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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Environmental Impact Assessment Law in China's courts: A study of 107 judicial decisions

The article explores the practices of Environmental Impact Assessment (EIA) Law in China's courts by examining 107 judicial decisions. Each of the 107 judicial decisions has been analyzed to determine the time/location of the decision, what type of EIA document was referred to, what specific claim was made by the plaintiffs, and what the court's ruling was on the case. The results indicate that: unlike in Germany or Japan, all kinds of EIA decisions made by environment protect bureaus (EPBs) in China were widely taken as justiciable, and China's courts generally allowed local residents to have standing and thus challenge the EPBs' decisions made during the EIA process. On the other hand, the research also shows the EPBs overwhelmingly prevailed in those EIA lawsuits. It is also found that China's reviewing judges were highly self-restrained, giving obvious deference to the technocrat with the substantial contents of EIA documents. Also, the concept of “flaw” was created when it came to procedural issues. These two factors, among others, were both helping the EPBs' prevailing successes. - Highlights: • 107 judicial decisions referring to China's EIA law are examined. • The justiciability of EPB's EIA decisions were taken for granted. • The defenders overwhelmingly prevailed in those EIA lawsuits. • The reviewing judges were highly self-restrained, defering to the technocrat with the EIA documents. • A functional concept, “flaw”, was created by reviewing judges when it came to procedural issues.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Reimagining How Flood Warnings Can Inform Decision‐Making and Community Actions

Society faces increasingly severe flood hazards, intensifying demand for flood early warning systems (FEWS) that deliver accurate and actionable information. However, most existing FEWS remain prediction‐centric, treating decision‐making as a downstream consumer of hazard forecasts while offering limited support for uncertainty interpretation, risk communication, and real‐world response. This Perspective presents a vision and blueprint for a novel inland FEWS‐decision‐making (FEWS‐DM) framework that repositions decision‐making as an equal partner in the forecasting process—not a passive recipient of its outputs. The framework is built on three tightly coupled, co‐evolving thrusts: Physical Science (T1), which advances flood prediction with quantified uncertainty informed by decision relevance; Human Science (T2), which incorporates psychology, behavior, and cultural and institutional context; and Decision Science (T3), which unifies physical predictions and human factors through principled, utility‐based decision support with end‐to‐end uncertainty management. Rather than treating T1 as a solved problem, FEWS‐DM recognizes that forecast development itself must be shaped by decision needs through continuous bidirectional feedback. We identify key scientific, behavioral, and operational challenges limiting such integration and discuss the enabling role of AI, while emphasizing human‐centered design and community feedback as essential for building trust and improving flood risk management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impact Mapping for Geospatial Reasoning and Decision Making

Objective The reported study evaluated a novel approach to aiding geospatial reasoning and decision making. Background Impact mapping aims to alleviate the cognitive demands of geospatial tasks in part by externalizing data in the form of an integrated decision surface. This is achieved by aggregating data across multiple sources of information and visualizing their combined utility rather than objective measurements or individual utility. Previous research has shown that geospatial decisions improve when aided in this manner, but it remains unknown if dynamic decision making, often plagued by fatigue and anchoring bias, would benefit similarly. Method The experiment implemented a systematic manipulation of the presence of a composite impact map and the number of attributes present in a two-stage disaster relief, resource allocation task to investigate when and how impact mapping is beneficial or deleterious to decision makers. Results The presence of the composite impact map increased the utility of selected sites, increased re-planning decisions, reduced information display views, and reduced workload. Generally, the effect of the composite impact map was greater when participants were asked to evaluate more attributes. Conclusion Composite impact maps appear to improve repeated geospatial reasoning and minimize anchoring bias because they alleviate the cognitive demands otherwise necessary to interpret and maintain information from multiple attributes. Application Data visualization techniques, such as impact mapping, can improve repeated geospatial decision making in environments that include high cognitive demand.

Illingworth, David A.↗
DOE OSTI2024

Clear Sky Toolkit: Decision Support User Guide

The Clear Sky Decision Support Template is a Microsoft Excel-based resource that enables users to screen and prioritize critical facilities for the addition of solar + storage. The Decision Support Template consolidates and streamlines the most relevant resources from the emergency management discipline, the electric utility sector, and national energy research institutions to assist in further site analysis and decision-making. The Decision Support Template is intended for use by local government and private sector decision-makers who aim to mitigate the effects of power outages on critical infrastructure, understand the potential for deploying photovoltaic (PV) and storage solutions on critical facilities, and align emergency management planning processes and utility needs with community resilience objectives. The Decision Support Template is divided into five modules that provide a structured, step-by-step approach users can follow and apply to unique circumstances. This process guides a more thorough and accurate assessment of a site's potential to accommodate a solar + storage system, provide grid benefits to utilities, and support community resilience and disaster mitigation for the broader region. After identifying potentially viable and preferred sites for solar + storage using the Decision Support Template, users should conduct a more detailed evaluation and consultation with a solar professional.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Decision-Making in Complex Dynamical Systems of Systems With One Opposing Subsystem

Many complex dynamical systems consist of a large number of interacting subsystems that operate harmoniously and make decisions that are designed for the benefit of the entire enterprise. If, in an attempt to disrupt the operation of the entire system, one subsystem gets attacked and is made to operate in a manner that is adversarial with the others, then the entire system suffers, resulting in an adversarial decision-making environment among its subsystems. Such an environment may affect not only the decision-making process of the attacked subsystem but also possibly the other remaining subsystems as well. The disruption caused by the attacked subsystem may cause the remaining subsystems to either coalesce as a unified team making team-based decisions, or disintegrate and act as independent decision-making entities. The decision-making process in these types of complex systems of systems is best analyzed within the general framework of cooperative and non-cooperative game theory. In this paper, we will develop an analysis that provides a theoretical basis for modeling the decision-making process in such complex systems. We show how cooperation among the subsystems can produce Noninferior Nash Strategies (NNS) that are fair and acceptable to all subsystems within the team while at the same time provide the subsystems in the team with the security of the Nash equilibrium against the opposing attacked subsystem. We contrast these strategies with the all Nash Strategies (NS) that would result if the operation of the entire system disintegrated and became adversarial among all subsystems as a result of the attack. An example of a system consisting of three subsystems with one opposing subsystem as a result of an attack is included to illustrate the results.

Talebi, Shahriar↗
NASA NTRS1972

A control theory model for human decision making

The optimal control model for pilot-vehicle systems has been extended to handle certain types of human decision tasks. The model for decision making incorporates the observation noise, optimal estimation, and prediction concepts that form the basis of the model for control behavior. Experiments are described for the following task situations: (1) single decision tasks; (2) two decision tasks; and (3) simultaneous manual control and decision tasks. Using fixed values for model parameters, single-task and two-task decision performance scores to within an accuracy of 10 percent can be predicted. The experiment on simultaneous control and decision indicates the presence of task interference in this situation, but the results are not adequate to allow a conclusive test of the predictive capability of the model.

Levison, W. H.↗
NASA NTRS1992

Determining rules for closing customer service centers: A public utility company's fuzzy decision

In the present work, we consider the general problem of knowledge acquisition under uncertainty. A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision was made. Unique to this work is the fuzzy set representation of the conditions or attributes upon which the decision make may base his fuzzy set decision. From our examples, we infer certain and possible rules containing fuzzy terms. It should be stressed that the procedure determines how closely the expert follows the conditions under consideration in making his decision. We offer two examples pertaining to the possible decision to close a customer service center of a public utility company. In the first example, the decision maker does not follow too closely the conditions. In the second example, the conditions are much more relevant to the decision of the expert.

Dekorvin, Andre↗
NASA NTRS2005

D-Side: A Facility and Workforce Planning Group Multi-criteria Decision Support System for Johnson Space Center

"To understand and protect our home planet, to explore the universe and search for life, and to inspire the next generation of explorers" is NASA's mission. The Systems Management Office at Johnson Space Center (JSC) is searching for methods to effectively manage the Center's resources to meet NASA's mission. D-Side is a group multi-criteria decision support system (GMDSS) developed to support facility decisions at JSC. D-Side uses a series of sequential and structured processes to plot facilities in a three-dimensional (3-D) graph on the basis of each facility alignment with NASA's mission and goals, the extent to which other facilities are dependent on the facility, and the dollar value of capital investments that have been postponed at the facility relative to the facility replacement value. A similarity factor rank orders facilities based on their Euclidean distance from Ideal and Nadir points. These similarity factors are then used to allocate capital improvement resources across facilities. We also present a parallel model that can be used to support decisions concerning allocation of human resources investments across workforce units. Finally, we present results from a pilot study where 12 experienced facility managers from NASA used D-Side and the organization's current approach to rank order and allocate funds for capital improvement across 20 facilities. Users evaluated D-Side favorably in terms of ease of use, the quality of the decision-making process, decision quality, and overall value-added. Their evaluations of D-Side were significantly more favorable than their evaluations of the current approach. Keywords: NASA, Multi-Criteria Decision Making, Decision Support System, AHP, Euclidean Distance, 3-D Modeling, Facility Planning, Workforce Planning.

Tavana, Madjid↗
NASA NTRS2008

Experiences in Bridging the Gap between Science and Decision Making at NASA's GSFC Earth Science Data and Information Services Center (GES DISC)

Recognizing the significance of NASA remote sensing Earth science data in monitoring and better understanding our planet s natural environment, NASA has implemented the Decision Support Through Earth Science Research Results program (NASA ROSES solicitations). a) This successful program has yielded several monitoring, surveillance, and decision support systems through collaborations with benefiting organizations. b) The Goddard Space Flight Center (GSFC) Earth Sciences Data and Information Services Center (GES DISC) has participated in this program on two projects (one complete, one ongoing), and has had opportune ad hoc collaborations gaining much experience in the formulation, management, development, and implementation of decision support systems utilizing NASA Earth science data. c) In addition, GES DISC s understanding of Earth science missions and resulting data and information, including data structures, data usability and interpretation, data interoperability, and information management systems, enables the GES DISC to identify challenges that come with bringing science data to decision makers. d) The purpose of this presentation is to share GES DISC decision support system project experiences in regards to system sustainability, required data quality (versus timeliness), data provider understanding of how decisions are made, and the data receivers willingness to use new types of information to make decisions, as well as other topics. In addition, defining metrics that really evaluate success will be exemplified.

Kempler, Steven↗
DOE OSTI2024

A Tool to Incorporate Non-Energy Impacts in Energy Efficiency Investment Decision Making for Firms

Energy efficiency is a key demand-side strategy for sustainability, recently identified by the United States Department of Energy as a pillar of industrial decarbonization. The increased focus on decarbonization and the requirement for efficiency to enable electrification, another decarbonization pillar, due to the spark spread between natural gas and electricity prices, make energy efficiency increasingly relevant. Still, industries face challenges in adopting energy efficiency measures. Researchers have long found a gap in adoption of even those measures with a profitable net present value, attributed to lack of strategic value among other barriers (see for rigorous exploration and taxonomy). One solution to facilitate energy efficiency projects is the inclusion of non-energy impacts, as this has been shown to double potential deployment of such projects at system level. Energy efficiency can provide valuable benefits outside of simple operating cost reductions, from decreased pollution to enhanced productivity. The inclusion of these benefits in decision making assessments faces hurdles due to inconsistency of ancillary benefits across projects, difficulties in quantifying impacts and the need for additional measurement to quantify them. The decision-making tools to support this have been designed primarily for the European context. We begin with a stakeholder engagement process to better characterize the U.S. decision making process surrounding adoption of energy efficiency investments. Characterization of non-energy impacts has developed substantially over recent decades. Cagno et al. provided a framework for studying the applicability of these impacts to energy efficiency projects, listing 120 key performance indicators focused mainly on reductions of costs/harms. Other researchers have included impacts on the strategic and revenue side that can be merged into this framework as well. We seek a tractable set of impacts that can be included in a decision-making tool in the US, and as such are well suited to US industry, management and decision making processes. We also seek to understand how to best quantify or characterize these impacts. This work will demonstrate the results of a survey conducted among US manufacturing industry decision makers to assess the decision making landscape of stakeholders as well as the most relevant performance indicators for energy efficiency projects.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2024

A Model-Based Systems Engineering Approach for Effective Decision Support of Modern Energy Systems Depicted with Clean Hydrogen Production

A holistic approach to decision-making in modern energy systems is vital due to their increase in complexity and interconnectedness. However, decision makers often rely on narrowly-focused strategies, such as economic assessments, for energy system strategy selection. The approach in this paper helps considers various factors such as economic viability, technological feasibility, environmental impact, and social acceptance. By integrating these diverse elements, decision makers can identify more economically feasible, sustainable, and resilient energy strategies. While existing focused approaches are valuable since they provide clear metrics of a potential solution (e.g., an economic measure of profitability), they do not offer the much needed system-as-a-whole understanding. This lack of understanding often leads to selecting suboptimal or unfeasible solutions, which is often discovered much later in the process when a change may not be possible. This paper presents a novel evaluation framework to support holistic decision-making in energy systems. The framework is based on a systems thinking approach, applied through systems engineering principles and model-based systems engineering tools, coupled with a multicriteria decision analysis approach. The systems engineering approach guides the development of feasible solutions for novel energy systems, and the multicriteria decision analysis is used for a systematic evaluation of available strategies and objective selection of the best solution. The proposed framework enables holistic, multidisciplinary, and objective evaluations of solutions and strategies for energy systems, clearly demonstrates the pros and cons of available options, and supports knowledge collection and retention to be used for a different scenario or context. The framework is demonstrated in case study evaluation solutions for a novel energy system of clean hydrogen generation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Multistage distributionally robust mixed-integer programming with decision-dependent moment-based ambiguity sets

We study multistage distributionally robust mixed-integer programs under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisions made in previous stages. We first consider two ambiguity sets defined by decision-dependent bounds on the first and second moments of uncertain parameters and by mean and covariance matrix that exactly match decision-dependent empirical ones, respectively. For both sets, we show that the subproblem in each stage can be recast as a mixed-integer linear program (MILP). Moreover, we extend the general moment-based ambiguity set in to the multistage decision-dependent setting, and derive mixed-integer semidefinite programming (MISDP) reformulations of stage-wise subproblems. We develop methods for attaining lower and upper bounds of the optimal objective value of the multistage MISDPs, and approximate them using a series of MILPs. We deploy the Stochastic Dual Dynamic integer Programming (SDDiP) method for solving the problem under the three ambiguity sets with risk-neutral or risk-averse objective functions, and conduct numerical studies on multistage facility-location instances having diverse sizes under different parameter and uncertainty settings. Furthermore, our results show that the SDDiP quickly finds optimal solutions for moderate-sized instances under the first two ambiguity sets, and also finds good approximate bounds for the multistage MISDPs derived under the third ambiguity set. We also demonstrate the efficacy of incorporating decision-dependent distributional ambiguity in multistage decision-making processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A framework for guiding sustainability assessment and on-farm strategic decision making

Responding to future challenges and societal needs, various actions are taken in agriculture to evolve towards more sustainable farming practices. These actions imply strategic choices and suppose adequate sustainability assessments to identify, measure, evaluate and communicate sustainable development. However, literature is scarce on the link between strategic decision making and sustainability assessment. As questions emerge on how, what and when to measure, the objective of this paper is to construct a framework for guiding sustainability assessment and on-farm strategic decision making. Qualitative research on own experiences from the past and a recent project revealed four categories of actual needs farmers, advisors and experts have regarding sustainability assessment: context, flexibility, focus on farm and farmer and communication. These stakeholders' needs are then incorporated into a two-dimensional framework that marries the intrinsic complexity of sustainability assessment tools and the time frame of strategic decision making. The framework allows a farm-specific and flexible approach leading to harmonized actions towards sustainable farming. As this framework is mainly a procedural instrument to guide the use of sustainability assessment tools within strategic decision making, it fits to incorporate, even guide, future research on sustainability assessment tools themselves and on their adoption on farms. - Highlights: • How to link sustainability assessment and on-farm strategic decision making is unclear. • Two-dimensional framework incorporating stakeholders' needs regarding sustainability assessment • Linking complexity of sustainability assessment tools and the time frame of strategic decision making • Farm-specific and flexible approach to harmonize action towards sustainable farming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Distributionally robust facility location problem under decision-dependent stochastic demand

While the traditional facility location problem considers exogenous demand, in some applications, locations of facilities could affect the willingness of customers to use certain types of services, e.g., carsharing, and therefore they also affect realizations of random demand. Moreover, a decision maker may not know the exact distribution of such endogenous demand and how it is affected by location choices. In this paper, we consider a distributionally robust facility location problem, in which we interpret the moments of stochastic demand as functions of facility-location decisions. We reformulate a two-stage decision-dependent distributionally robust optimization model as a monolithic formulation, and then derive exact mixed-integer linear programming reformulation as well as valid inequalities when the means and variances of demand are piecewise linear functions of location solutions. We conduct extensive computational studies, in which we compare our model with a decision-dependent deterministic model, as well as stochastic programming and distributionally robust models without the decision-dependent assumption. Here, the results show superior performance of our approach with remarkable improvement in profit and quality of service under various settings, in addition to computational speed-ups given by formulation enhancements. These results draw attention to the need of considering the impact of location decisions on customer demand within this strategic-level planning problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. Furthermore, the MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.

42 ENGINEERING↗
DOE OSTI2015

Multiple stakeholders in multi-criteria decision-making in the context of Municipal Solid Waste Management: A review

Highlights: • We review Municipal Solid Waste Management studies with focus on multiple stakeholders. • We focus on studies with multi-criteria decision analysis methods and discover their trends. • Most studies do not offer solutions for situations where stakeholders compete for more benefits or have unequal voting powers. • Governments and experts are the most participated stakeholders and AHP is the most dominant method. - Abstract: Municipal Solid Waste Management (MSWM) is a complicated process that involves multiple environmental and socio-economic criteria. Decision-makers look for decision support frameworks that can guide in defining alternatives, relevant criteria and their weights, and finding a suitable solution. In addition, decision-making in MSWM problems such as finding proper waste treatment locations or strategies often requires multiple stakeholders such as government, municipalities, industries, experts, and/or general public to get involved. Multi-criteria Decision Analysis (MCDA) is the most popular framework employed in previous studies on MSWM; MCDA methods help multiple stakeholders evaluate the often conflicting criteria, communicate their different preferences, and rank or prioritize MSWM strategies to finally agree on some elements of these strategies and make an applicable decision. This paper reviews and brings together research on the application of MCDA for solving MSWM problems with more focus on the studies that have considered multiple stakeholders and offers solutions for such problems. Results of this study show that AHP is the most common approach in consideration of multiple stakeholders and experts and governments/municipalities are the most common participants in these studies.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

SU-E-J-04: A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

Purpose: To develop a decision support tool to predict a patient's potential overall survival (OS) and radiation induced toxicity (RIT) based on clinical factors and responses during the course of radiotherapy, and suggest appropriate radiation dose adjustments to improve therapeutic effect. Methods: Important relationships between a patient's basic information and their clinical features before and during the radiation treatment are identified from historical clinical data by using statistical learning and data mining approaches. During each treatment period, a data analysis (DA) module predicts radiotherapy features such as time to local progression (TTLP), time to distant metastases (TTDM), radiation toxicity to different organs, etc., under possible future treatment plans based on patient specifics or responses. An information fusion (IF) module estimates intervals for a patient's OS and the probabilities of RIT from a treatment plan by integrating the outcomes of module DA. A decision making (DM) module calculates “satisfaction” with the predicted radiation outcome based on trade-offs between OS and RIT, and finds the best treatment plan for the next time period via multi-criteria optimization. Results: Using physical and biological data from 130 lung cancer patients as our test bed, we were able to train and implement the 3 modules of our decision support tool. Examples demonstrate how it can help predict a new patient's potential OS and RIT with different radiation dose plans along with how these combinations change with dose, thus presenting a range of satisfaction/utility for use in individualized decision support. Conclusion: Although the decision support tool is currently developed from a small patient sample size, it shows the potential for the improvement of each patient's satisfaction in personalized radiation therapy. The radiation treatment outcome prediction and decision making model needs to be evaluated with more patients and demonstrated for use in radiation treatments for other cancers. P01-CA59827;R01CA142840.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m–km, days–weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

60 APPLIED LIFE SCIENCES↗