Empirical Legal Studies

New Junior Faculty Forum Focusing on STEM fields--Call For Papers

Dave Schwartz (Northwestern), along with his counterparts at Penn and Stanford law schools, are converging to launch a new Junior Faculty Forum focusing on "interdisciplinary scholarship focusing on the intersection of Law and Science-Technology-Engineering-Mathematics (STEM)." Penn Law (specifically, Penn's CTIC) will host the the inaugural Forum on October 6-7, 2017. Forum organizers currently seek paper submissions from interested junior faculty. The deadline for paper submissions is Friday, June 9, 2017.

Those with questions about the Forum should contact either Dave Schwartz (Northwestern), Christopher Yoo (Penn), or Mark Lemley (Stanford). The overlap with ELS Blog readers is potentially rich. As Dave notes, "while the field of law-STEM is somewhat ambiguous, it includes many topics related to ELS. For instance, predictive analytics, machine learning, and computational law all would be included."

SCOTUS Tenure Data

An interesting (and timely) data crunching post over at Empirical SCOTUS focuses on Justice tenure (here). As the post notes, with an eye towards assessing President Trump's potential to influence the Court's future through appointments: "If we look over time to all Justices confirmed to the Court or elevated from Associate to Chief Justice, we see that the average time between appointments has increased only minimally to around two years (the Justices are listed sequentially in the order in which they were nominated/elevated and the minimum of zero years is when two Justices were confirmed within the same year)." Notably, the "time between nominations" trend remains essentially flat despite the concurrent emergence two related trends. First, the average age at nomination has "declined about five years on average since the beginning of the 20^th century." Second, Justices last on the Court "for more years and until they are older."

Reserach Projects at HLS' A2J Lab

Under Jim Greiner's direction, work at HLS' Access to Justice Lab (previously described here) is underway. One recent courthouse project in Dane County, WI, involves an evaluation of that court's Public Safety Assessment ("PSA"). As this post notes, "Criminal process in this jurisdiction now includes additional, scientifically based information in a randomly selected subset of cases to inform pre-disposition release decisions. The judicial official–known here as a Commissioner–receives risk scores and a recommendation for release through the PSA and its static criminal history inputs to consider in reaching those decisions." (The A2J link also includes an informative video.)

Federal Judicial Center, ICPSR, Data Access, and Change

Back in the "old days," virtually immediate access to ICPSR's massive--and massively important--federal court data holdings was simple, easy, and, quite literally, involved only a few "clicks" of the mouse. A few years ago, "something happened," and that something resulted in the imposition of a formal ICPSR application requirement along with pre-IRB approvals and restricted use agreements. And all this for publicly available data. Gone were the days when researchers could simply "browse" datasets without such impediments. While ICPSR data remain "available," access now requires a comparatively dogged effort and these barriers to access have dampened overall use of ICPSR data.

But change is coming, and some of it has now arrived. One supplier of ICPSR data germane to legal scholars, the Federal Judicial Center, in conjunction with the U.S. Administrative Office, has effectively "side-stepped" ICPSR and made the FJC's Integrated Database (through 2016) directly available to users. FJC's Joe Cecil, who was instrumental in this data access project, sums it up nicely:

"The Federal Judicial Center’s Integrated Database, containing records of all federal court cases, is now available without the necessity of a restricted use agreement at www.fjc.gov/research/idb (or here). An interactive query function allows selection of subsets of civil cases based on federal circuit, district, nature of suit, disposition, origin, jurisdiction, class action designation, filing date, termination date, and docket number. Annual case filing and termination datasets through 2016 for civil, criminal defendant, appellate, and bankruptcy cases also can be downloaded in SAS format."

The FJC deserves well-earned applause from empirical legal scholars for its efforts.

The Empirics of the "Hot Hand" Thesis--Updated

While we've delved into this topic previously (here), the NCAA basketball tournaments' "championship weekend" provides an opportune time to update the Tversky et al. paper, Misconceptions of Chance Processes in Basketball (1985). To refresh, one of the co-authors recently summarized the paper's key insight.

GILOVICH: "Well, everyone who’s ever played the game of basketball knows you get this feeling where the game seems to slow down. It becomes easier, or you almost don’t even have to aim that carefully. The ball’s going to go in. It’s one of the most compelling feelings that you can have. And it turns out if you statistically analyze people’s shots — whether it’s professional games, college basketball players shooting in a gym, although the feeling exists when you make several shots in a row — you will feel hot. That feeling very surprisingly doesn’t predict how you’re going to do in the next shot or the next several shots — the distribution of hits and misses in the game of basketball looks just like the distribution of heads and tails when you’re flipping a coin. although of course, not every player shoots 50%. Very few of them do."

Not surprisingly, the "hot hand" thesis attracted scrutiny. Gelman's take on the thesis is quite clear: "That’s wrong. The distribution of hits and misses in the game of basketball does not look just like the distribution of heads and tails when you’re flipping a coin." Gelman's criticism echos a 2015 paper (revised in 2016), Surprised by the Gambler's and Hot Hand Fallacies? A Truth in the Law of Small Numbers, by Miller and Sanjurjo, where the authors find:

"... a subtle but substantial bias in a standard measure of the conditional dependence of present outcomes on streaks of past outcomes in sequential data. The mechanism is driven by a form of selection bias, which leads to an underestimate of the true conditional probability of a given outcome when conditioning on prior outcomes of the same kind. The biased measure has been used prominently in the literature that investigates incorrect beliefs in sequential decision making — most notably the Gambler’s Fallacy and the Hot Hand Fallacy. Upon correcting for the bias, the conclusions of some prominent studies in the literature are reversed."