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How do Humans Interact with Algorithms? Experimental Evidence from Health Insurance

Kate Bundorf, Maria Polyakova, Ming Tai-Seale

NBER Working Paper No. 25976
Issued in June 2019
NBER Program(s):Aging, Health Care, Health Economics, Industrial Organization, Labor Studies, Public Economics

Algorithms increasingly assist consumers in making their purchase decisions across a variety of markets; yet little is known about how humans interact with algorithmic advice. We examine how algorithmic, personalized information affects consumer choice among complex financial products using data from a randomized, controlled trial of decision support software for choosing health insurance plans. The intervention significantly increased plan switching, cost savings, time spent choosing a plan, and choice process satisfaction, particularly when individuals were exposed to an algorithmic expert recommendation. We document systematic selection - individuals who would have responded to treatment the most were the least likely to participate. A model of consumer decision-making suggests that our intervention affected consumers’ signals about both product features (learning) and utility weights (interpretation).

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Document Object Identifier (DOI): 10.3386/w25976

 
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