Results for “decision making” — Page 7

DOE OSTI2015

Assessment of New Approaches in Geothermal Exploration Decision Making

Geothermal exploration projects have significant amount of risk associated with uncertainties encountered in the discovery of the geothermal resource. Understanding when and how to proceed in an exploration program, and when to walk away from a site, are two of the largest challenges for increased geothermal deployment. Current methodologies for exploration decision making is left to subjective by subjective expert opinion which can be incorrectly biased by expertise (e.g. geochemistry, geophysics), geographic location of focus, and the assumed conceptual model. The aim of this project is to develop a methodology for more objective geothermal exploration decision making at a given location, including go-no-go decision points to help developers and investors decide when to give up on a location. In this scope, two different approaches are investigated: 1) value of information analysis (VOIA) which is used for evaluating and quantifying the value of a data before they are purchased, and 2) enthalpy-based exploration targeting based on reservoir size, temperature gradient estimates, and internal rate of return (IRR). The first approach, VOIA, aims to identify the value of a particular data when making decisions with an uncertain outcome. This approach targets the pre-drilling phase of exploration. These estimated VOIs are highly affected by the size of the project and still have a high degree of subjectivity in assignment of probabilities. The second approach, exploration targeting, is focused on decision making during the drilling phase. It starts with a basic geothermal project definition that includes target and minimum required production capacity and initial budgeting for exploration phases. Then, it uses average temperature gradient, reservoir temperature estimates, and production capacity to define targets and go/no-go limits. The decision analysis in this approach is based on achieving a minimum IRR at each phase of the project. This second approach was determined to be less subjective, since it requires less subjectivity in the input values.

15 GEOTHERMAL ENERGY↗

DOE OSTI2015

Assessment of New Approaches in Geothermal Exploration Decision Making: Preprint

Geothermal exploration projects have significant amount of risk associated with uncertainties encountered in the discovery of the geothermal resource. Understanding when and how to proceed in an exploration program, and when to walk away from a site, are two of the largest challenges for increased geothermal deployment. Current methodologies for exploration decision making is left to subjective by subjective expert opinion which can be incorrectly biased by expertise (e.g. geochemistry, geophysics), geographic location of focus, and the assumed conceptual model. The aim of this project is to develop a methodology for more objective geothermal exploration decision making at a given location, including go-no-go decision points to help developers and investors decide when to give up on a location. In this scope, two different approaches are investigated: 1) value of information analysis (VOIA) which is used for evaluating and quantifying the value of a data before they are purchased, and 2) enthalpy-based exploration targeting based on reservoir size, temperature gradient estimates, and internal rate of return (IRR). The first approach, VOIA, aims to identify the value of a particular data when making decisions with an uncertain outcome. This approach targets the pre-drilling phase of exploration. These estimated VOIs are highly affected by the size of the project and still have a high degree of subjectivity in assignment of probabilities. The second approach, exploration targeting, is focused on decision making during the drilling phase. It starts with a basic geothermal project definition that includes target and minimum required production capacity and initial budgeting for exploration phases. Then, it uses average temperature gradient, reservoir temperature estimates, and production capacity to define targets and go/no-go limits. The decision analysis in this approach is based on achieving a minimum IRR at each phase of the project. This second approach was determined to be less subjective, since it requires less subjectivity in the input values.

15 GEOTHERMAL ENERGY↗

DOE OSTI2016

Minimizing Uncertainties Impact in Decision Making with an Applicability Study for Economic Power Dispatch

This report summaries the work performed under the LDRD project on the preliminary study on knowledge automation, where specific focus has been made on the investigation of the impact of uncertainties of human decision making onto the optimization of the process operation. At first the statistics on signals from the Brain-Computing Interface (BCI) is analyzed so as to obtain the uncertainties characterization of human operators during the decision making phase using the electroencephalogram (EEG) signals. This is then followed by the discussions of an architecture that reveals the equivalence between optimization and closed loop feedback control design, where it has been shown that all the optimization problems can be transferred into the control design problem for closed loop systems. This has led to a “closed loop” framework, where the structure of the decision making is shown to be subjected to both process disturbances and controller’s uncertainties. The latter can well represent the uncertainties or randomness occurred during human decision making phase. As a result, a stochastic optimization problem has been formulated and a novel solution has been proposed using probability density function (PDF) shaping for both the cost function and the constraints using stochastic distribution control concept. A sufficient condition has been derived that guarantees the convergence of the optimal solution and discussions have been made for both the total probabilistic solution and chanced constrained optimization which have been well-studied in optimal power flows (OPF) area. A simple case study has been carried out for the economic dispatch of powers for a grid system when there are distributed energy resources (DERs) in the system, and encouraging results have been obtained showing that a significant savings on the generation cost can be expected.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2015

City-Level Energy Decision Making. Data Use in Energy Planning, Implementation, and Evaluation in U.S. Cities

The Cities-LEAP technical report, City-Level Energy Decision Making: Data Use in Energy Planning, Implementation, and Evaluation in U.S. Cities, explores how a sample of cities incorporates data into making energy-related decisions. This report provides the foundation for forthcoming components of the Cities-LEAP project that will help cities improve energy decision making by mapping specific city energy or climate policies and actions to measurable impacts and results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1994

Fuzzy Logic Approaches to Multi-Objective Decision-Making in Aerospace Applications

Fuzzy logic allows for the quantitative representation of multi-objective decision-making problems which have vague or fuzzy objectives and parameters. As such, fuzzy logic approaches are well-suited to situations where alternatives must be assessed by using criteria that are subjective and of unequal importance. This paper presents an overview of fuzzy logic and provides sample applications from the aerospace industry. Applications include an evaluation of vendor proposals, an analysis of future space vehicle options, and the selection of a future space propulsion system. On the basis of the results provided in this study, fuzzy logic provides a unique perspective on the decision-making process, allowing the evaluator to assess the degree to which each option meets the evaluation criteria. Future decision-making should take full advantage of fuzzy logic methods to complement existing approaches in the selection of alternatives.

Hardy, Terry L.↗

NASA NTRS1991

Sharing intelligence: Decision-making interactions between users and software in MAESTRO

By combining the best of automated and human decision-making in scheduling many advantages can accrue. The joint performance of the user and system is potentially much better than either alone. Features of the MAESTRO scheduling system serve to illustrate concepts of user/software cooperation. MAESTRO may be operated at a user-determinable and dynamic level of autonomy. Because the system allows so much flexibility in the allocation of decision-making responsibilities, and provides users with a wealth of information and other support for their own decision-making, better overall schedules may result.

Geoffroy, Amy L.↗

NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗

NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗

DOE OSTI2018

IAEA Initiatives that Integrate Stakeholders, Models and Decision Making - 18646

The International Atomic Energy Agency (IAEA) has initiated an effort to provide stakeholder support services as part of Phase II of the Constraints to Implementing Decommissioning and Environmental Remediation (CIDER II) programs. The intent of this sub-group of CIDER II is to provide members states support in areas of stakeholder communication, engagement and participation, with the goal of building trust in decommissioning and environmental remediation (D and ER) decisions for radioactive contamination and facilities. IAEA has also initiated a follow-on effort to address Modeling and Data for Radiological Impact Assessments (MODARIA II), which is aimed at enhancing the capabilities of Member States to simulate radionuclide transfer in the environment, and to ensure appropriate levels of protection from ionizing radiation. Working Group 1 of MODARIA II is focused on the integration of modeling and decision analysis. These two groups have complementary and supporting objectives, which provides an opportunity for collaboration and mutual benefit. The technical integrator is the approach of structured decision making, which follows a five-step process that combines stakeholder values with models through a technically defensible decision analysis. Briefly, the steps are: 1. Provide the context and decision context for the problem; 2. Obtain costs, values and concerns from stakeholders, to develop objectives that need to be achieved (e.g., improve human health, minimize costs, increase jobs); 3. Identify options that might achieve the objectives (e.g., cleanup, institutional controls, education); 4. Develop probabilistic models that connect options to objectives so that the objectives can be evaluated collectively (e.g., a fate and transport and risk model to address the objective of reducing human health risk, a cost model to address costs of taking action); and, 5. Model evaluation, including sensitivity analysis, to determine if an action can be taken with sufficient certainty, or if collection of more data is needed to decrease uncertainty before a decision is made. This approach also supports adaptive management as new data are collected. This approach clarifies the roles of stakeholders and subject matter experts in the decision-making process, where stakeholders specify values, and subject matter experts focus on the models that are needed to evaluate the options. It also provides a technically defensible, reproducible, transparent and traceable approach to all of the components necessary to properly solve the types of complex decision problems that plague radioactive waste management, decommissioning and environmental remediation. This paper introduces the major challenges to implementing such a broad scale of decision analysis, and approaches to overcoming those challenges, which include not only the technical challenges, but also the need to develop capacity in a range of skills not usually accommodated in environmental modeling and includes the need for the IAEA to address decision making in different countries, and with different cultures, religions, political affiliations, and languages. This paper explores the potential for IAEA to benefit from both the CIDER II and MODARIA II programs, and hence to develop a fully integrated approach to effective decision making for radioactive waste management and environmental remediation. Note that there are many people involved in these efforts at the IAEA, and credit belongs to those people for the progress that has been made to date. Our intent here is simply to introduce these IAEA programs at the WM2018 Symposium to provide our perspective and some awareness of some of the directions in which IAEA appears to be headed regarding stakeholder engagement, environmental and risk modeling, data science, and decision analysis. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Stakeholder Engaged Structured Decision Making to Improve Performance and Reduce Costs While Protecting Human Health and the Environment - 18645

The objective of the Best Practice is to facilitate a paradigm shift when it comes to approaches to the decision making process for nuclear waste management, disposal, and remediation decisions. The stakeholder-engaged structured decision making (SDM) paradigm shift provides a transparent framework for developing optimal solutions to complex problems (Keeney, 1992, Gregory et al, 2012). This is a deliberative-analytical process. The deliberative side centers on understanding stakeholder concerns and values, and developing objectives while identifying options that may achieve those objectives. SDM is implemented with the aid of computer tools that are aimed specifically at capturing these deliberative aspects of a decision analysis. SDM captures this information in a structured format that allows access to the values, objectives, and options that have been identified to help. Through this approach we achieve transparency, traceability, and reproducibility. The goal of SDM at this stage is to provide a formal structure for capturing and processing deliberative information. The deliberative part sets the stage for the analytical part of SDM. A variety of Subject matter experts (SMEs) are engaged in how to evaluate the options through the objectives. Objectives often include minimizing human health risk and minimizing cost, but in the full scope of a sustainability-based approach to decision making, it can include objectives related to economic, environmental and social issues. Subject matter experts might provide other options for achieving the objectives, but their primary role is to evaluate/model the options to the endpoint defined by the objectives or in other words perform a consequence analysis. The structure of SDM makes it clear exactly what is needed from the SMEs, because the options are identified, and the endpoint (objectives) are defined - evaluations and/or models are needed to connect options to objectives. This consequence analysis completes the evaluation, and directly addresses which of the options is the best option. The same SDM approach to finding the best options can also be used for prioritization and resource allocation. Because all of the information is captured in a formal system with the help of computer tools (a software framework for implementing SDM), the decision models that are developed for an application can be fully evaluated numerically and for the insights gained from using a formal process for managing the multiple factors that need to be considered for complex decisions. This includes uncertainty and sensitivity analysis that can be used to guide the need for further data/information collection if the optimal decision is not adequately supported - that is, if there is insufficient confidence in the decision. Also, this feeds directly into adaptive management therefore if more data/information are collected then the efficacy of the current decision can be evaluated, potentially leading to a change in decisions if warranted. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

A game theory perspective on environmental assessment: What games are played and what does this tell us about decision making rationality and legitimacy?

Game theory provides a useful theoretical framework to examine the decision process operating in the context of environmental assessment, and to examine the rationality and legitimacy of decision-making subject to Environmental Assessment (EA). The research uses a case study of the Environmental Impact Assessment and Sustainability Appraisal processes undertaken in England. To these are applied an analytical framework, based on the concept of decision windows to identify the decisions to be assessed. The conditions for legitimacy are defined, based on game theory, in relation to the timing of decision information, the behaviour type (competitive, reciprocal, equity) exhibited by the decision maker, and the level of public engagement; as, together, these control the type of rationality which can be brought to bear on the decision. Instrumental rationality is based on self-interest of individuals, whereas deliberative rationality seeks broader consensus and is more likely to underpin legitimate decisions. The results indicate that the Sustainability Appraisal process, conducted at plan level, is better than EIA, conducted at project level, but still fails to provide conditions that facilitate legitimacy. Game theory also suggests that Sustainability Appraisal is likely to deliver ‘least worst’ outcomes rather than best outcomes when the goals of the assessment process are considered; this may explain the propensity of such ‘least worst’ decisions in practise. On the basis of what can be learned from applying this game theory perspective, it is suggested that environmental assessment processes need to be redesigned and better integrated into decision making in order to guarantee the legitimacy of the decisions made. - Highlights: • Decision legitimacy is defined in terms of game theory. • Game theory is applied to EIA and SA decision windows. • Game theory suggests least worst outcomes prevail. • SA is more likely to be perceived legitimate than EIA.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1998

Development of a Search and Rescue Simulation to Study the Effects of Prolonged Isolation on Team Decision Making

The goals of this project were to identify and investigate aspects of team and individual decision-making and risk-taking behaviors hypothesized to be most affected by prolonged isolation. A key premise driving our research approach is that effects of stressors that impact individual and team cognitive processes in an isolated, confined, and hazardous environment will be projected onto the performance of a simulation task. To elicit and investigate these team behaviors we developed a search and rescue task concept as a scenario domain that would be relevant for isolated crews. We modified the Distributed Dynamic Decision-making (DDD) simulator, a platform that has been extensively used for empirical research in team processes and taskwork performance, to portray the features of a search and rescue scenario and present the task components incorporated into that scenario. The resulting software is called DD-Search and Rescue (Version 1.0). To support the use of the DDD-Search and Rescue simulator in isolated experiment settings, we wrote a player's manual for teaching team members to operate the simulator and play the scenario. We then developed a research design and experiment plan that would allow quantitative measures of individual and team decision making skills using the DDD-Search and Rescue simulator as the experiment platform. A description of these activities and the associated materials that were produced under this contract are contained in this report.

Entin, Elliot E.↗

NASA NTRS1992

Acquisition and production of skilled behavior in dynamic decision-making tasks

Currently, two main approaches exist for improving the human-machine interface component of a system in order to improve overall system performance - display enhancement and intelligent decision making. Discussed here are the characteristic issues of these two decision-making strategies. Differences in expert and novice decision making are described in order to help determine whether a particular strategy may be better for a particular type of user. Research is outlined to compare and contrast the two technologies, as well as to examine the interaction effects introduced by the different skill levels and the different methods for training operators.

Kirlik, Alex↗

NASA NTRS1994

Multi-objective decision-making under uncertainty: Fuzzy logic methods

Selecting the best option among alternatives is often a difficult process. This process becomes even more difficult when the evaluation criteria are vague or qualitative, and when the objectives vary in importance and scope. Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.

Hardy, Terry L.↗

NASA NTRS2010

NASA Risk-Informed Decision Making Handbook

This handbook provides guidance for conducting risk-informed decision making in the context of NASA risk management (RM), with a focus on the types of direction-setting key decisions that are characteristic of the NASA program and project life cycles, and which produce derived requirements in accordance with existing systems engineering practices that flow down through the NASA organizational hierarchy. The guidance in this handbook is not meant to be prescriptive. Instead, it is meant to be general enough, and contain a sufficient diversity of examples, to enable the reader to adapt the methods as needed to the particular decision problems that he or she faces. The handbook highlights major issues to consider when making decisions in the presence of potentially significant uncertainty, so that the user is better able to recognize and avoid pitfalls that might otherwise be experienced.

Dezfuli, Homayoon↗

DOE OSTI2017

Development of the Delphi display concept for alternative explanations decision making support - 120

Human operators of advanced technology systems, such as nuclear power, command and control, and deepwater oil drilling, must be able to evaluate and respond to a wide array of operational anomalies. Unfortunately, these circumstances are often complex and ambiguous, complicating decision making tasks. One common decision making error, called confirmation bias, occurs when decision makers prematurely focus on one explanation instead of systematically considering all plausible alternative explanations that could equally well account for an operational anomaly (Nickerson, 1998). The Delphi display is designed to counteract decision makers' tendencies towards confirmation bias and to encourage them to consider all plausible explanations, including high risk explanations, they might otherwise overlook. An initial Delphi display prototype was designed to help identify the underlying cause of operational anomalies in the complex, high-consequence technology of deepwater drilling. This prototype decision aid incorporates major indicators related to operational anomalies and presents corresponding plausible explanations for each indicator, ranging from high-risk system-critical explanations to lower-risk but still significant events. The Delphi display incorporates several innovative features to help decision makers detect and monitor operational anomalies and view the relationships among indicators and their associated plausible explanations. Preliminary evaluations of an early prototype Delphi display for detecting kicks in deepwater oil drilling operations have been promising. Moreover, the methodology used to design and develop the Delphi display can be readily generalized to numerous other operational contexts that require humans to make system-critical decisions. To illustrate, an initial Delphi display design for nuclear power plant control is described. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Critical Infrastructure Decision-Making under Long-Term Climate Hazard Uncertainty: The Need for an Integrated, Multidisciplinary Approach

U.S. critical infrastructure assets are often designed to operate for decades, and yet long-term planning practices have historically ignored climate change. With the current pace of changing operational conditions and severe weather hazards, research is needed to improve our ability to translate complex, uncertain risk assessment data into actionable inputs to improve decision-making for infrastructure planning. Decisions made today need to explicitly account for climate change – the chronic stressors, the evolution of severe weather events, and the wide-ranging uncertainties. If done well, decision making with climate in mind will result in increased resilience and decreased impacts to our lives, economies, and national security. We present a three-tier approach to create the research products needed in this space: bringing together climate projection data, severe weather event modeling, asset-level impacts, and contextspecific decision constraints and requirements. At each step, it is crucial to capture uncertainties and to communicate those uncertainties to decision-makers. While many components of the necessary research are mature (i.e., climate projection data), there has been little effort to develop proven tools for long-term planning in this space. The combination of chronic and acute stressors, spatial and temporal uncertainties, and interdependencies among infrastructure sectors coalesce into a complex decision space. By applying known methods from decision science and data analysis, we can work to demonstrate the value of an interdisciplinary approach to climate-hazard decision making for longterm infrastructure planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2013

Test and Evaluation Metrics of Crew Decision-Making And Aircraft Attitude and Energy State Awareness

NASA has established a technical challenge, under the Aviation Safety Program, Vehicle Systems Safety Technologies project, to improve crew decision-making and response in complex situations. The specific objective of this challenge is to develop data and technologies which may increase a pilot's (crew's) ability to avoid, detect, and recover from adverse events that could otherwise result in accidents/incidents. Within this technical challenge, a cooperative industry-government research program has been established to develop innovative flight deck-based counter-measures that can improve the crew's ability to avoid, detect, mitigate, and recover from unsafe loss-of-aircraft state awareness - specifically, the loss of attitude awareness (i.e., Spatial Disorientation, SD) or the loss-of-energy state awareness (LESA). A critical component of this research is to develop specific and quantifiable metrics which identify decision-making and the decision-making influences during simulation and flight testing. This paper reviews existing metrics and methods for SD testing and criteria for establishing visual dominance. The development of Crew State Monitoring technologies - eye tracking and other psychophysiological - are also discussed as well as emerging new metrics for identifying channelized attention and excessive pilot workload, both of which have been shown to contribute to SD/LESA accidents or incidents.

Bailey, Randall E.↗