Country Innovation Advantage Ranking: A New Approach and comparison with the Global Innovation Index

We consider a special multi-objective decision-making problem, aiming to identify the ‘best countries’ in terms of their innovative advantage. We propose using rating/ranking-theory methods for the solution to this problem. Moreover, we also show that voting theory methods can be useful to aggregate the results from different ranking methods.
For illustrative purposes, our investigation uses a dataset from the Global Innovation Index (GII) 2019. Using different rating/ranking methods, we obtain new alternative ratings/rankings of the innovative advantages of countries. In Particular, the following methods was used as examples: the Buchholz method, Colley method, the Markov-chain method, Perron-Frobenius and geometric mean versions of the analytical hierarchy process (AHP), and the entropy method.
The dataset presents the results of our calculations. Namely, the new ratings/rankings of the innovative advantages of countries and their comparison with GII -2019. A detailed description of the calculation methods will be presented in the corresponding publication.
The dataset is presented as a sheet of an MS Excel file (.xlsx): which include the following columns:
Column #: ID;
Column Country: Country name;
Column 3 Code: ISO alpha 3 code of country;
Column GII-Rating: GII-2019 score
Column rE: rating score by the entropy method;
Column AHPpf: rating score by the AHP Perron-Frobenius version;
Column AHPgm: rating score by the AHP geometric mean version;
Column rB: rating score by the Buchholz method;
Column rC: rating score by the Colley method;
Column Mch: rating score by the Markov-chain method;
Column GII-Rank: GII-2019 rank;
Column Rank-rE: ranking by the entropy method;
Column Rank-AHPpf: ranking by the AHP Perron-Frobenius version;
Column Rank-AHPgm: ranking by the AHP geometric mean version;
Column Rank-rB: ranking by the Buchholz method;
Column Rank-rC: ranking by the Colley method;
Column Rank-Mch: ranking by the Markov-chain method;
Column AggR: ranking obtained by aggregation previous 6 ranks by the Borda method.

本研究探讨了一种特殊的多目标决策问题，旨在根据各国创新优势识别‘最佳国家’。针对此问题，我们提出采用评级/排名理论方法进行解决。此外，我们还表明，投票理论方法可用于汇总不同排名方法的结果。为了直观展示，本研究采用全球创新指数（GII）2019年的数据集。通过运用不同的评级/排名方法，我们获得了关于各国创新优势的新评级/排名。具体而言，以下方法被作为示例：Buchholz方法、Colley方法、马尔可夫链方法、Perron-Frobenius与几何平均版本的层次分析法（AHP）、以及熵方法。该数据集展示了我们的计算结果，即各国创新优势的新评级/排名及其与GII-2019的比较。详细的计算方法将在相应的出版物中予以介绍。数据集以MS Excel文件（.xlsx）的表格形式呈现，其中包含以下列：列号、国家名称、ISO alpha 3代码、GII-2019评分、熵方法评级得分、AHP Perron-Frobenius版本评级得分、AHP几何平均版本评级得分、Buchholz方法评级得分、Colley方法评级得分、马尔可夫链方法评级得分、GII-2019排名、熵方法排名、AHP Perron-Frobenius版本排名、AHP几何平均版本排名、Buchholz方法排名、Colley方法排名、马尔可夫链方法排名，以及通过Borda方法聚合前六个排名得到的综合排名。