The enhancement of operator situation awareness (SA) has become a major designgoal for those developing operator interfaces, automation concepts and training programs ina wide variety of fields, including aircraft, air traffic control, power plants, and advancedmanufacturing systems. This dramatic growth in interest in SA, beginning in the mid-1980’s and accelerating through the 1990’s, was spurred on by many factors, chief amongthem the challenges of a new class of technology. One can easily see that situation awareness has always been needed in order forpeople to perform tasks effectively. Prehistoric man undoubtedly needed to be aware ofmany cues in his environment in order to successfully hunt and keep from being hunted.For many years, having good situation awareness was largely a matter of training andexperience — learning the important cues to watch for and what they meant. With the advent of the machine age, our emphasis shifted to creating a new class oftools to help people perform tasks, largely those physical in nature. The computer age andnow the information age have followed rapidly on the heals of basic mechanization. Thetools provided are no longer simple; they are amazingly complex, focused on not justphysical tasks, but elaborate perceptual and cognitive tasks as well. The pilot of today’saircraft, the air traffic controller, the power plant operator, the anesthesiologist: all mustperceive and comprehend a dazzling array of data which is often changing very rapidly. Ihave taken to calling this challenge the information gap (Figure 1).Today’s systems are capable of producing a huge amount of data, both on the statusof their own components, and on the status of the external environment. Due toachievements in various types of datalink and internet technologies, systems can alsoprovide data on almost anything anywhere in the world. The problem with today’s systemsis not a lack of information, but finding what is needed when it is needed.Unfortunately, in the face of this torrent of data, many operators may be even lessinformed than ever before. This is because there is a huge gap between the tons of databeing produced and disseminated and people’s ability to find the bits that are needed andprocess them together with the other bits to arrive at the actual information that is requiredfor their decisions. This information must be integrated and interpreted correctly as well; afrequently tricky task. This problem is real and ongoing, whether the job is in the cockpitor behind a desk. It is becoming widely recognized that more data does not equal moreinformation. Issues of automation and “intelligent systems” have frequently onlyexacerbated the problem, rather than aided it (Endsley & Kiris, 1995; Sarter & Woods,1995).The criteria for what we are seeking from system designs have correspondinglychanged. In addition to designing systems that provide the operator with the neededinformation and capabilities, we must also insure that it is provided in a way that is useablecognitively as well as physically. We want to know how well the system design supportsthe operator’s ability to get the needed information under dynamic operational constraints.(i.e. How well does it bridge the information gap?) This design objective and measure ofmerit has been termed situation awareness.

/pdf/theoretical-underpinnings-of-situation-awareness-a-critical-2zswqslgyi.pdf

Theoretical Underpinnings of Situation Awareness, A Critical Review

Eutrophication: A new wine in an old bottle?

This commentary examines an "inconvenient truth" that phosphorus (P)-based nutrient mitigation, long regarded as the key tool in eutrophication management, in many cases has not yet yielded the desired reductions in water quality and nuisance algal growth in rivers and their associated downstream ecosystems. We examine why the water quality and aquatic ecology have not recovered, in some case after two decades or more of reduced P inputs, including (i) legacies of past land-use management, (ii) decoupling of algal growth responses to river P loading in eutrophically impaired rivers; and (iii) recovery trajectories, which may be nonlinear and characterized by thresholds and alternative stable states. It is possible that baselines have shifted and that some disturbed river environments may never return to predisturbance conditions or may require P reductions below those that originally triggered ecological degradation. We discuss the practical implications of setting P-based nutrient criteria to protect and improve river water quality and ecology, drawing on a case study from the Red River Basin in the United States. We conclude that the challenges facing nutrient management and eutrophication control bear the hallmarks of "postnormal" science, where uncertainties are large, management intervention is urgently required, and decision stakes are high. We argue a case for a more holistic approach to eutrophication management that includes more sophisticated regime-based nutrient criteria and considers other nutrient and pollutant controls and river restoration (e.g., physical habitat and functional food web interactions) to promote more resilient water quality and ecosystem functioning along the land-freshwater continuum.

Phosphorus Mitigation to Control River Eutrophication: Murky Waters, Inconvenient Truths, and “Postnormal” Science

Diagnostic hypothesis-generation processes are ubiquitous in human reasoning. For example, clinicians generate disease hypotheses to explain symptoms and help guide treatment, auditors generate hypotheses for identifying sources of accounting errors, and laypeople generate hypotheses to explain patterns of information (i.e., data) in the environment. The authors introduce a general model of human judgment aimed at describing how people generate hypotheses from memory and how these hypotheses serve as the basis of probability judgment and hypothesis testing. In 3 simulation studies, the authors illustrate the properties of the model, as well as its applicability to explaining several common findings in judgment and decision making, including how errors and biases in hypothesis generation can cascade into errors and biases in judgment.

http://www.damlab.umd.edu/PDF%20articles/2008%20Thomas,Dougherty,Sprenger,Harbison_PsycReview.pdf

Diagnostic hypothesis generation and human judgment.

Human activities both depend upon and have consequences on the environment. Environmental risk assessment (ERA) is a process of estimating the probability and consequences of the adverse effects of human activities and other stressors on the environment. Bayesian networks (BNs) can synthesize different types of knowledge and explicitly account for the probabilities of different scenarios, therefore offering a useful tool for ERA. Their use in formal ERA practice has not been evaluated, however, despite their increasing popularity in environmental modeling. This paper reviews the use of BNs in ERA based on peer-reviewed publications. Following a systematic mapping protocol, we identified studies in which BNs have been used in an environmental risk context and evaluated the scope, technical aspects, and use of the models and their results. The review shows that BNs have been applied in ERA, particularly in recent years, and that there is room to develop both the model implementation and participatory modeling practices. Based on this review and the authors' experience, we outline general guidelines and development ideas for using BNs in ERA. Integr Environ Assess Manag 2021;17:62-78. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Bayesian Networks in Environmental Risk Assessment: A Review

In an experiment using the Patriot air defense training simulator, we found that the order in which the same information was presented to experienced Patriot officers significantly affected their probability estimates, identification judgments, and engagement decisions. In all cases the results represented a primacy effect, contradicting the recency effect predictions of the Hogarth and Einhorn (1992) model. In the present study we found that early in an aircraft's track history, the order effect is caused by the overweighting of prior information; later in the history, the late order effect is caused by participants reinterpreting the meaning of information based on what preceded it. These findings help to explain the differential effectiveness of a proposed display and the importance of naturalistic research for testing the generality of models of decision making.

Research with Patriot Air Defense Officers: Examining Information Order Effects:

1. Stream reaches found to be impaired by physical, chemical or biological assessment generally
are associated with greater extent of urban and agricultural land uses, and lesser amount of
undeveloped lands. However, because stream condition commonly is influenced by multiple
stressors as well as underlying natural gradients, it can be difficult to establish mechanistic
relationships between altered land use and impaired stream condition.
2. This study explores the use of Bayesian belief networks (BBNs) to model presumed causal
relationships between stressors and response variables. A BBN depicts the chain of causal
relationships resulting in some outcome such as environmental impairment and can make use of
evidence from expert judgment as well as observational and experimental data.
3. Three case studies illustrate the flexibility of BBN models. Expert elicitation in a workshop
setting was employed to model the effects of sedimentation on benthic invertebrates. A second
example used empirical data to explore the influence of natural and anthropogenic gradients on
stream habitat in a highly agricultural watershed. The third application drew on several forms of
evidence to develop a decision support tool linking grazing and forestry practices to stream reach
condition.
4. Although data limitations challenge model development and our ability to narrow the range of
possible outcomes, model formulation forces participants to conceptualise causal mechanisms and
consider how to resolve data shortfalls. With sufficient effort and resources, models with greater
evidentiary strength from observational and experimental data may become practical tools to
guide management decisions.
5. Such models may be used to explore possible outcomes associated with a range of scenarios,
thus benefiting management decision-making, and to improve insight into likely causal
relationships.

/pdf/investigating-the-relationships-between-environmental-2m3kuazcot.pdf

Investigating the relationships between environmental stressors and stream condition using Bayesian belief networks

There is a growing desire to develop effective and efficient computational methods and tools that facilitate environmental analysis, evaluation and problem solving. Environmental problems of interest may include concerns as apparently dissimilar as revitalization of contaminated land, and effective management of inland and coastal waters. The approach to effective problem solving in both of these examples can involve the development of what are commonly called Decision Support Systems (DSSs).

Basic Steps for the Development of Decision Support Systems

Assessing the Probability of Inadvertent Human Intrusion at the Nevada Test Site Radioactive Waste Management Sites

SMARTe (Sustainable Management Approaches and Revitalization Tools – electronic) is a free, open-source, web-based, decision-support system that helps revitalization stakeholders (communities, developers, regulators, etc.) overcome obstacles to revitalization and allows them to develop and evaluate future reuse scenarios for potentially contaminated sites (e.g., brownfields). SMARTe currently has four primary components:


eDocument: Through a series of menus (i.e., table of contents), SMARTe provides information, resources, links and case studies for all aspects of revitalization including visioning/planning, environmental risk management, social acceptance, and economic viability.


Tool Box: SMARTe contains stand-alone analysis tools and checklists for many aspects of revitalization including human health risk, site characterization and monitoring data analysis, net revenues, public involvement, and selecting a lawyer, environmental consultant, and developer.


My Project: A project-specific, password protected portion of SMARTe contains an integrated decision support system that helps users evaluate and assess both market and non-market costs and benefits of potential reuse options (i.e., future alternatives). This part of SMARTe also enables users to develop a revitalization plan.


Search Engine: A search bar available on the SMARTe banner enables users to directly search for specific information within the system.

SMARTe: An MCDA Approach to Revitalize Communities and Restore the Environment

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