Christian Lebiere - US grants

Cyber-Physical Systems (CPS) encompass a large variety of systems including for example future energy systems (e.g. smart grid), homeland security and emergency response, smart medical technologies, smart cars and air transportation. One of the most important challenges in the design and deployment of Cyber-Physical Systems is how to formally guarantee that they are amenable to effective human control. This is a challenging problem not only because of the operational changes and increasing complexity of future CPS but also because of the nonlinear nature of the human-CPS system under realistic assumptions. Current state of the art has in general produced simplified models and has not fully considered realistic assumptions about system and environmental constraints or human cognitive abilities and limitations. To overcome current state of the art limitations, our overall research goal is to develop a theoretical framework for complex human-CPS that enables formal analysis and verification to ensure stability of the overall system operation as well as avoidance of unsafe operating states. To analyze a human-CPS involving a human operator(s) with bounded rationality three key questions are identified: (a) Are the inputs available to the operator sufficient to generate desirable behaviors for the CPS? (b) If so, how easy is it for the operator with her cognitive limitations to drive the system towards a desired behavior? (c) How can areas of poor system performance and determine appropriate mitigations be formally identified? The overall technical approach will be to (a) develop and appropriately leverage general cognitive models that incorporate human limitations and capabilities, (b) develop methods to abstract cognitive models to yield tractable analytical human models (c) develop innovative techniques to design the abstract interface between the human and underlying system to reflect mutual constraints, and (d) extend current state-of-the-art reachability and verification algorithms for analysis of abstract interfaces, iin which one of the systems in the feedback loop (i.e., the user) is mostly unknown, uncertain, highly variable or poorly modeled.

The research will provide contributions with broad significance in the following areas: (1) fundamental principles and algorithms that would serve as a foundation for provably safe robust hybrid control systems for mixed human-CPS (2) methods for the development of analytical human models that incorporate cognitive abilities and limitations and their consequences in human control of CPS, (3) validated techniques for interface design that enables effective human situation awareness through an interface that ensures minimum information necessary for the human to safely control the CPS, (4) new reachability analysis techniques that are scalable and allow rapid determination of different levels of system safety. The research will help to identify problems (such as automation surprises, inadequate or excessive information contained in the user interface) in safety critical, high-risk, or expensive CPS before they are built, tested and deployed. The research will provide the formal foundations for understanding and developing human-CPS and will have a broad range of applications in the domains of healthcare, energy, air traffic control, transportation systems, homeland security and large-scale emergency response. The research will contribute to the advancement of under-represented students in STEM fields through educational innovation and outreach. The code, benchmarks and data will be released via the project website.

Formal descriptions of models of human cognition are in general incompatible with formal models of the Cyber Physical System (CPS) the human operator(s) control. Therefore, it is difficult to determine in a rigorous way whether a CPS controlled by a human operator will be safe or stable and under which circumstances. The objective of this research is to develop an analytic framework of human-CPS systems that encompasses engineering compatible formal models of the human operator that preserve the basic architectural features of human cognition. In this project the team will develop methodologies for building such models as well as techniques for formal verification of the human-CPS system so that performance guarantees can be provided. They will validate models in a variety of domains ranging from air traffic control to large scale emergency response to the administration of anesthesia.

Forecasts of how the COVID-19 epidemic will progress, in terms of regional rate of infections and deaths, are made by epidemiological models. The projections of these models influence the decisions of public health and other officials, as well as members of the general public. In the absence of a vaccine, it is crucial that epidemiological models accurately predict how the rate of transmission changes in response to non-pharmaceutical interventions such as advisories about social distancing, wearing masks, washing hands, etc. This requires accurate and precise modeling of how people respond both psychologically and behaviorally to this guidance. People in different regions and subgroups may have very different individual mindsets and capabilities so that they respond differently to different guidance, which may change over time, e.g., ?shelter-in-place fatigue?. Current epidemiological models are do not incorporate scientifically established computational models of human psychology and behavior change. This project is about developing agents that represent an individual, and populations of agents simulating the human population of a given area to be part of a new kind of epidemiological model for forecasting Covid-19 cases.

Individual agents will be built upon prior models of decision-making and behavior-change. This will model relevant individual-level responses and resulting population dynamics for a select set of US regions. Online media and datasets will be used to seed populations of agents to model populations of the selected US regions. New algorithms for cognitive content mining of attitudes, beliefs, intentions, and preferences for a regional population will be developed and validated quantitatively against observed behavior and epidemiological data in a set of US state-level data (four states and their sub-regions) using a mix of statistical modeling and agent-based modeling. Improvements in regional forecasting of Covid-19 incidence rates, estimated transmission rates in response to community guidance, and behavior compliance using cell-phone mobility and non-essential visit data to measure effectiveness of the newly designed agents and enhance the design of messages to contain COVID-19.

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.

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.