At Columbia Business School, our faculty members are at the forefront of research in their respective fields, offering innovative ideas that directly impact the practice of business today. A quick glance at our publication on faculty research, CBS Insights, will give you a sense of the breadth and immediacy of the insight our professors provide.
As a student at the School, this will greatly enrich your education. In Columbia classrooms, you are at the cutting-edge of industry, studying the practices that others will later adopt and teach. As any business leader will tell you, in a competitive environment, being first puts you at a distinct advantage over your peers. Learn economic development from Ray Fisman, the Lambert Family Professor of Social Enterprise and a rising star in the field, or real estate from Chris Mayer, the Paul Milstein Professor of Real Estate, a renowned expert and frequent commentator on complex housing issues. This way, when you complete your degree, you'll be set up to succeed.
Columbia Business School in conjunction with the Office of the Dean provides its faculty, PhD students, and other research staff with resources and cutting edge tools and technology to help push the boundaries of business research.
Specifically, our goal is to seamlessly help faculty set up and execute their research programs. This includes, but is not limited to:
All these activities help to facilitate and streamline faculty research, and that of the doctoral students working with them.
The article offers the authors' views on expatriate management programs and the benefits from executives interacting with the people and institutions of the host country. The idea that international experience or interaction between foreign managers and local people will help managers become more creative, entrepreneurial, and successful is discussed. The concept of integrative complexity in bi-cultural managers which enhances job performance is mentioned.
The Kidney Case is multi-person exercise that involves the allocation of a single kidney. Students read profiles of eight candidates for the kidney and make a first allocation decision. Each candidate was designed to be high on some allocation principles but low or unknown on others (e.g., best, match, time in cue, age, personal responsibility for disease, future benefits to society, etc.). Then, students are put into groups and assigned to advocate for one of the candidates. Each group will prepare and give a 3-minute presentation on why their candidate should receive the kidney.
Emissions abatement alone cannot address the consequences of global warming for weather disasters. We model how society adapts to manage disaster risks to capital stock. Optimal adaptation — a mix of firm-level efforts and public spending — varies as society learns about the adverse consequences of global warming for disaster arrivals. Taxes on capital are needed alongside those on carbon to achieve the first best.
What are the sources of the returns to education? We study the allocation of higher education graduates from public institutions in Ohio across firms. We present three results. First, we confirm findings in the earlier literature of large pay differences across degrees. Second, we show that up to one quarter of pay premiums for higher degrees are explained by between-firm pay differences. Third, higher education degrees are associated with greater representation at the best-paying firms.
This article focuses on the role of threats in negotiations. Broadly speaking, a threat is a proposition that issues demands and warns of the costs of noncompliance. Even if neither party resorts to them, potential threats shadow most negotiations. Researchers have found that people actually evaluate their counterparts more favorably when they combine promises with threats rather than extend promises alone. Whereas promises encourage exploitation, the threat of punishment motivates cooperation.
An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. We quantify news novelty – changes in the distribution of news text – through an entropy measure, calculated using a recurrent neural network applied to a large news corpus. Entropy is a better out-of-sample predictor of market returns than a collection of standard measures. Cross-sectional entropy exposure carries a negative risk premium, suggesting that assets that positively covary with entropy hedge the aggregate risk associated with shifting news language.
Problem definition: Motivated by ride-hailing platforms such as Uber, Lyft and Didi, we study the problem of matching riders with self-interested drivers over a spatial network.
We consider experimentation in settings where, due to interference or other concerns, experimental units are coarse. ‘Region-split’ experiments on online platforms, where an intervention is applied to a single region over some experimental horizon, are one example of such a setting. Synthetic control is the state-of-the-art approach to inference in such experiments. The cost of these experiments is high since the opportunity cost of a sub-optimal intervention is borne by an entire region over the length of the experiment.
We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework. With this framework, we define an intuitive family of control policies that include Thompson sampling (TS) and the Bayesian optimal policy as endpoints. Analogous to TS, which, at each decision epoch pulls an arm that is best with respect to the randomly sampled parameters, our algorithms sample entire future reward realizations and take the corresponding best action.
This paper studies product ranking mechanisms of a monopolistic online platform in the presence of social learning. The products’ quality is initially unknown, but consumers can sequentially learn it as online reviews accumulate. A salient aspect of our problem is that consumers, who want to purchase a product from a list of items displayed by the platform, incur a search cost while scrolling down the list. In this setting, the social learning dynamics, and hence the demand, is affected by the interplay of two unique features: substitution and ranking effects.
We developed a predictive statistical model to identify donor–recipient characteristics related to kidney graft survival in the Chilean population. Given the large number of potential predictors relative to the sample size, we implemented an automated variable selection mechanism that could be revised in future studies as more national data is collected. Materials and Methods: A retrospective multicenter study was conducted to analyze data from 822 adult kidney transplant recipients from adult donors between 1998 and 2018.
Emergency Departments (EDs) typically have multiple areas where patients of different acuity levels receive treatments. In practice, different areas often operate with fixed nurse staffing levels. When there are substantial imbalances in congestion among different areas, it could be beneficial to deviate from the original assignment and reassign nurses. However, reassignments typically are only feasible at the beginning of 8-12-hour shifts, providing partial flexibility in adjusting staffing levels.
We develop a model of information and portfolio choice in which ex ante identical investors choose to specialize because of fixed attention costs required in learning about securities. Without this friction, investors would invest in all securities and would be indifferent across a wide range of information choices. When securities' dividends depend on an aggregate (macro) risk factor and an idiosyncratic (micro) shocks, fixed attention costs lead investors to specialize in either macro or micro information.
Background
Equitable protocols to triage life-saving resources must be specified prior to shortages in order to promote transparency, trust and consistency. How well proposed utilitarian protocols perform to maximize lives saved is unknown. We aimed to estimate the survival rates that would be associated with implementation of the New York State 2015 guidelines for ventilator triage, and to compare them to a first-come-first-served triage method.
Methods
The COVID-19 pandemic has caused great disruption to the service sector, and it has, in turn, adapted by implementing measures that reduce physical contact among employees and users; examples include home-office work and the setting of occupancy restrictions at indoor locations.
The composition of natural liquidity has been changing over time. An analysis of intraday volumes for the S&P500 constituent stocks illustrates that (i) volume surprises, i.e., deviations from their respective forecasts, are correlated across stocks, and (ii) this correlation increases during the last few hours of the trading session.
We consider a liquidation problem in which a risk-averse trader tries to liquidate a fixed quantity of an asset in the presence of market impact and random price fluctuations. When deciding the liquidation strategy, the trader encounters a trade-off between the transaction costs incurred due to market impact and the volatility risk of holding the position.
Service systems are typically limited resource environments where scarce capacity is reserved for the most urgent customers. However, there has been a growing interest in the use of proactive service when a less urgent customer may become urgent while waiting. On one hand, providing service for customers when they are less urgent could mean that fewer resources are needed to fulfill their service requirement. On the other hand, using limited capacity for customers who may never need the service in the future takes the capacity away from other more urgent customers who need it now.
Online allocation problems with resource constraints are central problems in revenue management and online advertising. In these problems, requests arrive sequentially during a finite horizon and, for each request, a decision maker needs to choose an action that consumes a certain amount of resources and generates reward. The objective is to maximize cumulative rewards subject to a constraint on the total consumption of resources.
When an inventory manager attempts to construct probabilistic models of demand based on past data, demand samples are almost never available: only sales data can be used. This limitation, referred to as demand censoring, introduces an exploration-exploitation trade-off as the ordering decisions impact the information collected. Much of the literature has sought to understand how operational decisions should be modified to incorporate this trade-off. We ask an even more basic question: when does the exploration-exploitation trade-off matter?
Bitcoin provides its users with transaction-processing services which are similar to those of traditional payment systems. This article models the novel economic structure implied by Bitcoin’s innovative decentralized design, which allows the payment system to be reliably operated by unrelated parties called miners. We find that this decentralized design protects users from monopoly pricing. Competition among service providers within the platform and free entry imply no entity can profitably affect the level of fees paid by users.
The Covid-19 pandemic crisis is ongoing, and it its wake has brought tremendous loss of life and economic loss, disruption and uncertainty. It has simultaneously brought to the surface important challenges about global healthcare systems, political systems and institutions and their response to the multi-faceted crisis, the connectedness and dependencies of modern economies through global supply chains, and issues of inequity, as manifested in segments of the population that have been most affected in terms of health and economic outcomes through the crisis.
In modern equity markets, participants have a choice of many exchanges at which to trade. Exchanges typically operate as electronic limit order books under a "price-time" priority rule and, in turn, can be modeled as multi-class FIFO queueing systems. A market with multiple exchanges can be thought of as a decentralized, parallel queueing system. Heterogeneous traders that submit limit orders select the exchange, i.e., the queue, in which to place their orders by trading off financial considerations against anticipated delays until their orders may fill.
Pricing is central to many industries and academic disciplines ranging from Operations Research to Computer Science and Economics. In the present paper, we study data-driven optimal pricing in low informational environments. We analyze the following fundamental problem: how should a decision-maker optimally price based on a single sample of the willingness-to-pay (WTP) of customers. The decision-maker's objective is to select a general pricing policy with maximum competitive ratio when the WTP distribution is only known to belong to some broad set.
We develop a model of investor information choices and asset prices where the availability of information about fundamentals is time-varying. A competitive research sector produces more information when more investors are willing to pay for that research. This feedback, from investor willingness to pay for information to more information production, generates two regimes in equilibrium, one having high prices and low volatility, the other the opposite. The low-price, high-volatility regime is associated with greater information asymmetry between informed and uninformed investors.
We study the classical newsvendor problem in which the decision-maker must trade-off underage and overage costs. In contrast to the typical setting, we assume that the decision-maker does not know the underlying distribution driving uncertainty but has only access to historical data. In turn, the key questions are how to map existing data to a decision and what type of performance to expect as a function of the data size.
We consider the pricing problem faced by a revenue maximizing platform matching price-sensitive customers to flexible supply units within a geographic area. This can be interpreted as the problem faced in the short-term by a ride-hailing platform. We propose a two-dimensional framework in which a platform selects prices for different locations, and drivers respond by choosing where to relocate in equilibrium based on prices, travel costs and driver congestion levels.
Many service systems are staffed by workers who work in shifts. In this work, we study the dynamic assignment of servers to different areas of a service system at the beginning of discrete time-intervals, i.e., shifts. The ability to reassign servers at discrete intervals, rather than continuously, introduces a partial flexibility that provides an opportunity for reducing the expected waiting time of customers.
Patients whose transfer to the Intensive Care Unit (ICU) is unplanned are prone to higher mortality rates and longer length-of-stay than those who were admitted directly to the ICU. Recent advances in machine learning to predict patient deterioration have introduced the possibility of proactive transfer from the ward to the ICU. In this work, we study the problem of finding robust patient transfer policies which account for uncertainty in statistical estimates due to data limitations when optimizing to improve overall patient care.
Deciding between the use of market orders and limit orders is an important question in practical optimal trading problems. A key ingredient in making this decision is understanding the uncertainty of the execution of a limit order, that is, the fill probability or the probability that an order will be executed within a certain time horizon. Equivalently, one can estimate the distribution of the time-to-fill. We propose a data-driven approach based on a recurrent neural network to estimate the distribution of time-to-fill for a limit order conditional on the current market conditions.
We study the problems of offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action an oracle with full knowledge of the objective function would have taken. We aim to minimize regret, which is defined as the difference between our losses and the ones incurred by an all-knowing oracle.
We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on observations at a single historical price? The decision-maker optimizes over (potentially randomized) pricing policies to maximize the worst-case ratio of the revenue it can garner compared to an oracle with full knowledge of the distribution of values, when the latter is only assumed to belong to broad non-parametric set. In particular, our framework applies to the widely used regular and monotone non-decreasing hazard rate (mhr) classes of distributions.
One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions including e-mails, display ads and search ads to eventual conversion. Several heuristics are used for attribution in practice; however, there is no formal justification for them and many of these fail even in simple canonical settings. The main contribution in this work is to develop an axiomatic framework for attribution in online advertising.
We analyze methods for selecting topics in news articles to explain stock returns. We find, through empirical and theoretical results, that supervised Latent Dirichlet Allocation (sLDA) implemented through Gibbs sampling in a stochastic EM algorithm will often overfit returns to the detriment of the topic model. We obtain better out-of-sample performance through a random search of plain LDA models. A branching procedure that reinforces effective topic assignments often performs best. We test these methods on an archive of over 90,000 news articles about S&P 500 firms.
Auctions are widely used in practice. While also extensively studied in the literature, most of the developments rely on the significant common prior assumption. We study the design of optimal prior-independent selling mechanisms: buyers do not have any information about their competitors and the seller does not know the distribution of values, but only a general class it belongs to.
The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence. The Fact Extraction and VERification (FEVER) dataset provides such a resource for evaluating end-to-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction.
The Centers for Medicare & Medicaid Services (CMS) and the National Quality Forum have endorsed the 30-day mortality rate as an important indicator of hospital quality. Concerns have been raised, however, as to whether post-discharge mortality rates are reasonable measures of hospital quality as they consider the frequency of an event that occurs after a patient is discharged and no longer under the watch and care of hospital staff. Estimating the causal effect of length-of-stay (LOS) on post-discharge mortality from retrospective data introduces a number of econometric challenges.
Objectives:
To examine whether and how step-down unit admission after ICU discharge affects patient outcomes.
Design:
Retrospective study using an instrumental variable approach to remove potential biases from unobserved differences in illness severity for patients admitted to the step-down unit after ICU discharge.
Setting:
Ten hospitals in an integrated healthcare delivery system in Northern California.
Patients:
The multi-armed bandit problem has been the subject of decades of intense study in statistics, operations research, electrical engineering, computer science, and economics. A “one-armed bandit” is a somewhat antiquated term for a slot machine, which tends to “rob” players of their money. The colorful name for our problem comes from a motivating story in which a gambler enters a casino and sits down at a slot machine with multiple levers, or arms, that can be pulled. When pulled, an arm produces a random payout drawn independently of the past.
Many financial markets are operated as electronic limit order books (LOBs). Over short time scales, seconds to minutes, LOBs can be best understood and modeled as stochastic dynamical systems, and, specifically, ones that exhibit interesting and relevant queueing phenomena. I will offer a brief overview of algorithmic trading in a limit order book, and highlight how queueing phenomena play an important role in trade execution, and as a consequence in market behavior.
In a multi-armed bandit (MAB) problem a gambler needs to choose at each round of play one of K arms, each characterized by an unknown reward distribution. Reward realizations are only observed when an arm is selected, and the gambler's objective is to maximize cumulative expected earnings over some planning horizon of length T. To do this, the gambler needs to acquire information about arms (exploration) while simultaneously optimizing immediate rewards (exploitation). The gambler's policy is measured relative to a (static) oracle that knows the identity of the best arm a priori.
We find that an increase in the "unusualness" of news with negative sentiment predicts an increase in stock market volatility. Similarly, unusual positive news forecasts lower volatility. Our analysis is based on more than 360,000 articles on 50 large financial companies, published in 1996–2014. Unusualness interacted with sentiment forecasts volatility at both the company-specific and aggregate level. These effects persist for several months. Furthermore, unusual news is reflected in volatility more slowly at the aggregate than at the company-specific level.
Motivated by the proliferation of user-generated product-review information and its widespread use, this note studies a market where consumers are heterogeneous in terms of their willingness-to-pay for a new product. Each consumer observes the binary reviews (like or dislike) of consumers who purchased the product in the past and uses Bayesian updating to infer the product quality.
The well-documented underreaction of stock prices to news exhibits substantial time variation. Higher risk-bearing capacity of financial intermediaries, lower passive ownership of stocks, and more informative news increase price responses to contemporaneous news; surprisingly, they also increase price responses to lagged news (underreaction). Our findings are not driven by short-sale constraints, serial correlation in news flow, or improved information processing capacity. We discuss possible mechanisms based on investor behavior and strategic order-splitting by institutions.
We consider a fundamental pricing model in which a fixed number of units of a reusable resource are used to serve customers. Customers arrive to the system according to a stochastic process and upon arrival decide whether or not to purchase the service, depending on their willingness-to-pay and the current price. The service time during which the resource is used by the customer is stochastic and the firm may incur a service cost. This model represents various markets for reusable resources such as cloud computing, shared vehicles, rotable parts, and hotel rooms.
The paper's introduction offers a high-level review of Bitcoin's features, especially its governance by protocol. The paper proceeds to summarize Bitcoin's analysis as a payment system. It pays particular attention to a comparison between Bitcoin and a firm-run payment system.