The Game Theory of How Algorithms Can Drive Up Prices

Computer scientists at the University of Pennsylvania have proved that pricing algorithms can drive up prices even when they lack the capacity to collude. Aaron Roth and four colleagues studied so-called no-swap-regret algorithms, which are designed to minimize losses and were previously thought to guarantee competitive pricing. The researchers found that when such an algorithm faces an opponent using a nonresponsive strategy -- one that randomly selects from predetermined price probabilities without reacting to competitor moves -- both players can end up in equilibrium at high prices.

Neither has an incentive to switch strategies because their profits are nearly equal and as high as possible under the circumstances. The nonresponsive strategy cannot express threats because it does not respond to opponent behavior, yet it effectively coaxes the learning algorithm into raising prices. Mallesh Pai, an economist at Rice University not involved in the research, said the finding matters because regulators have no clear grounds to intervene without evidence of threats or agreements. Roth conceded however that he lacks a solution to the regulatory challenge his team identified.

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