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

M. Kate Bundorf, Maria Polyakova and Ming Tai-Seale

No 25976, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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 use data from a randomized controlled trial of algorithmic advice in the context of prescription drug insurance to examine these questions. We propose that algorithmic recommendations can affect decision-making by influencing consumer beliefs about either product features (learning) or how to value those features (interpretation). We use data from the trial to estimate the importance of each mechanism. We find evidence that algorithms influence choices through both channels. Further, we document substantial selection into the use of algorithmic expert advice. Consumers who we predict would have responded more to algorithmic advice were less likely to demand it. Our results raise concerns regarding the ability of algorithmic advice to alter consumer preferences as well as the distributional implications of greater access to algorithmic advice.

JEL-codes: D1 D12 D8 D81 D82 D83 D9 D90 D91 G22 H51 I13 (search for similar items in EconPapers)
Date: 2019-06
New Economics Papers: this item is included in nep-big, nep-exp, nep-hea, nep-ias and nep-upt
Note: AG EH IO LS PE
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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