrete, harmful examples of algorithmic price coordination driving up essential costs.