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All sources NASA NTRS DOE OSTI

DOE OSTI2017

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets' planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.

Zhan, Yiduo↗

DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

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↗

DOE OSTI2022

Decision Science for Machine Learning (DeSciML)

The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring rely on machine learning for aspects of their analyses. Likewise, future technologies, such as incorporation of data-driven methods into the stockpile surveillance and predictive failure analysis for weapons components, will all rely on decision-making that incorporates the output of machine learning models. In this project, our main focus was the development of decision scientific methods that combine uncertainty estimates for machine learning predictions, with a domain-specific model of error costs. Other focus areas include uncertainty measurement in ML predictions, designing decision rules using multiobjecive optimization, the value of uncertainty reduction, and decision-tailored uncertainty quantification for probability estimates. By laying foundations for rigorous decision making based on the predictions of machine learning models, these approaches are directly relevant to every national security mission that applies, or will apply, machine learning to data, most of which entail some decision context.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Making a Water Data System Responsive to Information Needs of Decision Makers

Evidence-based environmental management requires data that are sufficient, accessible, useful and used. A mismatch between data, data systems, and data needs for decision making can result in inefficient and inequitable capital investments, resource allocations, environmental protection, hazard mitigation, and quality of life. In this paper, we examine the relationship between data and decision making in environmental management, with a focus on water management. We focus on the concept of decision-driven data systems —data systems that incorporate an assessment of decision-makers' data needs into their design. The aim of the research was to examine the process of translating data into effective decision making by engaging stakeholders in the development of a water data system. Using California's legislative mandate for state agencies to integrate existing water and other environmental data as a case study, we developed and applied a participatory approach to inform data-system design and identify unmet data needs. Using workshops and focused stakeholder meetings, we developed 20 diverse use cases to assess data sources, availability, characteristics, gaps, and other attributes of data used for representative decisions. Federal and state agencies made up about 90% of the data sources, and could readily adapt to a federated data system, our recommended model for the state. The remaining 10% of more-specialized data, central to important decisions across multiple use cases, would require additional investment or incentives to achieve data consistency, interoperability, and compatibility with a federated system. Based on this assessment, we propose a typology of different types of data limitations and gaps described by stakeholders. We also propose technical, governance, and stakeholder engagement evaluation criteria to guide planning and building environmental data systems. Data-system governance involving both producers and users of data was seen as essential to achieving workable standards, stable funding, convenient data availability, resilience to institutional change, and long-term buy-in by stakeholders. Our work provides a replicable lesson for using decision-maker and stakeholder engagement to shape the design of an environmental data system, and inform a technical design that addresses both user and producer needs.

Cantor, Alida↗

NASA NTRS1991

Incorporating affective bias in models of human decision making

Research on human decision making has traditionally focused on how people actually make decisions, how good their decisions are, and how their decisions can be improved. Recent research suggests that this model is inadequate. Affective as well as cognitive components drive the way information about relevant outcomes and events is perceived, integrated, and used in the decision making process. The affective components include how the individual frames outcomes as good or bad, whether the individual anticipates regret in a decision situation, the affective mood state of the individual, and the psychological stress level anticipated or experienced in the decision situation. A focus of the current work has been to propose empirical studies that will attempt to examine in more detail the relationships between the latter two critical affective influences (mood state and stress) on decision making behavior.

Nygren, Thomas E.↗

NASA NTRS1992

Crew decision making under stress

Flight crews must make decisions and take action when systems fail or emergencies arise during flight. These situations may involve high stress. Full-missiion flight simulation studies have shown that crews differ in how effectively they cope in these circumstances, judged by operational errors and crew coordination. The present study analyzed the problem solving and decision making strategies used by crews led by captains fitting three different personality profiles. Our goal was to identify more and less effective strategies that could serve as the basis for crew selection or training. Methods: Twelve 3-member B-727 crews flew a 5-leg mission simulated flight over 1 1/2 days. Two legs included 4 abnormal events that required decisions during high workload periods. Transcripts of videotapes were analyzed to describe decision making strategies. Crew performance (errors and coordination) was judged on-line and from videotapes by check airmen. Results: Based on a median split of crew performance errors, analyses to date indicate a difference in general strategy between crews who make more or less errors. Higher performance crews showed greater situational awareness - they responded quickly to cues and interpreted them appropriately. They requested more decision relevant information and took into account more constraints. Lower performing crews showed poorer situational awareness, planning, constraint sensitivity, and coordination. The major difference between higher and lower performing crews was that poorer crews made quick decisions and then collected information to confirm their decision. Conclusion: Differences in overall crew performance were associated with differences in situational awareness, information management, and decision strategy. Captain personality profiles were associated with these differences, a finding with implications for crew selection and training.

Orasanu, J.↗

NASA NTRS2005

Decision Facilitator for Launch Operations using Intelligent Agents

Launch operations require millions of micro-decisions which contribute to the macro decision of 'Go/No-Go' for a launch. Knowledge workers"(such as managers and technical professionals) need information in a timely precise manner as it can greatly affect mission success. The intelligent agent (web search agent) uses the words of a hypertext markup language document which is connected through the internet. The intelligent agent's actions are to determine if its goal of seeking a website containing a specified target (e.g., keyword or phrase), has been met. There are few parameters that should be defined for the keyword search like "Go" and "No-Go". Instead of visiting launch and range decision making servers individually, the decision facilitator constantly connects to all servers, accumulating decisions so the final decision can be decided in a timely manner. The facilitator agent uses the singleton design pattern, which ensures that only a single instance of the facilitator agent exists at one time. Negotiations could proceed between many agents resulting in a final decision. This paper describes details of intelligent agents and their interaction to derive an unified decision support system.

Thirumalainambi, Rajkumar↗

NASA NTRS2012

Key Decision Record Creation and Approval Module

Retaining good key decision records is critical to ensuring the success of a project or operation. Having adequately documented decisions with supporting documents and rationale can greatly reduce the amount of rework or reinvention over a project's, vehicle's, or facility's lifecycle. Stennis Space Center developed and uses a software tool that automates the Key Decision Record (KDR) process for its engineering and test projects. It provides the ability for a user to log key decisions that are made during the course of a project. By customizing Parametric Technology Corporation's (PTC) Windchill product, the team was able to log all information about a decision, and electronically route that information for approval. Customizing the Windchill product allowed the team to directly connect these decisions to the engineering data that it might affect and notify data owners of the decision. The user interface was created in JSP and Javascript, within the OOTB (Out of the Box) Windchill product, allowing users to create KDRs. Not only does this interface allow users to create and track KDRs, but it also plugs directly into the OOTB ability to associate these decision records with other relevant engineering data such as drawings, designs, models, requirements, or specifications

Hebert, Barrt↗

NASA NTRS2015

Decision Making and Communications Process Assessment of NASA Using Three Change Requests from the Space Launch System Program

This thesis investigated the communication and decision making process as part of the Systems Engineering practices at the NASA/Marshall Center to determine its level of effectiveness. Data was collected across three change requests to assess how decisions were made, how the decisions were communicated, and whether a process mattered in the formulation and dissemination of those decisions. Data results revealed the comprehensive decision making process for the technical change requests to be effective. Evidence revealed that the process was sufficiently tailored to accommodate the need of each individual technical change which promoted effective communication amongst the stakeholders in the formulation of the strategic decision recommendations elevated to upper management. However, data results also revealed the dissemination of the final decision and approval of the change requests from the higher organizational level down to all stakeholders was less effective. An establishment of a culmination meeting at the end of the change request decision process in which to close the communication loop with all entities would be beneficial.

Hicks, Karen Campbell↗

NASA NTRS2018

Information Theory Applied to Decision Making Structures

Decision making structures, such as control boards, process information on many topics as they select options for the system and project. The decisions are based on the information known to the board members and presenters (subject matter experts) and shared at the board during discussion. This information flow through this process can be modelled using information theory. Information theory provides a mathematical basis to understand the flow of information through the decision making process and the information needed for a particular decision. Information theory also provides the mathematical relationships on which to base the optimal decision making structure for a specific system development and organizational structure. Since decision making bodies provide control for the system or project, control theory can be used to construct a decision making model. This provides a starting point for adding cognitive science models. Information processing by each individual board participant can be represented through cognitive processes which are integrated across the board participants through information theory relationship. The set theory view of information theory provides a structure in which to look at the relationships between the participants in a decision making structure.

Watson, Michael D.↗

NASA NTRS2018

Information Theory Applied to Decision Making Structures

Watson, Michael D.↗

DOE OSTI2018

Overview of IAEA MODARIA II Program Working Group 1 on Assessment and Decision Making of Existing Exposure Situations for NORM and Nuclear Legacy Sites - 18308

The International Atomic Energy Agency (IAEA) launched the Modelling and Data for Radiological Impact Assessments (MODARIA) II Programme in October. As part of the IAEA effort to enhance the capabilities of its Member States in assessing radiological impacts of radionuclides released to the environment from sites that were not operated in accordance with current standards, and to ensure protection of the public and the environment, the MODARIA II Programme aims to: (1) test the performance of models developed for predicting the transfer of radionuclides in the environment and assessing radiological impacts to the public and the environment; (2) develop and improve models for particular environments and, where appropriate, to agree on data sets that are generally applicable for use in testing environmental transfer models; and (3) provide an international forum for the exchange of experiences, ideas and research information. The Programme will run for 3 years from 2016 to 2019. The Programme comprises seven working groups covering a wide range of topics (https://wwwns.iaea.org/projects/modaria/modaria2.asp?s=8 and l=129). MODARIA II Working Group 1 (WG1) is focused on dose assessment and decision making regarding existing exposure situations due to naturally occurring radioactive material (NORM) and nuclear legacy sites. WG1 was formed as a combination of two MODARIA I WGs: WG1 focused on application of decision-aiding tools in remediation; and WG3 focused on risk assessment for NORM and radioactively contaminated legacy sites (https://wwwns.iaea.org/projects/modaria/default.asp?s=8 and l=129). It was recognized that the decision-making needs will inform the scope of risk assessments, and the risk insights and structured decision analyses will aid in informed decision-making. Therefore, MODARIA II WG1 is focused on developing methods and toolsets for risk-informed decision-making for NORM and nuclear legacy sites, including demonstration of decision-making processes and tools through case studies. MODARIA II WG1 has 91 registered participants from 33 Member States and IAEA (as of November 2017), some 30 of whom are actively engaged in various collaborative research and development activities within WG1. Members of WG1 participated in the First Technical Meeting of MODARIA II, which was held in Vienna, Austria from 31 October to 4 November 2016. The group then held its First Interim Meeting in Brussels, Belgium from 26 to 30 June 2017, which included a field trip to the legacy Tessenderlo Chemie (TCH) Phosphate Processing Site. At this meeting, the TCH Site and the Pridneprovsky Chemical Plant (PChP) in Ukraine were selected for case studies. The Los Alamos National Laboratory (LANL) Material Disposal Area B in the USA and the Beaverlodge Uranium Mill and Mill Site in northern Canada were selected as examples for sharing of good practices and lessons learned in risk-informed decision-making for environmental remediation and land conveyance. At the Second Technical Meeting held in Vienna on 30 October to 3 November 2017, an additional NORM site for tin processing in Brazil was added to the case studies, with another NORM site in Yunnan, China included for consideration at the Second Interim Meeting of WG1. The Second Interim Meeting will be held in in early to mid-May, 2018. The group has also decided to prepare an Interim Report by October 2018 to document the WG1 discussions and analyses for the period of 2016-2018, which will be finalized at the conclusion of MODARIA II in October 2019. Initial assignments have been discussed for IM1 participants to collect data, conduct modelling and analyses, and prepare draft sections for the Interim Report. This paper will provide an overview of the work plans for WG1. It will also highlight some near-term activities that the group is currently working on. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one’s peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the “pattern” by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game decides among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make a similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Reducing Subjectivity in Geothermal Exploration Decision Making (Presentation); NREL(National Renewable Energy Laboratory)

Geothermal exploration projects have a significant amount of risk associated with uncertainties encountered in the discovery of the geothermal resource. Two of the largest challenges for increased geothermal deployment are 1) understanding when and how to proceed in an exploration program, and 2) when to walk away from a site. Current methodologies for exploration decision-making are formulatedby 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 alocation. 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 aparticular 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 basicgeothermal 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 secondapproach was determined to be less subjective, since numerical inputs come from the collected data. And it helps to facilitate communication between project managers and exploration geologists in making objective go/no-go decisions throughout the different project phases.

15 GEOTHERMAL ENERGY↗

DOE OSTI2013

Status Update on the NCRP Scientific Committee SC 5-1 Report: Decision Making for Late-Phase Recovery from Nuclear or Radiological Incidents - 13450

In August 2008, the U.S. Department of Homeland Security (DHS) issued its final Protective Action Guide (PAG) for radiological dispersal device (RDD) and improvised nuclear device (IND) incidents. This document specifies protective actions for public health during the early and intermediate phases and cleanup guidance for the late phase of RDD or IND incidents, and it discusses approaches to implementing the necessary actions. However, while the PAG provides specific guidance for the early and intermediate phases, it prescribes no equivalent guidance for the late-phase cleanup actions. Instead, the PAG offers a general description of a complex process using a site-specific optimization approach. This approach does not predetermine cleanup levels but approaches the problem from the factors that would bear on the final agreed-on cleanup levels. Based on this approach, the decision-making process involves multifaceted considerations including public health, the environment, and the economy, as well as socio-political factors. In an effort to fully define the process and approach to be used in optimizing late-phase recovery and site restoration following an RDD or IND incident, DHS has tasked the NCRP with preparing a comprehensive report addressing all aspects of the optimization process. Preparation of the NCRP report is a three-year (2010-2013) project assigned to a scientific committee, the Scientific Committee (SC) 5-1; the report was initially titled, Approach to Optimizing Decision Making for Late- Phase Recovery from Nuclear or Radiological Terrorism Incidents. Members of SC 5-1 represent a broad range of expertise, including homeland security, health physics, risk and decision analysis, economics, environmental remediation and radioactive waste management, and communication. In the wake of the Fukushima nuclear accident of 2011, and guided by a recent process led by the White House through a Principal Level Exercise (PLE), the optimization approach has since been expanded to include off-site contamination from major nuclear power plant accidents as well as other nuclear or radiological incidents. The expanded application under the current guidance has thus led to a broadened scope of the report, which is reflected in its new title, Decision Making for Late-Phase Recovery from Nuclear or Radiological Incidents. The NCRP report, which is due for publication in 2013, will substantiate the current DHS guidance by clarifying and elaborating on the processes required for the development and implementation of procedures for optimizing decision making for late-phase recovery, enabling the establishment of cleanup goals on a site-specific basis. The report will contain a series of topics addressing important issues related to the long-term recovery from nuclear or radiological incidents. Special topics relevant to supporting the optimization of the decision-making process will include cost-benefit analysis, radioactive waste management, risk communication, stakeholder interaction, risk assessment, and decontamination approaches and techniques. The committee also evaluated past nuclear and radiological incidents for their relevance to the report, including the emerging issues associated with the Fukushima nuclear accident. Thus, due to the commonality of the late-phase issues (such as the potential widespread contamination following an event), the majority of the information pertaining to the response in the late-phase decision-making period, including site-specific optimization framework and approach, could be used or adapted for use in case of similar situations that are not due to terrorism, such as those that would be caused by major nuclear facility accidents or radiological incidents. To ensure that the report and the NCRP recommendations are current and relevant to the effective implementation of federal guidance, SC 5-1 has actively coordinated with the agencies of interest and other relevant stakeholders throughout the duration of the project. The resulting report will be an important resource to guide those involved in late-phase recovery efforts following a nuclear or radiological incident. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Risk Model for EM Decision Support Toolsets

The Government Office of Accountability (GAO) has published several reports identifying the need for the Department of Energy (DOE) Office of Environmental Management (EM) to address mounting costs for DoE's cleanup program. DOEEM could greatly benefit from independent decision tool-sets/models that would allow them to evaluate options and inform business decision at the enterprise level considering site-specific life cycle costs and system plans. Program goals: Develop a tool-set that enables EM to independently evaluate alternatives, assess outcomes from different contracting strategies, and inform critical decisions for the enterprise. Project goals: Adaption of a Risk Model for integration with complimentary tool-sets for project level decision making. Operational Events: Discrete event model that evaluates operational variables (e.g. capacity, throughput, maintenance constraints) and identify bottlenecks. Identifying and Bounding Project Risk: Identify risks that impact confidence in meeting goals (e.g. production). Life cycle Cost: Evaluate impacts of staffing levels, inventory, and capital investments on life cycle costs. Methods and approach: Monte Carlo Analysis is being executed to generate results: Input derives from Risk Register Data; Tied to Projected and Target Schedules; Incorporates float duration within the model. Assumes associated risk mitigation actions being completed within a timeline of five fiscal years. Metrics include: Confidence in Meeting Production Goal; Confidence in Safety Standards; Confidence in Continuous Operation; Other Metrics can be added as appropriate regarding specific site needs. The adapted risk model can be used as a stand alone decision tool or can be integrated complementary tool-sets (i.e. process and cost models) for project-specific decisions. These support tool-sets can then be integrated with others for site- and complex- level evaluations. Future work includes designing an adaptable and modular framework that would allow integration of multiple tool-sets for holistic and/or targeted evaluation of alternative strategies for decision making that could lead to risk and cost reduction across the enterprise.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

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↗