# Can Social Agents Efficiently Perform in Automated Negotiation?
## 3 Proposed Social Agent
### Opponent Modeling
#### 3.5.2 Issue Weight Estimation
Moreover, the focus of the agent proposed in [52] is not necessarily social utility maximization but individual utility maximization. Proposing another agent that is supported by a variety of negotiation strategies [53], the authors presented a multi-party negotiation agent for which the decisions (e.g., bidding, acceptance, etc.) are built by aggregating the decisions of multiple winning agents from previous ANAC competitions. While our agent is also supported by several strategies, the behavior of the agent proposed in [53] does not adapt according to the negotiation context, as the aggregation rules remain the same. In addition, the objective of our agent is that of being social, while the work presented in [PERSON] et al. [53] solely strives for utility maximization. [PERSON] [54] proposed an adaptive agent that changes the concession speed according to previous negotiation sessions with the opponent and the current negotiation state. The strategy uses an opponent modeling mechanism that maps the opponent bids to its utility function by considering time, mean received utility, and the deviation of the received utility. Then, the agent classifies opponents according to the Thomas-Killman Conflict Mode Instrument and adapts its concession speed accordingly. If the opponent is classified as cooperative or passive, the proposed agent demonstrates a more cooperative behavior. In addition to this, the agent also employs a genetic algorithm to search for Pareto optimal bids by generating variations of the opponent's first bid. It assumes, with a high likelihood, that bids generated from the first opponent's bids are the best for the opponent. The work presented in [54] also adapts its behavior and may respond cooperatively to other agents' cooperation. However, the agent requires multiple negotiation sessions to learn about the opponent's style, while our agent is designed to work for single negotiations where we may not have information about the opponent. [PERSON] et al. [55] proposed a distributed multi-objective genetic algorithm for solving bilateral negotiations. In their proposal, each agent carries out an NSGA-II genetic algorithm that employs one's utility function and the evaluation of the opponent for a subset of offers (i.e., partial disclosure on one's preferences). Once near Pareto optimal bids are generated, the agents negotiate on which bid from the estimated Pareto frontier they should agree on. The agents proposed by [PERSON] et al. are cooperative, as they disclose information about their preferences. However, the negotiation carried out on the estimated Pareto frontier does not necessarily need to be cooperative and ends up in a social agreement. In addition to our agent aiming for social agreements, the other main difference between the proposal presented in [PERSON] et al. and our proposal is that we do not assume any information disclosure in the negotiation apart from exchanged bids. Therefore, our negotiation strategy is also adequate for less cooperative scenarios. [PERSON] et al. [56] presented a negotiating agent that searches high joint utility agreements around the Nash bargaining solution. This agent also employs a frequentist opponent model, assuming that the most frequently received issue values are the most important for the opponent. In their work, these values are named as prior values. On the other hand, issues with high deviation are priors for the opponent. In their study, the agent calculates standard deviations for each of the issue values, and then it estimates the standard deviation of the aforementioned deviations, coined as the Issue Standard Deviation (ISD). ISD is used as a bridge to estimate the opponent's preferences on issues. In contrast to our work, they do not have a bid/time window approach in the opponent model, which may face the problems mentioned throughout our article. Moreover, our social agent strategy adapts its bidding and acceptance strategy based on the information available during the negotiation, while the proposal in [PERSON] et al. does not.