Robust multidimensional welfare comparisons: One vector of weights, one vote

Arbeitspapier

Many aspects of social welfare are intrinsically multidimensional. Composite indices at-tempting to reduce this complexity to a unique measure abound in many areas of economics and public policy. Comparisons based on such measures depend, sometimes critically, on how the di erent dimensions of performance are weighted. Thus, a policy maker maker may wish to take into account imprecision over composite index weights in a systematic manner. In this paper, such weight imprecision is parameterized via the -contamination framework of Bayesian statistics. Subsequently, combining results from polyhedral geometry, social choice, and theoretical computer science, an analytical procedure is presented that yields a provably robust ranking of the relevant alternatives in the presence of weight imprecision. The main idea is to consider a vector of weights as a voter and a continuum of weights as an electorate. The procedure is illustrated on recent versions of the Rule of Law and Human Development indices.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Nota di Lavoro ; No. 40.2013

Classification and Topics

• Classification: Wirtschaft ; Mathematical Methods ; Optimization Techniques; Programming Models; Dynamic Analysis ; Microeconomic Policy: Formulation, Implementation, and Evaluation ; Social Choice; Clubs; Committees; Associations ; General Welfare; Well-Being
• Subject: Multidimensional Welfare ; Composite Index ; ε-contamination ; Polyhedral Geometry ; Social Choice ; Approximation Algorithms

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Athanassoglou, Stergios
• Event: Veröffentlichung
• (who): Fondazione Eni Enrico Mattei (FEEM)
• (where): Milano
• (when): 2013

Further information

• Handle: http://hdl.handle.net/10419/74813
• Last update: 10.03.2025, 11:44 AM CET