Jun Zhuang, Ph.D. - US grants

This Grant for Rapid Response Research (RAPID) project will identify the key dynamics for optimal distribution and sustainable partnership in Haitian disaster recovery following the tragic 7.0 magnitude earthquake which struck just 10 miles from the Haitian capital of Port-au-Prince on January 12th, 2010. This study will be conducted during the military troop withdrawal and the unique conditions propagated by the transition from international response to Haitian-led recovery. Observations during this time period can provide insight into many of the inter-agency problems that have plagued large-scale responses in the past. The body of knowledge on dynamic partnership formation has the potential to be greatly advanced and expanded through the identification of key characteristics in effective inter-agency partnerships and through observation of the practical utilization of these partnerships during formation and under strain.

Data to be collected during the Haitian visit includes insight from emergency managers and responders. Using naturalistic data collection methods, quantitative and qualitative data on the efficiency of aid acquisition, transportation, and distribution will be collected using a knowledgeable researcher embedded within an active distribution organization. By focusing on the dynamics of local-international partnerships and their efficacy, this study will identify initial results from analysis of the collected data and will motivate future studies aimed at developing accurate models for organizational dynamics, distribution locations, and transport methodology under dynamic conditions. The information gathered will provide practical and testable insight into future response operations across the United States and around the world. The recent earthquake in Chile and ongoing preparation for similar earthquake disasters (e.g. the New Madrid Seismic Zone in the US) motivate the need for the increased understanding of inter-organizational dynamics which this study can provide.

The objective of this award is to explore a new class of decision models to provide structural insights for robust screening when dealing with adaptive applicants and incomplete information. This research is motivated by public concerns on balancing congestion and safety due to security screening. Such screening has been used to identify and deter potential threats (e.g., terrorists, attackers, smugglers, spies) among normal applicants wishing to enter an organization, location, or facility. In-depth screening could reduce the risk of being attacked. However it may also create delays and deter normal applicants, which decreases the welfare of both the approver (authority, manager, screener) and the normal applicants. This research will consider the factors of security, congestion, equity, and the strategic and non-strategic responses from various applicant types. In particular, this research will study the applicants' strategies of applying, reneging, learning, and deceiving. This research will also study the approver's strategies of screening, dynamic service rates, multiple-servers and priority processing, multi-layer screening, and secrecy and deception.

If successful, this research will lead to new frameworks that decision makers can use for screening diverse groups of strategic applicants. These new frameworks have the potential to reduce costs, avoid unnecessary waiting and inconvenience, and improve effectiveness and efficiency of the approval processes. Potential applications of this research include immigration systems, job market background checks, and airport/container/border controls. The relevance is illustrated by the recent national debate on selective "pat-downs" and "advanced imaging" screening, and the associated changing travel patterns. This research will engage many graduate, undergraduate, and high school students, including those from under-represented groups. The results of this research will be disseminated broadly to local, national and international communities.

The objective of this research is to model and analyze partnership creation, length, and conclusion in networks of agencies responding to extreme events. The researchers develop models to explore how characteristics of partnerships could be used to predict dynamics in agency investment, commitment length, partnership selection, and exit timing. The research collects and validates data by interviewing agencies active in responding to extreme events. This project compares agency behavior in two separate disaster response scenarios, mathematically models the life-cycle of agency partnerships during disaster operations, and conducts controlled experiments to analyze agency decision making.

Extreme events have had a significant impact on the world over the last several years, including earthquakes, tsunamis, hurricanes, and tropical storms. The scale and scope of events like Hurricane Sandy, the earthquake and tsunami in Japan, and the tornados around the United States make it imperative to increase our understanding of how government, non-governmental, and business agencies interact with one another in the aftermath of such extreme events. This study builds on previous work in emergency management to provide an analysis of the partnership selection and resource sharing processes that occur following an extreme event. By documenting the dynamical change in roles and flow of resources following extreme events, this work tests hypotheses regarding agencies and how they are impacted by partnerships, goals, roles, and prior involvement. With the support of a host of agencies that are actively involved in relief operations in the U.S. and around the world, the model results are checked for relevance, accuracy, and correct representation of the agencies and individuals responding to extreme events.

The goal of this award is to help provide a solid foundation for motivating more comprehensive ways to assess the risk tradeoffs in multi-stakeholder disaster management and resource allocation. This will be accomplished by taking advantage of theoretical decision frameworks such as game theory and prospect theory, and will use robust optimization techniques to address the uncertainty that surrounds disasters. This project will address under-studied questions such as: (a) How should governments and private sectors balance between the funding for emergency preparedness and the funding for disaster relief, when they are uncertain about the disaster location and consequences? (b) How should governments distribute incentives to reduce vulnerability to disasters? and (c) How should decision makers balance equity, efficiency, and effectiveness when preparing for and responding to disasters?

As a 2012 National Research Council report states, "there is currently no comprehensive framework to guide private-public collaboration focused on disaster preparedness, response, and recovery." If successful, this project will help to address this issue by providing insights, practical guidelines, and decision support tools to help save lives and property in the face of disasters. This research will engage many graduate, undergraduate, and high school students, including those from under-represented groups. The models, results, and insight gained will be shared with international, federal, and local representatives through seminars, conferences, publication, media coverage, and websites.

Social media users significantly impact what information is spreading and how fast it spreads during a crisis such as a hospital fire or a flood. Rumors spread on social media have posed great risks to crisis response activities by spreading inaccurate information on social media. The spreading of rumors can worsen situation awareness and contribute to bad decisions. Regarding rumors, social media can exacerbate problems by the ease of information sharing by only clicking buttons; on the other hand, the rumors can also be debunked more quickly. Better knowledge of the rumor responding behaviors of social media users would be essential for a better rumor control on social media during crises.

This research investigates responding behaviors of social media users during crises. The research team identified four crises for which data are available of what happened on social media. From these data the team explores how the rumors were debunked. Besides the data from different social media platforms, the research also includes in-depth interviews to ascertain variables that influence responses to rumors and to rumor-debunking efforts. Then the team develops and validates a decision-making model to understand the decision-making processes of misinformed social media users. This research provides novel insights into a better rumor management during crises.

Forms of social media like Twitter are increasingly relied upon by members of the public as sources of information in emergencies like hurricanes and floods. The downside of these sources of information is that they may also be the source of unfounded rumors as were spread regarding a host of issues during hurricanes Harvey and Irma. The instant sharing features of social media make rumors difficult to control because information from official sources to debunk misinformation often comes late. Motivated by those issue, this project will collect and analyze data related to how rumors were spread and controlled during Hurricane Harvey and Hurricane Irma. The objectives of this project are to study (a) how rumor spreads on social media; (b) what effective debunking network constitutes; and (c) how the public deals with risk information that stipulates subsequent communication behaviors such as information processing and sharing.

The study will use social network analysis, content analysis, survey, interviews, optimization and simulation, to (a) identify rumor response behaviors of social media users during disasters; (b) identify motivations behind social media users' risk communication behaviors such as information processing and information sharing; (c) build a decision making model of individual social media users to predict future responses to rumors; (d) investigate social media usage in rumor control and potential collaborations among disaster response agencies in rumor management; and (e) design potential collaboration methods among disaster response agencies on social media to minimize the costs and damages generated by rumors.

Defending against adversarial threats has been a central and longstanding focus for governments throughout the world. One prominent approach to improve defensive capabilities is to deploy new technologies among venues of interest (e.g., metal detectors at airports). When it comes to deploying new security and defense technologies, agencies must decide how the related information should be released to the public. Given that adversaries, such as terrorist organizations, can access information that is publicly disseminated, it is critical to understand the implications of releasing different types of information. For instance, security agencies may choose to release information on only a subset of venues where new technology is deployed, creating an uncertainty for an adversary regarding the deployment of the technology at other venues (e.g., airports). This research addresses this difficult information disclosure problem by building on previous work in game theory and adversarial decision making to develop models and experiments that provide insights into defensive information disclosure, adversarial beliefs, and adversarial target selection decisions.<br/><br/>The research objectives of this proposed effort are to mathematically model, experimentally test, and robustly analyze technology deployment and information disclosure strategies in the context of security and defense. To meet these objectives, this project develops and analyzes a novel game-theoretic signaling model, which provides insightful analyses into optimal information disclosure strategies. Further, human experiments –designed to mimic the game model – are conducted to (i) study human beliefs and decision making in this context, and (ii) compare the game-theoretic prescriptions with actual human decisions. Successful completion of this project helps to inform the long-term effectiveness of technology deployments within the security and defense sectors. Although this research is motivated by problems in security and defense, the mathematical model and experimental framework is generalizable to any application in which the strategic release of information across multiple venues is of interest.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.