Previous experimental work showed that newsvendors tend to order closer to mean demand than prescribed by the normative critical fractile solution. A recently proposed explanation for this mean ordering behavior assumes that the decision maker commits random choice errors, and predicts the mean ordering pattern because there is more room to err toward mean demand than away from it. Do newsvendors exhibit mean ordering simply because they make random errors? We subject this hypothesis to an empirical test that rests on the fact that the random error explanation is insensitive to context. Our results strongly support the existence of context-sensitive decision strategies that rely directly on (biased) order-to-demand mappings, such as mean demand anchoring, demand chasing, and inventory error minimization.newsvendor model, task context, heuristics, random choice
W e analyze how individuals make forecasts based on time-series data. Using a controlled laboratory experiment, we find that forecasting behavior systematically deviates from normative predictions: Forecasters overreact to forecast errors in relatively stable environments, but underreact to errors in relatively unstable environments. The performance loss that is due to such systematic judgment biases is larger in stable than in unstable environments.
T his research analyzes how individual differences affect performance in judgmental time-series forecasting. Decision makers with the ability to balance intuitive judgment with cognitive deliberation, as measured by the cognitive reflection test, tend to have lower forecast errors. This relationship holds when controlling for intelligence. Furthermore, forecast errors increase for very fast or very slow decisions. We provide evidence that forecast performance can be improved by manipulating decision speed.
W e investigate retailers' dynamic pricing decisions in a stylized two-period setting with possible supply constraints and demand from both myopic and strategic consumers. We present an analytical model and then test its predictions in a behavioral experiment in which human subjects played the role of pricing managers. We find that the fraction of strategic consumers in the market systematically moderates the optimal pricing structure. When this fraction exceeds a certain threshold, the retailer offers relatively small late season markdowns to discourage strategic consumers from waiting and to incentivize them to buy during the early season; otherwise, the retailer offers relatively large markdowns to divert all strategic consumers to the late season, where the majority of revenue is made. Our model analyses suggest that the latter policy is optimal under fairly broad conditions. Our experiment shows that after some significant learning, aggregate behavior is able to approximate the key qualitative predictions from our model analysis, with one notable deviation: in the presence of a mixture of myopic and strategic consumers, subjects act somewhat myopicallythey underprice and oversell in the main selling season, which significantly limits their ability to generate revenue in the markdown season.
Firms require demand forecasts at different levels of aggregation to support a variety of resource allocation decisions. For example, a retailer needs store-level forecasts to manage inventory at the store, but also requires a regionally aggregated forecast for managing inventory at a distribution center. In generating an aggregate forecast, a firm can choose to make the forecast directly based on the aggregated data or indirectly by summing lower-level forecasts (i.e., bottom up). Our study investigates the relative performance of such hierarchical forecasting processes through a behavioral lens. We identify two judgment biases that affect the relative performance of direct and indirect forecasting approaches: a propensity for random judgment errors and a failure to benefit from the informational value that is embedded in the correlation structure between lower-level demands. Based on these biases, we characterize demand environments where one hierarchical process results in more accurate forecasts than the other. This paper was accepted by Martin Lariviere, operations management.
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