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 that leverages 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.

Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimerà and Sales-Pardo (2011), Ruger et al. (2004), andMartin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States . Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court's overall affirm / reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized, and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).

Scholars have increasingly investigated "crowdsourcing" as an alternative to expert-based judgment or purely data-driven approaches to predicting the future. Under certain conditions, scholars have found that crowdsourcing can outperform these other approaches. However, despite interest in the topic and a series of successful use cases, relatively few studies have applied empirical model thinking to evaluate the accuracy and robustness of crowdsourcing in real-world contexts. In this paper, we offer three novel contributions. First, we explore a dataset of over 600,000 predictions from over 7,000 participants in a multi-year tournament to predict the decisions of the Supreme Court of the United States. Second, we develop a comprehensive crowd construction framework that allows for the formal description and application of crowdsourcing to real-world data. Third, we apply this framework to our data to construct more than 275,000 crowd models. We find that in out-of-sample historical simulations, crowdsourcing robustly outperforms the commonly-accepted null model, yielding the highest-known performance for this context at 80.8% case level accuracy. To our knowledge, this dataset and analysis represent one of the largest explorations of recurring human prediction to date, and our results provide additional empirical support for the use of crowdsourcing as a prediction method.

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Since Democrats lost their filibuster-proof majority in the Senate in the 2010 midterm elections-due in large part to the rising unpopularity of the Affordable Care Act (ACA) and the growing strength of the Tea Party-President Obama was largely unable to advance his agenda through the legislative process. Faced with Republican opposition at every step, Obama increasingly turned to executive power to take action where Congress would not. By 2011, the mantra of his presidency became "We can't wait." Charlie Savage reported that the President coined this slogan at a strategy meeting to "more aggressively use executive power to govern in the face of Congressional obstructionism." 1 When Congress will not legislate to the President's satisfaction, he will act alone. In a White House blog post fittingly titled "We Can't Wait" the administration listed all of the President's executive actions, stressing that he "is not letting congressional gridlock slow our economic growth." 2 By my count, Obama has repeated this phrase at least a dozen times to justify taking executive action where Congress would not pass him the bill he wants. 3 * Associate Professor, South Texas College of Law, Houston. This essay is adapted from Unraveled: Obamacare, Religious Liberty, & Executive Power (Cambridge University Press 2016). I would like to thank the members of the FIU Law Review and all of the participants of the symposium. 1 Charlie Savage, Shift on Executive Power Lets Obama Bypass Rivals, N.Y. TIMES (Apr. 22, 2012), http://nyti.ms/1SgzZnC. 2 We Can't Wait, WHITEHOUSE.GOV (Oct. 24, 2011), www.whitehouse.gov/economy/jobs/wecant-wait. 3 Press Release, The White House, Remarks by the President on the Economy and Housing (Oct. 24, 2011, 2:15 PM) ("So I'm here to say to all of you-and to say to the people of Nevada and the people of Las Vegas-we can't wait for an increasingly dysfunctional Congress to do its job. Where they won't act, I will. In recent weeks, we decided to stop waiting for Congress to fix No Child Left Behind, and decided to give states the flexibility they need to help our children meet higher standards.") (emphasis added), www.whitehouse.gov/the-press-office/2011/10/24/remarks-president-economy-andhousing; Press Release, The White House, Remarks by the President at a Campaign Event (Oct. 25, 2011, 7:36 PM) ("And so we're going to keep on putting pressure on them, but in the meantime we're saying we can't wait for Congress, and we're going to go ahead and do everything we can through executive actions-whether it's this refinancing program, or tomorrow I'm going to be talking about making college more affordable for young people-we're not going to wait for Congress.") (emphasis added), www.whitehouse.gov/the-press-office/2011/10/25/remarks-president-campaign-event-1; Press Release, The White House, Remarks by the President at Signing of Executive Order (Oct. 31, 2011, 12:50 PM) ("Congress has been trying since February to do something about this. It has not yet been able to get it done. And it is the belief of this admin...