Comparing Weight Aggregation Strategies in Group Decision Analysis

Lakmayer, S. & Danielson, M. (2027). Comparing Weight Aggregation Strategies in Group Decision Analysis. In: Advances and Trends in Artificial Intelligence. Theory and Applications. Eds. Fujita, H., Selamat, A., Ghazali, M., & Ali, M., pp. 358-371 Singapore: Springer Nature Singapore. ISBN 978-981-92-2888-1 10.1007/978-981-92-2888-1_29.

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Abstract

This paper examines the role of surrogate weights derived from ordinal criteria information within additive models of multi-criteria decision analysis in group decision-making contexts. In particular, we compare how surrogate weights perform when applied at different stages of the decision-making process and analyse the resulting outcomes. The findings are presented along two dimensions. First, we identify the stage at which aggregating the preferences of multiple decision-makers is most effective, showing that aggregation based on surrogate weights or final utilities clearly outperforms aggregation of individual ordinal rankings. Second, we investigate how excluding extreme (i.e., potentially unrealistic) weight vectors influences group results. Overall, the results indicate that surrogate weights provide a practical and effective approach for aggregating preferences in group decision-making settings, not least when using the preferred (SWA/UTA) concatenation pathway.

Item Type:	Book Section
Research Programs:	Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Cooperation and Transformative Governance (CAT)
Depositing User:	Luke Kirwan
Date Deposited:	19 Aug 2026 07:46
Last Modified:	19 Aug 2026 07:46
URI:	https://pure.iiasa.ac.at/21817

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