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This paper documents the differences in pricing strategies between online and offline (brick-and-mortar) channels. We collect price data for identical products from leading online grocery retailers in the United States and complement it with offline data for the same products from scanner data. Our findings reveal a consistent pattern: online retailers exhibit higher price dispersion than their offline counterparts. More specifically, online grocers employ price algorithms that amplify price discrimination in three key dimensions: (1) over time (through frequent price changes), (2) across locations (by charging varying prices based on delivery zipcodes), and (3) across sellers (by setting dispersed prices for identical products across rival retailers).

We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN), a method for the generation of time-series data that is designed to support decision-making. The framework adopts a multi-Wasserstein loss on decision-related quantities and an overlapped block-sampling approach for sample efficiency. We characterize the generalization properties of DAT-CGAN and in application to a multi-period portfolio choice problem and financial time series data, we demonstrate better training stability and generative quality in regard to both raw data and decision-related quantities than strong GAN-based baselines.

Spatio-temporal modeling is an essential lens to understand many real-world phenomena from traffic to finance. There has been exciting work that explores spatio-temporal modeling with temporal graph convolutional networks. Often these methods assume that the spatial structure is static. We propose a new model Dyn-GWN for spatio-temporal learning from time-varying graphs. Our model relies on a novel module called the Tensor Graph Convolutional Module (TGCM), which captures dynamic trends in graphs effectively in the time-varying graph representations. This module has two components: (i) it applies temporal dilated convolutions both on the time-varying graph adjacency space and the time-varying features. (ii) it aggregates the higher-level latent representations from both time-varying components through a proposed layer TGCL. Experiments demonstrate the efficacy of these model across time-series data from finance and traffic domains. Dyn-GWN can give up to better out-of-sample performance than prior methods that learn from time-varying graphs, e.g., EvolveGCN and TM-GCN. Interestingly, Dyn-GWN can be ∼ 300 × faster than EvolveGCN, which is the more competitive baseline from state-of-the-art models that cater to time-varying graphs.

This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep learning models, as demonstrated by extensive experiments on both tabular and image data sets. The results are further validated by ablation experiments and SHAP value analysis, which reveal the interactions and trade-offs between the different evaluation metrics. To support practitioners applying our framework, we provide a prescriptive approach that offers recommendations for selecting an appropriate training loss function based on their specific objectives. All the code to reproduce the results can be found at https://github.com/kimvc7/HDL

This paper focuses on the development of an integrated system that can rapidly and accurately extract the geometrical dimensions of a physical object assisted by a robotic hand and generate a 3D model of an object in a popular commercial Computer-Aided Design (CAD) software using computer vision. Two sets of experiments were performed: one with a simple cubical object and the other with a more complex geometry that needed photogrammetry to redraw it in the CAD system. For the accurate positioning of the object, a robotic hand was used. An Internet of Things (IoT) based camera unit was used for capturing the image and wirelessly transmitting it over the network. Computer vision algorithms such as GrabCut, Canny edge detector, and morphological operations were used for extracting border points of the input. The coordinates of the vertices of the solids were then transferred to the Computer-Aided Design (CAD) software via a macro to clean and generate the border curve. Finally, a 3D solid model is generated by linear extrusion based on the curve generated in CATIA. The results showed excellent regeneration of an object. This research makes two significant contributions. Firstly, it introduces an integrated system designed to achieve precise dimension extraction from solid objects. Secondly, it presents a method for regenerating intricate 3D solids with consistent cross-sections. The proposed system holds promise for a wide range of applications, including automatic 3D object reconstruction and quality assurance of 3D-printed objects, addressing potential defects arising from factors such as shrinkage and calibration, all with minimal user intervention.

Copyright: © 2018 INFORMS The value added by an active investor is traditionally measured using alpha, tracking error, and the information ratio. However, these measures do not characterize the dynamic component of investor activity, nor do they consider the time horizons over which weights are changed. In this paper, we propose a technique to measure the value of active investment that captures both the static and dynamic contributions of an investment process. This dynamic alpha is based on the decomposition of a portfolio’s expected return into its frequency components using spectral analysis. The result is a static component that measures the portion of a portfolio’s expected return resulting from passive investments and security selection and a dynamic component that captures the manager’s timing ability across a range of time horizons. Our framework can be universally applied to any portfolio and is a useful method for comparing the forecast power of different investment processes. Several analytical and empirical examples are provided to illustrate the practical relevance of this decomposition.

Probability matching, also known as the “matching law” or Herrnstein’s Law, has long puzzled economists and psychologists because of its apparent inconsistency with basic self-interest. We conduct an experiment with real monetary payoffs in which each participant plays a computer game to guess the outcome of a binary lottery. In addition to finding strong evidence for probability matching, we document different tendencies towards randomization in different payoff environments—as predicted by models of the evolutionary origin of probability matching—after controlling for a wide range of demographic and socioeconomic variables. We also find several individual differences in the tendency to maximize or randomize, correlated with wealth and other socioeconomic factors. In particular, subjects who have taken probability and statistics classes and those who self-reported finding a pattern in the game are found to have randomized more, contrary to the common wisdom that those with better understanding of probabilistic reasoning are more likely to be rational economic maximizers. Our results provide experimental evidence that individuals—even those with experience in probability and investing—engage in randomized behavior and probability matching, underscoring the role of the environment as a driver of behavioral anomalies.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2019, Cataloged from PDF version of thesis., Includes bibliographical references (pages 162-168)., This dissertation explores, in real time, key institutional factors contributing to the diffusion and impact of a breakthrough technology from its very first days. The studies combine rigorous quantitative empirical methods with a deep understanding of the institutions of a novel setting that allows for a nuanced picture of the actors, institutions, technologies, and rules necessary to make recommendations on policies and strategies for the diffusion of emerging innovations. The first chapter examines whether the introduction of a breakthrough technology, the CRISPR DNA-editing system, affects the trajectory of a scientific field through project selection and new entry. Using proprietary data from the primary distributor of CRISPR to academic scientists, Addgene, the study shows that the relative proportion of scientists focusing on editing mammalian cells after the introduction of CRISPR increased over their counterparts working in bacteria and other eukaryotes., The shift towards mammalian research may result mostly from entry of new authors. The second chapter (with Neil Thompson), explores whether characteristics of individual scientists who experiment with CRISPR differ from those who incorporate that experimentation into a new project. Using Addgene data we separately observe both groups by matching CRISPR orders to scientists' publication histories. We find that some characteristics (e.g., proximity to the discoverers) do not impact experimentation but do influence the ability to publish, empirically showing that access to a complex new tool does not automatically translate into the ability to use the tool. The third chapter builds on the previous two by noting that many new tools require specialized complementary know-how to be applied effectively and delving into how teams form to acquire that know-how., Teams in any research domain face the tradeoff of either acquiring this know-how themselves or working with scarce external tool specialists who also have a choice over domain teams. CRISPR enables identification of external tool specialists on research teams by exploiting natural difficulties of applying the tool across disease domains. External tool specialists appear more often in teams for difficult diseases, especially in subsequent innovations, suggesting that external tool specialists may be more attracted to complex but influential problems., by Samantha Zyontz., Ph. D., Ph.D. Massachusetts Institute of Technology, Sloan School of Management

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2019, Cataloged from PDF version of thesis., Includes bibliographical references., This dissertation consists of three chapters. Chapter 1 proposes a dynamic general equilibrium model to study jointly (i) the pace of technological progress and (ii) asset pricing properties of investment in innovation. Risk is a key characteristic that links the two together. Both empirically and in my theory innovation activity is associated with elevated levels of idiosyncratic risk. In the model, idiosyncratic risk is driven by uncertain productivity improvements and disruption that emerge in the process of innovation. Thus idiosyncratic risk is an instrumental determinant of the rate of technological progress and expected returns on investment in innovation. A calibrated version of the model provides an accurate quantitative description of the venture capital cycles both in terms of investment flows and financial returns., Joint study with Hui Chen and Jiang Wang in Chapter 2 investigates the effects of market-wide trading halts, also called circuit breakers, on stock prices and trading behavior. We develop a model to examine how circuit breakers impact the market when investors trade to share risk. We show that a downside circuit breaker tends to lower the stock price, increase its volatility and raise the likelihood of reaching the triggering price. The volatility amplification effect becomes stronger when the wealth share of the relatively pessimistic agent is small. In Chapter 3 I develop a theory that shows how search frictions in the labor market shape asset pricing properties of stock returns. My theory reconciles and links together two empirical facts: (i) economic downturns are associated with higher pace of job reallocation and (ii) rapidly growing firms on average yield lower returns on their stocks compared to shrinking firms., In the model, firms with more growth opportunities benefit from recessions due to more slack in the labor market, since in recessions growing firms can hire more and expand quicker. This feature makes growth firms' value less cyclical. A calibrated version of the model successfully replicates predictability of returns in the cross-section by a range of growth indicators., by Anton Petukhov., Ph. D., Ph.D. Massachusetts Institute of Technology, Sloan School of Management

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2019, Cataloged from PDF version of thesis., Includes bibliographical references., This dissertation contains four essays concerning the economics of information technology, intangible capital, and artificial intelligence. In the first essay, "Engineering Value: The Returns to Technological Talent and Investments in Artificial Intelligence" I describe how firms can appropriate some of the value of their employees' human capital by assigning firm-specific tasks. I then use a database of employment records to document dynamics in the valuation of publicly traded firms as they relate to different types of employment, focusing especially on AI skills. The second essay, "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies" (coauthored with Erik Brynjolfsson and Chad Syverson) addresses the concern that new technologies with wide applicability throughout the economy can cause both underestimation and overestimation of total factor productivity., As capital is accumulated, intangible investment output, and therefore productivity growth, will be underestimated only to later generate a yield (at which point productivity growth will be overestimated). Presenting a theoretical description of how to use corporate valuations to recover hidden investment value, we discuss how productivity growth and levels can be adjusted to accommodate these changes. Implications for research and development, computer hardware, and computer software investments are considered. The third essay, "Machine Learning and Occupational Change" (coauthored with Erik Brynjolfsson and Tom Mitchell), develops and implements a method to measure the labor market impact potential of machine learning technologies. Tasks are evaluated for their Suitability for Machine Learning (SML). We find that few occupations can be fully automated with machine learning, but many occupations will potentially be redesigned., The final essay, "Do Labor Demand Shifts Occur Within Firms or Across Them? Non-Routine-Biased Technological Change 2000-2016" (coauthored with Seth Benzell and Guillermo Lagarda) decomposes labor share shifts of occupational groups into changes between firms, within firms, and due to entry and exit. We find that within-firm compositional shifts are an important component of changes in the overall labor market. We also find that the rate of within-firm shifts has declined in the period from 2000 to 2016. Together, these essays offer insights into how artificial intelligence technologies, particularly machine learning, will impact the U.S. economy., by Daniel Rock., Chapter 1. Engineering Value: The Returns to Technological Talent and Investments in Artificial Intelligence -- Chapter 2. The Productivity J-Curve: How Intangibles Complement General Purpose Technologies -- Chapter 3. Machine Learning and Occupational Change -- Chapter 4. Do Labor Demand Shifts Occur Within Firms or Across Them? Non-Routine Biased Technological Change, 2000-2016., Ph. D., Ph.D. Massachusetts Institute of Technology, Sloan School of Management

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2019, Cataloged from PDF version of thesis., Includes bibliographical references (pages 41-46)., I investigate the effect of corporate patent disclosures on innovation. Using the American Inventor's Protection Act (AIPA) as a plausibly exogenous shock to corporate patent disclosures, I find evidence of the AIPA shaping innovation through two simultaneous channels. First, the AIPA encourages a firm to innovate by facilitating access to the scientific information contained in other firms' patent disclosures. Second, the AIPA discourages a firm from innovating by increasing the risk of leaking business-related strategies through its own patent disclosures. These findings are consistent with the view that corporate patents contain information useful for both science and business, and highlight their respective roles in generating both spillover benefits and proprietary costs of mandating patent disclosures. Finally, using textual analysis, I find that firms with high proprietary costs respond to the AIPA by strategically changing their patent disclosures to obfuscate exploitable business-related signals., by Jinhwan Kim., Ph. D., Ph.D. Massachusetts Institute of Technology, Sloan School of Management

Thesis: M. Fin., Massachusetts Institute of Technology, Sloan School of Management, Master of Finance Program, 2019, Cataloged from PDF version of thesis., Includes bibliographical references (page 55)., I estimate the out-of-sample performance of the equal weight, minimum variance and mean-variance model portfolios in different settings. In each setting, I vary the loss function used when estimating returns and covariances, length of the estimation window, and number of factors used in our estimation model. I find that when measuring performance by Sharpe ratio, choice of loss function strongly influences whether the mean-variance model portfolio outperforms the equal weight or minimum variance portfolio, and that the optimal loss function depends on the length of the estimation window and the dimension of the return model. It appears that we don't gain much by using more factors. The 3-factor model does a pretty good job based on Sharpe ratio, and the results are consistently the best for MVO(10). With more factors, it seems clear that we need longer estimation windows, but even then we do not gain anything in terms of Sharpe Ratio. However, when measuring performance by the certainty-equivalent return, I find that the mean-variance model portfolio does not outperform the minimum variance portfolio or the equal weight portfolio in any setting. This suggests that choosing a loss function carefully is imperative to managing estimation errors and that an investor's utility preferences and attitude towards risk should be taken into account when choosing a measure of performance., by Dhruv Ramesh., M. Fin., M.Fin. Massachusetts Institute of Technology, Sloan School of Management, Master of Finance Program

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2019, Cataloged from PDF version of thesis., Includes bibliographical references., This dissertation considers how innovation and entrepreneurship are developed, encouraged, and evaluated with the theoretical lens of economic sociology. The first chapter investigates who becomes an entrepreneur among the pool of general consumers. The process by which individuals become entrepreneurs is often described as a decisive moment of transition, yet it necessarily involves a series of smaller steps. By breaking down the transition stages of knitting hobbyists' transition to producers who sell their original design patterns, the study examines the distinctive characteristics that affect users' decision to (a) create new products and (b) commercialize them. The second chapter examines the role of social capital in revealing and encouraging entrepreneurship. To the question of how social capital benefits innovation and entrepreneurship, existing literature has provided one dominant answer: access to information and resources., In this study, I suggest an alternative mechanism how social capital benefits an individual's entrepreneurial transition: social networks provide potential entrepreneurs self-confidence on the promise of their new ideas and encourages their entry into the market. Using a matched sample of potential innovators, I show that an individual's participation in a local group encourages her transition to an entrepreneur, especially for those who already have the necessary skills for the transition. The empirical analysis resonates with qualitative evidence that hobbyists make the transition to entrepreneurs when encouraged by their friends. The third chapter (co-authored with Pierre Azoulay and Ezra Zuckerman) considers commitment-based typecasting among knit designers. We show that "commitment-based typecasting" has two characteristic features: asymmetry in audience valuation and retrospective reevaluation., When a novice performer experiences an "identity shock" that suggests that she is more committed to the audience for one category than another, "betrayed" audience tends to regard her as having always been less committed to the rival audience/category. We test this theory in the domain of knitting, where there is a divide between avant-garde knitters and traditional knitters, and we show that when a novice knit designer is first published in the publication associated with one category, this elicits a retrospective devaluation of her prior work by the audience of the opposing category., by Hyejun Kim., Chapter 1. Sharing or Selling: Multiple Stages of Entrepreneurial Transitions in the Hobbyist Community -- Chapter 2. Knitting Community: The Role of Social Capital in Revealing and Encouraging Entrepreneurship -- Chapter 3. Never Really One of Us: Commitment-based Typecasting among Knit Designers., Ph. D., Ph.D. Massachusetts Institute of Technology, Sloan School of Management

Thesis: S.M. in Management Studies, Massachusetts Institute of Technology, Sloan School of Management, 2019, Cataloged from PDF version of thesis., Includes bibliographical references (page 53)., A number of regional research on the operations of private funds in Africa exist with varying levels of depth and detail. These research focus on the continent's regional hubs-North Africa, West Africa, East Africa, Central Africa, and South Africa. Given the varying economic realities among countries within these hubs, there is a need for more in-depth analysis within those regional entities at the sub-regional or national level. Additionally, the vast majority of those studies concentrate only on later stage investment funds. There is a gap in observations, analysis, and development of frameworks for early stage investment funds to successfully invest in this part of the world. The present work aims to study the challenges and opportunities for private funds, with a focus on early-stage investment funds that currently invest or wish to invest in Western-African countries at both sub-regional and national level., by Bouame Donald Magloire Aka., S.M. in Management Studies, S.M.inManagementStudies Massachusetts Institute of Technology, Sloan School of Management

Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1984., Bibliography: leaves 120-122., by Sharon Beth Sinsky.

This paper aims to develop distributed algorithms for nonconvex optimization problems with complicated constraints associated with a network. The network can be a physical one, such as an electric power network, where the constraints are nonlinear power flow equations, or an abstract one that represents constraint couplings between decision variables of different agents. Despite the recent development of distributed algorithms for nonconvex programs, highly complicated constraints still pose a significant challenge in theory and practice. We first identify some difficulties with the existing algorithms based on the alternating direction method of multipliers (ADMM) for dealing with such problems. We then propose a reformulation that enables us to design a two-level algorithm, which embeds a specially structured three-block ADMM at the inner level in an augmented Lagrangian method framework. Furthermore, we prove the global and local convergence as well as iteration complexity of this new scheme for general nonconvex constrained programs, and show that our analysis can be extended to handle more complicated multi-block inner-level problems. Finally, we demonstrate with computation that the new scheme provides convergent and parallelizable algorithms for various nonconvex applications, and is able to complement the performance of the state-of-the-art distributed algorithms in practice by achieving either faster convergence in optimality gap or in feasibility or both.

Objective To compare temporal changes of ablation zones and lymph nodes following lung microwave ablation (MWA) and cryoablation. Methods This retrospective cohort study compared lung ablation zones and thoracic lymph nodes following MWA and cryoablation performed 2006–2020. In the ablation zone cohort, ablation zone volumes were measured on serial CT for 12 months. In the lymph node cohort, the sum of bidimensional products of lymph node diameters was measured before (baseline) and up to 6 months following ablation. Cumulative incidence curves estimated the time to 75% ablation zone reduction and linear mixed-effects regression models compared the temporal distribution of ablation zones and lymph node sizes between modalities. Results Ablation zones of 59 tumors treated in 45 sessions (16 MWA, 29 cryoablation) in 36 patients were evaluated. Differences in the time to 75% volume reduction between modalities were not detected. Following MWA, half of the ablation zones required an estimated time of 340 days to achieve a 75% volume reduction compared to 214 days following cryoablation (p = .30). Thoracic lymph node sizes after 33 sessions (13 MWA, 20 cryoablation) differed between modalities (baseline–32 days, p = .01; 32–123 days, p = .001). Following MWA, lymph nodes increased on average by 38 mm2 (95%CI, 5.0–70.7; p = .02) from baseline to 32 days, followed by an estimated decrease of 50 mm2 (32–123 days; p = .001). Following cryoablation, changes in lymph nodes were not detected (baseline–32 days, p = .33). Conclusion The rate of ablation zone volume reduction did not differ between MWA and cryoablation. Thoracic lymph nodes enlarged transiently after MWA but not after cryoablation. Key Points • Contrary to current belief, the rate of lung ablation zone volume reduction did not differ between microwave and cryoablation. • Transient enlargement of thoracic lymph nodes after microwave ablation was not associated with regional tumor spread and decreased withi

© 2020 The Authors How can individual scientists most effectively spread the adoption of open science practices? Engaging in open science practices presents a social dilemma because they are individually costly (given the current incentive schemes in academia) but collectively beneficial (due to production of higher quality and more accessible science). Mechanisms for promoting cooperation in social dilemmas typically rely on normativity—but open science practices are still comparatively rare. Further, individuals may be tempted to dishonestly “virtue signal” due to growing support for open science. We formulate a solution based on the theory of credibility-enhancing displays: advocates who are known to themselves practice the behavior they are advocating for (particularly if they are prestigious) are more effective at convincing others—specifically because their actions provide an honest signal of their belief in the behavior's value. Thus, advocates for open science practices should find ways to engage in those practices visibly and often.

Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

© 2020 We use a new data set on macroprudential foreign exchange (FX) regulations to evaluate their effectiveness and unintended consequences. Our results support the predictions of a model in which banks and markets lend in different currencies, but only banks can screen firm productivity. Regulations significantly reduce bank FX borrowing, and firms respond by increasing FX debt issuance. Moreover, regulations reduce bank sensitivity to exchange rates but are less effective at reducing the sensitivity of the broader economy. Therefore, FX regulations mitigate bank vulnerability to currency fluctuations and the global financial cycle, but appear to partially shift the snowbanks of vulnerability elsewhere.

The recent interior point algorithm by Dahl and Andersen [10] for nonsymmetric cones as well as earlier works [18, 21] require derivative information from the conjugate of the barrier function of the cones in the problem. Besides a few special cases, there is no indication of when this information is efficient to evaluate. We show how to compute the gradient of the conjugate barrier function for seven useful nonsymmetric cones. In some cases, this is helpful for deriving closed-form expressions for the inverse Hessian operator for the primal barrier.

Significance Scientists and managers alike have been preoccupied with the question of whether and, if so, under what conditions groups of interacting problem solvers outperform autonomous individuals. Here we describe an experiment in which individuals and groups were evaluated on a series of tasks of varying complexity. We find that groups are as fast as the fastest individual and more efficient than the most efficient individual when the task is complex but not when the task is simple. We then precisely quantify synergistic gains and process losses associated with interacting groups, finding that the balance between the two depends on complexity. Our study has the potential to reconcile conflicting findings about group synergy in previous work.

Abstract We provide evidence that risk aversion leads pharmaceutical firms to underinvest in radical innovation. We introduce a new measure of drug novelty based on chemical similarity and show that firms face a risk-reward trade-off: novel drug candidates are less likely to obtain FDA approval but are based on more valuable patents. Consistent with a simple model of costly external finance, we show that a positive shock to firms’ net worth leads firms to develop more novel drugs. This suggests that even large firms may behave as though they are risk averse, reducing their willingness to investment in potentially valuable radical innovation.

Scholars of street-level bureaucracy and institutional research focus primarily on the relationships between advocates and their larger bureaucratic and social systems, assuming that advocates have little need to satisfy their beneficiaries. We find otherwise in our two-year ethnographic study of public defenders advocating for disadvantaged clients in interactions with district attorneys. In our analysis of 82 advocacy opportunities, we demonstrate that, when existing bureaucratic and social systems put beneficiaries at a disadvantage, advocates may be concerned about managing fraught relationships with their beneficiaries in addition to navigating barriers within the bureaucratic and social systems. We further show a tension between the two; ironically, engaging in advocacy work on behalf of beneficiaries can lead to beneficiary mistrust. As a result, advocates engage in triadic advocacy work—managing impressions with their beneficiaries while also influencing powerful actors within the system on behalf of these same beneficiaries. Understanding the process by which advocates navigate this tension is critical to understanding beneficiary outcomes. By reconceptualizing advocacy work as a triadic process among advocate, bureaucratic system, and beneficiary rather than as a dyadic process between advocate and bureaucratic system, this paper develops new theory about how advocates can attempt to garner benefits that advance the rights and opportunities of the disadvantaged.

We consider a heterogeneous distributed service system consisting of n servers with unknown and possibly different processing rates. Jobs with unit mean arrive as a renewal process of rate proportional to n and are immediately dispatched to one of several queues associated with the servers. We assume that the dispatching decisions are made by a central dispatcher with the ability to exchange messages with the servers and endowed with a finite memory used to store information from one decision epoch to the next, about the current state of the queues and about the service rates of the servers. We study the fundamental resource requirements (memory bits and message exchange rate) in order for a dispatching policy to be always stable. First, we present a policy that is always stable while using a positive (but arbitrarily small) message rate and [Formula: see text] bits of memory. Second, we show that within a certain broad class of policies, a dispatching policy that exchanges [Formula: see text] messages per unit of time, and with [Formula: see text] bits of memory, cannot be always stable.

Ground delay programs (GDPs) comprise the main interventions to optimize flight operations in congested air traffic networks. The core GDP objective is to minimize flight delays, but this may not result in optimal outcomes for passengers—especially with connecting itineraries. This paper proposes a novel passenger-centric optimization approach to GDPs by balancing flight and passenger delays in large-scale networks. For tractability, we decompose the problem using a rolling procedure, enabling the model’s implementation in manageable runtimes. Computational results based on real-world data suggest that our modeling and computational framework can reduce passenger delays significantly at small increases in flight delay costs through two main mechanisms: (i) delay allocation (delaying versus prioritizing flights) and (ii) delay introduction (holding flights to avoid passenger misconnections). In practice, however, passenger itineraries are unknown to air traffic managers; accordingly, we propose statistical learning models to predict passenger itineraries and optimize GDP operations accordingly. Results show that the proposed passenger-centric approach is highly robust to imperfect knowledge of passenger itineraries and can provide significant benefits even in the current decentralized environment based on collaborative decision making.

© 2018 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Australia, Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. We evaluate the impact of explicitly representing irrigated land and water scarcity in an economy-wide model with and without a global carbon policy. The analysis develops supply functions of irrigable land from a water resource model for 282 river basins and applies them within a global economy-wide model. The analysis reveals two key findings. First, explicitly representing irrigated land has a small impact on global food, bioenergy and deforestation outcomes. This is because this modification allows irrigated and rainfed land to expand in different proportions, which counters the effect of rising marginal costs for the expansion of irrigated land. Second, changes in water availability have small impacts on global food prices, bioenergy production, land use change and the overall economy, even with large-scale (c. 150 exajoules) bioenergy production, due in part to endogenous irrigation and storage responses. However, representing water scarcity and changes in water availability can be important regionally, with relatively arid areas and/or areas with rapidly growing populations fully exhausting our estimated maximum irrigation capacity that allows for improved irrigation efficiency, lining of canals to limit water loss, and expanding storage to fully capture average annual water flows.

Transitioning from military service is stressful for veterans with service-connected disabilities seeking civilian employment. This descriptive study examined self-assessed mental health, well-being, and substance use of men and women shortly before or after transition from US military service, compared to norms from community and military samples. As part of a prospective study evaluating an innovative employment program, researchers interviewed 229 current and former service members with service-connected disabilities transitioning from U.S. military service. Compared to published norms, respondents reported significantly poorer outcomes on 5 of 6 standardized measures, indicating less life satisfaction, poorer mental health, more symptoms of depression and posttraumatic stress disorder, and greater financial distress. In the previous year, 42% were prescribed opioid medications, over twice the annual opioid prescription rate of 19% in the general US population. Systematic strategies are needed to ensure access for transitioning veterans with serious behavioral health issues to appropriate evidence-based practices.

According to the previous literature, only a few papers found better accuracy than a chance to detect dishonesty, even when more information and verbal cues (VCs) improve precision in detecting dishonesty. A new classification of dishonesty profiles has recently been published, allowing us to study if this low success rate happens for all people or if some people have higher predictive ability. This paper aims to examine if (dis)honest people can detect better/worse (un)ethical behavior of others. With this in mind, we designed one experiment using videos from one of the most popular TV shows in the UK where contestants make a (dis)honesty decision upon gaining or sharing a certain amount of money. Our participants from an online MTurk sample (N = 1,582) had to determine under different conditions whether the contestants would act in an (dis)honest way. Three significant results emerged from these two experiments. First, accuracy in detecting (dis)honesty is not different than chance, but submaximizers (compared to maximizers) and radical dishonest people (compare to non-radicals) are better at detecting honesty, while there is no difference in detecting dishonesty. Second, more information and VCs improve precision in detecting dishonesty, but honesty is better detected using only non-verbal cues (NVCs). Finally, a preconceived honesty bias improves specificity (honesty detection accuracy) and worsens sensitivity (dishonesty detection accuracy).

Stacking long short-term memory (LSTM) cells or gated recurrent units (GRUs) as part of a recurrent neural network (RNN) has become a standard approach to solving a number of tasks ranging from language modeling to text summarization. Although LSTMs and GRUs were designed to model long-range dependencies more accurately than conventional RNNs, they nevertheless have problems copying or recalling information from the long distant past. Here, we derive a phase-coded representation of the memory state, Rotational Unit of Memory (RUM), that unifies the concepts of unitary learning and associative memory. We show experimentally that RNNs based on RUMs can solve basic sequential tasks such as memory copying and memory recall much better than LSTMs/GRUs. We further demonstrate that by replacing LSTM/GRU with RUM units we can apply neural networks to real-world problems such as language modeling and text summarization, yielding results comparable to the state of the art.

AbstractInterventions that shift users attention toward the concept of accuracy represent a promising approach for reducing misinformation sharing online. We assess the replicability and generalizability of this accuracy prompt effect by meta-analyzing 20 experiments (with a total N = 26,863) completed by our group between 2017 and 2020. This internal meta-analysis includes all relevant studies regardless of outcome and uses identical analyses across all studies. Overall, accuracy prompts increased the quality of news that people share (sharing discernment) relative to control, primarily by reducing sharing intentions for false headlines by 10% relative to control in these studies. The magnitude of the effect did not significantly differ by content of headlines (politics compared with COVID-19 related news) and did not significantly decay over successive trials. The effect was not robustly moderated by gender, race, political ideology, education, or value explicitly placed on accuracy, but was significantly larger for older, more reflective, and more attentive participants. This internal meta-analysis demonstrates the replicability and generalizability of the accuracy prompt effect on sharing discernment.

We present a general-purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix at almost every iteration. Our algorithm is very robust, placing no requirements on the problem data such as positive definiteness of the objective function or linear independence of the constraint functions. It can be configured to be division-free once an initial matrix factorization is carried out, making it suitable for real-time applications in embedded systems. In addition, our technique is the first operator splitting method for quadratic programs able to reliably detect primal and dual infeasible problems from the algorithm iterates. The method also supports factorization caching and warm starting, making it particularly efficient when solving parametrized problems arising in finance, control, and machine learning. Our open-source C implementation OSQP has a small footprint, is library-free, and has been extensively tested on many problem instances from a wide variety of application areas. It is typically ten times faster than competing interior-point methods, and sometimes much more when factorization caching or warm start is used. OSQP has already shown a large impact with tens of thousands of users both in academia and in large corporations.

While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, and few other nuances, offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs).

Background Variant interpretation is the main bottleneck in medical genomic sequencing efforts. This usually involves genome analysts manually searching through a multitude of independent databases, often with the aid of several, mostly independent, computational tools. To streamline variant interpretation, we developed the GeneTerpret platform which collates data from current interpretation tools and databases, and applies a phenotype-driven query to categorize the variants identified in the genome(s). The platform assigns quantitative validity scores to genes by query and assembly of the genotype–phenotype data, sequence homology, molecular interactions, expression data, and animal models. It also uses the American College of Medical Genetics and Genomics (ACMG) criteria to categorize variants into five tiers of pathogenicity. The final output is a prioritized list of potentially causal variants/genes. Results We tested GeneTerpret by comparing its performance to expert-curated genes (ClinGen’s gene-validity database) and variant pathogenicity reports (DECIPHER database). Output from GeneTerpret was 97.2% and 83.5% concordant with the expert-curated sources, respectively. Additionally, similar concordance was observed when GeneTerpret’s performance was compared with our internal expert-interpreted clinical datasets. Conclusions GeneTerpret is a flexible platform designed to streamline the genome interpretation process, through a unique interface, with improved ease, speed and accuracy. This modular and customizable system allows the user to tailor the component-programs in the analysis process to their preference. GeneTerpret is available online at https://geneterpret.com .

Problem definition: Loyalty programs (LPs) introduce a new currency—the points—through which customers transact with firms. Such points represent a promise for future service, and their monetary value thus counts as a liability on the issuing firms’ balance sheets. Consequently, adjusting the value of points has a first-order effect on profitability and performance and emerges as a core operating decision. We study the problem of optimally setting the points’ value in view of their associated liabilities. Academic/practical relevance: Firms across numerous industries increasingly utilize LPs. The sheer magnitude of LPs coupled with recent changes in accounting rules have turned the associated liabilities into significant balance-sheet items, amounting to billions of dollars. Managers (from chief financial officers to chief marketing officers) struggle with the problem of adjusting the points’ value in view of these liabilities. Academic work is primarily aimed at understanding LPs as marketing tools, without studying the liability angle. Methodology: We develop a multiperiod model and use dynamic programming techniques and comparative statics analysis. Results: We show that the optimal policies depend on a new financial metric, given by the sum of the firm’s realized cash flows and outstanding deferred revenue, which we refer to as the profit potential. The total value of loyalty points is set to hit a particular target, which increases with the profit potential. We find that loyalty programs can act as buffers against uncertainty, with the value of points increasing (decreasing) under strong (weak) operating performance and increasing with uncertainty. Managerial implications: Setting the point values and adjusting operating decisions in view of LP liabilities should be done by tracking the firm’s profit potential. Loyalty programs can act as hedging tools against uncertainty in future operating performance, which provides a new rationale for their existence, even

AbstractNanoparticle corona phase (CP) design offers a unique approach toward molecular recognition (MR) for sensing applications. Single-walled carbon nanotube (SWCNT) CPs can additionally transduce MR through its band-gap photoluminescence (PL). While DNA oligonucleotides have been used as SWCNT CPs, no generalized scheme exists for MR prediction de novo due to their sequence-dependent three-dimensional complexity. This work generated the largest DNA-SWCNT PL response library of 1408 elements and leveraged machine learning (ML) techniques to understand MR and DNA sequence dependence through local (LFs) and high-level features (HLFs). Out-of-sample analysis of our ML model showed significant correlations between model predictions and actual sensor responses for 6 out of 8 experimental conditions. Different HLF combinations were found to be uniquely correlated with different analytes. Furthermore, models utilizing both LFs and HLFs show improvement over that with HLFs alone, demonstrating that DNA-SWCNT CP engineering is more complex than simply specifying molecular properties.

On online platforms, consumers face an abundance of options that are displayed in the form of a position ranking. Only products placed in the first few positions are readily accessible to the consumer, and she needs to exert effort to access more options. For such platforms, we develop a two-stage sequential search model where, in the first stage, the consumer sequentially screens positions to observe the preference weight of the products placed in them and forms a consideration set. In the second stage, she observes the additional idiosyncratic utility that she can derive from each product and chooses the highest-utility product within her consideration set. For this model, we first characterize the optimal sequential search policy of a welfare-maximizing consumer. We then study how platforms with different objectives should rank products. We focus on two objectives: (i) maximizing the platform’s market share and (ii) maximizing the consumer’s welfare. Somewhat surprisingly, we show that ranking products in decreasing order of their preference weights does not necessarily maximize market share or consumer welfare. Such a ranking may shorten the consumer’s consideration set due to the externality effect of high-positioned products on low-positioned ones, leading to insufficient screening. We then show that both problems—maximizing market share and maximizing consumer welfare—are NP-complete. We develop novel near-optimal polynomial-time ranking algorithms for each objective. Further, we show that, even though ranking products in decreasing order of their preference weights is suboptimal, such a ranking enjoys strong performance guarantees for both objectives. We complement our theoretical developments with numerical studies using synthetic data, in which we show (1) that heuristic versions of our algorithms that do not rely on model primitives perform well and (2) that our model can be effectively estimated using a maximum likelihood estimator.