Skip to main content area Skip to institutional navigation Skip to search Skip to section navigation

Crime, Law & Economics Workshop


Thursday September 12, 2013
4:30-6:00 pm | Shuster Room, Silverman 147

Dr. Doron Teichman
Senior Lecturer, Faculty of Law,  Hebrew University of Jerusalem

Seeing is Believing: The Anti-Inference Bias

Joint event with the Donald and Margaret Sherman Violence Prevention Lecture

Thursday October 17, 2013 (Updated Speaker and Topic)
4:30-6:00 pm | Shuster Room, Silverman 147

Anthony A. Braga
Don M. Gottfredson Professor of Evidence-Based Criminology in the School of Criminal Justice at Rutgers University; Senior Research Fellow in the Program in Criminal Justice Policy and Management at Harvard University.

“Beyond Putting ‘Cops on Dots’: Applying Empirical Evidence to Improve Police Responses to Violent Places.”

Over the last two decades, crime scholars and practitioners have pointed to the potential benefits of focusing police crime prevention efforts on crime places.  Research suggests that there is significant clustering of violent crime in small places or “hot spots.” Moreover, these violent crime concentrations at specific places are very stable over long periods of time. There is a strong and growing body of rigorous scientific evidence that the police can control crime hot spots without simply displacing violence to other places.  Putting police officers in high crime locations may be an old and well-established idea. The age and popularity of an idea, however, does not necessarily mean that it is being done in the most productive way.  New empirical evidence suggests how the police address crime hot spots matters.  Police officers should strive to use problem-oriented policing and situational crime prevention techniques to address the place dynamics, situations, and characteristics that cause a “spot” to be “hot.”  Increased traditional policing strategies, such as patrol and arrest-based interventions, do not deal with the underlying criminal opportunity structures that cause crime places to persist over time. In this lecture, the implementation and evaluation of evidence-based violence prevention practices in police departments in Lowell and Boston, Massachusetts are used to develop and support these ideas.

Thursday November 14, 2013
4:30-6:00 pm | Faculty Lounge, Silverman 144

Richard Berk
Professor of Criminology and Statistics, Wharton School, University of Pennsylvania

Using Big Data and Machine Learning to Forecast Criminal Justice Outcomes

Thursday December 5, 2013
4:30-6:00 pm | Shuster Room, Silverman 147

Dr. Yehonatan Givati
Hebrew University of Jerusalem

Organizational Structure, Police Activity and Crime

Thursday, February 20, 2014
4:30-6:00 pm | Faculty Lounge, Silverman 144

Marc Meredith
Assistant Professor of Political Science, University of Pennsylvania

The Politics of the Restoration of Ex-Felon Voting Rights: The Case of Iowa

Marc Meredith has been an assistant professor of Political Science and (by courtesy) Business Economics and Public Policy at the University of Pennsylvania since 2009. He received his Ph.D. in Political Economics from the Stanford Graduate School of Business. His research examines the political economy of American elections, with a particular focus on the application of causal inference methods. Marc’s substantive research interests include election administration, local political institutions, political campaigns, and voter decision-making, particularly as it relatives to economic conditions. He is currently working on a book project on the public administration of felony disenfranchisement laws. His research appears in the American Political Science Review, the Journal of Politics, the Proceedings of the National Academies of Science, Political Analysis, and the Quarterly Journal of Political Science, among other outlets.

Thursday, March 06, 2014
4:30-6:00 pm | Faculty Lounge, Silverman 144

Cynthia Rudin
Associate Professor of Statistics, MIT Sloan School of Management 

Finding Patterns with a Rotten Core: Data Mining for Crime Series with Core Sets

Professor Rudin’s goal is to automatically detect patterns of crime. Among a large set of crimes that happen every year in a major city, it is challenging, time-consuming, and labor-intensive for crime analysts to determine which ones may have been committed by the same individual(s).  If automated, data-driven tools for crime pattern detection are made available to assist analysts, these tools could help police to better understand patterns of crime, leading to more precise attribution of past crimes, and the apprehension of suspects.

She will describe work from the Prediction Analysis Lab at MIT and the Crime Analysis Unit of the Cambridge Police Department. She and her co-authors have been developing algorithms that find patterns of crimes from within a database. These methods have had promising results on identifying patterns within Cambridge’s historical crime data.

Cynthia Rudin is an associate professor of statistics at the Massachusetts Institute of Technology. Previously, Prof. Rudin was an associate research scientist at the Center for Computational Learning Systems at Columbia University, and prior to that, an NSF postdoctoral research fellow at NYU. She holds an undergraduate degree from the University at Buffalo where she received the College of Arts and Sciences Outstanding Senior Award in Sciences and Mathematics, and she received a PhD in applied and computational mathematics from Princeton University in 2004. She is the recipient of the 2013 INFORMS Innovative Applications in Analytics Award. She was given an NSF CAREER award in 2011. Her work has been featured in IEEE Computer, Businessweek, The Wall Street Journal, the Boston Globe, the Times of London, Fox News (Fox & Friends), the Toronto Star, WIRED Science, Yahoo! Shine, U.S. News and World Report, Slashdot, CIO magazine, and on Boston Public Radio.

Thursday, May 01, 2014
12:00-1:15 pm | Faculty Lounge, Silverman 144

Benjamin Hansen
Assistant Professor of Economics, University of Oregon

Phoning Home?: Technological Innovation and its Effects on Inmate Violence, Drug Use and Misconducts

Ben Hansen is an assistant professor of economics at the University of Oregon, where he has taught since 2010. He received a PhD. in Economics from the University of California Santa Barbara. His research focuses on education, health and labor topics with a particular focus on factors influencing human capital accumulation, adolescent and adult risky behavior, and crime. His research has appeared in the National Tax Journal, the Industrial and Labor Relations Review, and is forthcoming in the American Economic Journal and the Journal of Law and Economics. He will be presenting his research on changes in prisoner behavior in response to changes in good time credits within an American prison system.


Thursday, October 18, 2012
4:30-6:00 pm | Michael A. Fitts Auditorium

Philip Cook
ITT/Sanford Professor of Public Policy and Professor of Economics and Sociology at Duke University

Private Self-Protection and Prevention in Crime Control (PDF)


Thursday January 24, 2013
4:30-6:00 pm | Faculty Lounge, Silverman 144

Daniel Nagin
Teresa and H. John Heinz III University Professor of Public Policy and Statistics

Deterrence and the Death Penalty
Full Report

In 1976, the Supreme Court decision in Gregg v. Georgia (428 U.S. 153) ended the 4-year moratorium on executions that had resulted from its 1972 decision in Furman v. Georgia (408 U.S. 238). In the immediate aftermath of Gregg, an earlier report of the National Research Council (NRC) reviewed the evidence relating to the deterrent effect of the death penalty that had been gathered through the mid-1970s. That review was highly critical of the earlier research and concluded (National Research Council, 1978, p. 9) that “available studies provide no useful evidence on the deterrent effect of capital punishment.” 

During the 35 years since Gregg, and particularly in the past decade, many additional studies have renewed the attempt to estimate the effect of capital punishment on homicide rates. Most researchers have used post-Gregg data from the United States to examine the statistical association between homicide rates and the legal status, the actual implementation of the death penalty, or both. The studies have reached widely varying, even contradictory, conclusions. Some studies conclude that executions save large numbers of lives; others conclude that executions actually increase homicides; and still others conclude that executions have no effect on homicide rate. Commentary on the scientific validity of the findings has sometimes been acrimonious. The Committee on Deterrence and the Death Penalty was convened against this backdrop of conflicting claims about the effect of capital punishment on homicide rates. 


Wednesday February 6, 2013 
4:30-6:00 pm | Faculty Lounge, Silverman 144

Jeffrey Brantingham
Professor of Anthropology, UCLA  

The Los Angeles Predictive Policing Experiment

The term predictive policing refers broadly to the use of data analysis to inform the allocation of police resources.  Not surprisingly, the vagueness of this definition allows many different data analysis programs to be called predictive policing.  Here I define predictive policing as a formal process that (1) uses data to assign explicit probabilities to future crime events in space and time, (2) presents crime event probabilities in a useable framework to law enforcement decision makers, and (3) leads to resource deployment patterns conditioned on crime probabilities.  For predictive policing to be effective it also must hold that (4) the accuracy of predictions be evaluated and (5) law enforcement be willing to act on probabilistic information.  Here I outline the behavioral and mathematical architecture underlying our approach to predictive policing.  I then review the results of the Los Angeles Predictive Policing Experiment, a real-time single-blind field deployment of predictive policing in multiple divisions of the LAPD.  The experiment establishes a predictive accuracy six-times random and more than two-times that achievable by a dedicated crime analyst.  The experiment also underscores the critical importance of officer ‘buy-in’ for successful real-world deployments.