You are invited to attend a briefing on Capitol Hill on "Evidence Based Counterterrorism Policy". The event will feature the work of many START researchers, as well as scholars from other leading research organizations. WHEN: Monday, September 10 10:00am through 12:15pm, with reception to follow WHERE: Russell Senate Office Building, SR-188 Capitol Hill, Washington DC Online RSVP Required BRIEFING SUMMARY Researchers will present empirical findings on the effects - both intended and unintended - of counterterrorism interventions. Despite the increase in the use and generation of strategies to counter terrorism, there continues to be only a small amount of existing research that has evaluated these programs. The findings presented will demonstrate how research can be used to provide an evidence base for such strategies to inform policy. PRESENTERS AND THEIR RESEARCH Dr. Laura Dugan (START, University of Maryland): The Global Terrorism Database and counterterrorism efforts in Northern Ireland Dr. Brian Forst, Mr. Mark McInerney (American University) and Dr. Ashley Nellis (START): Target attractiveness, fear, and the prevention of terrorism Dr. Brian Jackson (RAND Corporation): Understanding terrorist efforts to overcome defensive technologies Dr. Leslie W. Kennedy (Rutgers University): Creating a research infrastructure for counterterrorism Dr. Cynthia Lum (George Mason University) Findings of collateral effects of airport security screening at BWI Airport Dr. Peggy Maddox (George Mason University): Disaster and emergency preparedness among nurses in the Washington, DC area Dr. David Ortiz (RAND Corporation): Evaluating the security and efficiency of the global containerized supply chain Dr. Clay Ramsay (START and PIPA, University of Maryland): Muslim public opinion on military counterterrorism interventions Dr. Ivan Sascha Sheehan (University of Massachusetts): The impact of the war on terrorism on subsequent terrorist activity Dr. Maksim Tsvetovat (George Mason University): The recovery of terrorist networks using simulation models
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