# Can Social Agents Efficiently Perform in Automated Negotiation?
## 3 Proposed Social Agent
### Opponent Modeling
#### 3.5.2 Issue Weight Estimation
The opponent modeling mechanism proposed in this article is a frequentist model that is based on the novel idea of windows of bids to detect general changes in the opponent's bidding and to avoid unwanted effects on the opponent model by abrupt and punctual changes in the opponent's concession and hardheaded behaviors. The overall negotiation strategy represents different general states that may be faced by a negotiating strategy: a strict exploration where no offers are accepted, an exploration phase where offers may be accepted, two optimizer phases where the agent aims to employ the available information about the opponent to ensure a social deal by using similarity metrics or a frequentist opponent model, and an eager state where the agent aims to close a deal as soon as possible. The agent moves through these states as more information is available about the opponent. First, the agent explores the negotiation space to understand the opponent's preferences. If enough information is available about the opponent's preferences, the social agent then exploits the available information by proposing estimated Nash bids and attempt to end up with a social agreement. If no information is available, the agent resorts to a similarity metric that aims to offer bids that are the most similar to the best offer received from the opponent, trying to be social with the scarce information available. Finally, when the deadline approaches, the agent aims to close an agreement. We have carried out experiments in an agent society formed by some top-performing agents from previous negotiation competitions and some social agents. The experiments have shown that our agent is not only the top-performing with respect to one's utility maximization but also the agent that participates in those negotiations that are the closest to the Nash Bargaining and the Kalai point, both measures of social fairness. A more in-depth analysis also revealed that our social agent is one of the pure equilibrium strategies to play in the proposed agent society. Furthermore, our social agent also has notorious importance in all of the mixed equilibrium strategies carried out by agents in an agent society. This shows how an agent can be social yet can efficiently perform in an agent society, confirming our main claim in this article. These results suggest that our social agent (i) obtains a high utility in heterogeneous environments, (ii) achieves socially efficient agreements in heterogeneous environments, and (iii) represents a rational and efficient choice even for self-interested agents in an agent society. Conceptualization, V.S.-A., O.T., R.A., and V.J. ; methodology, V.S.-A., R.A. and V.J. ; software, V.S.-A. and O.T. ; validation, V.S.-A., V.S.-A., O.T., R.A., and V.J. ; formal analysis, V.S.-A. ; writing--original draft preparation, V.S.-A., O.T., R.A.; supervision, V.J. ; funding acquisition, R.A. and V.J. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government and partially supported by a grant of The Scientific and Research Council of Turkey (TUBITAK) with grant number 118E197. The contents of this article reflect the ideas and positions of the authors and do not necessarily reflect the ideas or positions of TUBITAK. Not applicable. The authors declare no conflict of interest. ## References

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