The Impact of DSS Use and Information Load on Errors and Decision Quality

This paper uses a laboratory experiment to examine the effect of DSS use on the decision maker‘s error patterns and decision quality. The DSS used in our experiments is the widely used Expert Choice (EC) implementation of the Analytic Hierarchy Process. Perhaps surprisingly, our experiments do not provide general support for the often tacit assumption that the use of a DSS such as EC improves decision quality. Rather, we find that, whereas a DSS can help decision makers develop a better understanding of the essence of a decision problem and can reduce logical errors (especially if the information load is high), it is also susceptible to introducing accidental effects such as mechanical errors. In some cases, as in our study, the accidental errors may outweigh the benefits of using a DSS, leading to lower quality decisions

Principles For Aiding Complex Military Decision Making

Paper presented to the Second International Command and Control Research and Technology Symposium, Monterey, Ca.The Tactical Decision Making Under Stress (TADMUS) program is being conducted to apply recent developments in decision theory and human-system interaction technology to the design of a decision support system for enhancing tactical decision making under the highly complex conditions involved in anti-air warfare scenarios in littoral environments. Our goal is to present decision support information in a format that minimizes any mismatches between the cognitive characteristics of the human decision maker and the design and response characteristics of the decision support system. Decision makers are presented with decision support tools which parallel the cognitive strategies they already employ, thus reducing the number of decision making errors. Hence, prototype display development has been based on decision making models postulated by naturalistic decision-making theory. Incorporating current human-system interaction design principles is expected to reduce cognitive processing demands and thereby mitigate decision errors caused by cognitive overload, which have been documented through research and experimentation. Topics include a discussion of: (1) the theoretical background for the TADMUS program; (2) a description of the cognitive tasks performed; (3) the decision support and human- system interaction design principles incorporated to reduce the cognitive processing load on the decision maker; and (4) a brief description of the types of errors made by decision makers and interpretations of the cause of these errors based on the cognitive psychology literature.Funding for the research cited in this paper was received from the Cognitive and Neural Science and Technology Division of the Office of Naval Research

Decision Support Systems and its Impact on Organization Empowerment Field Study at Jordanian Universities

The concept and applications of Decision support systems (DSS) help companies to make better business decisions in order to attain the organizational objectives in an efficient way. Ample evidence indicated that building empowerment is important for having access to information and resources, thinking critically, being effective, create effecting change and building confidence. The study aims to identify the level of DSS applications and empowerment in the Jordanian universities, as well analyzing the impact of decision support systems on empowerment. The study developed a conceptual framework that consists of two parts which simulate the study model. The target population of the study comprised of all faculty members in the colleges of economics and business in the Jordanian universities (state and private). An equal stratified random sampling of (5) public universities and (5) private ones were taken, (150) surveys were distributed, (142) surveys were included in the analysis, (38) items were designed based on previous studies to meet the study objectives. The Study revealed that DSS generators had a significant effect at level  (P? 0.05) on organization empowerment in the Jordanian universities, also the study found a statistically significant effect of  DSS generators on personal and collective empowerment  in the Jordanian universities.   Keywords: Information systems, Decision support systems, Organization empowerment, Jordanian universities

Analytical enrichment: A target to source approach for missing requirements in decision support systems

Operational Systems collect transactional data and support the execution of business processes in an organization. These systems are often the data source for Decision Support Systems (DSS), i.e. analytical systems designed to aid business users in the decision-making process. For this reason, several problems in Operational Systems, such as missing data requirements or data quality issues, can lead to unfulfilled analytical needs of the DSS and, consequently, have a negative effect on the Decision Making Process since relevant business queries may not be answered. The objective of this study is to understand the impact of the integration of DSS requirements in the design of operational systems. To achieve this objective, this dissertation uses a real use case DSS to identify the missing requirements and develop a DSS prototype to demonstrate the positive impact on the Decision-Making process when these requirements are fulfilled. Throughout this development, ways of dealing with the various types of missing requirements are going to be addressed. Additionally, a methodology to evaluate the missing requirements is suggested, along with a proposal to classify and understand the missing requirements and how they can be dealt with. Also, the evaluation method is applied, and the developed prototype is compared to the baseline system in order to measure the impact. Finally, the benefits of this integration are shown, as well as other factors that can also constrain the DSS requirements.Os sistemas operacionais recolhem dados transacionais e apoiam a execução de processos de negócio numa organização. Estes sistemas são frequentemente a fonte de dados para os Sistemas de Apoio à Decisão (SAD), ou seja, sistemas analíticos concebidos para auxiliar os utilizadores empresariais no processo de tomada de decisão. Por esta razão, vários problemas nos Sistemas Operacionais, tais como requisitos de dados em falta ou questões de qualidade de dados, podem levar a necessidades analíticas não satisfeitas do SAD e, consequentemente, ter um efeito negativo no Processo de Tomada de Decisão, uma vez que as questões de negócio relevantes podem não ser respondidas. O objetivo do presente estudo é compreender o impacto da integração dos requisitos do SAD na conceção dos sistemas operacionais. Para atingir este objetivo, esta dissertação utiliza um caso de estudo real de um SAD para identificar os requisitos em falta e desenvolver um SAD protótipo para demonstrar o impacto positivo no processo de Tomada de Decisão quando estes requisitos são cumpridos. Ao longo deste desenvolvimento, as formas de lidar com os vários tipos de requisitos em falta serão abordadas. É também proposto um método de avaliação para compreender e categorizar os requisitos em falta e a forma como podem ser tratados. Além disso, o método de avaliação é aplicado, e o protótipo desenvolvido é comparado com o sistema de base, no sentido de medir o impacto. Finalmente, são mostrados os benefícios desta integração, bem como outros fatores que também podem limitar os requisitos do SAD

Building information modeling for facility managers

A Decision Support System (DSS) can help facility managers to improve building performance, occupants’ comfort, and energy efficiency during the Operation and Maintenance (O&M) phase. These DSSs are normally data-intensive and have specific data requirements. Building Information Modeling (BIM) has the potential to advance and transform facilities O&M by providing facility managers with a digitalized virtual environment that allows them to retrieve, analyze, and process such data. However, the implementation of BIM in O&M phases is still limited. The majority of issues in the BIM-O&M context lie in the interoperability between different software that requires different data structures and formats. In a BIM environment, there are issues associated with extracting, storing, managing, integrating, and disseminating data so that interoperability is assured. Considering the aforementioned aspects, the aim of this thesis is to enable interoperability between BIM models and the DSSs for building performance aspects such as building condition, maintenance, and occupants’ comfort. This integration automatizes the data transfer process which can assist Facility Management (FM) team in properly establishing the necessary measurements to moderate the negative consequences on buildings and thereby improve their performance and occupants’ comfort. The approach can also provide FM teams with an effective platform for data visualization in a user-friendly manner that can assist in integrating digital insights into FM decision-making processes and converting them into positive strategic actions. The proposed approach is validated in existing software as a case study. It is possible to demonstrate the applicability of this approach by ensuring that its interactions and outcomes are feasible using case studies. Case studies also identify how much the task efficiencies are in comparison with the manual method, helping facility managers to optimize operation strategies of buildings in order to enhance their performance. Verification tests are also performed on the information exported from a software program. The results demonstrate an efficiency increase in high-quality FM data collection for different kinds of DSS, reducing the time and effort that the FM team spends on searching for information and entering data. A Dynamo script is designed to allow administrators to include as much information as they wish in BIM models. Moreover, a novel approach is proposed to create a new category in BIM to assist public and business administrations with managing assets efficiently. In addition, building performance aspects can also be analyzed using the proposed method of integrating occupants' feedback into BIM models. By implementing the proposed approach, FM teams are able to correctly establish measurements which can be applied to mitigate the negative effects on buildings, thus improving their performance and enhancing their occupants’ comfort. Besides, the proposed approach enables BIM to be a more useful tool for visualization by using the most appropriate charts and formatting.Un Sistema de Soporte de decisiones (SSD) puede ayudar a los gestores de edificios a mejorar su rendimiento, su eficiencia energética y el confort de sus ocupantes. Para el buen funcionamiento de los SSD se requieren muchos datos. El Building Information Modeling (BIM) permite mejorar la gestión de las operaciones y el mantenimiento de los edificios al proporcionar un entorno virtual digitalizado que permite recuperar, analizar y procesar los datos requeridos por los SSD. Sin embargo, la implementación de BIM en las fases de Operación y Mantenimimento (O&M) aún es escasa. La mayoría de los problemas en el contexto de BIM-O&M radican en la interoperabilidad entre diferentes programas que requieren diferentes estructuras y formatos de datos. En un entorno BIM, existen problemas asociados a la extracción, el almacenamiento, la gestión, la integración y la difusión de datos para garantizar la interoperabilidad. Teniendo en cuenta los aspectos antes mencionados, el objetivo de esta tesis es facilitar la interoperabilidad entre los modelos BIM y los SSD relacionados con el rendimiento de los edificios, su estado de conservación y el confort de los ocupantes. Esta integración automatiza el proceso de transferencia de datos que puede ayudar a los gestores de edificios a establecer correctamente las medidas necesarias para mejorar su rendimiento y el confort de sus ocupantes. Esta integración también va a proporcionar a los gestores de edificios una plataforma eficaz para la visualización de datos de una manera fácil de usar que puede ayudar a integrar resultados de los SSD y convertirlos en acciones estratégicas. Para demostrar la aplicabilidad y la eficiencia de este integración, ésta se valida a través de casos de estudio. También se realizan pruebas de verificación sobre la información exportada en los diferentes sistemas. Los resultados demuestran un aumento de la eficiencia en la recopilación de datos de alta calidad para diferentes tipos de DSS, lo que reduce el tiempo y el esfuerzo que los gestores de edificios dedican a buscar información e introducir datos en la diferentes aplicaciones. Un script de Dynamo está diseñado para permitir que los gestores incluyan tanta información como deseen en los modelos BIM. Además, se propone un enfoque novedoso para crear una nueva categoría en BIM para ayudar a las administraciones públicas y empresariales a gestionar los activos de manera eficiente. Además, los aspectos del rendimiento del edificio también se pueden analizar utilizando el método propuesto de integrar los comentarios de los ocupantes en los modelos BIM. Al implementar el enfoque propuesto, los gestores de edificios pueden establecer correctamente las medidas que se pueden aplicar para mitigar los efectos negativos en los edificios, mejorando así su rendimiento y el confort de sus ocupantes. Además, la integración propuesta permite que BIM sea una herramienta más útil para la visualización mediante el uso de los gráficos y las opciones de formato más apropiados, guiando a la toma de decisiones para gestionar los edificiosPostprint (published version

The Effects of Information Load on Decision Making In a Decision Support Environment

The conflicting results of previous studies examining DSS effectiveness suggest that other factors may be affecting a user’s ability to process information. Several research studies in the marketing, accounting and psychology disciplines have examined the effects information load has on decision quality involving manual decision making tasks. Their results strongly indicate that decision-makers working under increased loads of information beyond an optimal point perform poorly or render poorer decisions. This study examines the relationship between information load and decision quality in a DSS (computer-aided problem solving) environment. The results suggest that in spite of information technology’ s support, information load can affect a user’s decisions

Absolute flux density calibrations of radio sources: 2.3 GHz

A detailed description of a NASA/JPL Deep Space Network program to improve S-band gain calibrations of large aperture antennas is reported. The program is considered unique in at least three ways; first, absolute gain calibrations of high quality suppressed-sidelobe dual mode horns first provide a high accuracy foundation to the foundation to the program. Second, a very careful transfer calibration technique using an artificial far-field coherent-wave source was used to accurately obtain the gain of one large (26 m) aperture. Third, using the calibrated large aperture directly, the absolute flux density of five selected galactic and extragalactic natural radio sources was determined with an absolute accuracy better than 2 percent, now quoted at the familiar 1 sigma confidence level. The follow-on considerations to apply these results to an operational network of ground antennas are discussed. It is concluded that absolute gain accuracies within + or - 0.30 to 0.40 db are possible, depending primarily on the repeatability (scatter) in the field data from Deep Space Network user stations

Automation bias and prescribing decision support – rates, mediators and mitigators

Purpose: Computerised clinical decision support systems (CDSS) are implemented within healthcare settings as a method to improve clinical decision quality, safety and effectiveness, and ultimately patient outcomes. Though CDSSs tend to improve practitioner performance and clinical outcomes, relatively little is known about specific impact of inaccurate CDSS output on clinicians. Although there is high heterogeneity between CDSS types and studies, reviews of the ability of CDSS to prevent medication errors through incorrect decisions have generally been consistently positive, working by improving clinical judgement and decision making. However, it is known that the occasional incorrect advice given may tempt users to reverse a correct decision, and thus introduce new errors. These systematic errors can stem from Automation Bias (AB), an effect which has had little investigation within the healthcare field, where users have a tendency to use automated advice heuristically. Research is required to assess the rate of AB, identify factors and situations involved in overreliance and propose says to mitigate risk and refine the appropriate usage of CDSS; this can provide information to promote awareness of the effect, and ensure the maximisation of the impact of benefits gained from the implementation of CDSS. Background: A broader literature review was carried out coupled with a systematic review of studies investigating the impact of automated decision support on user decisions over various clinical and non-clinical domains. This aimed to identify gaps in the literature and build an evidence-based model of reliance on Decision Support Systems (DSS), particularly a bias towards over-using automation. The literature review and systematic review revealed a number of postulates - that CDSS are socio-technical systems, and that factors involved in CDSS misuse can vary from overarching social or cultural factors, individual cognitive variables to more specific technology design issues. However, the systematic review revealed there is a paucity of deliberate empirical evidence for this effect. The reviews identified the variables involved in automation bias to develop a conceptual model of overreliance, the initial development of an ontology for AB, and ultimately inform an empirical study to investigate persuasive potential factors involved: task difficulty, time pressure, CDSS trust, decision confidence, CDSS experience and clinical experience. The domain of primary care prescribing was chosen within which to carry out an empirical study, due to the evidence supporting CDSS usefulness in prescribing, and the high rate of prescribing error. Empirical Study Methodology: Twenty simulated prescribing scenarios with associated correct and incorrect answers were developed and validated by prescribing experts. An online Clinical Decision Support Simulator was used to display scenarios to users. NHS General Practitioners (GPs) were contacted via emails through associates of the Centre for Health Informatics, and through a healthcare mailing list company. Twenty-six GPs participated in the empirical study. The study was designed so each participant viewed and gave prescriptions for 20 prescribing scenarios, 10 coded as “hard” and 10 coded as “medium” prescribing scenarios (N = 520 prescribing cases were answered overall). Scenarios were accompanied by correct advice 70% of the time, and incorrect advice 30% of the time (in equal proportions in either task difficulty condition). Both the order of scenario presentation and the correct/incorrect nature of advice were randomised to prevent order effects. The planned time pressure condition was dropped due to low response rate. Results: To compare with previous literature which took overall decisions into account, taking individual cases into account (N=520), the pre advice accuracy rate of the clinicians was 50.4%, which improved to 58.3% post advice. The CDSS improved the decision accuracy in 13.1% of prescribing cases. The rate of AB, as measured by decision switches from correct pre advice, to incorrect post advice was 5.2% of all cases at a CDSS accuracy rate of 70% - leading to a net improvement of 8%. However, the above by-case type of analysis may not enable generalisation of results (but illustrates rates in this specific situation); individual participant differences must be taken into account. By participant (N = 26) when advice was correct, decisions were more likely to be switched to a correct prescription, when advice was incorrect decisions were more likely to be switched to an incorrect prescription. There was a significant correlation between decision switching and AB error. By participant, more immediate factors such as trust in the specific CDSS, decision confidence, and task difficulty influenced rate of decision switching. Lower clinical experience was associated with more decision switching (but not higher AB rate). The rate of AB was somewhat problematic to analyse due to low number of instances – the effect could potentially have been greater. The between subjects effect of time pressure could not be investigated due to low response rate. Age, DSS experience and trust in CDSS generally were not significantly associated with decision switching. Conclusion: There is a gap in the current literature investigating inappropriate CDSS use, but the general literature supports an interactive multi-factorial aetiology for automation misuse. Automation bias is a consistent effect with various potential direct and indirect causal factors. It may be mitigated by altering advice characteristics to aid clinicians’ awareness of advice correctness and support their own informed judgement – this needs further empirical investigation. Users’ own clinical judgement must always be maintained, and systems should not be followed unquestioningly

Uncertainty In Measurements And Cognitive Engineering Analysis Of A Decision Support System For Power System Reconfiguration

Accuracy of the measurement data used for the decision making process or for shipboard operations and control is very important to ensure the reliability and survivability. The uncertainties present in measurement data need to be minimized for reliable system operation. In this work, a fuzzy logic based model is developed to deal with uncertainty in the meter data. Operational and historical parameters of the meters were used to determine a ‘trust’ value of individual meter. A fuzzy correction system for measurement data was used to generate an input dataset for a genetic algorithm based reconfiguration system. Additionally, with the goal of optimizing the performance of power system operator, the effects of Decision Support System (DSS) on the quality of decisions taken by the operator were examined. Unaided and aided interface prototypes were developed and usability tests were carried out on interface prototypes with users having knowledge of power systems