scholarly journals A Qualitative Study on the US Forest Service’s Risk Management Assistance Efforts to Improve Wildfire Decision-Making

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 344
Author(s):  
Courtney A. Schultz ◽  
Lauren F. Miller ◽  
Sarah Michelle Greiner ◽  
Chad Kooistra
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Decision Support ◽  
Qualitative Study ◽  
Fire Management ◽  
Perceived Value ◽  
Forest Service ◽  
The Us ◽  
Decision Biases ◽  
Line Officers

To support improved wildfire incident decision-making, in 2017 the US Forest Service (Forest Service) implemented risk-informed tools and processes, together known as Risk Management Assistance (RMA). The Forest Service is developing tools such as RMA to improve wildfire decision-making and implements these tools in complex organizational environments. We assessed the perceived value of RMA and factors that affected its use to inform the literature on decision support for fire management. We sought to answer two questions: (1) What was the perceived value of RMA for line officers who received it?; and (2) What factors affected how RMA was received and used during wildland fire events? We conducted a qualitative study involving semi-structured interviews with decision-makers to understand the contextualized and interrelated factors that affect wildfire decision-making and the uptake of a decision-support intervention such as RMA. We used a thematic coding process to analyze our data according to our questions. RMA increased line officers’ ability to communicate the rationale underlying their decisions more clearly and transparently to their colleagues and partners. Our interviewees generally said that RMA data analytics were valuable but did not lead to changes in their decisions. Line officer personality, pre-season exposure to RMA, local political dynamics and conditions, and decision biases affected the use of RMA. Our findings reveal the complexities of embracing risk management, not only in the context of US federal fire management, but also in other similar emergency management contexts. Attention will need to be paid to existing decision biases, integration of risk management approaches in the interagency context, and the importance of knowledge brokers to connect across internal organizational groups. Our findings contribute to the literature on managing change in public organizations, specifically in emergency decision-making contexts such as fire management.

scholarly journals Towards enhanced risk management: planning, decision making and monitoring of US wildfire response

2017 ◽  
Vol 26 (7) ◽  
pp. 551 ◽  
Author(s):  
Christopher J. Dunn ◽  
David E. Calkin ◽  
Matthew P. Thompson
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Fire Management ◽  
The United States ◽  
Management Decision ◽  
Current Response ◽  
Ongoing Research ◽  
Response Strategies ◽  
Management Decision Making ◽  
Response Decisions

Wildfire’s economic, ecological and social impacts are on the rise, fostering the realisation that business-as-usual fire management in the United States is not sustainable. Current response strategies may be inefficient and contributing to unnecessary responder exposure to hazardous conditions, but significant knowledge gaps constrain clear and comprehensive descriptions of how changes in response strategies and tactics may improve outcomes. As such, we convened a special session at an international wildfire conference to synthesise ongoing research focused on obtaining a better understanding of wildfire response decisions and actions. This special issue provides a collection of research that builds on those discussions. Four papers focus on strategic planning and decision making, three papers on use and effectiveness of suppression resources and two papers on allocation and movement of suppression resources. Here we summarise some of the key findings from these papers in the context of risk-informed decision making. This collection illustrates the value of a risk management framework for improving wildfire response safety and effectiveness, for enhancing fire management decision making and for ushering in a new fire management paradigm.

Science-policy in environmental and health risk assessment: if we cannot do without, can we do better?

2006 ◽  
Vol 25 (1) ◽  
pp. 29-43 ◽  
Author(s):  
P F Ricci ◽  
L A Cox ◽  
T R MacDonald
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Risk Management ◽  
Health Risk ◽  
Human Health ◽  
Empirical Evidence ◽  
Health Risk Assessment ◽  
Science Policy ◽  
The Us ◽  
Us Epa

How can empirical evidence of adverse effects from exposure to noxious agents, which is often incomplete and uncertain, be used most appropriately to protect human health? We examine several important questions on the best uses of empirical evidence in regulatory risk management decision–making raised by the US Environmental Protection Agency (EPA)'s science–policy concerning uncertainty and variability in human health risk assessment. In our view, the US EPA (and other agencies that have adopted similar views of risk management) can often improve decision–making by decreasing reliance on default values and assumptions, particularly when causation is uncertain. This can be achieved by more fully exploiting decision–theoretic methods and criteria that explicitly account for uncertain, possibly conflicting scientific beliefs and that can be fully studied by advocates and adversaries of a policy choice, in administrative decision–making involving risk assessment. The substitution of decision–theoretic frameworks for default assumption–driven policies also allows stakeholder attitudes toward risk to be incorporated into policy debates, so that the public and risk managers can more explicitly identify the roles of risk–aversion or other attitudes toward risk and uncertainty in policy recommendations. Decision theory provides a sound scientific way explicitly to account for new knowledge and its effects on eventual policy choices. Although these improvements can complicate regulatory analyses, simplifying default assumptions can create substantial costs to society and can prematurely cut off consideration of new scientific insights (e.g., possible beneficial health effects from exposure to sufficiently low ‘hormetic’ doses of some agents). In many cases, the administrative burden of applying decision–analytic methods is likely to be more than offset by improved effectiveness of regulations in achieving desired goals. Because many foreign jurisdictions adopt US EPA reasoning and methods of risk analysis, it may be especially valuable to incorporate decision–theoretic principles that transcend local differences among jurisdictions.

scholarly journals Developing the US Wildland Fire Decision Support System

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Erin K. Noonan-Wright ◽  
Tonja S. Opperman ◽  
Mark A. Finney ◽  
G. Thomas Zimmerman ◽  
Robert C. Seli ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Program Effectiveness ◽  
Fire Management ◽  
Wildland Fire ◽  
The United States ◽  
Informed Decision ◽  
Informed Decision Making

A new decision support tool, the Wildland Fire Decision Support System (WFDSS) has been developed to support risk-informed decision-making for individual fires in the United States. WFDSS accesses national weather data and forecasts, fire behavior prediction, economic assessment, smoke management assessment, and landscape databases to efficiently formulate and apply information to the decision making process. Risk-informed decision-making is becoming increasingly important as a means of improving fire management and offers substantial opportunities to benefit natural and community resource protection, management response effectiveness, firefighter resource use and exposure, and, possibly, suppression costs. This paper reviews the development, structure, and function of WFDSS, and how it contributes to increased flexibility and agility in decision making, leading to improved fire management program effectiveness.

A structural design of clinical decision support system for chronic diseases risk management

Open Medicine ◽  
2007 ◽  
Vol 2 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Chi-Chang Chang ◽  
Chuen-Sheng Cheng
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Structural Design ◽  
Clinical Decision Support System ◽  
Clinical Decision Making ◽  
Clinical Decision

AbstractIn clinical decision making, the event of primary interest is recurrent, so that for a given unit the event could be observed more than once during the study. In general, the successive times between failures of human physiological systems are not necessarily identically distributed. However, if any critical deterioration is detected, then the decision of when to take thei ntervention, given the costs of diagnosis and therapeutics, is of fundamental importance This paper develops a possible structural design of clinical decision support system (CDSS) by considering the sensitivity analysis as well as the optimal prior and posterior decisions for chronic diseases risk management. Indeed, Bayesian inference of a nonhomogeneous Poisson process with three different failure models (linear, exponential, and power law) were considered, and the effects of the scale factor and the aging rate of these models were investigated. In addition, we illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease. The proposed structural design of CDSS facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert’s opinions and the sampling information which will furnish decision makers with valuable support for quality clinical decision making.

scholarly journals Assessing the role of short-term weather forecasts in fire manager tactical decision-making: a choice experiment

Fire Ecology ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Claire E. Rapp ◽  
Robyn S. Wilson ◽  
Eric L. Toman ◽  
W. Matt Jolly
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Choice Experiment ◽  
Fire Management ◽  
Fire Weather ◽  
Decision Support Tools ◽  
Weather Forecasts ◽  
Weather Information ◽  
Support Tools ◽  
In Fire

Abstract Background Weather plays an integral role in fire management due to the direct and indirect effects it has on fire behavior. However, fire managers may not use all information available to them during the decision-making process, instead utilizing mental shortcuts that can bias decision-making. Thus, it is important to evaluate if (and how) fire managers use information like weather forecasts when making tactical decisions. We explore USDA Forest Service fire manager confidence in relative humidity, precipitation, and wind models. We then use a choice experiment where key weather attributes were varied to explore how sensitive fire managers were to changes in specific weather variables when choosing to directly or indirectly attack a fire that is transitioning to extended attack. Results Respondents were less confident in the accuracy of wind and precipitation forecasts than relative humidity or weather forecasts more generally. The influence of weather information on the decision depended on the framing used in the choice experiment; specifically, whether respondents were told the initial strategy had been to directly or indirectly attack the fire. Across conditions, fire managers generally preferred to indirectly attack the fire. Decisions about the tactics to apply going forward were more sensitive to time in season when the fire was occurring and wind and precipitation forecasts than to other attributes. Conclusions The results have implications for the design of decision support tools developed to support fire management. Results suggest how fire managers’ use of fire weather information to evaluate forecast conditions and adjust future management decisions may vary depending on the management decision already in place. If fire weather-based decision support tools are to support the use of the best available information to make fire management decisions, careful attention may be needed to debias any effect of prior decisions. For example, decision support tools may encourage users to “consider the opposite,” i.e., consider if they would react differently if different initial decision with similar conditions were in place. The results also highlight the potential importance of either improving wind and precipitation forecast models or improving confidence in existing models.

scholarly journals Are robots perceived as good decision makers? A study investigating trust and preference of robotic and human linesman-referees in football

2021 ◽  
Vol 12 (1) ◽  
pp. 287-296
Author(s):  
Kaustav Das ◽  
Yixiao Wang ◽  
Keith E. Green
Keyword(s):  
Decision Making ◽  
Qualitative Study ◽  
Decision Makers ◽  
Exposure Level ◽  
Robot Design ◽  
Good Decision ◽  
Online Study ◽  
The Us ◽  
Explainable Ai ◽  
Different Types

Abstract Increasingly, robots are decision makers in manufacturing, finance, medicine, and other areas, but the technology may not be trusted enough for reasons such as gaps between expectation and competency, challenges in explainable AI, users’ exposure level to the technology, etc. To investigate the trust issues between users and robots, the authors employed in this study, the case of robots making decisions in football (or “soccer” as it is known in the US) games as referees. More specifically, we presented a study on how the appearance of a human and three robotic linesmen (as presented in a study by Malle et al.) impacts fans’ trust and preference for them. Our online study with 104 participants finds a positive correlation between “Trust” and “Preference” for humanoid and human linesmen, but not for “AI” and “mechanical” linesmen. Although no significant trust differences were observed for different types of linesmen, participants do prefer human linesman to mechanical and humanoid linesmen. Our qualitative study further validated these quantitative findings by probing possible reasons for people’s preference: when the appearance of a linesman is not humanlike, people focus less on the trust issues but more on other reasons for their linesman preference such as efficiency, stability, and minimal robot design. These findings provide important insights for the design of trustworthy decision-making robots which are increasingly integrated to more and more aspects of our everyday lives.

scholarly journals Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study (Preprint)

2021 ◽  
Author(s):  
Stina Matthiesen ◽  
Søren Zöga Diederichsen ◽  
Mikkel Klitzing Hartmann Hansen ◽  
Christina Villumsen ◽  
Mats Christian Højbjerg Lassen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Qualitative Study ◽  
Real World ◽  
Remote Monitoring ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Great Promise ◽  
Prediction Tool

BACKGROUND Artificial intelligence (AI), such as machine learning (ML), shows great promise for improving clinical decision-making in cardiac diseases by outperforming statistical-based models. However, few AI-based tools have been implemented in cardiology clinics because of the sociotechnical challenges during transitioning from algorithm development to real-world implementation. OBJECTIVE This study explored how an ML-based tool for predicting ventricular tachycardia and ventricular fibrillation (VT/VF) could support clinical decision-making in the remote monitoring of patients with an implantable cardioverter defibrillator (ICD). METHODS Seven experienced electrophysiologists participated in a near-live feasibility and qualitative study, which included walkthroughs of 5 blinded retrospective patient cases, use of the prediction tool, and questionnaires and interview questions. All sessions were video recorded, and sessions evaluating the prediction tool were transcribed verbatim. Data were analyzed through an inductive qualitative approach based on grounded theory. RESULTS The prediction tool was found to have potential for supporting decision-making in ICD remote monitoring by providing reassurance, increasing confidence, acting as a second opinion, reducing information search time, and enabling delegation of decisions to nurses and technicians. However, the prediction tool did not lead to changes in clinical action and was found less useful in cases where the quality of data was poor or when VT/VF predictions were found to be irrelevant for evaluating the patient. CONCLUSIONS When transitioning from AI development to testing its feasibility for clinical implementation, we need to consider the following: expectations must be aligned with the intended use of AI; trust in the prediction tool is likely to emerge from real-world use; and AI accuracy is relational and dependent on available information and local workflows. Addressing the sociotechnical gap between the development and implementation of clinical decision-support tools based on ML in cardiac care is essential for succeeding with adoption. It is suggested to include clinical end-users, clinical contexts, and workflows throughout the overall iterative approach to design, development, and implementation.

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