Person: CANYAKMAZ, Caner
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Caner
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CANYAKMAZ
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ArticlePublication Metadata only Human and machine: The impact of machine input on decision making under cognitive limitations(Informs, 2023-03) Boyacı, T.; Canyakmaz, Caner; de Vericourt, F.; Business Administration; CANYAKMAZ, CanerThe 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.ArticlePublication Metadata only Queueing systems with rationally inattentive customers(Informs, 2023-01) Canyakmaz, Caner; Boyacı, T.; Business Administration; CANYAKMAZ, CanerProblem definition: Classical models of queueing systems with rational and strategic customers assume queues to be either fully visible or invisible, while service parameters are known with certainty. In practice, however, people only have "partial information" on the service environment, in the sense that they are not able to fully discern prevalent uncertainties. This is because assessing possible delays and rewards is costly, as it requires time, attention, and cognitive capacity, which are all limited. On the other hand, people are also adaptive and endogenously respond to information frictions. Methodology: We develop an equilibrium model for a single-server queueing system with customers having limited attention. Following the theory of rational inattention, we assume that customers optimize their learning strategies by deciding the type and amount of information to acquire and act accordingly while internalizing the associated costs. Results: We establish the existence and uniqueness of a customer equilibrium when customers allocate their attention to learn uncertain queue lengths and delineate the impact of service characteristics. We provide a complete spectrum of the impact of information costs on throughput and show numerically that throughput might be nonmonotone. This is also reflected in social welfare if the firm's profit margin is high enough, although customer welfare always suffers from information costs. Managerial implications: We identify service settings where service firms and social planners should be most cautious for customers' limited attention and translate our results to advisable strategies for information provision and service design. For example, we recommend firms to avoid partial hindrance of queue-length information when a low-demand service is not highly valued by customers. For a popular service that customers value reasonably highly, however, partial hindrance of information is particularly advisable. Academiclpractical relevance: We propose a microfounded framework for strategic customer behavior in queues that links beliefs, rewards, and information costs. It offers a holistic perspective on the impact of information prevalence (and information frictions) on operational performance and can be extended to analyze richer customer behavior and complex queue structures, rendering it a valuable tool for service design.ArticlePublication Metadata only A newsvendor problem with markup pricing in the presence of within-period price fluctuations(Elsevier, 2022-08-16) Canyakmaz, Caner; Özekici, S.; Karaesmen, F.; Business Administration; CANYAKMAZ, CanerWe consider a single-item single-period joint inventory management and pricing problem of a retailer selling an item that has selling price uncertainties. Unlike most of the literature on the newsvendor problem, we assume that price-dependent demand arrives randomly according to a stochastic arrival process whose rate depends on the fluctuating market input price process. The retailer's problem is to choose the order quantity and a proportional price markup over the input price to maximize the expected profit. This setting is mostly encountered by retailers that trade in different currencies or have to purchase and convert commodities for seasonal sales. For this setting, we characterize both the optimal inventory and markup levels. We present monotonicity properties of the expected profit function with respect to each decision variable. We also show that more volatile input price processes lead to lower expected profits.