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ESMT Working Paper
ESMT Working Paper No. 22-01
Mirko Kremer, Francis de Véricourt (2022)
Subject(s)
Management sciences, decision sciences and quantitative methods
Keyword(s)
congestion, diagnostic accuracy, experiments, partially observable markov decision process, path-dependent decision making, undertesting, task completion bias
To study the effect of congestion on the fundamental trade-off between diagnostic accuracy and speed, we empirically test the predictions of a formal sequential testing model in a setting where the gathering of additional information can improve diagnostic accuracy, but may also take time and increase congestion as a result. The efficient management of such systems requires a careful balance of congestion-sensitive stopping rules. These include diagnoses made based on very little or no diagnostic information, and the stopping of diagnostic processes while waiting for information. We test these rules under controlled laboratory conditions, and link the observed biases to system dynamics and performance. Our data shows that decision makers (DMs) stop diagnostic processes too quickly at low congestion levels where information acquisition is relatively cheap. But they fail to stop quickly enough when increasing congestion requires the DM to diagnose without testing, or diagnose while waiting for test results. Essentially, DMs are insufficiently sensitive to congestion. As a result of these behavioral patterns, DMs manage the system with both lower-than-optimal diagnostic accuracy and higher-than-optimal congestion cost, underperforming on both sides of the accuracy/speed trade-off.
Pages
40
ISSN (Print)
1866–3494
ESMT Working Paper
ESMT Working Paper No. 20-01 (R3)
Forthcoming in Management Science.
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management; Technology, R&D management
Keyword(s)
Data, machine learning, data product, pricing, incentives, contracting
This paper explores how firms that lack expertise in machine learning (ML) can leverage the so-called AI Flywheel effect. This effect designates a virtuous cycle by which, as an ML product is adopted and new user data are fed back to the algorithm, the product improves, enabling further adoptions. However, managing this feedback loop is difficult, especially when the algorithm is contracted out. Indeed, the additional data that the AI Flywheel effect generates may change the provider's incentives to improve the algorithm over time. We formalize this problem in a simple two-period moral hazard framework that captures the main dynamics among ML, data acquisition, pricing, and contracting. We find that the firm's decisions crucially depend on how the amount of data on which the machine is trained interacts with the provider's effort. If this effort has a more (less) significant impact on accuracy for larger volumes of data, the firm underprices (overprices) the product. Interestingly, these distortions sometimes improve social welfare, which accounts for the customer surplus and profits of both the firm and provider. Further, the interaction between incentive issues and the positive externalities of the AI Flywheel effect has important implications for the firm's data collection strategy. In particular, the firm can boost its profit by increasing the product's capacity to acquire usage data only up to a certain level. If the product collects too much data per user, the firm's profit may actually decrease, i.e., more data is not necessarily better.
Pages
48
ISSN (Print)
1866–3494
ESMT Working Paper
ESMT Working Paper No. 20-03 (R2)
Forthcoming in Management Science.
Francis de Véricourt, Huseyin Gurkan, Shouqiang Wang (2021)
Subject(s)
Health and environment; Information technology and systems
Keyword(s)
Public health, epidemic control, information design, strategic behavior
This paper explores how governments may efficiently inform the public about an epidemic to induce compliance with their confinement measures. Using an information design framework, we find the government has an incentive to either downplay or exaggerate the severity of the epidemic if it heavily prioritizes the economy over population health or vice versa. Importantly, we find that the level of economic inequality in the population has an effect on these distortions. The more unequal the disease's economic impact on the population is, the less the government exaggerates and the more it downplays the severity of the epidemic. When the government weighs the economy and population health sufficiently equally, however, the government should always be fully transparent about the severity of the epidemic.


Pages
41
ISSN (Print)
1866–3494
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management; Technology, R&D management
Keyword(s)
Machine-learning, rational inattention, human-machine collaboration, cognitive effort
The rapid adoption of AI technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks. In fact, when used in collaboration, machines can significantly enhance the complementary strengths of humans. Indeed, because of their immense computing power, machines can perform specific tasks with incredible accuracy. In contrast, human decision-makers (DM) are flexible and adaptive but constrained by their limited cognitive capacity. This paper investigates how machine-based predictions may affect the decision process and outcomes of a human DM. We study the impact of these predictions on decision accuracy, the propensity and nature of decision errors as well as the DM's cognitive efforts. To account for both flexibility and limited cognitive capacity, we model the human decision-making process in a rational inattention framework. In this setup, the machine provides the DM with accurate but sometimes incomplete information at no cognitive cost. We fully characterize the impact of machine input on the human decision process in this framework. We show that machine input always improves the overall accuracy of human decisions, but may nonetheless increase the propensity of certain types of errors (such as false positives). The machine can also induce the human to exert more cognitive efforts, even though its input is highly accurate. Interestingly, this happens when the DM is most cognitively constrained, for instance, because of time pressure or multitasking. Synthesizing these results, we pinpoint the decision environments in which human-machine collaboration is likely to be most beneficial.
Pages
40
ISSN (Print)
1866–3494
ESMT Working Paper
ESMT Working Paper No. 17-03
Francis de Véricourt, Jeffrey Hales, Gilles Hilary, Jordan Samet (2017)
Subject(s)
Finance, accounting and corporate governance
Keyword(s)
Budgetary controls, budgets, creativity, capital constraints, originality
When setting budgets, managers may place constraints on how resources can be used in an effort to mitigate opportunistic behavior by subordinates. These restrictions can affect the ability of the subordinate to succeed in the budgeted task, but may also have an unintended spillover effect on the ability to innovate. Using an experiment, we find that individuals working under higher budgetary constraints are more efficient in their use of budgeted resources, but are less successful in the budgeted tasks, than their counterparts working under lower budgetary constraints. Importantly, we find that imposing budgetary constraints also causes employees to subsequently generate fewer highly original and creative ideas in an unrelated activity. These findings suggest that budget structures can have unintended consequences on the innovative capabilities of organizations. This paper contributes to the expansive budgeting literature by showing budgetary control design has organizational performance implications beyond the specified budgeted activity.
Pages
29
ISSN (Print)
1866–3494
ESMT Working Paper
ESMT Working Paper No. 14-05
Tian Chan, Francis de Véricourt, Omar Besbes (2014)
Subject(s)
Product and operations management
Keyword(s)
Maintenance repair, service contracting, co-production, empirical operations management, service chain value, healthcare industry
Equipment manufacturers offer different types of maintenance service plans (MSPs) that delineate payment structures between equipment operators and maintenance service providers. These MSPs allocate risks differently and thus induce different kinds of incentives. A fundamental question, therefore, is how such structures impact service performance and the service chain value. We answer empirically this question. Our study is based on a unique panel data covering the sales and service records of over 700 diagnostic medical body scanners. By exploiting the presence of a standard warranty period, we overcome the key challenge of isolating the incentive effects of MSPs on service performance from the confounding effects of adverse selection. We found that moving an operator from a basic pay-per-service plan to a fixed-fee full-protection plan leads to both a reduction in reliability and an increase in service costs. We further show that the increase in cost is driven by both the operator and the service provider. Our results point to the presence of losses in service chain value in the maintenance of medical equipment, and provide the first evidence that a basic pay-per-service plan, where the risk of equipment failure is borne by the operator, can actually improve performance and costs.
Pages
32
ISSN (Print)
1866–3494
ESMT Working Paper
ESMT Working Paper No. 14-03
Francis de Véricourt, Denis Gromb (2014)
Subject(s)
Finance, accounting and corporate governance; Management sciences, decision sciences and quantitative methods
Keyword(s)
Capacity, optimal contracts, financial constraints, newsvendor model
This paper studies the interplay between the operational and financial facets of capacity investment. We consider the capacity choice problem of a firm with limited liquidity and whose access to external capital markets is hampered by moral hazard. The firm must therefore not only calibrate its capacity investment and the corresponding funding needs, but also optimize its sourcing of funds. Importantly, the set of available sources of funds is derived endogenously and includes standard financial claims (debt, equity, etc.). We find that when higher demand realizations are more indicative of high effort, debt financing is optimal for any given capacity level. In this case, the optimal capacity is never below the efficient capacity level but sometimes strictly above that level. Further, the optimal capacity level increases with the moral hazard problem's severity and decreases with the firm's internal funds. This runs counter to the newsvendor logic and to the common intuition that by raising the cost of external capital and hence the unit capacity cost, financial market frictions should lower the optimal capacity level. We trace the value of increasing capacity beyond the efficient level to a bonus effect and a demand elicitation effect. Both stem from the risk of unmet demand, which is characteristic of capacity decisions under uncertainty.
Pages
32
ISSN (Print)
1866–3494
Subject(s)
Management sciences, decision sciences and quantitative methods