Breaking the Cycle: How the News and Markets Created a Negative Feedback Loop in COVID-19
New research from CBS Professor Harry Mamaysky reveals how negativity in the news and markets can escalate a financial crisis.
New research from CBS Professor Harry Mamaysky reveals how negativity in the news and markets can escalate a financial crisis.
Adapted from “Global Value Chains in Developing Countries: A Relational Perspective from Coffee and Garments,” by Laura Boudreau of Columbia Business School, Julia Cajal Grossi of the Geneva Graduate Institute, and Rocco Macchiavello of the London School of Economics.
Adapted from “Online Advertising as Passive Search,” by Raluca M. Ursu of New York University Stern School of Business, Andrey Simonov of Columbia Business School, and Eunkyung An of New York University Stern School of Business.
This paper from Columbia Business School, “Meaning of Manual Labor Impedes Consumer Adoption of Autonomous Products,” explores marketing solutions to some consumers’ resistance towards autonomous products. The study was co-authored by Emanuel de Bellis of the University of St. Gallen, Gita Johar of Columbia Business School, and Nicola Poletti of Cada.
Co-authored by John B. Donaldson of Columbia Business School, “The Macroeconomics of Stakeholder Equilibria,” proposes a model for a purely private, mutually beneficial financial agreement between worker and firm that keeps decision-making in the hands of stockholders while improving the employment contract for employees.
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.
We address the simultaneous determination of pricing, production, and capacity investment decisions by a monopolistic firm in a multi-period setting under demand uncertainty. We analyze the optimal decision with particular emphasis on the relationship between price and capacity. We consider models that allow for either bi-directional price changes or models with markdowns only, and in the latter case we prove that capacity and price are strategic substitutes.
We consider the Kiefer-Wolfowitz (KW) stochastic approximation algorithm and derive general upper bounds on its mean-squared error. The bounds are established using an elementary induction argument and phrased directly in the terms of tuning sequences of the algorithm. From this we deduce the non- necessity of one of the main assumptions imposed on the tuning sequences in the Kiefer-Wolfowitz paper and essentially all subsequent literature.
We consider the Kiefer-Wolfowitz (KW) stochastic approximation algorithm and derive general upper bounds on its mean-squared error. The bounds are established using an elementary induction argument and phrased directly in the terms of tuning sequences of the algorithm. From this we deduce the non- necessity of one of the main assumptions imposed on the tuning sequences in the Kiefer-Wolfowitz paper and essentially all subsequent literature.
In many service industries, companies compete with each other on the basis of the waiting time their customers experience, along with other strategic instruments such as the price they charge for their service. The objective of this paper is to conduct an empirical study of an important industry to measure to what extent waiting time performance impacts different firms' market shares and price decisions.
We consider a pricing problem in an environment where the customers' willingness-to-pay (WtP) distribution may change at some point over the selling horizon. Customers arrive sequentially and make purchase decisions based on a quoted price and their private reservation price. The seller knows the WtP distribution pre- and post-change, but does not know the time at which this change occurs. The performance of a pricing policy is measured in terms of regret: the loss in revenues relative to an oracle that knows the time of change prior to the start of the selling season.
We consider a pricing problem in an environment where the customers' willingness-to-pay (WtP) distribution may change at some point over the selling horizon. Customers arrive sequentially and make purchase decisions based on a quoted price and their private reservation price. The seller knows the WtP distribution pre- and post-change, but does not know the time at which this change occurs. The performance of a pricing policy is measured in terms of regret: the loss in revenues relative to an oracle that knows the time of change prior to the start of the selling season.
We propose and analyze a general periodic-review model in which the firm has access to a set of potential suppliers, each with specific yield and price characteristics. Assuming that unsatisfied demand is backlogged, the firm incurs three types of costs: (i) procurement costs, (ii) inventory-carrying costs for units carried over from one period to the next, and (iii) backlogging costs.
We propose a message-passing paradigm for resource allocation problems. This serves to connect ideas from the message-passing literature, which has primarily grown out of the communications, statistical physics, and artificial intelligence communities, with a problem central to operations research. This also provides a new framework for decentralized management that generalizes price-based systems by allowing incentives to vary across activities and consumption levels.
Consider a firm that sells products over repeated seasons, each of which includes a full-price period and a markdown period. The firm may deliberately understock products in the markdown period to induce high-value customers to purchase early at full price. Customers cannot perfectly anticipate availability. Instead, they use observed past capacities to form capacity expectations according to a heuristic smoothing rule. Based on their expectations of capacity, customers decide to buy either in the full-price period or in the markdown period.
Many companies do not know their marketing ROI because their organizations are not set up to evaluate marketing ROI.
This special issue features articles from the 9th Annual INFORMS Revenue Management and Pricing Section Conference at the Kellogg School of Management, Northwestern University during 22–23 June 2009. The conference featured 42 half hour talks by practitioners and researchers, as well as keynote addresses by Professor Anton Kleywegt of Georgia Tech and by Dr Matthew Schrag, the Director of Operations Research and Industrial Engineering at Delta Airlines. The conference was organized by Martin Lariviere and Baris Ata.
Applies economic, marketing, and finance concepts to develop a metric, Customer Value Added, that explains how marketing activities drive the financial performance of an organization. Includes empirical results for a consumer packaged goods company where Customer Value Added predicted revenue and contribution with R-squared values greater than 0.90.
How to view pricing, cross-selling, and customer loyalty during difficult economic times. (Reprinted from "Marketing in Difficult Times," Effective Executive, July, 2009, pp. 11-18.)
Discussion of different marketing strategies to employ during difficult times. (Reprinted from "Marketing in Difficult Times," Effective Executive, July, 2009, pp. 11-18.)
We consider an agent interacting with an unmodeled environment. At each time, the agent makes an observation, takes an action, and incurs a cost. Its actions can influence future observations and costs. The goal is to minimize the long-term average cost. We propose a novel algorithm, known as the active LZ algorithm, for optimal control based on ideas from the Lempel-Ziv scheme for universal data compression and prediction.
We establish that the min-sum message-passing algorithm and its asynchronous variants converge for a large class of unconstrained convex optimization problems, generalizing existing results for pairwise quadratic optimization problems. The main sufficient condition is that of scaled diagonal dominance. This condition is similar to known sufficient conditions for asynchronous convergence of other decentralized optimization algorithms, such as coordinate descent and gradient descent.
This article explains how the metric, Customer Value Added (CVA), can be applied to develop effective marketing and branding strategies. Strategies that are successful against competitors should focus on creating CVA that is greater than those produced by competitors. To do so, one must first regularly measure and monitor CVA by examining its components, perceived value and variable costs per unit. Next, one must develop strategies and tactics to increase CVA effectively and efficiently. In the long run, the organization that succeeds in achieving and maintaining the highest CVA wins.
Why companies have had difficulties determining marketing ROI and how they should approach evaluating marketing ROI. (Reprinted from Columbia Ideas at Work, "Many Happy Returns on Marketing," 8/31/2009, pp. 1-2.)
We study a capacity sizing problem in a service system that is modeled as a single-class queue with multiple servers and where customers may renege while waiting for service. A salient feature of the model is that the mean arrival rate of work is random (in practice this is a typical consequence of forecasting errors). The paper elucidates the impact of uncertainty on the nature of capacity prescriptions, and relates these to well established rules-of-thumb such as the square root safety staffing principle.
We study scheduling of multimedia traffic on the downlink of a wireless communication system. We examine a scenario where multimedia packets are associated with strict deadlines and are equivalent to lost packets if they arrive after their associated deadlines. Lost packets result in degradation of playout quality at the receiver, which is quantified in terms of the "distortion cost" associated with each packet. Our goal is to design a scheduler which minimizes the aggregate distortion cost over all receivers. We study the scheduling problem in a dynamic programming (DP) framework.
Discrete choice models are appealing for airline revenue management (RM) because they offer a means to profitably exploit preferences for attributes such as time of day, routing, brand, and price. They are also good at modeling demand for unrestricted fare class structures, which are widespread throughout the industry. However, there is little empirical research on the practicality and effectiveness of choice-based RM models. Toward this end, we report the results of a study of choice-based RM conducted with a major U.S. airline.
Discrete choice models are appealing for airline revenue management (RM) because they offer a means to profitably exploit preferences for attributes such as time of day, routing, brand, and price. They are also good at modeling demand for unrestricted fare class structures, which are widespread throughout the industry. However, there is little empirical research on the practicality and effectiveness of choice-based RM models. Toward this end, we report the results of a study of choice-based RM conducted with a major U.S. airline.
Consider a firm that owns a fixed capacity of a resource that is consumed in the production or delivery of multiple products. The firm strives to maximize its total expected revenues over a finite horizon, either by choosing a dynamic pricing strategy for each product or, if prices are fixed, by selecting a dynamic rule that controls the allocation of capacity to requests for the different products.
Economics is the study of how scarce resources are allocated. Operations research studies how to accomplish goals in the least costly manner. These fields have much to offer each other in terms of challenging problems that need to be solved and the techniques to solve them. This was the case after World War II, partly because the individuals who went on to be the leading scholars in economics and operations research worked together during WWII. In fact, the two fields share many early luminaries, including Arrow, Dantzig, Holt, Kantorovich, Koopmans, Modigliani, Scarf, and von Neumann.
The fields of statistics and econometrics have developed powerful methods for testing the validity (specification) of a model based on its fit to underlying data. Unlike statisticians, managers are typically more interested in the performance of a decision rather than the statistical validity of the underlying model. We propose a framework and a statistical test that incorporates decision performance into a measure of statistical validity. Under general conditions on the objective function, asymptotic behavior of our test admits a sharp and simple characterization.
The fields of statistics and econometrics have developed powerful methods for testing the validity (specification) of a model based on its fit to underlying data. Unlike statisticians, managers are typically more interested in the performance of a decision rather than the statistical validity of the underlying model. We propose a framework and a statistical test that incorporates decision performance into a measure of statistical validity. Under general conditions on the objective function, asymptotic behavior of our test admits a sharp and simple characterization.
Hospital ambulance diversions are prevalent and increasing nationwide as emergency departments experience growing congestion. Using negative binomial regressions, this paper links the number of acute myocardial infarction (AMI) deaths to the level and extent of diversion in the five boroughs of New York City. The results indicate that both high levels of ambulance diversion and simultaneous diversion across hospitals are associated with increasing numbers of deaths from AMI.
We characterize the equilibrium behavior in a broad class of competition models in which the competing firms' market shares are given by an attraction model, and the aggregate sales in the industry depend on the aggregate attraction value according to a general function. Each firm's revenues and costs are proportional with its expected sales volume, with a cost rate that depends on the firm's chosen attraction value according to an arbitrary increasing function.
We consider a single product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve), is not known.
We consider a single product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve), is not known.
We analyze a planning model for a firm or public organization that needs to cover uncertain demand for a given item by procuring supplies from multiple sources. The necessity to employ multiple suppliers arises from the fact that when an order is placed with any of the suppliers, only a random fraction of the order size is usable. The model considers a single demand season with a given demand distribution, where all supplies need to be ordered simultaneously before the start of the season.
We consider a revenue maximizing make-to-order manufacturer that serves a market of price and delay sensitive customers and operates in an environment in which the market size varies stochastically over time. A key feature of our analysis is that no model is assumed for the evolution of the market size. We analyze two main settings: i) the size of the market is observable at any point in time; and ii) the size of the market is not observable and hence cannot be used for decision-making.
We consider a revenue maximizing make-to-order manufacturer that serves a market of price and delay sensitive customers and operates in an environment in which the market size varies stochastically over time. A key feature of our analysis is that no model is assumed for the evolution of the market size. We analyze two main settings: i) the size of the market is observable at any point in time; and ii) the size of the market is not observable and hence cannot be used for decision-making.
Due to the increase in diversity of wireless devices, streaming media systems must be capable of serving multiple types of users. Scalable coding allows for adaptations without re-encoding. To account for various viewing capabilities of each user, such as different spatial resolutions, multiple distortion measures are used. In this paper, we examine the question of how to broadcast media packets with multiple distortion measures to multiple users. We cast the problem as a stochastic shortest path problem and use Dynamic Programming to find the optimal policy.
Donald E. Sexton, PhD, a professor of marketing at Columbia University and president of The Arrow Group, Ltd., discusses one key way to link marketing activity to financial performance.
We consider a call center model with multiple customer classes and multiple server pools. Calls arrive randomly over time, and the instantaneous arrival rates are allowed to vary both temporally and stochastically in an arbitrary manner. The objective is to minimize the sum of personnel costs and expected abandonment penalties by selecting an appropriate staffing level for each server pool.
We establish the convergence of the min-sum message passing algorithm for minimization of a quadratic objective function given a convex decomposition. Our results also apply to the equivalent problem of the convergence of Gaussian belief propagation.
This paper presents a general class of dynamic stochastic optimization problems we refer to as Stochastic Depletion Problems. A number of challenging dynamic optimization problems of practical interest are stochastic depletion problems. Optimal solutions for such problems are difficult to obtain, both from a pragmatic computational perspective as also from a theoretical perspective. As such, simple heuristics are desirable.
We develop a model for the competitive interactions in service industries where firms cater to multiple customer classes or market segments with the help of shared service facilities or processes so as to exploit pooling benefits. Different customer classes typically have distinct sensitivities to the price of service as well as the delays encountered.
Cross-selling is becoming an increasingly prevalent practice in call centers, due, in part, to its unique capability to allow firms to dynamically segment their callers and customize their product offerings accordingly. This paper considers a call center with cross-selling capability that serves a pool of customers that are differentiated in terms of their revenue potential and delay sensitivity. It studies the operational decisions of staffing, call routing, and cross-selling under various forms of customer segmentation.
We develop a competitive pricing model which combines the complexity of time-varying demand and cost functions and that of scale economies arising from dynamic lot sizing costs. Each firm can replenish inventory in each of the T periods into which the planing horizon is partitioned. Fixed as well as variable procurement costs are incurred for each procurement order, along with inventory carrying costs. Each firm adopts, at the beginning of the planning horizon, a (single) price to be employed throughout the horizon.
We develop a competitive pricing model which combines the complexity of time-varying demand and cost functions and that of scale economies arising from dynamic lot sizing costs. Each firm can replenish inventory in each of the T periods into which the planning horizon is partitioned. Fixed as well as variable procurement costs are incurred for each procurement order, along with inventory carrying costs. Each firm adopts, at the beginning of the planning horizon, a (single) price to be employed throughout the horizon.
Managing shipping vessel profitability is a central problem in marine transportation. We consider two commonly used types of vessels—liners (ships whose routes are fixed in advance) and trampers (ships for which future route components are selected based on available shipping jobs)—and formulate a vessel profit maximization problem as a stochastic dynamic program. For liner vessels, the profit maximization reduces to the problem of minimizing refueling costs over a given route subject to random fuel prices and limited vessel fuel capacity.
Traditional monopoly pricing models assume that firms have full information about the market demand and consumer preferences. In this article, we study a prototypical monopoly pricing problem for a seller with limited market information and different levels of demand learning capability under relative performance criterion of the competitive ratio (CR). We provide closed-form solutions for the optimal pricing policies for each case and highlight several important structural insights.
Generalizing earlier work on staffing and routing in telephone call centers, we consider a processing network model with large server pools and doubly stochastic input flows. In this model the processing of a job may involve several distinct operations. Alternative processing modes are also allowed. Given a finite planning horizon, attention is focused on the two-level problem of capacity choice and dynamic system control. A pointwise stationary fluid model (PSFM) is used to approximate system dynamics, which allows development of practical policies with a manageable computational burden.
Affine jump-diffusion (AJD) processes constitute a large and widely used class of continuous-time asset pricing models that balance tractability and flexibility in matching market data. The prices of e.g., bonds, options, and other assets in AJD models are given by extended pricing transforms that have an exponential-affine form; these transforms have been characterized in great generality by Duffie et al. [2000. Transform analysis and asset pricing for affine jump-diffusions. Econometrica 68, 1343–1376].
Previous research concludes that options are mispriced based on the high average returns, CAPM alphas, and Sharpe ratios of various put selling strategies. One criticism of these conclusions is that these benchmarks are ill suited to handle the extreme statistical nature of option returns generated by nonlinear payoffs. We propose an alternative way to evaluate the statistical significance of option returns by comparing historical statistics to those generated by option pricing models.
We consider the one-armed bandit problem of Woodroofe [J. Amer. Statist. Assoc. 74 (1979) 799–806], which involves sequential sampling from two populations: one whose characteristics are known, and one which depends on an unknown parameter and incorporates a covariate. The goal is to maximize cumulative expected reward. We study this problem in a minimax setting, and develop rate-optimal polices that involve suitable modifications of the myopic rule.
This paper introduces new variance reduction techniques and computational improvements to Monte Carlo methods for pricing American-style options. For simulation algorithms that compute lower bounds of American option values, we apply martingale control variates and introduce the local policy enhancement, which adopts a local simulation to improve the exercise policy. For duality-based upper bound methods, specifically the primal-dual simulation algorithm, we have developed two improvements.