ABX-LEX: An Argument-Driven Approach for the Digital Facilitation of Efficient Group Decisions
Graham Horton () and
Jana Goers ()
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Graham Horton: Computer Science Department, Universität Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany
Jana Goers: Computer Science Department, Universität Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany
International Journal of Information Technology & Decision Making (IJITDM), 2021, vol. 20, issue 01, 137-164
Abstract:
The quality of a group decision depends on its members sharing and adopting their various perspectives. Multi-criteria models provide an appropriate vehicle for managing this information sharing because they break down the decision into small, single-issue questions that can be treated independently. We present a new lexicographic decision method with three equivalence classes for use by a group. Our algorithm is the first to combine the hidden profile and multi-criteria paradigms and thereby improving the consensus by identifying critical disagreements and inviting the decision makers to share their mental models of them. A computer simulation demonstrates the algorithm’s effectiveness and efficiency. Unless the problem parameters are very unfavorable, the method achieves the desired choice set or a close approximation to it significantly faster than the commonly used compensatory methods. Our algorithm can either provide step-by-step instructions to a human facilitator, or it can be implemented as a software agent acting as a fully automated digital facilitator, allowing decision makers to participate “on the go” using their internet-connected mobile devices.
Keywords: Multi-criteria decision making (MCDM); multi-attribute group decision making (MAGDM); hidden profiles; lexicographic decision models; satisficing; collaboration engineering (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:20:y:2021:i:01:n:s0219622020500431
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DOI: 10.1142/S0219622020500431
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