TY - GEN
T1 - Can we reach Pareto optimal outcomes using bottom-up approaches?
AU - Sanchez-Anguix, Victor
AU - Aydoğan, Reyhan
AU - Baarslag, Tim
AU - Jonker, Catholijn M.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Classically, disciplines like negotiation and decision making have focused on reaching Pareto optimal solutions due to its stability and efficiency properties. Despite the fact that many practical and theoretical algorithms have successfully attempted to provide Pareto optimal solutions, they have focused on attempting to reach Pareto Optimality using horizontal approaches, where optimality is calculated taking into account every participant at the same time. Sometimes, this may prove to be a difficult task (e.g., conflict, mistrust, no information sharing, etc.). In this paper, we explore the possibility of achieving Pareto Optimal outcomes in a group by using a bottom-up approach: discovering Pareto optimal outcomes by interacting in subgroups.We analytically show that the set of Pareto optimal outcomes in a group covers the Pareto optimal outcomes within its subgroups. This theoretical finding can be applied in a variety of scenarios such as negotiation teams, multi-party negotiation, and team formation to social recommendation. Additionally, we empirically test the validity and practicality of this proof in a variety of decision making domains and analyze the usability of this proof in practical situations.
AB - Classically, disciplines like negotiation and decision making have focused on reaching Pareto optimal solutions due to its stability and efficiency properties. Despite the fact that many practical and theoretical algorithms have successfully attempted to provide Pareto optimal solutions, they have focused on attempting to reach Pareto Optimality using horizontal approaches, where optimality is calculated taking into account every participant at the same time. Sometimes, this may prove to be a difficult task (e.g., conflict, mistrust, no information sharing, etc.). In this paper, we explore the possibility of achieving Pareto Optimal outcomes in a group by using a bottom-up approach: discovering Pareto optimal outcomes by interacting in subgroups.We analytically show that the set of Pareto optimal outcomes in a group covers the Pareto optimal outcomes within its subgroups. This theoretical finding can be applied in a variety of scenarios such as negotiation teams, multi-party negotiation, and team formation to social recommendation. Additionally, we empirically test the validity and practicality of this proof in a variety of decision making domains and analyze the usability of this proof in practical situations.
KW - Agreement technologies
KW - Artificial intelligence
KW - Group decision making
KW - Multi-agent systems
KW - Pareto optimality
UR - http://www.scopus.com/inward/record.url?scp=85018693323&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-57285-7_2
DO - 10.1007/978-3-319-57285-7_2
M3 - Conference contribution
AN - SCOPUS:85018693323
SN - 9783319572840
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 19
EP - 35
BT - Conflict Resolution in Decision Making - 2nd International Workshop, COREDEMA 2016, Revised Selected Papers
A2 - Baarslag, Tim
A2 - Jonker, Catholijn M.
A2 - Julian, Vicente
A2 - Gerding, Enrico
A2 - Aydogan, Reyhan
A2 - Sanchez-Anguix, Victor
PB - Springer
T2 - 2nd International Workshop on Conflict Resolution in Decision Making, COREDEMA 2016
Y2 - 29 August 2016 through 30 August 2016
ER -