Reaching Unanimous Agreements Within Agent-Based Negotiation Teams With Linear and Monotonic Utility Functions

Abstract

In this article, an agent-based negotiation model for negotiation teams that negotiate a deal with an opponent is presented. Agent-based negotiation teams are groups of agents that join together as a single negotiation party because they share an interest that is related to the negotiation process. The model relies on a trusted mediator that coordinates and helps team members in the decisions that they have to take during the negotiation process: which offer is sent to the opponent, and whether the offers received from the opponent are accepted. The main strength of the proposed negotiation model is the fact that it guarantees unanimity within team decisions since decisions report a utility to team members that is greater than or equal to their aspiration levels at each negotiation round. This work analyzes how unanimous decisions are taken within the team and the robustness of the model against different types of manipulations. An empirical evaluation is also performed to study the impact of the different parameters of the model.

A win–win team formation problem based on the negotiation

“…In general, the negotiation process is an iterated process until the goal of the negotiation is achieved successfully. It is essential to set a deadline of the negotiation round in order to assure the results of the negotiation, and in the meanwhile to avoid the infinite iteration (Sánchez-Anguix et al, 2011, 2012b, 2013b. Firstly, we validate the effectiveness of the team formation under different team contexts.…”

“…Then, Fan and Yen (2004) and Sánchez-Anguix et al (2011) pointed out that the human behaviors could be simulated well by agent, especially for mentalistic notions such as beliefs, desires, and intentions. Finally, Sánchez-Anguix et al (2012b, 2013a assumed that different agents master different skills. The more agents can form a team with comprehensive skills to deal with difficult negotiation problems efficiently.…”

Can We Reach Pareto Optimal Outcomes Using Bottom-Up Approaches?

“…It should be highlighted that we are not depicting achieving Pareto optimality as a simple task. However, there is value in computing Pareto optimality in smaller groups as long as we are able to use those solutions in more challenging scenarios: -Negotiation teams: In this scenario, a group of individuals negotiate as a party with opponent(s) to achieve a deal [22,27,26,25,23]. In that case, finding the outcomes that are Pareto optimal within the team may play in favor of the team as (i) if the team sticks to these outcomes while negotiating with opponents, it can ensure efficiency in the final outcome, (ii) the set of calculated deals may be reused in multiple negotiations with different opponents as they remain Pareto optimal, and (iii) finding Pareto optimal outcomes once may reduce the time spent in negotiation threads as the team exactly knows which outcomes are more beneficial for team members.…”

“…Such kind of property can be used in some complex group decision making scenarios. Imagine that a group of agents is negotiating in unison with an unknown opponent [22,27,25]. If the agents can find the Pareto optimal outcomes within the team, they may use these outcomes in their bidding strategy to reach a Pareto optimal agreement with their opponent.…”

Thesis summaries

“…When an organization negotiates it sends individuals from different departments due to their specialized knowledge. This thesis focuses on this latter case, where teammates have diverging preferences (i.e., intra-team conflict) and they should decide on which negotiation steps to take in order to reach an agreement with the opponent (i.e., external conflict) [7][8][9].…”