An Economist's Guide to U.S. v. Microsoft

We analyze the central economic issues raised by U.S. v Microsoft. Network effects and economies of scale in applications programs created a barrier to entry for new operating system competitors, which the combination of Netscape Navigator and the Java programming language potentially could have lowered. Microsoft took actions to eliminate this threat to its operating system monopoly, and some of Microsoft's conduct very likely harmed consumers. While we recognize the risks of the government's proposed structural remedy of splitting Microsoft in two, we are pessimistic that a limited conduct remedy would be effective in this case.

A General Approach for Predicting the Behavior of the Supreme Court of the United States

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time evolving random forest classifier which leverages some unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.Comment: version 2.02; 18 pages, 5 figures. This paper is related to but distinct from arXiv:1407.6333, and the results herein supersede arXiv:1407.6333. Source code available at https://github.com/mjbommar/scotus-predict-v

Market Structure, Organizational Structure, and R&D Diversity

We examine the effects of market structure and the internal organization of firms on equilibrium R&D projects. We compare a monopolist's choice of R&D portfolio to that of a welfare maximizer. We next show that Sah and Stiglitz's finding that the market portfolio of R&D is independent of the number of firms under Bertrand competition extends to neither Cournot oligopoly nor a cartel. We also show that the ability of firms to pre-empt R&D by rivals along particular research paths can lead to socially excessive R&D diversification. Lastly, using Sah and Stiglitz's definition of hierarchy, we establish conditions under which larger hierarchies invest in smaller portfolios.

Stop-Catalyzed Baryogenesis Beyond the MSSM

Non-minimal supersymmetric models that predict a tree-level Higgs mass above the Minimal Supersymmetric Standard Model (MSSM) bound are well motivated by naturalness considerations. Indirect constraints on the stop sector parameters of such models are significantly relaxed compared to the MSSM; in particular, both stops can have weak-scale masses. We revisit the stop-catalyzed electroweak baryogenesis (EWB) scenario in this context. We find that the LHC measurements of the Higgs boson production and decay rates already rule out the possibility of stop-catalyzed EWB. We also introduce a gauge-invariant analysis framework that may generalize to other scenarios in which interactions outside the gauge sector drive the electroweak phase transition.Comment: 9 pages, 3 figures. v2: Minor changes. Added appendix with the details of the higgs couplings fit. References adde

Do we need to rethink guidance on repeated interviews?

Within the legal system, children are frequently interviewed about their experiences more than once, with different information elicited in different interviews. The presumed positive and negative effects of multiple interviewing have generated debate and controversy within the legal system and among researchers. Some commentators emphasise that repeated interviews foster inaccurate recall and are inherently suggestive, whereas others emphasise the benefits of allowing witnesses more than one opportunity to recall information. In this article we briefly review the literature on repeated interviewing before presenting a series of cases highlighting what happens when children are interviewed more than once for various reasons. We conclude that, when interviewers follow internationally recognised best-practice guidelines emphasising open-questions and free memory recall, alleged victims of abuse should be interviewed more than once to ensure that more complete accounts are obtained. Implications for current legal guidelines concerning repeated interviewing are discussed