Human and Machine: The Impact of Machine Input on Decision Making Under Cognitive Limitations

Tamer Boyacı (), Caner Canyakmaz () and Francis de Véricourt ()
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Tamer Boyacı: European School of Management and Technology Berlin, 10178 Berlin, Germany
Caner Canyakmaz: Faculty of Business, Ozyegin University, Istanbul 34794, Turkey
Francis de Véricourt: European School of Management and Technology Berlin, 10178 Berlin, Germany

Management Science, 2024, vol. 70, issue 2, 1258-1275

Abstract: The rapid adoption of artificial intelligence (AI) technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks. In fact, when used in collaboration, machines can significantly enhance the complementary strengths of humans. Indeed, because of their immense computing power, machines can perform specific tasks with incredible accuracy. In contrast, human decision makers (DMs) are flexible and adaptive but constrained by their limited cognitive capacity. This paper investigates how machine-based predictions may affect the decision process and outcomes of a human DM. We study the impact of these predictions on decision accuracy, the propensity and nature of decision errors, and the DM’s cognitive efforts. To account for both flexibility and limited cognitive capacity, we model the human decision-making process in a rational inattention framework. In this setup, the machine provides the DM with accurate but sometimes incomplete information at no cognitive cost. We fully characterize the impact of machine input on the human decision process in this framework. We show that machine input always improves the overall accuracy of human decisions but may nonetheless increase the propensity of certain types of errors (such as false positives). The machine can also induce the human to exert more cognitive efforts, although its input is highly accurate. Interestingly, this happens when the DM is most cognitively constrained, for instance, because of time pressure or multitasking. Synthesizing these results, we pinpoint the decision environments in which human-machine collaboration is likely to be most beneficial.

Keywords: machine learning; rational inattention; human-machine collaboration; cognitive effort (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:70:y:2024:i:2:p:1258-1275

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