On Penalty-Based Aggregation Functions and Consensus

  1. Gleb Beliakov 1
  2. Tomasa Calvo 2
  3. Simon James
  1. 1 Deakin University
    info

    Deakin University

    Geelong, Australia

    ROR https://ror.org/02czsnj07

  2. 2 Universidad de Alcalá
    info

    Universidad de Alcalá

    Alcalá de Henares, España

    ROR https://ror.org/04pmn0e78

Llibre:
Consensual Processes
  1. Herrera-Viedma, Enrique (coord.)
  2. García-Lapresta, J.L. (coord.)
  3. Kacprzyk, J. (coord.)
  4. Fedrizzi, M. (coord.)
  5. Nurmi, H. (coord.)
  6. Zadrożny, S. (coord.)

Editorial: Springer Alemania

ISBN: 978-3-642-20533-0

Any de publicació: 2011

Pàgines: 23-40

Tipus: Capítol de llibre

DOI: 10.1007/978-3-642-20533-0_2 DIALNET GOOGLE SCHOLAR

Resum

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.

Fuente de los datos: Dialnet