Adaptive decision support systems

Fazlollahi, Bijan; Parikh, Mihir A.; Verma, Sameer

doi:10.1016/s0167-9236(97)00014-6

Cited by 42 publications

(18 citation statements)

References 19 publications

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“…5 Numerous studies have conceptualized ADSSs that support problem formulation, interpretation of the dynamic problem space, and final decision outcomes in response to environments that are known to change within a single decision or across multiple decisions. 5 Piramuthu & Shaw 10 for instance, suggest that an ADSS must have a learning component that can incrementally renew its knowledge base through continuous feedback from the environment. Others have proposed that ADSS must adapt to users' personalities 11 and decision support needs.…”

Section: Adaptive Dssmentioning

confidence: 99%

“…Our intent is not to provide a technical design of AFDSS components, as has been done in. 5,10 Rather, considering the scope of this study, we focus on how the CST could feed into specific components of AFDSS. In essence, we specify the broad frameworks proposed by earlier studies to the context of FDSS.…”

Section: A Framework For Adaptive Forecasting Decision Support Systemsmentioning

confidence: 99%

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Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

Adya

Lusk

2016

Decision Support Systems

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Abstract:Evidence from forecasting research gives reason to believe that understanding time series complexity can enable design of adaptive forecasting decision support systems (FDSSs) to positively support forecasting behaviors and accuracy of outcomes. Yet, such FDSS design capabilities have not been formally explored because there exists no systematic approach to identifying series complexity. This study describes the NOT THE PUBLISHED VERSION; this is the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page.Decision Support Systems, Vol 83 (March 2016): pg. 70-82. DOI. This article is © Elsevier and permission has been granted for this version to appear in e-Publications@Marquette. Elsevier does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Elsevier.2 development and validation of a rule-based complexity scoring technique (CST) that generates a complexity score for time series using 12 rules that rely on 14 features of series. The rule-based schema was developed on 74 series and validated on 52 holdback series using well-accepted forecasting methods as benchmarks. A supporting experimental validation was conducted with 14 participants who generated 336 structured judgmental forecasts for sets of series classified as simple or complex by the CST. Benchmark comparisons validated the CST by confirming, as hypothesized, that forecasting accuracy was lower for series scored by the technique as complex when compared to the accuracy of those scored as simple. The study concludes with a comprehensive framework for design of FDSS that can integrate the CST to adaptively support forecasters under varied conditions of series complexity. The framework is founded on the concepts of restrictiveness and guidance and offers specific recommendations on how these elements can be built in FDSS to support complexity.

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Section: Adaptive Dssmentioning

confidence: 99%

Section: A Framework For Adaptive Forecasting Decision Support Systemsmentioning

confidence: 99%

Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

Adya

Lusk

2016

Decision Support Systems

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“…Researchers have long realised that DSS need to offer several forms of support to decision-makers. Criticising decisions, monitoring decision-makers' actions and providing appropriate warnings are some of them (Fazlollahi et al 1997). For example, in the context of decision-support, Vahidov and Elrod (1999) propose a framework for developing positive and negative critique agents.…”

Section: Decision-support Characteristics Required In Debiasing Softwmentioning

confidence: 99%

An agent-based debiasing framework for investment decision-support systems

Bhandari¹,

Hassanein²

2012

Behaviour & Information Technology

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Though researchers agree on the role of psychological forces on individuals' decision-making and emphasise the need for developing decision-support systems (DSS) that make individuals aware of these forces, a framework that can guide us in building such systems is still non-existent. In this article, we attempt to bridge this gap by proposing an agent-based debiasing framework for developing investment DSS. Identifying the primary characteristics of major biases influencing investment decisions through a thorough literature review, we propose a taxonomy to categorise them as cognitive, affective or conative. Cognitive biases are information-processing biases. Affective biases involve general moods and emotions. Conative biases are relatively stable personality traits such as overconfidence and inertia. We then outline debiasing strategies for each of these bias categories and identify decision-support characteristics necessary in software agents to carry out the appropriate debiasing tasks. An agentbased DSS architecture is then proposed, and a detailed trading example triggering a sample bias detection and debiasing algorithm is discussed to demonstrate the feasibility of the proposed system.

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“…However, one of the most flexible support systems was presented by Fazlollahi, Parikh, and Verma (1997). They suggested designing a DSS that would be capable of monitoring the decision-making process and of recording the history of interaction.…”

Section: Flexibilitymentioning

confidence: 99%

Revisiting Decision Support Systems for Cognitive Readiness

Cegarra

Wezel

2012

Journal of Cognitive Engineering and Decision Making

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In this article, the authors focus on scheduling situations. Because of their unstructured nature and hard combinatorial complexity, scheduling situations have always been a predominant application area for decision support systems (DSSes). After setting out the generic characteristics of a DSS, the authors summarize some of their known limitations in scheduling situations. They argue that scheduling situations focus excessively on performance and effectiveness and neglect the potential of human schedulers. The authors then review field studies to understand the way in which humans perform scheduling, demonstrating the importance of cognitive readiness if schedulers are to schedule efficiently. On the basis of design issues and field studies, the authors go on to refine three factors that influence cognitive readiness that therefore need to be taken into account when designing a DSS, namely, adaptability, flexibility, and acceptability. Finally, the authors show how these concepts can be used to assess DSS user interfaces and algorithms from a cognitive readiness perspective.

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Adaptive decision support systems

Cited by 42 publications

References 19 publications

Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

An agent-based debiasing framework for investment decision-support systems

Revisiting Decision Support Systems for Cognitive Readiness

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