1986 — 1988 |
Carley, Kathleen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Knowledge Acquisition as a Social Phenomenon (Information Science) @ Carnegie-Mellon University |
1 |
1987 — 1990 |
Carley, Kathleen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Frame Base Decision Making in a Distributed Decision Making Environment @ Carnegie-Mellon University
The decision to launch a space shuttle is an example of a decision that is made in a distributed decision making environment. Distributed decision making (DDM) typically refers to decision making when the decision makers or the information are distributed across information processing centers. A decision maker may be a group of humans, a single human, or a basic transaction (piece of a computer program). The concern of this proposal is with DDM environments where the decision makers are humans organized hierarchically and dispersed across information processing centers that may or may not be geographically separate. Most work on human decision making has been concerned with the behavior of individual decision makers. It is quite clear that it is unrealistic to lump together identical people into an "economic man" or "ideal decision maker". Such models tend to make erroneous predictions vis-a-vis group decision making. Consequently, what is needed is a way of aggregating individual models that does not violate the constraints placed on individual decision making due to the fact that are involved in a distributed decision making enterprise. There is a need for a model of the human decision maker in a DDM environment. Such a model would decrease reliance on expensive field exercises and war and organizational games where parameters affecting decision errors are difficult to control. Further, such a model would enable researchers to make predictions about human decision making behavior in distributed environments which could then be empirically tested.
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1 |
1993 — 1997 |
Kiesler, Sara [⬀] Carley, Kathleen Wholey, Douglas |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Learning Teamwork: Studies of Training in Software Development @ Carnegie-Mellon University
Scientists, engineers and technical managers often work in teams. The ability to work as part of a team contributes importantly to organizational productivity. Yet despite a strong tradition of theory and research on groups, little is known about the acquisition of teamwork skills, especially in technical domains. This research investigates how technical students and professionals learn teamwork in software development. A theoretical framework for examining individual and group change in coordination behavior and teamwork skills is developed. Then, studies of the development of teamwork skills in student software development teams are conducted. The research examines the effect of students' experience on changes in team structure and communication, and of how patterns of team coordination are associated with team performance and with individual performance and cognitive learning. The analysis is then extended to actual organizations with studies of changes in patterns of coordination and learning in software development teams in two organizations. Two field studies are conducted: (1) a cross-sectional study of how coordination patterns in teams are associated with cognitions and attitudes about teamwork, and (2) a longitudinal study of socialization for teamwork, examining the influence of experienced group members on changes in the coordination behaviors, cognitions, and attitudes of novices.
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1 |
1993 — 2000 |
Argote, Linda (co-PI) [⬀] Carley, Kathleen Fichman, Mark Krackhardt, David (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Groups, Technology and Organizational Effectiveness: a Proposed Graduate Program @ Carnegie-Mellon University
9354995 Fichman Carnegie Mellon University if proposing an innovative GRT program that focuses on the joint impact of groups and technology on organizational effectiveness. As the primary work unit in organizations continues to move from individuals to groups, organizational effectiveness increasingly depends on the effective use of group-oriented organizational designs. At the same time, the expansion of new technologies in organizations both enhances opportunities for the formation and maintenance of work groups and poses new challenges for effective work group functioning. The ability of organizational scientists to contribute the organizational effectiveness in the future will depend on an understanding of groups, technology, and their interaction. We propose a GRT program that is innovative in form. It is a university wide program, operated by faculty in the schools of business, social sciences, and public management. This structure will expose students to a range of theoretical and methological approaches to studying groups and technology in both basic and applied settings. Training will be interdisciplinary in nature. Students will have opportunities to bridge theory and application in newly designed internships and project courses. ***
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1 |
1998 — 2001 |
Carley, Kathleen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
The Dynamics of Cyberspace: Examining and Modeling Electronic Group Development @ Carnegie-Mellon University
The media, many businesses, and many academicians are proclaiming the information highway and the web as providing universal access to information and as providing the infrastructure in which a Oglobal villageO and Ovirtual organizationsO will emerge. Within this infrastructure, individuals are assumed to be able to freely interact and have their ideas compete in an open market. This assumption, however, neglects the fact that people continually form groups and that groups develop norms and procedures that restrict the flow of information and prevent non-group members from having the same benefits as group members. IndividualsO communication behavior is significantly affected by groups that emerge in on-line communities. This proposal is to investigate the evolution of electronic groups and to ascertain the factors affecting group formation and maintenance on this information highway. One of the outcomes of this work will be an empirically validated model of electronic group development. By investigating the development of naturally occurring electronic groups, this work is expected to contribute to the study of group dynamics and investigations of the impact and use of new telecommunication technologies.
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1 |
1999 — 2009 |
Carley, Kathleen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Igert Formal Proposal: Multidisciplinary Training Program in Computational Analysis of Social and Organizational Systems @ Carnegie-Mellon University
This Integrative Graduate Education and Research Training (IGERT) award supports the establishment of a multidisciplinary graduate training program of education and research in Computational Analysis of Social and Organizational Systems (CASOS). In the past decade the computational social sciences (e.g., computational organization theory) and the social computer sciences (e.g., multi-agent systems) have emerged. This has resulted in a unified perspective on groups, organizations, institutions and societies as intelligent adaptive agents composed of networks of socially-embedded, intelligent and adaptive agents that can be reasoned about, and supported by, computationally and socially sophisticated models and agents. The CASOS program will prepare students for this field by teaching them how to use computers to understand the complexities of human behavior and how to use knowledge of humans as social actors to improve computational agents. Program features include: integrated social and computer science curriculum; studio courses emphasizing real-world corporate concerns, model design, implementation and validation; mentoring program; research practicum, proposal competition; distance learning; and integrated summer workshop and conference. Classes and research will take place in the classroom/office of the future -- a distributed intelligent space where people have ubiquitous access to and can provide/receive information wherever, whenever and to/from whomever they want in an unbounded network of agents (human, webbots, robots, corporations, etc.); thus, reducing classroom/research barriers, enabling real-time computational analysis, data collection, and model validation, and increasing interaction with faculty and students at other institutions.
IGERT is an NSF-wide program intended to facilitate the establishment of innovative, research-based graduate programs that will train a diverse group of scientists and engineers to be well-prepared to take advantage of a broad spectrum of career options. IGERT provides doctoral institutions with an opportunity to develop new, well-focussed multidisciplinary graduate programs that transcend organizational boundaries and unite faculty from several departments or institutions to establish a highly interactive, collaborative environment for both training and research. In this second year of the program, awards are being made to twenty-one institutions for programs that collectively span all areas of science and engineering supported by NSF. This specific award is supported by funds from the Directorates for Social, Behavioral, and Economic Sciences, for Computer and Information Science and Engineering, and for Education and Human Resources.
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1 |
2001 — 2003 |
Carley, Kathleen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Doctoral Dissertation Research: Spatial Models of Large-Scale Interpersonal Networks @ Carnegie-Mellon University
Given geographical constraints, the ability of humans to be aware of or establish contact with others falls off rapidly with physical distance. Indeed, problems as diverse as the spread of disease within human populations, the diffusion of cultural norms, migration, racial/ethnic segregation, and susceptibility to panic or revolution have all been linked to the distance-based structure of human relations. Yet, this spatial element of social network formation has received little attention. This project therefore develops mathematical network models based on physical distance, evaluates competing network models using published data on interpersonal relations across space, and presents measures of network structures. Using simulation methods, it identifies the linkages between large-scale social processes, such as those described above, and the social networks that span social groups and organizations. The mathematical methods thus provide insights into the general structures of human interaction that shape the more specific experiences and behaviors of individuals in a variety of types of groups.
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1 |
2002 — 2003 |
Carley, Kathleen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Doctoral Dissertation Research: Talking the Talk -- Isomorphism in Organizational Discourse @ Carnegie-Mellon University
This dissertation research will analyze organizational texts to evaluate the degree of organizational isomorphism: the extent to which organizations come to resemble each other in response to external institutional expectations. Texts from universities and corporations will provide the principal data for these analyses. Comparisons will be made between texts from individual speakers (e.g., university presidents) and from organizational sources (e.g., privacy policies). Texts will be coded using two established computer programs and human coders.
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1 |
2002 — 2007 |
Carley, Kathleen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Itr: Modeling Distributed Denial of Service Attacks and Defenses @ Carnegie-Mellon University
Distributed denial of service (DDOS) attacks have emerged as a prevalent way to take down web sites and have imposed financial losses to companies. The CSI/FBI survey (CSI 2001) shows that 36% of respondents in the last 12-months period have detected denial of service, which imposed more than $4.2 million financial losses. The effectiveness of DDOS defenses depends on many factors such that the nature of the network's topology, the specific attack scenario, and various characteristics of the network routers. However, little research has focused on the tradeoffs inherent in this complex system. The researchers are developing a computational testbed to study security policies and the associated technologies that provide defenses against DDOS attacks. The researchers are using this framework to evaluate various policies and technologies. Out model and the ensuing analyses are informed by research in the areas of computer science, information science, organizational theory and social networks.
There have been a number of proposals on how to control the on-going DDOS attack traffic. None have been widely deployed. The effectiveness of DDOS defenses depends on many factors, such as the type of network topology, the type of attacks and whether all ISPs are compliant in establishing defenses. However, little is known about the interactions among these factors. Knowing what tradeoffs will occur as these factors vary will enable stakeholders to make more informed security policy decisions in which they adjust for the chance that others may not make the same decisions. Our research illuminates these tradeoffs. Moreover, the computational model the researchers are building enables the user to examine the tradeoffs associated with various DDOS defenses and attack scenarios at the router level.
The researchers focus on two basic research questions. First, how do ISPs provide DDOS defenses at the lowest cost while their subscribers remain satisfied with the availability of network connections during attacks? A cost-performance analysis of the effectiveness of DDOS defenses is being conducted using results from the computational model. This cost-performance analysis will aid ISPs and local network administrators in their evaluation of DDOS defenses. Second, the researchers ask where are the critical points in a network to deploy defenses? The researchers examine the impact of network topology on the deployment location of defenses. Graph level indices and models from social network studies will be used to categorize network topologies and to select deployment locations for defenses. This analysis will provide guidance to decision makers.
Benefits of this work research include: The policy framework the researchers are developing will help ISPs and subscribers to consider the benefits of providing DDOS defenses and to realize the tradeoffs in DDOS defenses. Results from this study will enable decision makers to make more informed security policy decisions for computer networks. It is costly and unethical to conduct real world experiments of DDOS attacks on large networks. This research will provide a cost effective and ethical means for evaluating various attack scenarios and defenses. Further, topological measures developed in this research should be useful for studies of other large-scale topologies. As such, this work extends social network measures typically used on small person-to-person networks to large-scale computer networks. Finally, this research provides a theoretical basis for evaluating DDOS defenses building on interdisciplinary studies from the fields of computer science, information science, organizational theory and social network analysis.
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1 |
2004 — 2005 |
Carley, Kathleen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Dynamics For Social Networks Processes: Comparing Statistical Models With Intelligent Agents @ Carnegie-Mellon University
The goal of this project is to reconcile two methods for modeling change in social networks over time: a class of statistical models and intelligent agent models. The research contrasts the properties of these two approaches, exploring what qualitative dynamic behaviors in social networks are captured usefully and interpretably by each. The primary tools are latent variable representations and dynamical systems analysis. The result is a framework that characterizes the strengths and limitations of the two classes of models in multiple settings, and provides feedback that stimulates refinement of existing theories for social networks and development of new theory.
Social network theory has produced a wealth of paradigms to describe the nature and evolution of groups of people who interact, possibly in subgroups, with one another. Settings in which social network theory has been applied range from friend to corporations to political blocs to religious groups. Counterterrorism is one application of particular current importance, in which the notion that the network might have latent (unobservable) characteristics that are important to its evolution over time is especially germane. Currently, however, there is not good understanding of strengths and limitations of different models for the dynamics of social networks. The researchers on this project, drawn from the social sciences, statistics and applied mathematics, are constructing a conceptual and operational framework that allows the principled comparison of different models in multiple contexts. In addition to advances in theory and methodology, the framework enables social sciences researchers and other analysts to choose a model whose dynamic properties are most appropriate to each application.
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1 |
2005 — 2009 |
Kraut, Robert (co-PI) [⬀] Kraut, Robert (co-PI) [⬀] Carley, Kathleen Herbsleb, James [⬀] Mockus, Audris |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Coordination, Communication, and Collaboration in Open Source Software Development @ Carnegie-Mellon University
This project will investigate how, when and why open source collaborative software development projects succeed or fail. Open source software runs the Internet, dominates the web server market and competes successfully in operating systems and even applications markets. This success is remarkable given the major communication and coordination problems that typically plague other forms of geographically distributed collaboration. But despite the visible successes of open source, it is also true that most projects fail. This project will conduct theory-driven empirical studies of coordination, social networks, and outcomes. The first step will be to replicate studies of commercial software development projects to determine if coordination in open source follows the same processes, or if it is fundamentally different. The next step will be to perform comparisons of open source and commercial developments to see if the theoretically-predicted differences are observed. Next, a computational model of open source software development will be constructed. Model predictions will be validated against actual observations. Finally, field experiments will be conducted to make specific coordination improvements in selected open source projects.
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1 |
2005 — 2009 |
Carley, Kathleen Lamb, Roberta |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Modeling the Social Actor
This research is based on a comprehensive multi-scaled, multi-disciplinary approach to study complex information age issues that involve the combination of people and the information and communication technologies they use. This study extends work on social actors, developed through prior NSF-funded research. The social actor is an empirically derived model of people+ information and communication technologies (ICTs). To make this model useable for a wider community of researchers and policy- makers, who need robust models with solid measures of attributes and behaviors that conflate people and their technologies, some formalization of the social actor is required. Using metrics derived from institutional theory, appropriate measures based on social actor concepts will be developed and related to formal models and theories of cognitive understanding and collaborative decision making to establish a better basis for reasoning about ICT-infused environments. A theoretically supported, empirically grounded, social actor model will then be used to simulate dynamic agents, with scenarios developed in collaboration with RAND organization researchers. The empirical work will involve modeling social actors as people + IC in organizational and institutional settings using agent-based systems (ABS). Agent-based modeling allows a researcher to examine complex adaptive systems with sophisticated computational models that integrate empirical research and formal modeling. These systems help researchers and policy-makers explore the dynamic issues that attend the complex interactions of collaborative and competitive social units.
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1 |
2005 — 2007 |
Carley, Kathleen M |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Optimization/Parallelization of Biowar @ Carnegie-Mellon University |
0.958 |
2006 — 2007 |
Carley, Kathleen M |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Large-Scale Agent-Based Modeling of Weaponized Biological Attacks On a Realisti @ Carnegie-Mellon University |
0.958 |
2007 — 2010 |
Brewer, Barbara Bagdasarian [⬀] Carley, Kathleen M Verran, Joyce A (co-PI) [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Dynads: a Dynamic Network Analysis Decision Support Tool For Nurse Managers
DESCRIPTION (provided by applicant): The quality of care in U.S. hospitals continues to alarm. As a case in point, medical errors persist at a distressing rate and incur enormous costs, despite extensive publicity and considerable research. Because healthcare is a complex sociotechnical system, these medical errors generally have no simple, single cause. Instead, the errors typically are the result of system failures that involve many individuals, many contributing factors, and encompass multiple levels of the health care system. In our previous research we have shown that computational modeling using OrgAhead is a valuable research tool for dealing with these types of complex, multi-level problems. Building on that work, we propose using a network-based theory of organizations as complex adaptive systems to develop desktop decision support tools and methods that help managers (a) assess their unit's current performance, and (b) predict the impact of potential unit innovations to improve targeted safety and quality outcomes. The purpose of the proposed project is to develop a simple virtual environment that allows nurse managers to assess, through a dynamic network analysis decision support tool (DyNADS), the organizational health of their patient care units and then engage in strategic planning by using automated "what-if" analysis techniques to test, on the virtual units, the likelihood of potential innovations to improve their actual units'safety and quality outcomes. Specific aims are to: 1. Refine and streamline data collection requirements for DyNADS to fit within the unit management workflow. 2. Design a prototype desktop dynamic network analysis decision support tool (DyNADS) that facilitates: (a) identifying structures or communication patterns that are the basis for system failures in acute care settings and (b) testing the likelihood of potential innovations to improve safety and quality outcomes. 3. Evaluate DyNADS'validity, scalability, and usefulness through a field test. 4. Evaluate the feasibility of implementing DyNADS or nursing units.
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0.91 |
2008 — 2009 |
Carley, Kathleen M |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Biodefense: Multi-Agent City-Scale Simulation of Disease Dispersion @ Carnegie-Mellon University
Biological Effect of Chemicals; CRISP; Cities; Communicable Diseases; Computer Retrieval of Information on Scientific Projects Database; Disease; Disorder; Funding; Grant; Grippe; Infectious Disease Pathway; Infectious Diseases; Infectious Diseases and Manifestations; Infectious Disorder; Influenza; Institution; Investigators; Modeling; NIH; National Institutes of Health; National Institutes of Health (U.S.); Research; Research Personnel; Research Resources; Researchers; Resources; Running; SARS; Severe Acute Respiratory Syndrome; Source; United States National Institutes of Health; biodefense; disease/disorder; experiment; experimental research; experimental study; flu infection; influenza infection; interest; research study; simulation; virtual
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0.958 |
2013 — 2016 |
Benham-Hutchins, Mary Margaret (co-PI) [⬀] Brewer, Barbara Bagdasarian [⬀] Carley, Kathleen M |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Measuring Network Stability and Fit
DESCRIPTION (provided by applicant): Our proposed basic research program will enhance the scientific application of social network analysis (SNA) to health care in several ways: First, we will compare information sharing and decision-making networks in hospital nursing units (wards) to further clarify how these network structures and processes relate to nursing- sensitive patient outcomes. Second, we will use *ORA, a dynamic network analysis tool, to identify a robust, but parsimonious, set of network properties (i.e., metrics or sets of metrics) that measure network stability and congruence (fit) with unit environmental features and association with patient safety and quality outcomes. Finally, we will use the new metrics to test a novel model of network stability and congruence based on a variety of hospital nursing units over time and under variations in unit physical layout, workgroup characteristics, staff expertise, communication technology, and workflow exceptions as a means to develop new theory. We have 4 specific aims: Aim 1. Compare the structure of nursing unit decision making and information sharing networks within and across shifts. Aim 2. Identify a robust, but parsimonious, set of key network metrics that can be used to measure information sharing and decision-making network stability longitudinally. Aim 3. Identify network metrics to measure congruence of nursing unit information sharing and decision- making networks to nursing unit contextual features (unit type, physical layout, workgroup characteristics, staff expertise, and communication technology). Aim 4. Develop and investigate nursing unit information sharing and decision-making network stability and congruence profiles associated with high and low levels of nursing-sensitive patient safety and quality outcomes. Our highly experienced team was the first to use SNA to explore how nursing unit information sharing networks relate to nursing-sensitive patient outcomes. Accomplishing these 4 specific aims will build on our previous findings to improve our understanding of how changes in information-sharing and decision-making networks over time and across shifts relate to patient safety and quality outcomes. Although hospital nursing units provide the context for this research, the problems we are addressing are generic in SNA research; therefore, we expect our results to generalize broadly and thereby expand the applicability of SNA to the healthcare arena.
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0.91 |
2018 — 2019 |
Carley, Kathleen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Advancing the Science of Social Cyber-Security Around Information Diffusion and Authenticity @ Carnegie-Mellon University
Social cybersecurity is an emerging scientific area aimed at understanding and forecasting changes in human behavior, social, and economic outcomes that might arise from threats enabled by online communication platforms. These threats range from increasingly realistic deceptive messages, such as spear-phishing or simulated but realistic videos, to manipulation of systems for creating and distributing content in social networks. This project will conduct a two-day workshop that brings together social and computer scientists interested in information dissemination with the goal of going beyond current work that focuses on labeling the veracity of particular information or actors, or encouraging information consumers to consider veracity. Instead, the workshop will take a broader theoretical lens that (a) analyzes how current models of information diffusion, rumor, social influence, communication, and so on need to adapt to novel and rapidly changing socio-technical environments and (b) focuses on the impact of these systems, identifying what is needed to characterize, measure, and affect the impact of information with different, and not necessarily consensus, credibility evaluations on the diverse consumers of that information, a problem of great import in domains ranging from news to health to crisis response.
The workshop organizers will invite participants from a large variety of institutions, representing a broad range of both intellectual backgrounds and participant demographics, to increase the diversity of perspectives on the problem. After an opening plenary session where participants can pose key challenges, questions, and gaps, the main part of the workshop will be devoted to breakout groups organized around those challenges; a closing plenary session will develop plans for moving forward. These sessions will be structured to produce three main outcomes. The first is a set of challenge descriptions that define forward-looking problems in the space of information diffusion and manipulation, characterize what different scientific disciplines can provide toward them, and identify the next steps needed to address these challenges. The second is a plan for developing a larger conference in the area of social cybersecurity for information diffusion that brings together researchers from different backgrounds and communities around the challenges identified. The third is a longer term road map describing a scientific path toward addressing these challenges, with the goal of illuminating how stakeholder communities can coordinate to make progress toward them.
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.
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1 |
2022 — 2024 |
Carley, Kathleen Lebiere, Christian (co-PI) [⬀] Pirolli, Peter Orr, Mark |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Pipp Phase I: Computational Theory of the Co-Evolution of Pandemics, (Mis)Information, and Human Mindsets and Behavior @ Florida Institute For Human and Machine Cognition, Inc.
Epidemiological models are used to predict the spread of highly contagious and lethal diseases such as COVID-19. Public health officials use such models to inform pandemic response policies and advisories. Yet these models require a rigorous scientific foundation about human psychology to better predict people’s responses to information and policies about pandemics. The recent COVID-19 pandemic illustrates the central role of human decision making and behavior in the spread of such a transmissible disease. People’s decisions regarding social isolation, social distancing, mask wearing, hand washing, and vaccination are correlated with the rate at which the COVID-19 virus spreads or the seriousness of getting infected. People have different individual mindsets, and these can vary across different regions and subgroups, so different groups of people respond differently to messaging and mandates and those responses change over time. There is also an ongoing scientific debate about the degree to which pandemic information or misinformation, or the perceived credibility of information sources, influences the degree to which people change their behavior. To address these scientific needs, this project involves activities to develop a multidisciplinary research core and agenda and to develop a strong plan for a cohesive research center for Predictive Intelligence for Pandemic Prevention. The activities include exploratory research on computational models of human psychology, information flow and influence, and resulting pandemic transmission. The project will also support the training and mentoring of graduate students who represent the next generation of researchers tackling these global challenges.<br/><br/>This project uses computational theories and models to examine the fundamental interdependent evolution of infection, behavior, and information at multiple levels and drawing upon multiple disciplines in order to support improved pandemic intelligence, prediction, explanation, and countermeasures. The project is organized into (1) interdisciplinary, strategic research thrusts to Accelerate Convergent Science towards the Grand Challenge, (2) three invitational meetings to draw in diverse researchers to address focal research topics and research questions, to fill in gaps in the Research Challenges, and develop a strong research and education agenda for a cohesive PIPP center, and (3) Pilot Studies to Demonstrate Feasibility of integrated computational models of information, human psychology, and pandemic transmission. For the pilot research, a multidisciplinary team combines empirical assessments with computational cognitive models in an agent-based modeling system. For data the investigators draw on vaccination discussions in mass media, Twitter, geolocated timeseries data on vaccination rates, infection, death and recovery rates, state and national mandates regarding COVID-19 policies about vaccination and mask wearing from February 2020 through December 2021 in the United States. These data will be segmented by state and major cities within those states. <br/><br/>This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).<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.
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0.903 |