cssi/SciSciGPT-SciSciNet huggingface dataset:
Stability and Evolution of Supernova Fallback Disks

We show that thin accretion disks made of carbon or oxygen are subject to the same thermal ionization instability as hydrogen and helium disks. We argue that the instability applies to disks of any metal content. The relevance of the instability to supernova fallback disks probably means that their power-law evolution breaks down when they first become neutral. We construct simple analytical models for the viscous evolution of fallback disks to show that it is possible for these disks to become neutral when they are still young (ages of a few 103 to 104 yr), compact in size (a few 109 to 1011 cm) and generally accreting at sub-Eddington rates ( ~ a few 1014-1018 g s-1). Based on recent results on the nature of viscosity in the disks of close binaries, we argue that this time may also correspond to the end of the disk activity period. Indeed, in the absence of a significant source of viscosity in the neutral phase, the entire disk will likely turn to dust and become passive. We discuss various applications of the evolutionary model, including anomalous X-ray pulsars and young radio pulsars. Our analysis indicates that metal-rich fallback disks around newly born neutron stars and black holes become neutral generally inside the tidal truncation radius (Roche limit) for planets at ≈1011 cm. Consequently, the efficiency of the planetary formation process in this context will mostly depend on the ability of the resulting disk of rocks to spread via collisions beyond the Roche limit. It appears easier for the merger product of a doubly degenerate binary, whether it is a massive white dwarf or a neutron star, to harbor planets because its remnant disk has a rather large initial angular momentum, which allows it to spread beyond the Roche limit before becoming neutral. The early super-Eddington phase of accretion is a source of uncertainty for the disk evolution models presented here.
Naturalness, Extra-Empirical Theory Assessments, and the Implications of Skepticism

Naturalness is an extra-empirical quality that aims to assess plausibility of a theory. Finetuning measures are often deputized to quantify the task. However, knowing statistical distributions on parameters appears necessary. Such meta-theories are not known yet. A critical discussion of these issues is presented, including their possible resolutions in fixed points. Both agreement to and skepticism of naturalness’s utility remains credible, as is skepticism to any extra-empirical theory assessment (SEETA) that claims to identify “more correct” theories that are equally empirically adequate. The severe implications of SEETA are set forward in some detail. We conclude with a summary and discussion of the viability of three main viewpoints toward naturalness and finetuning, where the “moderate naturalness position” is suggested to be most appealing, not suffering from the disquietudes of the extreme pro- and anti-naturalness positions.
Dynamical fermions by global acceptance steps

Abstract A study of principle is conducted on the inclusion of the fermionic determinant as a Metropolis acceptance correction. It is carried out in the 2-D Schwinger model to prepare later applications to the Schrodinger functional. A mixed stochastic/deterministic acceptance step is found that allows to include some problematic modes in a way to avoid the collapse of the acceptance rate due to fluctuations.
Rational thoughts in neural codes

Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals. A fundamental challenge in neuroscience is, How can we use behavior and neural activity to understand this internal model and its dynamic latent variables? Here we interpret behavioral data by assuming an agent behaves rationally—that is, it takes actions that optimize its subjective reward according to its understanding of the task and its relevant causal variables. We apply a method, inverse rational control (IRC), to learn an agent’s internal model and reward function by maximizing the likelihood of its measured sensory observations and actions. This thereby extracts rational and interpretable thoughts of the agent from its behavior. We also provide a framework for interpreting encoding, recoding, and decoding of neural data in light of this rational model for behavior. When applied to behavioral and neural data from simulated agents performing suboptimally on a naturalistic foraging task, this method successfully recovers their internal model and reward function, as well as the Markovian computational dynamics within the neural manifold that represent the task. This work lays a foundation for discovering how the brain represents and computes with dynamic latent variables.
Scattering equations in AdS: scalar correlators in arbitrary dimensions

We introduce a bosonic ambitwistor string theory in AdS space. Even though the theory is anomalous at the quantum level, one can nevertheless use it in the classical limit to derive a novel formula for correlation functions of boundary CFT operators in arbitrary space-time dimensions. The resulting construction can be treated as a natural extension of the CHY formalism for the flat-space S-matrix, as it similarly expresses tree-level amplitudes in AdS as integrals over the moduli space of Riemann spheres with punctures. These integrals localize on an operator-valued version of scattering equations, which we derive directly from the ambitwistor string action on a coset manifold. As a testing ground for this formalism we focus on the simplest case of ambitwistor string coupled to two cur- rent algebras, which gives bi-adjoint scalar correlators in AdS. In order to evaluate them directly, we make use of a series of contour deformations on the moduli space of punctured Riemann spheres and check that the result agrees with tree level Witten diagram computations to all multiplicity. We also initiate the study of eigenfunctions of scattering equations in AdS, which interpolate between conformal partial waves in different OPE channels, and point out a connection to an elliptic deformation of the Calogero-Sutherland model.
Minding the Gap: GW190521 as a Straddling Binary

Models for black hole (BH) formation from stellar evolution robustly predict the existence of a pair-instability supernova (PISN) mass gap in the range $\sim50$ to $\sim120$ solar masses. This theoretical prediction is supported by the binary black holes (BBHs) of LIGO/Virgo's first two observing runs, whose component masses are well-fit by a power law with a maximum mass cutoff at $m_\mathrm{max}=40.8^{+11.8}_{-4.4}\,M_\odot$. Meanwhile, the BBH event GW190521 has a reported primary mass of $m_1=85^{+21}_{-14}\,M_\odot$, firmly above the inferred $m_\mathrm{max}$, and secondary mass $m_2=66^{+17}_{-18}\,M_\odot$. Rather than concluding that both components of GW190521 belong to a new population of mass-gap BHs, we explore the conservative scenario in which GW190521's secondary mass belongs to the previously-observed population of BHs. We replace the default priors on $m_1$ and $m_2$, which assume that BH detector-frame masses are uniformly distributed, with this population-informed prior on $m_2$, finding $m_2 120\,M_\odot$, or 25\% probability for $m_1>130\,M_\odot$). As long as the prior odds for a double-mass-gap BBH are smaller than $\sim 1:15$, it is more likely that GW190521 straddles the pair-instability gap. We argue that GW190521 may be the first example of a straddling binary black hole, composed of a conventional stellar mass BH and a BH from the ``far side' of the PISN mass gap.
PDRS: A Fast Non-Iterative Scheme for Massive Grant-Free Access in Massive MIMO

Grant-free multiple-input multiple-output (MIMO) usually employs non-orthogonal pilots for joint user detection and channel estimation. However, existing methods are too complex for massive grant-free access in massive MIMO. This letter proposes pilot detection reference signal (PDRS) to greatly reduce the complexity. In PDRS scheme, no iteration is required. Direct weight estimation is also proposed to calculate combining weights without channel estimation. After combining, PDRS recovery errors are used to decide the pilot activity. The simulation results show that the proposed grant-free scheme performs good with a complexity reduced by orders of magnitude.
Student Log-Data from a Randomized Evaluation of Educational Technology: A Causal Case Study

Randomized evaluations of educational technology produce log data as a bi-product: highly granular data on student and teacher usage. These datasets could shed light on causal mechanisms, effect he...
A Glimpse of the First Eight Months of the COVID-19 Literature on Microsoft Academic Graph: Themes, Citation Contexts, and Uncertainties

As scientists worldwide search for answers to the overwhelmingly unknown behind the deadly pandemic, the literature concerning COVID-19 has been growing exponentially. Keeping abreast of the body of literature at such a rapidly advancing pace poses significant challenges not only to active researchers but also to society as a whole. Although numerous data resources have been made openly available, the analytic and synthetic process that is essential in effectively navigating through the vast amount of information with heightened levels of uncertainty remains a significant bottleneck. We introduce a generic method that facilitates the data collection and sense-making process when dealing with a rapidly growing landscape of a research domain such as COVID-19 at multiple levels of granularity. The method integrates the analysis of structural and temporal patterns in scholarly publications with the delineation of thematic concentrations and the types of uncertainties that may offer additional insights into the complexity of the unknown. We demonstrate the application of the method in a study of the COVID-19 literature.
Population-Scale Study of Human Needs During the COVID-19 Pandemic: Analysis and Implications

Most work to date on mitigating the COVID-19 pandemic is focused urgently on biomedicine and epidemiology. Yet, pandemic-related policy decisions cannot be made on health information alone. Decisions need to consider the broader impacts on people and their needs. Quantifying human needs across the population is challenging as it requires high geo-temporal granularity, high coverage across the population, and appropriate adjustment for seasonal and other external effects. Here, we propose a computational methodology, building on Maslow's hierarchy of needs, that can capture a holistic view of relative changes in needs following the pandemic through a difference-in-differences approach that corrects for seasonality and volume variations. We apply this approach to characterize changes in human needs across physiological, socioeconomic, and psychological realms in the US, based on more than 35 billion search interactions spanning over 36,000 ZIP codes over a period of 14 months. The analyses reveal that the expression of basic human needs has increased exponentially while higher-level aspirations declined during the pandemic in comparison to the pre-pandemic period. In exploring the timing and variations in statewide policies, we find that the durations of shelter-in-place mandates have influenced social and emotional needs significantly. We demonstrate that potential barriers to addressing critical needs, such as support for unemployment and domestic violence, can be identified through web search interactions. Our approach and results suggest that population-scale monitoring of shifts in human needs can inform policies and recovery efforts for current and anticipated needs.
Causal Strategic Linear Regression

In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to "game" the decision rule by changing their features so as to receive better decisions. Whereas the strategic classification literature has previously assumed that agents' outcomes are not causally affected by their features (and thus that strategic agents' goal is deceiving the decision-maker), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. As our main contribution, we provide efficient algorithms for learning decision rules that optimize three distinct decision-maker objectives in a realizable linear setting: accurately predicting agents' post-gaming outcomes (prediction risk minimization), incentivizing agents to improve these outcomes (agent outcome maximization), and estimating the coefficients of the true underlying model (parameter estimation). Our algorithms circumvent a hardness result of Miller et al. (2020) by allowing the decision maker to test a sequence of decision rules and observe agents' responses, in effect performing causal interventions through the decision rules.
General Identification of Dynamic Treatment Regimes Under Interference

In many applied fields, researchers are often interested in tailoring treatments to unit-level characteristics in order to optimize an outcome of interest. Methods for identifying and estimating treatment policies are the subject of the dynamic treatment regime literature. Separately, in many settings the assumption that data are independent and identically distributed does not hold due to inter-subject dependence. The phenomenon where a subject's outcome is dependent on his neighbor's exposure is known as interference. These areas intersect in myriad real-world settings. In this paper we consider the problem of identifying optimal treatment policies in the presence of interference. Using a general representation of interference, via Lauritzen-Wermuth-Freydenburg chain graphs (Lauritzen and Richardson, 2002), we formalize a variety of policy interventions under interference and extend existing identification theory (Tian, 2008; Sherman and Shpitser, 2018). Finally, we illustrate the efficacy of policy maximization under interference in a simulation study.
Optimal economic operation of liquid petroleum products pipeline systems

The majority of overland transport needs for crude petroleum and refined petroleum products are met using pipelines. Numerous studies have developed optimization methods for design of these systems in order to minimize construction costs while meeting capacity requirements. Here, we formulate problems to optimize the operations of existing single liquid commodity pipeline systems subject to physical flow and pump engineering constraints. The objectives are to maximize the economic value created for users of the system and to minimize operating costs. We present a general computational method for this class of continuous, non-convex nonlinear programs, and examine the use of pump operating settings and flow allocations as decision variables. The approach is applied to compute optimal operating regimes and perform engineering economic sensitivity analyses for a case study of a crude oil pipeline developed using publicly available data.
D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector

An iris recognition system is vulnerable to presentation attacks, or PAs, where an adversary presents artifacts such as printed eyes, plastic eyes, or cosmetic contact lenses to circumvent the system. In this work, we propose an effective and robust iris PA detector called D-NetPAD based on the DenseNet convolutional neural network architecture. It demonstrates generalizability across PA artifacts, sensors and datasets. Experiments conducted on a proprietary dataset and a publicly available dataset (LivDet-2017) substantiate the effectiveness of the proposed method for iris PA detection. The proposed method results in a true detection rate of 98.58% at a false detection rate of 0.2% on the proprietary dataset and outperforms state-of-the-art methods on the LivDet-2017 dataset. We visualize intermediate feature distributions and fixation heatmaps using t-SNE plots and Grad-CAM, respectively, in order to explain the performance of D-NetPAD. Further, we conduct a frequency analysis to explain the nature of features being extracted by the network. The source code and trained model are available at https://github.com/iPRoBe-lab/D-NetPAD.
Extrinsic Calibration of a 3D-LIDAR and a Camera

This work presents an extrinsic parameter estimation algorithm between a 3D LIDAR and a Projective Camera using a marker-less planar target, by exploiting Planar Surface Point to Plane and Planar Edge Point to back-projected Plane geometric constraints. The proposed method uses the data collected by placing the planar board at different poses in the common Field of View (FoV) of the LIDAR and the Camera. The steps include, detection of the target and the edges of the target in LIDAR and Camera frames, matching the detected planes and lines across both the sensing modalities and finally solving a cost function formed by the aforementioned geometric constraints that link the features detected in both the LIDAR and the Camera using non-linear least squares. We have extensively validated our algorithm using two Basler Cameras, Velodyne VLP-32 and Ouster OS1 LIDARs.
Computing the racing line using Bayesian optimization

A good racing strategy and, in particular, the racing line is decisive to winning races in Formula 1, MotoGP, and other forms of motor racing. The racing line defines the path followed around a track and the optimal speed profile along the path. The objective is to minimize lap time by driving the vehicle at the limits of friction and handling capability. The solution naturally depends upon the geometry of the track and vehicle dynamics. We introduce a novel method to compute the racing line using Bayesian optimization. Our approach is fully data-driven and computationally more efficient compared to other methods based on dynamic programming and naive random search. The approach is specifically relevant in autonomous racing, where teams can quickly compute the racing line for a new track and then exploit this information to design a motion planner and a controller to optimize real-time performance.
Pricing Eurodollar futures options using the BDT term structure model: The effect of yield curve smoothing

This article focuses on pricing Eurodollar futures options using the single‐factor Black, Derman, and Toy (1990) term structure model with particular emphasis on yield curve smoothing. Of the various approaches, the maximum smoothness forward rate approach developed by Adams and van Deventer (1994), cubic yield spline, and linear interpolation are used to produce finely spaced binomial trees. We compare the pricing accuracy associated with the use of yield curve smoothing techniques within the BDT framework. The findings provide the first supporting evidence that using a forward rate curve with maximum smoothness together with a time‐varying volatility structure improves best the performance of the BDT model. The empirical results are found to be robust across factors affecting the option price such as time‐to‐expiration, moneyness, and trading volume. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20:293–306, 2000
Asymmetric Social Interactions in Physician Prescription Behavior: The Role of Opinion Leaders

Abstract The authors quantify the impact of social interactions and peer effects in the context of physicians' prescription choices. Using detailed individual-level prescription data, along with self-reported social network information, the authors document that physician prescription behavior is significantly influenced by the behavior of research-active specialists, or “opinion leaders,” in the physician's reference group. The authors leverage a natural experiment in the category: New guidelines released about the therapeutic nature of the focal drug generated conditions in which physicians were more likely to be influenced by the behavior of specialist physicians in their network. The authors (1) find important, statistically significant peer effects that are robust across model specifications; (2) document asymmetries in response to marketing activity across nominators and opinion leaders; (3) measure the incremental value to firms of directing targeted sales force activity to these opinion leaders; a...
Equality Between Adults and Children: Its Meaning, Implications, and Opposition

Family law scholars have devoted much attention to equality among groups of adults and some attention to equality between groups of children. There has been little exploration, however, of the notion of equality between adults and children. In this Article, I first explain what it means at a basic, theoretical level to speak of such equality. I then identify some practical implications. Finally, I consider why there is great resistance to many practical implications of children’s equality, even among those who would consider themselves advocates for child welfare.
An MCMC approach to classical estimation

This paper studies computationally and theoretically attractive estimators called here Laplace type estimators (LTEs), which include means and quantiles of quasi-posterior distributions defined as transformations of general (nonlikelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods. The approach generates an alternative to classical extremum estimation and also falls outside the parametric Bayesian approach. For example, it offers a new attractive estimation method for such important semi-parametric problems as censored and instrumental quantile regression, nonlinear GMM and value-at-risk models. The LTEs are computed using Markov Chain Monte Carlo methods, which help circumvent the computational curse of dimensionality. A large sample theory is obtained and illustrated for regular cases.
In-RDBMS hardware acceleration of advanced analytics

The data revolution is fueled by advances in machine learning, databases, and hardware design. Programmable accelerators are making their way into each of these areas independently. As such, there is a void of solutions that enables hardware acceleration at the intersection of these disjoint fields. This paper sets out to be the initial step towards a unifying solution for in-Database Acceleration of Advanced Analytics (DAnA). Deploying specialized hardware, such as FPGAs, for in-database analytics currently requires hand-designing the hardware and manually routing the data. Instead, DAnA automatically maps a high-level specification of advanced analytics queries to an FPGA accelerator. The accelerator implementation is generated for a User Defined Function (UDF), expressed as a part of an SQL query using a Python-embedded Domain-Specific Language (DSL). To realize an efficient in-database integration, DAnA accelerators contain a novel hardware structure, Striders, that directly interface with the buffer pool of the database. Striders extract, cleanse, and process the training data tuples that are consumed by a multi-threaded FPGA engine that executes the analytics algorithm. We integrate DAnA with PostgreSQL to generate hardware accelerators for a range of real-world and synthetic datasets running diverse ML algorithms. Results show that DAnA-enhanced PostgreSQL provides, on average, 8.3× end-to-end speedup for real datasets, with a maximum of 28.2×. Moreover, DAnA-enhanced PostgreSQL is, on average, 4.0× faster than the multi-threaded Apache MADLib running on Greenplum. DAnA provides these benefits while hiding the complexity of hardware design from data scientists and allowing them to express the algorithm in ≈30-60 lines of Python.
In Pursuit of Happiness: Empirical Answers to Philosophical Questions

In this article, we provide an overview of what various philosophers throughout the ages have claimed about the nature of happiness, and we discuss to what extent psychological science has been able to substantiate or refute their claims. We first address concerns raised by philosophers regarding the possibility, desirability, and justifiability of happiness and then turn to the perennial question of how to be happy. Integrating insights from great thinkers of the past with empirical findings from modern behavioral sciences, we review the conditions and causes of happiness. We conclude our discussion with some thoughts about the future of happiness studies.
Evading the Treaty Power?: The Constitutionality of Nonbinding Agreements

The U.S. Constitution states that the President can make treaties with the advice and consent of the Senate, provided two thirds of Senators present concur. This high threshold for consent reflects the framers’ concern that treaties not be too easy to make. No one said the President alone could make treaties; many emphasized the contrary. James Wilson, for example, declared that “[n]either the President nor the Senate, solely, can complete a treaty; they are checks upon each other, and are so balanced as to produce security to the people”; Hamilton made similar observations in The Federalist. In modern times, however, Presidents on their own authority have made international agreements that look much like treaties. 2015 provides two examples. First, the President negotiated an agreement with Iran, China, France, Germany, Russia, Britain, and the European Union regarding Iran’s nuclear development. Known as the Joint Comprehensive Plan of Action (JCPOA) and announced in July 2015, its principal goal was to limit Iran to non-military nuclear development in return for lifting U.S. and international economic sanctions on Iran. Second, the President joined with leaders of over 150 nations to produce the Paris Agreement on climate change, with a final version announced in December 2015. The Agreement attempted to promote and coordinate controls on carbon emissions in response to concerns over human-caused global warming. Both agreements appear to involve substantial commitments by the United States, but neither will depend on approval by the Senate (or Congress).The President contends that these agreements are nonbinding under international law and so can be made on the President’s sole constitutional authority. This essay assesses that claim. It generally agrees with the President’s basic proposition but raises concerns about the application of that proposition to the Iran and Paris Agreements. It concludes that without adequate safeguards this approach can provide the President with substantial ability to evade the constitutional checks on the treaty-making power.
Demographic change and the demand for environmental regulation

Environmental regulation in the United States has increased pollution abatement expenditure as a percentage of gross national product from 1.7 percent in 1972 to an estimated 2.6 percent in the year 2000. This rise in regulation has coincided with demographic and economic changes that include rising educational levels, a growing minority population, an aging population, and decreasing employment in polluting industries. This paper examines whether these trends have contributed to increasing aggregate demand for environmental regulation. New evidence on voting on environmental ballots in California, local government environmental expenditures across the United States, and 25 years of congressional voting on environmental issues is examined to document the demographic correlates of environmental support. Minorities and the more educated are more pro-green, whereas manufacturing workers oppose environmental regulation. While demographics help explain observed differences in environmental support and thus can help predict long trends in the “average voter's” environmentalism, environmentalism varies substantially year to year unrelated to population demographics. © 2002 by the Association for Public Policy Analysis and Management.
Syzygies of Segre embeddings and $\Delta$-modules

We study syzygies of the Segre embedding of P(V1)×⋯×P(Vn), and prove two finiteness results. First, for fixed p but varying n and Vi, there is a finite list of master p-syzygies from which all other p-syzygies can be derived by simple substitutions. Second, we define a power series fp with coefficients in something like the Schur algebra, which contains essentially all the information of p-syzygies of Segre embeddings (for all n and Vi), and show that it is a rational function. The list of master p-syzygies and the numerator and denominator of fp can be computed algorithmically (in theory). The central observation of this paper is that by considering all Segre embeddings at once (i.e., letting n and the Vi vary) certain structure on the space of p-syzygies emerges. We formalize this structure in the concept of a Δ-module. Many of our results on syzygies are specializations of general results on Δ-modules that we establish. Our theory also applies to certain other families of varieties, such as tangent and secant varieties of Segre embeddings.
Rules and discretion in trade policy

Abstract We argue that the second-best nature of trade-policy suggests that the issue of time consistency will be an important consideration in determining the extent and efficacy of such intervention. If production decisions are made prior to consumption decisions, governments with sufficient policy discretion will be tempted to surprise producers with policies other than those announced in an effort to separate the consumption tax aspect from the production subsidy dimension of a tariff. This leads optimal trade policy to be time-inconsistent in a wide range of environments. We explore this idea and illustrate the results with specific examples.
Revitalizing Aristotle's Doctrine of Equity

This article argues for the contemporary relevance of Aristotle's doctrine of equity. Too often, similar arguments make this doctrine relevant by abstracting from the details of Aristotle's position or, conversely, fixating on details without due consideration of the epochal gap that separates us from Aristotle. After an initial survey of these limited approaches, the article proceeds to a more adequate account of Aristotle's doctrine of equity with the help of Heidegger. In particular, what Heidegger offers is a nuanced argument as to why Aristotle's manifest absorption in the concrete details of his face-to-face society is not a limitation to his doctrine, but a strength. We, no less than Aristotle, are enmeshed in logos, in a background ordering not at the command of our will, but we have a greater difficulty seeing this. Thus, where equity for Aristotle above all required expert engagement with logos, equity bids us first to acknowledge that the logos is.
A general multitiered supply chain network model of quality competition with suppliers

In this paper, we develop a general multitiered supply chain network equilibrium model consisting of competing suppliers and competing firms who purchase components for the assembly of their final branded products and, if capacity permits, and it enhances profits, produce their own components. The competitive behavior of each tier of decision-makers is described along with their strategic variables, which include quality of the components and, in the case of the firms, the quality of the assembly process itself. The governing equilibrium conditions of the supply chain network are formulated as a variational inequality and qualitative properties are presented. The algorithm, accompanied with convergence results, is then applied to numerical supply chain network examples, along with sensitivity analysis in which the impacts of capacity disruptions and complete supplier elimination are investigated.
SEMIOTICS OF ‘NONCOMPLIANT’ PATIENT

‘Noncompliant’ (NC) patient is a common label in medical records. While it encapsulates many dimensions of undesired patient behavior, the semiotics by which it is generated and applied is unclear: What data indicate noncompliance? How are the data analyzed and interpreted to label a patient as noncompliant? How does the label frame the physician’s thinking? How does it affect the physician’s diagnosis, treatment, instructions and actions? How does it affect medical outcomes? This lack of semiotic clarity can result in medical errors. We provide a framework (a) for conceptualizing the semiotics of NC, and (b) to understand the sources of potential medical errors. We illustrate the framework with a case study. The framework can be used to manage noncompliance effectively and reduce medical errors, especially with EMRs.
NLRA Values, Labor Values, American Values

The National Labor Relations Act (NLRA) was drafted to be far more than a technocratic exercise. The NLRA's policies do play the role - or should play the role - of any legislative policy: providing guidance in interpreting and applying the law. However, the important social, justice, and economic values expressed in - and underpinning - the NLRA's policies found in sections 1, 2(3), 7, and 13 means the policies also provide a means for transforming the fundamental values of this country. My book, Taking Back the Workers' Law, presents a comprehensive program for using the NLRA's policies - and its values - as part of a litigation strategy to restore its power to promote worker collective action. But, more important, it emphasizes that enforcing the law is but one part of a broader educational and litigation strategy to create a more just and democratic society. In other words, Taking Back the Workers' Law is more than a manual for smart litigation. Its basic premise is that unions cannot survive - and certainly cannot thrive - in a country whose core values are inhospitable to the essence of what unions as worker representatives are and do. Taking Back the Workers' Law urges taking a fresh look at the NLRA, seeing it as the Workers' Law with a potential for reinvigorating the labor movement and saving the soul of this country. That potential springs from the NLRA's policies - not as a list of simple statements of purpose, but, rather, as mapping to values of industrial and social democracy, solidarity, social and economic justice, fair wages and working conditions, equality, and industrial and social peace. The NLRA itself and its policies embody values that were intended to, and still can, transform our workplaces and our society.
How Reduced Search Costs and the Distribution of Bidder Participation Affect Auction Prices

Electronic commerce allows bidders to find and participate in auctions regardless of location. This reduction in bidders' search costs has important effects on bidders' participation patterns and sellers' revenue. The "demand expansion" effect occurs when reduced search costs allow bidders to participate in more auctions. The "demand distribution" effect occurs when reduced search costs allow bidders to distribute themselves more evenly across auctions. We focus on the latter effect by modeling when a more even distribution of bidder participation across auctions increases seller revenue. We apply our analytical insights to 65,718 sequential auctions comprising over 10 million vehicles in the wholesale used vehicle market. We show that reduced search costs can increase seller revenue by smoothing the distribution of bidder participation across auctions, even if the aggregate amount of bidder participation remains constant. This contributes new results to the auction theory literature and generates novel insights for sellers seeking increased revenue. This paper was accepted by Lorin Hitt, information systems.
Malcom McLean, containerization and entrepreneurship

Does the entrepreneurial market process reflect an equilibrating or disequilibrating tendency in the allocation of resources? We address this question by utilizing the case of Malcom McLean, who pioneered and introduced container shipping to international trade. We argue that Schumpeterian and Kirznerian entrepreneurship are distinct, yet complementary activities that drive the market process towards an equilibrating tendency. By realizing containerization as a lower cost method of shipping goods internationally, we argue that McLean acted simultaneously as a Schumpeterian and Kirznerian entrepreneur, illustrating that these two notions of entrepreneurship are different segments of the same equilibrating market process. Containerization had a disruptive effect on previous methods of ocean shipping, but its adoption was introduced through an act of arbitrage, namely by redeploying existing resources, such as cranes, ships, ports, and storage facilities from lower-valued uses to perceived higher-valued uses. In the process, McLean was able to realize previously unnoticed profit opportunities by correcting previously existing inefficiencies in intermodal transport.
Incentive Effect of Piece-Rate Contracts: Evidence from Two Small Field Experiments

We conducted two field experiments in a tree-thinning setting. In one experiment, we switched the pay of a randomly chosen half (the treatment group) from hourly wages to piece-rate pay. Workers in the control group were paid hourly wages throughout. In the second experiment, workers were switched from hourly to piece-rate pay all at once. The difference-in-difference and before-after estimates suggest that the productivity increase was on the order of 20-23 percent. Although the sample size is small, the estimates are statistically significant and robust. While the quality did not drop, the study highlights the measurement costs in setting up the right level of piece rates. We also discuss the strengths and weaknesses of using randomized control (and treatment) groups or not in conducting field experiments within firms.
Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy

Welch and Goyal (2008) find that numerous economic variables with in-sample predictive ability for the equity premium fail to deliver consistent out-of-sample forecasting gains relative to the historical average. Arguing that model uncertainty and instability seriously impair the forecasting ability of individual predictive regression models, we recommend combining individual forecasts. Combining delivers statistically and economically significant out-of-sample gains relative to the historical average consistently over time. We provide two empirical explanations for the benefits of forecast combination: (i) combining forecasts incorporates information from numerous economic variables while substantially reducing forecast volatility; (ii) combination forecasts are linked to the real economy. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.
The Doctrine of Preemptive Self-Defense

To the extent that the intervention in Iraq in 2003 is regarded as an act of preemptive self-defense, the aftermath of that intervention may presage an era where states resist resorting to large-scale preemptive self-defense. The intervention in Iraq highlighted considerable policy difficulties with the resort to preemptive self-defense: an inability to attract allies; the dangers of faulty intelligence regarding a foreign state's weapons programs and relations with terrorist groups; the political, economic and human costs in pursuing wars of choice; and the resistance of a local populace or radicalized factions to what is viewed as an unwarranted foreign invasion and occupation. Nevertheless, preemptive self-defense may continue to be used by powerful states on a smaller scale, such as missile attacks against weapons facilities or terrorist camps in rogue states. Unfortunately, the views of international lawyers are fractured on whether such conduct is lawful. Most international lawyers appear to fall into schools of thought that reject preemptive self-defense, but the debate is robust and will no doubt continue. As it continues, this essay urges international lawyers to focus more on the theory and methodology they employ in reaching their conclusions, and why their approach is superior to that of other schools of thought. In particular, the discourse among international lawyers regarding how to gauge state practice since 1945 is uneven, not joined, and at times breezy. Only by grappling squarely with issues of theory and methodology will international lawyers be able to achieve a greater level of convergence in their views, thereby providing policy-makers with better guidance and laying the groundwork for more stable international rules on the use of force.
The Inefficiency of Refinancing: Why Prepayment Penalties Are Good for Risky Borrowers

Abstract This paper provides a theoretical analysis of the efficiency of prepayment penalties in a dynamic competitive lending model with risky borrowers and costly default. When considering improvements in the borrower's creditworthiness as one of the reasons for refinancing mortgages, we show that refinancing penalties can be welfare improving and that they can be particularly beneficial to riskier borrowers in the form of lower mortgage rates, reduced defaults, and increased availability of credit. Thus, a high concentration of prepayment penalties among the riskiest borrowers can be an outcome of efficient equilibrium in a mortgage market. We also provide empirical evidence that is consistent with the key predictions of our model.
CEO Turnover, Information Uncertainty, and Debt Contracting

CEO turnovers are important corporate events that can lead to significant changes within the firm. We find that CEO departures are associated with a subsequent increase in bank loan financing. The ...
Mobility barriers and the value of incumbency

Publisher Summary This chapter discusses the difference between determinants of barriers to the mobility of capital and the welfare implications of entry and exit. The first task is confined for identifying situations in which a firm that is established in an industry benefits because of its incumbency. An incumbency advantage does not necessarily imply that welfare would be improved by encouraging entry. Consumers may be disadvantaged if forced to switch to another supplier. Industry costs would increase if entry eroded the benefits of economies of scale or learning. Entry prevention may result in both lower prices and lower costs than would occur if entry were allowed. The chapter describes Bain's determinants of barriers to entry and shows the way they may be interpreted in the light of recent contributions to the theory of strategic entry deterrence. The role of behavior in the determination of the conditions of entry is discussed in the chapter. Strategic entry deterrence is designed to influence the behavior of potential rivals. It is effective only to the extent that potential competitors look to current behavior as indicative of future market conditions.
The Slippery Slope: The Impact of Feature Alignability on Search and Satisfaction:

Abstract This research challenges the notion that increased search effort results in greater satisfaction with the choice by examining the impact of alignability on search quantity and search outcomes. Options that vary along comparable dimensions are alignable, whereas options that vary along unique dimensions are nonalignable. The results of three studies demonstrate greater search among nonalignable than among alignable options. Satisfaction initially increases but then declines with further search among nonalignable options. Although choice difficulty influences search and satisfaction, the primary mechanism driving the inverted U-shaped relationship of satisfaction with search among nonalignable options is feature learning. This research demonstrates a paradox: People continue searching more options precisely when their further search is detrimental to their satisfaction, falling down the slippery slope of search.
Dynamic Kidney Exchange

We study how barter exchanges should be conducted through a centralized mechanism in a dynamically evolving agent pool with time- and compatibility-based preferences. We derive the dynamically efficient two-way and multi-way exchange mechanisms that maximize total discounted exchange surplus. Recently several live-donor kidney exchange programs were established to swap incompatible donors of end-stage kidney disease patients. Since kidney exchange can be modeled as a special instance of our more general model, dynamically efficient kidney exchange mechanisms are derived as corollaries. We make policy recommendations using simulations.
Information and the Scope of Liability for Breach of Contract: The Rule of Hadley v. Baxendale

According to the contract law principle established in the famous nineteenth century English case of Hadley v. Baxendale, and followed ever since in the common law world, liability for a breach of contract is limited to losses "arising ... according to the usual course of things," or that may be reasonably supposed "to have been in the contemplation of both parties, at the time they made the contract, ..." Using a formal model, we attempt in this paper to analyze systematically the effects and the efficiency of this limitation on contract damages. We study two alternative rules: the limited liability rule of Hadley, and an unlimited liability rule. Our analysis focuses on the effects of the alternative rules on two types of decisions: buyers' decisions about communicating their valuations of performance to sellers; and sellers' decisions about their level of precautions to reduce the likelihood of nonperformance. We identify the efficient behavior of buyers and sellers. We then compare this efficient behavior with the decisions that buyers and sellers in fact make under the limited and unlimited liability rules. This analysis enables us to provide a full characterization of the conditions under which each of the rules induces, or fails to induce, efficient behavior, as well as the conditions under which each of the rules is superior to the other.
Effect of the Political Regime on Asset Returns in Emerging Markets: An Empirical Investigation:

The article explores the role of the political regime on asset returns in an International Capital Asset Pricing Model (CAPM) framework based on a sample of 17 emerging countries. The results revea...
Pursuing Environmental Justice: Obstacles and Opportunities—Lessons from the Field

The pursuit of environmental justice has not moved much beyond the rhetorical sphere, although the consistent efforts of communities have resulted in some hopeful signs from government leadership. Thirty years after environmental justice became a rallying cry, for example, there is still no regulation that accounts for cumulative air pollution at a neighborhood level. Government at all levels must implement ideas such as ending the grandfathering of new major sources and eliminating emission credits in highly polluted areas. The federal government must also initiate rulemaking to address nuisances. In addition to government action, citizens must organize to demand government action to address cumulative pollution.
Keeping your genes private.

This article discusses the development of genetic testing technology and the possibility that a person's genetic information will be shared among health providers and possibly a wider public. The potential for genetic information to provide a statistical measure of health status is described. Efforts to develop legal restrictions on the accessibility of health information in the Netherlands and Great Britain are described. INSETS: INTRIGUED BUT WARY;Should Family Members Be Warned?;GENE DETAILS COMING SOON.
Exchange rate regime transitions

Abstract The “hollowing-out”, or “two poles” hypothesis is tested in the context of a Markov chain model of exchange rate transitions. In particular, two versions of the hypothesis—that hard pegs are an absorbing state, or that fixes and floats form a closed set, with no transitions to intermediate regimes—are tested using two alternative classifications of regimes. While there is some support for the lack of exits from hard pegs (i.e. that they are an absorbing state), the data generally indicate that the intermediate cases will continue to constitute a sizable fraction of actual exchange rate regimes.