Publication records

Artificial intelligence systems are increasingly demonstrating their capacity to make better predictions than human experts. Yet, recent studies suggest that professionals sometimes doubt the quality of these systems and overrule machine based prescriptions. This paper explores the extent to which a decision maker (DM) supervising a machine to make high-stake decisions can properly assess whether the machine produces better recommendations. To that end, we study a set-up in which a machine performs repeated decision tasks (e.g., whether to perform a biopsy) under the DM’s supervision. Because stakes are high, the DM primarily focuses on making the best choice for the task at hand. Nonetheless, as the DM observes the correctness of the machine’s prescriptions across tasks, she updates her belief about the machine. However, the DM is subject to a so-called verification bias such that the DM verifies the machine’s correctness and updates her belief accordingly only if she ultimately decides to act on the task. In this set-up, we characterize the evolution of the DM’s belief and overruling decisions over time. We identify situations under which the DM hesitates forever whether the machine is better, i.e., she never fully ignores but regularly overrules it. Moreover, the DM sometimes wrongly believes with positive probability that the machine is better. We fully characterize the conditions under which these learning failures occur and explore how mistrusting the machine affects them. These findings provide a novel explanation for human-machine complementarity and suggest guidelines on the decision to fully adopt or reject a machine.

The rapid adoption of AI technologies by many organizations has recently raised concerns that AI may even-tually replace humans in certain tasks. In fact, when used in collaboration, machines can significantly enhancethe complementary strengths of humans. Indeed, because of their immense computing power, machines canperform specific tasks with incredible accuracy. In contrast, human decision-makers (DM) are flexible andadaptive but constrained by their limited cognitive capacity. This paper investigates how machine-basedpredictions may affect the decision process and outcomes of a human DM. We study the impact of thesepredictions on decision accuracy, the propensity and nature of decision errors as well as the DM’s cognitiveefforts. To account for both flexibility and limited cognitive capacity, we model the human decision-makingprocess in a rational inattention framework. In this setup, the machine provides the DM with accurate butsometimes incomplete information at no cognitive cost. We fully characterize the impact of machine inputon the human decision process in this framework. We show that machine input always improves the overallaccuracy of human decisions, but may nonetheless increase the propensity of certain types of errors (such asfalse positives). The machine can also induce the human to exert more cognitive efforts, even though its inputis 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 environmentsin which human-machine collaboration is likely to be most beneficial. Our main insights hold for differentinformation and reward structures, and when the DM mistrust the machine.

Markush structures are molecular skeletons containing not only specific atoms but also placeholders to represent broad sets of chemical (sub)structures. As genus claims, they allow a vast number of compounds to be claimed in a patent application without having to specify every single chemical entity. While Markush structures raise important questions regarding the functioning of the patent system, innovation researchers have been surprisingly silent on the topic. This paper summarizes the ongoing policy debate about Markush structures and provides first empirical insights into how Markush structures are used in patent documents in the pharmaceutical industry and how they affect important outcomes in the patent prosecution process. While not causing frictions in the patent prosecution process, patent documents con-taining Markush structures have an increased likelihood to restrict the patentability of follow-on inventions and to facilitate the construction of broad patent fences.

The literature on quality-management standards has generally focused on the drivers, motivations, and performance effects of adopting such standards. Yet the last decade has witnessed a substantial degree of decertification behavior, as organizations have increasingly decided to voluntarily withdraw from quality-management standards by not recertifying. While the drivers of the decision to initially adopt quality-management standards have been extensively studied, the drivers of the decision to decertify have received scant scholarly attention. We argue that innovative organizations are generally prone to retaining quality-management certification and thus exhibit a tendency to not abandon certification; however, radically-innovative organizations are more prone than incrementally-innovative organizations to discontinue quality-management standards and thereby exhibit a tendency to withdraw from quality certification. We compile World Bank data surveying facilities based in 50 countries and 103 industrial sectors across the 2003 to 2017 period. Taking advantage of the data’s panel properties yields a dataset composed of up to 1,755 facility-level observations of recertification decisions for empirical analysis. Our empirical testing employs a probit estimation technique that accounts for the appropriate fixed effects and generates results that support our theoretical priors regarding decertification behavior.

Anscombe & Aumann (1963) offer a definition of subjective probability in terms of comparisons with objective probabilities. That definition - which has provided the basis for much of the succeeding work on subjective probability - presumes that the subjective probability of an event is independent of the prize consequences of that event, a property we term Prize Independence. We design experiments to test Prize Independence and find that a large fraction of our subjects violate it; thus, they do not have subjective probabilities. These findings raise questions about the empirical relevance of much of the literature on subjective probability.

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.

An increasing number of countries have introduced some form of prohibition of abuses of economic dependence or broadened the scope of their existing legislation. Yet, very little has been written on the economics of economic dependence, that is on economic reasoning, tools or metrics that can be relied upon to identify whether a company is economically dependent on another company. The present paper aims to fill this gap, and argues that bargaining theory and the economics of relative market power can be helpful to characterise economic dependence. We summarise a number of takeaways from this literature, and describe empirical strategies that can be relied upon to try and quantify economic dependence in specific cases.

 

View all ESMT Working Papers in the ESMT Working Paper Series here. ESMT Working Papers are also available via SSRN, RePEc, EconStor, and the German National Library (DNB).

Product proliferation occurs in supply chains when manufacturers respond to diverse market needs by trying to produce a range of products from a limited variety of raw materials. In such a setting, manufacturers can establish market responsiveness and/or cost efficiency in alternative ways. Delaying the point of the proliferation helps manufacturers improve their responsiveness by postponing the ordering decisions of the final products until there is partial or full resolution of the demand uncertainty. This strategy can be implemented in two different ways: (1) redesigning the operations so that the point of proliferation is swapped with a downstream operation or (2) reducing the lead times. To establish cost efficiency, manufacturers can systematically reduce their operational costs or postpone the high-cost operations. We consider a multi-echelon and multi-product newsvendor problem with demand forecast evolution to analyze the value of each operational lever of the responsiveness and the efficiency. We use a generalized forecast-evolution model to characterize the demand-updating process, and develop a dynamic optimization model to determine the optimal order quantities at different echelons. Using anonymized data of Kordsa Inc., a global manufacturer of advanced composites and reinforcement materials, we show that our model outperforms a theoretical benchmark of the repetitive newsvendor model. We demonstrate that reducing the lead time of a downstream operation is more beneficial to manufacturers than reducing the lead time of an upstream operation by the same amount, whereas reducing the upstream operational costs is more favorable than reducing the downstream operational costs. We also indicate that delaying the proliferation may cause a loss of profit, even if it can be achieved with no additional costs. Finally, a decision typology is developed, which shows effective operational strategies depending on product/market characteristics and process flexibility.

The deployment of new broadband networks (NBNs) based on fiber-optic transmission technologies promises high gains in terms of productivity and economic growth, and has attracted subsidies worth billions from governments around the world in the form of various state aid programs. Yet, the effectiveness and the efficiency of such programs remains largely unstudied. We employ panel data from 32 OECD countries during 2002-2019 to provide robust empirical evidence of both. We find that state aid significantly increases NBNs by facilitating the deployment of new connections to 22% of households in the short term and 39.2% in the long term. By comparing the actual amounts of state aid support to the estimated impact on GDP growth, we also find it to be highly cost efficient, as the programs break even after three years on average.

 

View all ESMT Working Papers in the ESMT Working Paper Series here. ESMT Working Papers are also available via SSRN, RePEc, EconStor, and the German National Library (DNB).

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.