On Penalty-Based Aggregation Functions and Consensus
- Gleb Beliakov 1
- Tomasa Calvo 2
- Simon James
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1
Deakin University
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2
Universidad de Alcalá
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- Herrera-Viedma, Enrique (coord.)
- García-Lapresta, J.L. (coord.)
- Kacprzyk, J. (coord.)
- Fedrizzi, M. (coord.)
- Nurmi, H. (coord.)
- Zadrożny, S. (coord.)
Editorial: Springer Alemania
ISBN: 978-3-642-20533-0
Año de publicación: 2011
Páginas: 23-40
Tipo: Capítulo de Libro
Resumen
The problem of aggregating individual preferences in order to arrive at a group consensus arises naturally in elections where a candidate must be chosen that best represents the individuals’ differing opinions. Other contexts include the judging of sporting competitions and the fusion of sensor readings. In these applications it makes sense that the aggregated result should be as close as possible to the individual inputs, giving rise to the need for methods that minimize this difference. Penalty-based aggregation functions are precisely those functions that aim to accomplish this, drawing upon various notions of “difference” in varying situations.