The structure of network externalities influences platform competition and can determine whether a two-sided market is winner-takes-all or highly contestable. Yet, because of analytical challenges, relatively little is known about these structures in different markets, raising several research questions: How do network externalities arise in specific markets? What is the structure of network externalities? How does the structure influence the competition between two-sided platforms? What conditions lead to a monopoly or an oligopoly outcome, enabling or discouraging entry? We address these questions in the context of ride-hailing platforms, identifying the externalities that arise from congestion.

Graph anomaly detection has gained significant attention across various domains, particularly in critical applications like fraud detection in e-commerce platforms and insider threat detection in cybersecurity.Usually, these data are composed of multiple types (e.g., user information and transaction records for financial data), thus exhibiting view heterogeneity.However, in the era of big data, the heterogeneity of views and the lack of label information pose substantial challenges to traditional approaches.Existing unsupervised graph anomaly detection methods often struggle with high-dimensionality issues, rely on strong assumptions about graph structures or fail to handle complex multi-view graphs.To address these challenges, we propose a cluster aware multi-view graph anomaly detection method, called CARE.Our approach captures both local and global node affinities by augmenting the graph's adjacency matrix with the pseudo-label (i.e., soft membership assignments) without any strong assumption about the graph.To mitigate potential biases from the pseudo-label, we introduce a similarity-guided loss.Theoretically, we show that the proposed similarity-guided loss is a variant of contrastive learning loss, and we present how this loss alleviates the bias introduced by pseudolabel with the connection to graph spectral clustering.Experimental results on several datasets demonstrate the effectiveness and efficiency of our proposed framework.Specifically, CARE outperforms the second-best competitors by more than 39% on the Amazon dataset with respect to AUPRC and 18.7% on the YelpChi dataset with respect to AUROC.The code of our method is available at the GitHub link: https://github.com/zhenglecheng/CARE-demo.

We study the learning problem of revealed preference in a stochastic setting: a learner observes the utility-maximizing actions of a set of agents whose utility follows some unknown distribution, and the learner aims to infer the distribution through the observations of actions. The problem can be viewed as a single-constraint special case of the inverse linear optimization problem. Existing works all assume that all the agents share one common utility which can easily be violated under practical contexts. In this paper, we consider two settings for the underlying utility distribution: a Gaussian setting where the customer utility follows the von Mises-Fisher distribution, and a $\delta$-corruption setting where the customer utility distribution concentrates on one fixed vector with high probability and is arbitrarily corrupted otherwise. We devise Bayesian approaches for parameter estimation and develop theoretical guarantees for the recovery of the true parameter. We illustrate the algorithm performance through numerical experiments.

Having an interpretable, dynamic length-of-stay model can help hospital administrators and clinicians make better decisions and improve the quality of care. The widespread implementation of electronic medical record (EMR) systems has enabled hospitals to collect massive amounts of health data. However, how to integrate this deluge of data into healthcare operations remains unclear. We propose a framework grounded in established clinical knowledge to model patients’ lengths of stay. In particular, we impose expert knowledge when grouping raw clinical data into medically meaningful variables that summarize patients’ health trajectories. We use dynamic, predictive models to output patients’ remaining lengths of stay, future discharges, and census probability distributions based on their health trajectories up to the current stay. Evaluated with large-scale EMR data, the dynamic model significantly improves predictive power over the performance of any model in previous literature and remains medically interpretable. Summary of Contribution: The widespread implementation of electronic health systems has created opportunities and challenges to best utilize mounting clinical data for healthcare operations. In this study, we propose a new approach that integrates clinical analysis in generating variables and implementations of computational methods. This approach allows our model to remain interpretable to the medical professionals while being accurate. We believe our study has broader relevance to researchers and practitioners of healthcare operations.

Multiarmed bandit (MAB) problems, typically modeled as Markov decision processes (MDPs), exemplify the learning versus earning trade-off. An area that has motivated theoretical research in MAB designs is the study of clinical trials, where the application of such designs has the potential to significantly improve patient outcomes. However, for many practical problems of interest, the state space is intractably large, rendering exact approaches to solving MDPs impractical. In particular, settings that require multiple simultaneous allocations lead to an expanded state and action-outcome space, necessitating the use of approximation approaches. We propose a novel approximation approach that combines the strengths of multiple methods: grid-based state discretization, value function approximation methods, and techniques for a computationally efficient implementation. The hallmark of our approach is the accurate approximation of the value function that combines linear interpolation with bounds on interpolated value and the addition of a learning component to the objective function. Computational analysis on relevant datasets shows that our approach outperforms existing heuristics (e.g., greedy and upper confidence bound family of algorithms) and a popular Lagrangian-based approximation method, where we find that the average regret improves by up to 58.3%. A retrospective implementation on a recently conducted phase 3 clinical trial shows that our design could have reduced the number of failures by 17% relative to the randomized control design used in that trial. Our proposed approach makes it practically feasible for trial administrators and regulators to implement Bayesian response-adaptive designs on large clinical trials with potential significant gains.

Platforms facilitating the exchange of goods and services between individuals are prevalent: one can purchase goods from others on eBay, arrange accommodation through Airbnb, and find temporary projects/workers on online labor markets such as Upwork. The majority of these markets exhibit three key features. First, the platforms do not dictate the transaction prices, i.e., buyers and sellers determine at which price the goods/services will be exchanged. Second, not all buyers or sellers on a platform are compatible. This may be due to taste differences (a buyer may be interested only in the types of goods/services a subset of the sellers offer), geographical or import/export restrictions (e.g., being able to provide services only regionally), or other sources of mismatch (e.g., a mismatch in the desired and available skills in online labor markets). Finally, buyers/sellers are heterogeneous in their valuations for goods or services they receive/provide.

Risks exist in every aspect of our lives, and can mean different things to different people, while negatively in general they always cause a great deal of potential damage and inconvenience for the enterprise stakeholders. Investigation of risk analytics tools in today’s big data era is beneficial to both practitioners and academic researchers from industrial systems. The current special issue provides some view of how risk-based business intelligence can be applied to industrial systems faced with big data issues.

Clinical trials have traditionally followed a fixed randomized design, where patients are typically allocated once, at random, and usually equally to various treatments. Such designs provide a clean way of separating out the effects of alternate treatments. Response-adaptive designs, where assignment to treatments evolves as patient outcomes are observed, are gaining in popularity due to potential for improvements in both cost and efficiency over traditional designs. Such designs can be modeled as a Bayesian adaptive Markov decision process (BAMDP), essentially an MDP with unknown transition probabilities. Given the forward-looking nature of the underlying algorithms which solve BAMDP, the problem size grows as the trial becomes larger or more complex, often exponentially, making it computationally challenging to solve. In this study, we propose grid-based approximation as a way to reduce the problem dimensionality and the associated computationally burden. The proposed methods also open the possibility of implementing adaptive designs to large clinical trials. Further, we use numerical examples to demonstrate the effectiveness of our approach, including the effects of number of patient observations and the grid resolution.

Although decisions frequently have uncertain consequences, optimal-decision models often replace those uncertainties with averages or best estimates. Limited computational capability may have motivated this practice in the past. Recent computational advances have, however, greatly expanded the range of optimal-decision models with explicit consideration of uncertainties. This article describes the basic methodology for these stochastic programming models, recent developments in computation, and several practical applications.