Evolution of Ride Services: From Ride- Hailing to Autonomous Vehicles
In recent years the ride service industry has been evolving rapidly, driven by disruptive technologies such as mobile apps, AI, and autonomous vehicles (AVs). While platform-based decentralized ride hailing companies have gained significant market share, vertically-integrated robotaxi services using emerging AVs are starting to enter the market. In this paper, we aim to provide insights about the evolution and the future of ride services studying these two competing business approaches.
Stochastic Gradients: Optimization, Simulation, Randomization, and Sensitivity Analysis
Big data and high-dimensional optimization problems in operations research (OR) and artificial intelligence (AI) have brought stochastic gradients to the forefront. This article provides a view of research and applications in stochastic gradient estimation from multiple perspectives, as seminal advances have come from diverse and disparate research fields, including operations research/management science (OR/MS), industrial/systems engineering (ISE), optimal/stochastic control, statistics, and more recently from the computer science (CS) AI machine learning (ML) community.
Simulation Optimization and Artificial Intelligence
With the relentless increase in computing power and the ubiquitous availability of data in many industries, the fields of simulation optimization and artificial intelligence have emerged at the scientific and engineering forefront in their societal impact, manifested in the pervasiveness of technologies such as large language models, chatbots, digital twins, and agent-based systems. We examine cross-fertilization between simulation optimization and artificial intelligence, with a particular focus on reinforcement learning, highlighting research that has been mutually beneficial.
Online Learning with Survival Data
Decision-makers frequently use adaptive experiments to optimize time-to-event outcomes, such as accelerating healthcare screenings among patients who are not up to date or delaying customer churn. A common choice to run these adaptive experiments is a multi-armed bandit with a dichotomized outcome -- an experimenter sets some threshold (e.g. 1 month) and then uses the algorithm to identify the intervention with better performance on the dichotomized outcome (e.g. which algorithm maximizes the proportion of participants who get up to date on screening within a month of outreach).
Backfiring AI? AI Deployment in Workplace
AI in the workplace has the potential to change the competitive dynamics among employees. The AI system can learn from high-performing employees and make that knowledge available to others. In a competitive environment, this can disincentivize high-performing employees and ultimately backfire, leading to a decline in overall firm productivity.
Unintended Consequences of Closing Pay Gaps Across Multiple Groups: A Formal Modeling and Simulation Analysis of Allocation Methods
In recent years, many firms have prioritized both pay equity (i.e., closing pay gaps associated with target groups such as women and racial minorities) and equitable representation (i.e., ensuring these target groups are fairly represented across a firm’s hierarchy). We use formal modeling and simulations to show how efforts to close pay gaps across multiple groups can undermine equitable representation.
Market Formation, Pricing, and Revenue Sharing in Ride Hailing Services
Problem definition: We empirically study the market for ride-hailing services. In particular, we explore the following questions: (i) How do the two-sided market and prices jointly form in ride-hailing marketplaces? (ii) Does surge pricing create value and for whom? How can its efficiency be improved? (iii) Can platforms' strategy on revenue sharing with drivers be improved? (iv) What is the value generated by ride-hailing services, including hosting rival taxi services on ride-hailing apps?
Learning from Earnings Calls: Graph-Based Conversational Modeling for Financial Prediction
Earnings conference calls are valuable venues for business communication. Empirical research has shown that the content of earnings calls contains predictive signals about future market risks, which has motivated a line of computational studies that utilize earnings transcripts for financial forecasting tasks. However, earnings call transcripts are typically very long, and the predictive signals within them are often sparsely distributed across different sections of the transcript.
Can Employees' Past Helping Behavior Be Used to Improve Shift Scheduling? Evidence from ICU Nurses
Employees routinely make valuable contributions at work that are not part of their formal job description, such as helping a struggling coworker. These contributions, termed organizational citizenship behavior, are studied from many angles in the organizational behavior literature. However, the degree to which the past helping behavior of employees scheduled to a shift impacts that shift’s operational outcomes remains an underexplored question.
Cost-Saving Synergy: Energy Stacking in Battery Energy Storage Systems
Despite the great potential benefits of battery energy storage systems (BESSs) to electrical grids, most standalone uses of BESS are not economical due to batteries’ high upfront costs and limited lifespans. Energy stacking, a strategy of providing two or more services with a single BESS, has been of great interest to improve profitability. However, some key questions, for example, the underlying mechanism by which stacking works or why and how much it may improve profitability, remain unanswered in the literature.