# Can Social Agents Efficiently Perform in Automated Negotiation?
## 3 Proposed Social Agent
### Opponent Modeling
#### 3.5.2 Issue Weight Estimation
This positive result for the individual utility of the agent is related to the fact that the agent achieves deals that are close to the Kalai and Nash bargaining point. Usually, these points contain agreements that have a high utility for both parties, and no party suffers from exploitation. Therefore, by securing the socially optimal outcome, one can also guarantee a high utility for oneself. In this case, it was the highest overall average utility. This helps us answer our initial research question regarding the prospective performance of social agents in automated negotiation. Particularly, these results support one of our initial research questions, Q1 (i.e., _Can a social agent obtain a high utility in heterogeneous environments?_). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Agent** & **Utility** & **Nash Dist.** & **Kalai Dist.** & **Time** \\ \hline Social & **0.81** & **0.08** & **0.07** & 0.85 \\ \hline Agent K & 0.76 & 0.13 & 0.12 & 0.84 \\ \hline GAH & 0.78 & 0.21 & 0.20 & 0.97 \\ \hline HH & 0.76 & 0.22 & 0.21 & 0.95 \\ \hline IH2011 & 0.70 & 0.17 & 0.16 & 0.78 \\ \hline TNR & 0.75 & 0.27 & 0.26 & 0.97 \\ \hline CUHK & 0.79 & 0.27 & 0.27 & 0.99 \\ \hline IH2012 & 0.73 & 0.12 & 0.12 & **0.72** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average utility, distance to the Nash point, distance to the Kalai point, and negotiation time for the agents when facing themselves and all other opponents in the specified domains. The best performing values are in boldface. These results regarding the individual utility of agents are surprising at first, as most of the rest of the agents have been designed to be competitive and maximize one's utility as much as possible. Therefore, we carried out a more in-depth analysis of the negotiations of the tournament. This analysis showed why competitive agents were not able to guarantee the highest average utility. Many negotiations where these competitive agents participated finished without any agreement due to the competitive nature of agents participating in the tournament. For instance, 12.5% of the negotiations for The Negotiator Reloaded, 8.33% of the negotiations for HardHeaded, 5.52% of the negotiations for Gahboninho, and 4.68% of the negotiations for CUHK Agent finished without an agreement. The numbers were particularly worrying when some of these competitive agents faced themselves (e.g., 26.67% of negotiations failed when TheNegotiator Reloaded faced itself). No negotiation in which our agent participated finished in failure. We argue that, in an agent society, achieving agreements is of utmost importance as, otherwise, business opportunities and partnerships may be missed, and the reputation of the agent in the society may also decrease among other negotiators. In the end, if other agents in the society had bad experiences negotiating against another agent, they may avoid selecting the agent as a negotiating partner in the future, further decreasing the performance of that agent in the society. From these initial experiments, one can conclude that (i) the proposed agent indeed shows a social behavior by minimizing the distance of agreements achieved to both the Nash and the Kalai point, both measures of fairness in negotiation; (ii) although the design of the agent focuses on social performance and behavior, the agent is capable of achieving efficient results in terms of the individual utility of the agent. This latter result supports our initial hypothesis that social agents can also perform efficiently in automated negotiation. These results were obtained based on the assumption that there is a static agent society composed of the agents included in the experiment. In the next experiment, we show how our agent would perform in case that the agent society is dynamic and agents can decide on what strategy to play in negotiations.