Risk aversion and bidding theory
Theory of bidding behavior and formation of bidding model with risk aversion
Hanson, D. L.↗
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Selection of Next Priority IMPACT Medical Conditions Based on Available Terrestrial and Spaceflight Data
BACKGROUND: As the era of exploration class missions begins, identification of medical conditions that may occur and require management becomes essential for the modeling of medical risk. To this end, NASA has developed IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces), a suite of tools to assist in assessment of medical risk analysis. It has incorporated an initial list of the 120 conditions of highest concern, labeled the IMPACT condition list 1.0 (ICL 1.0). This abstract describes a method for prioritizing the 92 conditions included on the Proposed Future Conditions List (PFCL) for inclusion in future iterations of the ICL. OVERVIEW: To construct the Prioritized Proposed Future Conditions List (P-PFCL), each condition on the PFCL was scored as “low,” “medium,” or “high” on each of four variables: incidence, likelihood of significant task impairment, diagnostic and treatment complexity, and treatment futility. Qualitative assessment using clinical judgement was utilized to score complexity, futility, and likelihood of impairment. Incidence was assessed quantitatively using spaceflight data and/or analog populations where available then assigned a score using established cutoffs. Logarithmic numerical values were assigned to each category label. A Prioritization Score was generated for each condition by taking the product of incidence and likelihood of task impairment (risk) divided by the product of complexity and futility (difficulty of care), with higher values corresponding to higher priority for future inclusion in the ICL. DISCUSSION: The described methods allow for the generation of a ranked P-PFCL to act as a decision support tool for selection of the next generation of modeled medical conditions. Some of the conditions ranked highly on the P-PFCL include EVA-related upper and lower extremity sprain/strain, iron deficiency, delirium, and hypertension, among others. While this effort does not attempt to quantify the absolute risk associated with each condition, it does attempt to semi-quantitatively estimate the risk of each condition relative to the other possible conditions. This tool in concert with subject matter expert opinion could optimize the future use of limited resources thereby producing a more accurate medical risk model, which will be essential to the upcoming exploration class missions.
Michael Pohlen↗
Method for Tracking and Communicating Aggregate Risk Through the Use of Model-Based Systems Engineering (MBSE) Tools
Large, complex projects can identify a significant number and variety of risks, throughout the project life cycle. These risks are analyzed, mitigated, closed or accepted as independent uncertainties. Once closed or accepted, it is easy for projects to lose awareness of their impact. In reality, each of these risks contributes some amount to the overall risk posture of the project. The ability to track and effectively communicate this aggregate risk has represented a challenge to project management. There have been previous attempts to create a schema to communicate the aggregate effect of risks, without notable success. Most of these attempts have centered on some additive metric derived from the scoring of likelihood and consequence values. This, in and of itself, is a logical approach, but all too often the scores were then aggregated to a level where all context was lost. One weakness has been a lack of attempt to create linkages or logical groups of the risks upon which useful aggregation could then occur. The overall move to model-based (systems) engineering (MBSE) has opened up a vast frontier of opportunities to better integrate all project data. MBSE provides an underlying layer that links data items to each other. Objectives link to requirements, which then link to functions, functions to physical architecture items, and so on, as far down as projects want to model. While it started with a focus on modeling requirements based on things like use cases, efforts are now underway to integrate safety and mission assurance (S&MA) information and analyses, such as risks. This effort, called Model Based Mission Assurance (MBMA), is yielding models that are more useful and are a more accurate representations of the systems. MBSE models, with this ability to link related items, provide a new means of tracking and communicating aggregate risks. In the proposed method, risks are added into the models as distinct items, having attributes that communicate a scoring derived from the likelihood and consequence values as charted on the standard NASA 5x5 risk matrix. Like earlier efforts, each box in the 5x5 has an associated scoring, which may include both a current score and potential post-mitigation/control score. The risk items are then linked to elements of the model, such as system objectives/goals, requirements, functions, or physical architecture items, with "Risk to" relationships. These risks will then be communicated by use of reports generated from the model, detailing all risks and/or hazards linked to model elements. These reports can include aggregate impacts, including a current scoring and potential future state scoring based on the planned mitigations and/or controls. These reports will show all risks, open, accepted, and closed, linked to project objectives or requirements. When run as part of an upcoming risk acceptance discussion, these reports will serve to remind the team of all previous risks that relate to the effected portion of the system. When included as part of periodic program or project reviews, risk reviews, and safety reviews, this method can improve the overall understanding of the system's true risk posture. This proposed method takes full advantage of the advances that modern modeling techniques provide, with a minimal investment of additional time. Utilizing the model environment also enables a near constant access to current state of aggregate risks.
model based mission assurance↗
Prototype Biology-Based Radiation Risk Module Project
Biological effects of space radiation and risk mitigation are strategic knowledge gaps for the Evolvable Mars Campaign. The current epidemiology-based NASA Space Cancer Risk (NSCR) model contains large uncertainties (HAT #6.5a) due to lack of information on the radiobiology of galactic cosmic rays (GCR) and lack of human data. The use of experimental models that most accurately replicate the response of human tissues is critical for precision in risk projections. Our proposed study will compare DNA damage, histological, and cell kinetic parameters after irradiation in normal 2D human cells versus 3D tissue models, and it will use a multi-scale computational model (CHASTE) to investigate various biological processes that may contribute to carcinogenesis, including radiation-induced cellular signaling pathways. This cross-disciplinary work, with biological validation of an evolvable mathematical computational model, will help reduce uncertainties within NSCR and aid risk mitigation for radiation-induced carcinogenesis.
Terrier, Douglas↗
Risk-Based Prioritization of Research for Aviation Security Using Logic-Evolved Decision Analysis
The National Aeronautics and Space Administration is developing advanced technologies to reduce terrorist risk for the air transportation system. Decision support tools are needed to help allocate assets to the most promising research. An approach to rank ordering technologies (using logic-evolved decision analysis), with risk reduction as the metric, is presented. The development of a spanning set of scenarios using a logic-gate tree is described. Baseline risk for these scenarios is evaluated with an approximate reasoning model. Illustrative risk and risk reduction results are presented.
Eisenhawer, S. W.↗
Quantifying Medical Risk on a Long Duration Lunar Mission: A Demonstration of NASA’s IMPACT Tradespace Analysis Tool
Background NASA’s human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in-mission medical care. The distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and constraints on the medical evacuation of astronauts. Mass, volume, power, and data will be limited while higher demands will be placed on the crew to manage medical care. NASA’s Moon to Mars exploration strategy lays out increasingly complex Artemis missions both in terms of duration and operations. In these more challenging deep space missions, it is important to quantitatively estimate the human medical risk to inform a traditional heuristic approach to medical risk. Prior tools have been developed for missions in low Earth orbit, but a new tool is required to plan for future exploration missions. Methods IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a risk assessment tool developed by NASA to advance exploration mission medical system design by quantitatively estimating mission medical risk. IMPACT v1.0 includes a novel evidence library baselined to exploration environments; an expanded list of 119 medical conditions; the addition of medical resources; and the ability for rapid and iterative analysis. Medical system risk estimates include loss of crew life, consideration of the need for return to definitive care (medical evacuation), and an estimate of crew time affected due to medical conditions. A notional long duration lunar orbit and lunar surface design reference mission (DRM) was chosen with a 4-astronaut crew to represent a sustained exploration Artemis mission. Results/Discussion Overall, IMPACT successfully quantified medical risk and derived an optimized medical system to support crew on a long duration lunar mission. In this DRM, the calculated loss of crew life from a medical event was 0.008 events per mission, risk of potential need for evacuation was 0.30 events per mission, and cumulative crew time affected by medical conditions was 103 days. The medical conditions that most contributed to overall medical risk were decompression sickness, trauma conditions, and respiratory failure. The conditions that had the largest effects on crew performance included musculoskeletal injuries and lunar dust exposure. The IMPACT-generated medical system included resources that target the most common and highest risk conditions. This systematic analysis demonstrates the value of the IMPACT tool in medical system design for human exploration spaceflight missions.
Missions to Mars↗
Quantifying Medical Risk on a Long Duration Lunar Mission: A Demonstration of NASA’s IMPACT Tradespace Analysis Tool
Background NASA’s human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in-mission medical care. The distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and constraints on the medical evacuation of astronauts. Mass, volume, power, and data will be limited while higher demands will be placed on the crew to manage medical care. NASA’s Moon to Mars exploration strategy lays out increasingly complex Artemis missions both in terms of duration and operations. In these more challenging deep space missions, it is important to quantitatively estimate the human medical risk to inform a traditional heuristic approach to medical risk. Prior tools have been developed for missions in low Earth orbit, but a new tool is required to plan for future exploration missions. Methods IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a risk assessment tool developed by NASA to advance exploration mission medical system design by quantitatively estimating mission medical risk. IMPACT v1.0 includes a novel evidence library baselined to exploration environments; an expanded list of 119 medical conditions; the addition of medical resources; and the ability for rapid and iterative analysis. Medical system risk estimates include loss of crew life, consideration of the need for return to definitive care (medical evacuation), and an estimate of crew time affected due to medical conditions. A notional long duration lunar orbit and lunar surface design reference mission (DRM) was chosen with a 4-astronaut crew to represent a sustained exploration Artemis mission. Results/Discussion Overall, IMPACT successfully quantified medical risk and derived an optimized medical system to support crew on a long duration lunar mission. In this DRM, the calculated loss of crew life from a medical event was 0.008 events per mission, risk of potential need for evacuation was 0.30 events per mission, and cumulative crew time affected by medical conditions was 103 days. The medical conditions that most contributed to overall medical risk were decompression sickness, trauma conditions, and respiratory failure. The conditions that had the largest effects on crew performance included musculoskeletal injuries and lunar dust exposure. The IMPACT-generated medical system included resources that target the most common and highest risk conditions. This systematic analysis demonstrates the value of the IMPACT tool in medical system design for human exploration spaceflight missions.
Missions to Mars↗
Modeling Payload Stowage Impacts on Fire Risks On-Board the International Space Station
The purpose of this presentation is to determine the risks of fire on-board the ISS due to non-standard stowage. ISS stowage is constantly being reexamined for optimality. Non-standard stowage involves stowing items outside of rack drawers, and fire risk is a key concern and is heavily mitigated. A Methodology is needed to account for fire risk due to non-standard stowage to capture the risk. The contents include: 1) Fire Risk Background; 2) General Assumptions; 3) Modeling Techniques; 4) Event Sequence Diagram (ESD); 5) Qualitative Fire Analysis; 6) Sample Qualitative Results for Fire Risk; 7) Qualitative Stowage Analysis; 8) Sample Qualitative Results for Non-Standard Stowage; and 9) Quantitative Analysis Basic Event Data.
Anton, Kellie e.↗
Space Radiation Organ Doses for Astronauts on Past and Future Missions
We review methods and data used for determining astronaut organ dose equivalents on past space missions including Apollo, Skylab, Space Shuttle, NASA-Mir, and International Space Station (ISS). Expectations for future lunar missions are also described. Physical measurements of space radiation include the absorbed dose, dose equivalent, and linear energy transfer (LET) spectra, or a related quantity, the lineal energy (y) spectra that is measured by a tissue equivalent proportional counter (TEPC). These data are used in conjunction with space radiation transport models to project organ specific doses used in cancer and other risk projection models. Biodosimetry data from Mir, STS, and ISS missions provide an alternative estimate of organ dose equivalents based on chromosome aberrations. The physical environments inside spacecraft are currently well understood with errors in organ dose projections estimated as less than plus or minus 15%, however understanding the biological risks from space radiation remains a difficult problem because of the many radiation types including protons, heavy ions, and secondary neutrons for which there are no human data to estimate risks. The accuracy of projections of organ dose equivalents described here must be supplemented with research on the health risks of space exposure to properly assess crew safety for exploration missions.
Cucinotta, Francis A.↗
Sensitivity of Impact Risk to Uncertainty in Asteroid Properties and Entry Parameters
A central challenge in evaluating the threat posed by asteroids striking Earth is the large amount of uncertainty in potential asteroid properties and entry parameters, which can vary the resulting ground damage and affected population by orders of magnitude. We are using our Probabilistic Asteroid Impact Risk (PAIR) model to investigate the sensitivity of asteroid impact damage to these uncertainties. To assess the risk sensitivity, we alternately fix or vary the different input parameters and compare the damage distributions produced. In this study, we consider local ground damage from blast waves or thermal radiation for impactors 50-500m in diameter. The ongoing goal of this work is to help guide future efforts in asteroid characterization and model refinement by determining which properties most significantly affect the potential risk.
Wheeler, Lorien↗
A Team Mental Model Perspective of Pre-Quantitative Risk
This study was conducted to better understand how teams conceptualize risk before it can be quantified, and the processes by which a team forms a shared mental model of this pre-quantitative risk. Using an extreme case, this study analyzes seven months of team meeting transcripts, covering the entire lifetime of the team. Through an analysis of team discussions, a rich and varied structural model of risk emerges that goes significantly beyond classical representations of risk as the product of a negative consequence and a probability. In addition to those two fundamental components, the team conceptualization includes the ability to influence outcomes and probabilities, networks of goals, interaction effects, and qualitative judgments about the acceptability of risk, all affected by associated uncertainties. In moving from individual to team mental models, team members employ a number of strategies to gain group recognition of risks and to resolve or accept differences.
Cooper, Lynne P.↗
NASA/JSC orbital debris models
The orbital debris models developed within the framework of the NASA Johnson Space Center's (JSC's) orbital debris program, are categorized as environment definition and risk assessment models. The EVOLVE, CHAIN and the orbital debris engineering model 1996 (ORDEM96) computer programs determine the past, present and future orbital particulate environment, while the BUMPER and debris assessment software (DAS) computer programs provide a means for evaluating the risks of specific space missions. These models are presented. To support these models and to conduct specialized analyses, NASA/JSC employs a range of auxiliary models, including explosion and collision satellite breakup models, hypervelocity impact ballistic limit models, orbit propagation and decay models, space traffic models and solid rocket motor effluent models.
Johnson, Nicholas↗
Contribution of High Charge and Energy (HZE) Ions During Solar-Particle Event of September 29, 1989
The solar-particle event (SPE) of September 29, 1989, produced an iron-rich spectrum with energies approaching 1 A GeV with an approximate spectral slope parameter of 2.5. These high charge and energy (HZE) ions challenge conventional methods of shield design and assessment of astronaut risks. In the past, shield design and risk assessment have relied on proton shielding codes and biological response models derived from X-ray and neutron exposure data. Because the HZE spectra decline rapidly with energy and HZE attenuation in materials is limited by their penetration power, details of the mass distributions about the sensitive tissues (shielding materials and the astronaut's body) are important determining factors of the exposure levels and distributions of linear energy transfer. Local tissue environments during the SPE of September 29, 1989, with its f= components are examined to analyze the importance of these ions to human SPE exposure. Typical space suit and lightly shielded structures leave significant contributions from HZE components to certain critical body tissues and have important implications on the models for risk assessment. A heavily shielded equipment room of a space vehicle or habitat requires knowledge of the breakup of these ions into lighter components, including neutrons, for shield design specifications.
Kim, Myung-Hee Y.↗
Quantifying Cybersecurity Risk for NASA Missions
An end-to-end cyber risk assessment process is presented that is based on the combination of guidelines from the National Institute of Standards & Technology (NIST), the standard 5x5 risk matrix, and quantitative methods for generating loss exceedance curves. The NIST guidelines provide a framework for cyber risk assessment, and the standard 5x5 matrix is widely used across the industry for the representation of risk across multiple disciplines. Loss exceedance curves are a means of quantitatively assessing the loss that occurs due to a given risk profile. Combining these different techniques enables us to follow the guidelines, adhere to standard 5x5 risk management practices and develop quantitative metrics simultaneously. Our quantification process is based on the consideration of the NASA and JPL Cost Risk assessment modeling techniques as we define the cost associated with the cybersecurity risk profile of a mission as a function of the mission cost.
Miller, Robert L.↗
Root Cause Analysis of the Data Refinement Process – Medical Conditions Capability Resource Tables
The medical system for spaceflight thus far has been designed to support missions in low earth orbit (LEO). Crew capabilities are limited and heavily dependent on the team of medical support staff at Mission Control Center (MCC) to guide diagnosis and management. However, missions to the Moon and Mars will suffer from several constraints that will make this ground support focused approach to care ineffective. In order to update and modify medical system design, NASA has relied on Probabilistic Risk Assessment (PRA) modeling to mitigate medical risk through trade space analysis. Specifically, capability resource tables (CRT’s) were developed to create a dataset of resources required to manage a list of accepted medical conditions significant in exploration spaceflight. With 120 conditions, this dataset contained hundreds of capabilities and thousands of resources with tens of thousands of cells of data. Initially these tables were built in excel for high throughput during development, but ultimately had to be transferred, managed, and modified into the Evidence Library database for modeling purposes. The process of collating and reviewing the Evidence Library revealed numerous errors in the dataset that had to be corrected through iterative changes. Several error types emerged during this process and can be broken into specific classifications defined as “input”, “transcription”, “structural”, “branching”, and “information”. In reviewing these error types through the root cause analysis (RCA) approach, we were able to identify the contributors to these errors which included single data review points, changing product end goals, limited software selection, time constraints and several others. By reviewing and evaluating the underlying causes we can provide possible system improvements that can be implemented for current and future data management in PRA model inputs.
A. Anderson↗
Soyuz Landing Reconstructions
Introduction: Recent data show a landing-attributed injury rate among Soyuz crew members to be higher than predicted in original certification analysis. New vehicles being developed for NASA are being certified using analytical tools to quantify injury risk. This assessment will provide data to allow the Agency to evaluate whether these tools are able to capture true injury risk and allow a comparison between Soyuz landings and upcoming vehicles. Methods: A finite element model (FEM) of the Soyuz descent module (DM) and its landing environment were developed to recreate Soyuz landing conditions. Occupant breakout models composed of the Soyuz seat and Sokol helmet were then developed to evaluate occupant response. These models included both anthropomorphic test device (ATD) and human body model (HBM) surrogates in three anthropometric sizes. A generalized distribution analysis of the Soyuz landing environment was performed using landing variable distributions from available literature. Results were used to identify trends in vehicle landing acceleration with landing conditions and to compare landing model injury risk predictions with those estimated in vehicle certification analysis. Next, historic Soyuz landings identified as inducing occupant injury during were reconstructed using the developed landing model and estimated landing conditions. Results and Discussion: This assessment demonstrated the capability of certification metrics, which include a combination of vehicle- and occupant-level injury risk analysis, to identify elevated injury risk landing conditions. However, the developed landing load predictions were limited by available vehicle and landing condition data. Additional data, particularly landing acceleration data specific to historic Soyuz landing conditions, would greatly enhance this assessment’s findings.
Preston Greenhalgh↗
Assessing Environmental and Socioeconomic Factors of Urban Flood Vulnerability in Kansas City, Kansas
Pluvial flooding, over-saturated ground, and drainage systems disproportionately impact historically marginalized urban neighborhoods during extreme rainfall events. These communities are impacted by physical and socioeconomic factors that make them vulnerable to flooding events, such as high concentrations of impervious landcover, high precipitation rates, and a combined sewer system framework. Despite known vulnerability to environmental hazards, understanding potential pluvial street-level flooding events are largely unknown. Using the open-source National Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation model, NASA DEVELOP examined neighborhood scale runoff retention and potential economic damages for risk mapping throughout Kansas City, Kansas. The generated outputs aid in identifying vulnerable neighborhoods susceptible to flooding and in need of future intervention. Previous studies have applied this model framework to understand urban flood vulnerability regarding ecosystem services, urban planning, and flood mitigation strategies. We utilized the InVEST outputs to develop indices pertaining to environmental justice factors of race, socioeconomic status, social vulnerability, and health. The findings indicate that historically redlined neighborhoods in Kansas City, Kansas face disproportional impacts from flood events and are subject to greater environmental stressors. This research provides an approach to utilizing an open-source flood vulnerability model to empower neighborhood-scale environmental justice analysis, enhancing the local communities' understanding of present-day impacts of historical environmental injustices.
Hadwynne Gross↗
Probabilistic Risk Assessment for Decision Making During Spacecraft Operations
Decisions made during the operational phase of a space mission often have significant and immediate consequences. Without the explicit consideration of the risks involved and their representation in a solid model, it is very likely that these risks are not considered systematically in trade studies. Wrong decisions during the operational phase of a space mission can lead to immediate system failure whereas correct decisions can help recover the system even from faulty conditions. A problem of special interest is the determination of the system fault protection strategies upon the occurrence of faults within the system. Decisions regarding the fault protection strategy also heavily rely on a correct understanding of the state of the system and an integrated risk model that represents the various possible scenarios and their respective likelihoods. Probabilistic Risk Assessment (PRA) modeling is applicable to the full lifecycle of a space mission project, from concept development to preliminary design, detailed design, development and operations. The benefits and utilities of the model, however, depend on the phase of the mission for which it is used. This is because of the difference in the key strategic decisions that support each mission phase. The focus of this paper is on describing the particular methods used for PRA modeling during the operational phase of a spacecraft by gleaning insight from recently conducted case studies on two operational Mars orbiters. During operations, the key decisions relate to the commands sent to the spacecraft for any kind of diagnostics, anomaly resolution, trajectory changes, or planning. Often, faults and failures occur in the parts of the spacecraft but are contained or mitigated before they can cause serious damage. The failure behavior of the system during operations provides valuable data for updating and adjusting the related PRA models that are built primarily based on historical failure data. The PRA models, in turn, provide insight into the effect of various faults or failures on the risk and failure drivers of the system and the likelihood of possible end case scenarios, thereby facilitating the decision making process during operations. This paper describes the process of adjusting PRA models based on observed spacecraft data, on one hand, and utilizing the models for insight into the future system behavior on the other hand. While PRA models are typically used as a decision aid during the design phase of a space mission, we advocate adjusting them based on the observed behavior of the spacecraft and utilizing them for decision support during the operations phase.
dynamic fault trees↗