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Yong Tan

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2 papers

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EAAI Journal 2026 Journal Article

A knapsack-based entropy-clustering framework for multi-criteria decision making under epistemic uncertainty

Yong Tan
Abdollah Hadi-Vencheh
Jorge Antunes
Peter Wanke

Assessing sustainable socio-economic welfare using artificial intelligence is challenging due to the large number of interdependent environmental, social, and economic indicators involved and the uncertainty surrounding their relative importance. This study aims to develop an artificial intelligence–based decision support framework for sustainable socio-economic welfare assessment that identifies the most informative indicators, reveals structural differences among countries, and evaluates how alternative welfare representations affect national performance rankings. To achieve this, we propose a hybrid artificial intelligence–based methodology that integrates knapsack-based combinatorial optimization for variable selection, mutual information–driven clustering for structural grouping, and multi-criteria decision-making techniques for performance evaluation. This framework treats welfare assessment as a problem of informational uncertainty and structural heterogeneity rather than a fixed aggregation task. Applied to cross-country sustainability and socio-economic data, the approach uncovers distinct welfare profiles and shows that country rankings vary systematically depending on whether sustainability-oriented or socio-economic-oriented indicators are emphasized. The socio-economic profile exhibits greater dispersion and differentiation across countries, while the sustainability-oriented profile produces more clustered performance patterns. Sensitivity and robustness analyses confirm that these differences are structurally driven rather than artifacts of weighting choices. These findings demonstrate that welfare rankings are contingent on the informational structure of indicators and highlight the importance of variable selection in policy evaluation. Methodologically, the study contributes a generalizable artificial intelligence-enabled (AI-enabled) framework for high-dimensional decision analysis. Substantively, it provides policymakers with a more transparent way to understand trade-offs between sustainability and socio-economic development.

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TIST Journal 2020 Journal Article

Exploring Correlation Network for Cheating Detection

Ping Luo
Kai Shu
Junjie Wu
Li Wan
Yong Tan

The correlation network, typically formed by computing pairwise correlations between variables, has recently become a competitive paradigm to discover insights in various application domains, such as climate prediction, financial marketing, and bioinformatics. In this study, we adopt this paradigm to detect cheating behavior hidden in business distribution channels, where falsified big deals are often made by collusive partners to obtain lower product prices—a behavior deemed to be extremely harmful to the sale ecosystem. To this end, we assume that abnormal deals are likely to occur between two partners if their purchase-volume sequences have a strong negative correlation. This seemingly intuitive rule, however, imposes several research challenges. First, existing correlation measures are usually symmetric and thus cannot distinguish the different roles of partners in cheating. Second, the tick-to-tick correspondence between two sequences might be violated due to the possible delay of purchase behavior, which should also be captured by correlation measures. Finally, the fact that any pair of sequences could be correlated may result in a number of false-positive cheating pairs, which need to be corrected in a systematic manner. To address these issues, we propose a correlation network analysis framework for cheating detection. In the framework, we adopt an asymmetric correlation measure to distinguish the two roles, namely, cheating seller and cheating buyer, in a cheating alliance. Dynamic Time Warping is employed to address the time offset between two sequences in computing the correlation. We further propose two graph-cut methods to convert the correlation network into a bipartite graph to rank cheating partners, which simultaneously helps to remove false-positive correlation pairs. Based on a 4-year real-world channel dataset from a worldwide IT company, we demonstrate the effectiveness of the proposed method in comparison to competitive baseline methods.

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