COMPUTER-BASED SUPPORT OF ORGANIZATIONAL DECISION MAKING (original) (raw)
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A conceptualization and general architecture of intelligent decision support systems
Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation.
The paper presents an attempt to conceptualize decision support and various generic subtasks and to develop a general architecture of intelligent decision support systems. We decompose the task of decision support into subtasks whose input, output, and function are characterized. This is based on a small number of concepts: besides "decision", the essential ones are "observation", "situation", "goal", "action", and "process", which are in turn defined using elementary concepts for characterizing the system under consideration, or our model thereof. This is not an academic exercise aiming at providing definitions, but a prerequisite for a generic architecture of decision support systems with interfaces for certain generic functions, the comparison of basic modules implementing these functions, and the configuration of systems from a set of such modules. The primary subtasks whose (intelligent) solution is heavily dependent on domain knowledge are situation assessment, i.e. inferring what is happening in a system from a set of observations, and therapy proposal, i.e. developing plans for interventions to achieve certain goals starting from the current situation. Secondary tasks are situation and plan evaluation (checking whether and to what extent a situation or plan satisfies or violates goals), prediction (forecasting the future development starting from a situation with or without interventions), and observation/experiment proposal (designing activities to collect information, possibly after stimulating the system in a particular way, useful to disambiguate situation assessment and also situation evaluation).
Journal of Decision Systems, 2019
Looking forward, the goal of this article is to stimulate discussion and encourage novel, even radical thinking about computerized systems, especially decision systems including decision automation and decision support systems. Looking back 60 years, this article reviews definitions and articles related to the decision system concept and associated terms like automated decision system (ADS) and decision support system (DSS). This historical perspective differentiates and expands the phenomenon of a decision system to create a modern context for future applied and scholarly research and development. Looking forward, more automated decision systems will make and implement decisions. Analytics will be embedded in decision systems, decision support will proliferate, and decision systems will be part of ambient intelligent environments. Finally, computational organization research (Gasser, 1995) may expand the boundaries of computerized decision systems, help develop and test richer theory, and hence help us better understand organizational decision-making and behavior. This article expands the horizon for decisionmaking research by reviving the concept of a decision system. Perhaps this article will lead researchers to study decision systems more comprehensively.
Four models for a decision support system
Information & Management, 1999
We examine four decision support system (DSS) models ± the Symbiotic, Expert, Holistic, and Adaptive ± and distinguish them in terms of the impact of their knowledge management styles on their problem-processing behavior. We draw upon existing notions of knowledge types and their management to develop a knowledge-oriented view. We use it to categorize the models as being either Static or Dynamic. From this perspective, the Holistic DSS may be regarded as being the most advanced, as it postulates holistic problem recognition and processing capabilities. While progress has been made on digitally simulating holistic recognition, much remains to be done in developing practical processors and truly holistic systems that couple such processors and recognizers.
Can computers help overcome limitations in human decision making
Motivation: The article evaluates computer assisted decision support in the context of contemporary research on decisional thinking, outlines the potential that computers have for overcoming known limitations in this thinking and the problems that occur when some aspects of thinking are overlooked.
A new paradigm for computer-based decision support
Decision Support Systems, 2002
We identify and address a fundamental general problem which we regard as crucial for the widespread, effective use of decision support systems (DSS) in the future: how can we substantially improve the quality of interaction, and the degree of flexible engagement, between humans and computers? Rather than seeking an answer in additional technical functionality we propose a new paradigm for computing that is human-centred and that adopts a novel, observation-oriented approach to data modelling. We report recent practical work (a timetabling instrument) showing an unusual degree of openness for interaction, and evidence that our models can significantly generalise expert systems.
On Frameworks and Architectures for Intelligent Decision-Making Support Systems
Encyclopedia of Decision Making and Decision Support Technologies, 2008
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Decision Support and Problem Formulation Activity
Encyclopedia of Decision Making and Decision Support Technologies, 2008
means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark.
The division of labor between human and computer in the presence of decision support system advice
Decision Support Systems, 2002
Prior research suggests that decision support system (DSS) provide model advice and display non-modeled information for decision makers . We investigate whether decision makers (1) delegate the processing of the modeled information to the model, (2) cognitively process the non-modeled information, and (3) decide based on the model's advice adjusted for the nonmodeled information. Experimentally, decision makers were no more likely to execute normative strategies when they had requisite knowledge for the strategy than when they did not have the requisite knowledge. We observed alternative processing, including ignoring the advice altogether, and evaluating the advice. Our findings suggest that DSS builders must encourage decision strategies that capitalize on the relative strengths of human and computer in using those features. D