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We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that choices are more likely to be correct when the agent chooses to decide quickly, provided the agent’s prior beliefs are correct. This better matches the observed correlation between decision time and choice probability than does the classical drift-diffusion model (DDM), where the agent knows the utility difference between the choices.

We propose a model of job search with reference-dependent preferences, with loss aversion relative to recent income (the reference point). In this model, newly unemployed individuals search hard since consumption is below their reference point. Over time, though, they get used to lower income and thus reduce their search effort. In anticipation of a benefit cut, their search effort rises again, then declines once they get accustomed to the lower postcut benefit level. The model fits the typical pattern of exit from unemployment, even with no unobserved heterogeneity. To distinguish between this and other models, we use a unique reform in the unemployment insurance (UI) benefit path. In 2005, Hungary switched from a singlestep UI system to a two-step system, with overall generosity unchanged. The system generated increased hazard rates in anticipation of, and especially following, benefit cuts in ways the standard model has a hard time explaining. We estimate a model with optimal consumption, endogenous search effort, and unobserved heterogeneity. The reference-dependent model fits the hazard rates substantially better than plausible versions of the standard model, including habit formation. Our estimates indicate a slow-adjusting reference point and substantial impatience, likely reflecting present-bias.

This paper investigates how gender disparities affect the time to repay group micro-finance loans using survival analysis. We also control for the effect of the COVID-19 pandemic on the time needed by micro-finance loan borrowers to repay. We use a large sample of bank microfinance group loans from August 2017 to August 2021. Despite the fact that female borrowers’ overall default rate is smaller, our unconditional estimates show that female borrowers default almost the equivalent of three consecutive installments earlier. Moreover, this result persists when we control for micro, industry, and macroeconomic factors. We also observe that the COVID-19 pandemic materialized as a spike in aggregate default rates that gradually reduced afterward. Our study identified a potential gender gap that has been understudied in the literature.

We conduct an empirical investigation of the impact of queue management on patients’ average wait time and length of stay (LOS). Using an emergency department’s (ED) patient-level data from 2007 to 2010, we find that patients’ average wait time and LOS are longer when physicians are assigned patients under a pooled queuing system with a fairness constraint compared to a dedicated queuing system with the same fairness constraint. Using a difference-in-differences approach, we find the dedicated queuing system is associated with a 17% decrease in average LOS and a 9% decrease in average wait time relative to the control group—a 39-minute reduction in LOS and a four-minute reduction in wait time for an average patient of medium severity in this ED. Interviews and observations of physicians suggest that the improved performance stems from the physicians’ increased ownership over patients and resources that is afforded by a dedicated queuing system, which enables physicians to more actively manage the flow of patients into and out of ED beds. Our findings suggest that the benefits from improved flow management in a dedicated queuing system can be large enough to overcome the longer wait time predicted to arise from nonpooled queues. We conduct additional analyses to rule out alternate explanations for the reduced average wait time and LOS in the dedicated system, such as stinting and decreased quality of care. Our paper has implications for healthcare organizations and others seeking to reduce patient wait time and LOS without increasing costs. This paper was accepted by Serguei Netessine, operations management.

I analytically characterize the long-run behavior of an economy with two types of agents who differ in their beliefs and are endowed with homothetic recursive preferences. Agents with more incorrect beliefs dominate, or agents with different accuracy of their beliefs coexist in the long run, for broad ranges of plausible parameterizations when risk aversion is greater than the inverse of the intertemporal elasticity of substitution. The results highlight a crucial interaction between risk sharing, speculative behavior and consumption-saving choice of agents with heterogeneous beliefs, and the role of equilibrium prices in shaping long-run outcomes.

This article studies deep neural network expression rates for optimal stopping problems of discrete-time Markov processes on high-dimensional state spaces. A general framework is established in which the value function and continuation value of an optimal stopping problem can be approximated with error at most ε by a deep ReLU neural network of size at most κdqε−r. The constants κ,q,r≥0 do not depend on the dimension d of the state space or the approximation accuracy ε. This proves that deep neural networks do not suffer from the curse of dimensionality when employed to approximate solutions of optimal stopping problems. The framework covers for example exponential Lévy models, discrete diffusion processes and their running minima and maxima. These results mathematically justify the use of deep neural networks for numerically solving optimal stopping problems and pricing American options in high dimensions.

The simultaneous estimation and variable selection for Cox model has been discussed by several authors when one observes right-censored failure time data. However, there does not seem to exist an established procedure for interval-censored data, a more general and complex type of failure time data, except two parametric procedures. To address this, we propose a broken adaptive ridge (BAR) regression procedure that combines the strengths of the quadratic regularization and the adaptive weighted bridge shrinkage. In particular, the method allows for the number of covariates to be diverging with the sample size. Under some weak regularity conditions, unlike most of the existing variable selection methods, we establish both the oracle property and the grouping effect of the proposed BAR procedure. An extensive simulation study is conducted and indicates that the proposed approach works well in practical situations and deals with the collinearity problem better than the other oracle-like methods. An application is also provided.

In this paper, we study the impact of consumer-generated quality information (e.g., consumer reviews) on a firm’s dynamic pricing strategy in the presence of strategic consumers. Such information is useful, not only to the consumers that have not yet purchased the product but also to the firm. The informativeness of the consumer-generated quality information depends, however, on the volume of consumers who share their opinions and, thus, depends on the initial sales volume. Hence, via its initial price, the firm not only influences its revenue but also controls the quality information flow over time. The firm may either enhance or dampen the quality information flow via increasing or decreasing initial sales. The corresponding pricing strategy to steer the quality information flow is not always intuitive. Compared to the case without consumer-generated quality information, the firm may reduce the initial sales and lower the initial price. Interestingly, the firm may get strictly worse off due to the consumer-generated quality information. Even when the firm benefits from consumer-generated quality information, it may prefer less accurate information. Consumer surplus can also decrease due to the consumer-generated quality information, contrary to the conventional wisdom that word of mouth should help consumers. We examine extensions of our model that incorporate capacity investment, firm’s private information about quality, alternative updating mechanisms, as well as multiple sales periods, and show that our insights are robust. This paper was accepted by Yossi Aviv, operations management.

Redundancy is an important strategy for reducing response time in multi-server distributed queueing systems. This strategy has been used in a variety of settings, but only recently have researchers begun analytical studies. The idea behind redundancy is that customers can greatly reduce response time by waiting in multiple queues at the same time, thereby experiencing the minimum time across queues. Redundancy has been shown to produce significant response time improvements in applications ranging from organ transplant waitlists to Google’s BigTable service. However, despite the growing body of theoretical and empirical work on the benefits of redundancy, there is little work addressing the questions of how many copies one needs to make to achieve a response time benefit, and the magnitude of the potential gains. In this paper we propose a theoretical model and dispatching policy to evaluate these questions. Our system consists of k servers, each with its own queue. We introduce the Redundancy- d policy, under which each incoming job makes copies at a constant number of servers, d , chosen at random. Under the assumption that a job’s service times are exponential and independent across servers, we derive the first exact expressions for mean response time in Redundancy- d systems with any finite number of servers, as well as expressions for the distribution of response time which are exact as the number of servers approaches infinity. Using our analysis, we show that mean response time decreases as d increases, and that the biggest marginal response time improvement comes from having each job wait in only d = 2 queues. The e-companion is available at https://doi.org/10.1287/opre.2016.1582 .

One key factor contributing to emergency department (ED) overcrowding is prolonged waiting time for admission to inpatient wards, also known as ED boarding time. To gain insights into reducing this waiting time, we study operations in the inpatient wards and their interface with the ED. We focus on understanding the effect of inpatient discharge policies and other operational policies on the time-of-day waiting time performance, such as the fraction of patients waiting longer than six hours in the ED before being admitted. Based on an empirical study at a Singaporean hospital, we propose a novel stochastic processing network with the following characteristics to model inpatient operations: (1) A patient's service time in the inpatient wards depends on that patient's admission and discharge times and length of stay. The service times capture a two-time-scale phenomenon and are not independent and identically distributed. (2) Pre-and post-allocation delays model the extra amount of waiting caused by secondary bottlenecks other than bed unavailability, such as nurse shortage. (3) Patients waiting for a bed can overflow to a nonprimary ward when the waiting time reaches a threshold, where the threshold is time dependent. We show, via simulation studies, that our model is able to capture the inpatient flow dynamics at hourly resolution and can evaluate the impact of operational policies on both the daily and time-of-day waiting time performance. In particular, our model predicts that implementing a hypothetical policy can eliminate excessive waiting for those patients who request beds in mornings. This policy incorporates the following components: a discharge distribution with the first discharge peak between 8 A.M. and 9 A.M. and 26% of patients discharging before noon, and constant-mean allocation delays throughout the day. The insights gained from our model can help hospital managers to choose among different policies to implement depending on the choice of objective, such as to reduce the peak waiting in the morning or to reduce daily waiting time statistics.