On Penalty-Based Aggregation Functions and Consensus

• Gleb Beliakov ^[1] ; Tomasa Calvo ^[2] ; Simon James
  1. [1] Deakin University

     Deakin University

     Australia

     
  2. [2] Universidad de Alcalá

     Universidad de Alcalá

     Alcalá de Henares, España

     
• Localización: Consensual Processes / coord. por Enrique Herrera Viedma , José Luis García Lapresta , Janusz Kacprzyk , Mario Fedrizzi, Hannu Nurmi, Slawomir Zadrożny, 2011, ISBN 978-3-642-20533-0, págs. 23-40
• Idioma: inglés
• DOI: 10.1007/978-3-642-20533-0_2
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• 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.