# Can Social Agents Efficiently Perform in Automated Negotiation?
## 3 Proposed Social Agent
### Opponent Modeling
#### 3.5.2 Issue Weight Estimation
[PERSON] and [PERSON] presented a negotiating agent aiming not only to optimize its utility but also to optimize an outcome in combination with acceptability for human negotiators [26]. The agent determines its moves by following a set of predefined rules considering the opponent's last three moves. Similar to our work, that agent deploys an adaptive behavior-based mechanism. However, in contrast to our strategy, it has been mainly designed for human-agent negotiation settings where the negotiators have fewer rounds with limited bid exchanges. Another related negotiating agent was proposed by [PERSON] et al. [57]. Their agent follows an extension of the Boulware time-based concession tactic where the agent does not concede at the beginning of the negotiation while adjusting its target utility based on the highest estimated utility to be received. Unlike our time-independent target utility update mechanism with bid windows analysis, they strictly follow a time-based concession strategy led by the best-estimated bid for opponents given the list of acceptable bids for the agent itself. The opponent model is also implemented in a frequentist fashion, prioritizing received bid issues with the highest repetition having the least variation of values, but as mentioned, opponents' bid histories are not evaluated in windows. On the other hand, the opponent model is designed as an ensemble of individual models that learn each opponent separately, unlike our work. They also introduce the \"attention coefficient\", which is used to aggregate opponent models with higher values for the more conceding opponents. Given the adaptive expected utility-based target utility setting and weighted opponent modeling components, they claim that the agent seeks to maximize social welfare. [PERSON] et al. [58] proposed a social agent aiming to provide strategic advice in repeated interactions between humans and self-interested software agents. In the proposed model, the agent has complete information about the state of the world, but the human participant may not. The challenge of advice provision is choosing proposals that have some utility for the software agent and have high probabilities of being accepted by the human counterpart. The authors propose an adaptive model capable of gradually changing the weights assigned to each participant when providing advice. The experiments carried out by the authors show how the proposed social agent outperforms behavioral economics and psychological models. Both our agent and the agent proposed in [58] are social, and they show some form of adaptability. The main difference between [58] and our proposed agent is that we focus on bilateral negotiations between software agents where multiple bids may be exchanged between involved parties and where both parties have imperfect knowledge about the world. In addition to this, software agents may exhibit different behavior to that of human beings. ## 6 Conclusions

Many negotiator agents are selfish and purely driven by one's utility function maximization. Unfortunately, this means that they usually ignore the other counterparts' utility in the final agreed outcome. While this strategy may be apt for non-repeated interactions, we argue that, while optimizing one's utility function is important, agents in a society should not ignore the opponent's utility in the final agreement since this should improve the long-term perspectives of the agent in the system. In this article, we presented a social agent proposal supported by a portfolio of negotiation behaviors for which the selection depends on both the information about the negotiation and the behavior of the opponent. The agent employs a new variant of Tit-for-Tat based on a robust analysis of non-adjacent windows of bids and a new and robust frequency opponent modeling mechanism.