Integrating Actor Interactions into Multi-Criteria Decision Analysis for Electric Vehicle Adoption in Germany

Engineering notes

Our method systematically integrates key elements of interaction between actors into MCDA-based decision analyses without significantly increasing complexity, thereby maintaining practical applicability and interpretability compared to standard single-actor MCDA approaches.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

A successful transition to more sustainable structures, e.g., in the energy or mobility sector, requires understanding the preferences of those who might be affected by the transition, whether positively or negatively. Multi-Criteria Decision Analysis (MCDA) is a prominent approach for assessing these preferences. We propose a method for Multi-Actor Multi-Criteria Decision Analysis that endogenously incorporates interactions between different actors using impact matrices, which reflect the feedback of rankings, a) on the characteristics of options, or b) on the weightings of criteria by actors. Using the example of demand and supply of private cars in Germany, we demonstrate that this assessment of feedback loops can have significant impact on preferences compared to single-actor MCDA and that it leads to an improved understanding of how actors influence each other. Our method systematically integrates key elements of interaction between actors into MCDA-based decision analyses without significantly increasing complexity, thereby maintaining practical applicability and interpretability compared to standard single-actor MCDA approaches.

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