How do Humans Interact with Algorithms? Experimental Evidence from Health Insurance
Algorithms are increasingly available to help consumers make purchasing decisions. How does algorithmic advice affect human decisions and what types of consumers are likely to use such advice? We conducted a randomized, controlled trial comparing the effects of offering personalized information, either with or without algorithmic expert recommendations, relative to offering no personalized information for consumers choosing prescription drug insurance plans. Treated consumers were more likely to switch plans and to choose a plan that lowered their total spending on drugs. The behavioral response was more pronounced when information was combined with an algorithmic expert recommendation. We develop an empirical model of consumer choice to examine the mechanisms by which expert recommendations affect choices. Our experimental data are consistent with a model in which consumers have noisy beliefs not only about product features, but also about the parameters of their utility function. Expert advice, in turn, changes how consumers value product features by changing their beliefs about their utility function parameters. We further document substantial selection into who demands expert advice. Consumers who we predict would have responded more to algorithmic advice were less likely to demand it.
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Document Object Identifier (DOI): 10.3386/w25976