Decision Making & Negotiations

Who behaves dominantly—and why? Much compelling prior research spotlights motivational sources. We focus here on beliefs, proposing that people are less likely to behave dominantly when they expect dominance to incur greater relationship costs. We posit that this situation-specific expectancy is shaped by a general competitive worldview, seeing the social world as a “competitive jungle.” In five preregistered studies, we tested whether those with a competitive worldview expected dominance to incur less relationship harm and whether expected relationship harm predicted dominance.

The book gives evidence and advice for leveraging values as a concrete way to improve outcomes in leadership and life.  The first part of the book is about leveraging values as an individual, the second half is about organizational values.  The audience is thoughtful students of business, leaders, and scholars.

There has been growing interest in applying reinforcement learning (RL) to inventory management, either by optimizing over temporal transitions or by learning directly from full historical demand trajectories. This contrasts sharply with classical data-driven approaches, which first estimate demand distributions from past data and then compute well-structured optimal policies via dynamic programming.

We use topic modeling to construct novel news-based measures for tracking energy markets. Our parsimonious yet comprehensive set of indicators summarizes the information content of millions of news articles and forecasts oil spot, futures, and energy company stock returns, and changes in oil volatility, production, and inventories. Using an econometrically robust framework to evaluate both in- and out-of-sample predictive performance, we show that our measures are not spanned by existing text and nontext variables.

We study the dynamic pooling of multiple orders into a single trip, a strategy widely adopted by online delivery platforms. When an order has to be dispatched, the platform must determine which (if any) of the available orders to pool it with, weighing the immediate efficiency gains against the uncertain, differential benefits of holding each order for future pooling opportunities. In this paper, we demonstrate the effectiveness of using the delivery distance as a proxy for opportunity cost via a potential-based greedy algorithm (PB).

In the Newsvendor problem, the goal is to guess the number that will be drawn from some distribution, with asymmetric consequences for guessing too high vs. too low. In the data-driven version, the distribution is unknown, and one must work with samples from the distribution. Data-driven Newsvendor has been studied under many variants: additive vs. multiplicative regret, high probability vs. expectation bounds, and different distribution classes. This paper studies all combinations of these variants, filling in many gaps in the literature and simplifying many proofs.

Salespeople frequently face the dynamic screening decision of whether to persist in a conversation or abandon it to pursue the next lead. Yet, little is known about how these decisions are made, whether they are efficient, or how to improve them. We study these decisions in the context of high-volume outbound sales where leads are ample, but time is scarce and failure is common.

Deep Reinforcement Learning (DRL) provides a general-purpose methodology for training inventory policies that can leverage big data and compute. However, off-the-shelf implementations of DRL have seen mixed success, often plagued by high sensitivity to the hyperparameters used during training. In this paper, we show that by imposing policy regularizations, grounded in classical inventory concepts such as "Base Stock", we can significantly accelerate hyperparameter tuning and improve the final performance of several DRL methods.

A large portion of life as a consumer is spent mind-wandering from one off-task, spontaneous, and imaginative thought to the next. Psychology research has thoroughly documented the various characteristics of mind-wandering, showing that this default state of mind occupies much of our waking life and shapes outcomes ranging from goal pursuit and decision-making to present-moment experience. However, consumer research has largely overlooked mind-wandering as a phenomenon and mechanism that shapes consumption.