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Trade-offs between different outcome dimensions in negotiations are well documented by em- pirical studies for traditional – face-to-face or electronically mediated – negotiations between humans, e.g. concerning the effectiveness of negotiations – i.e. the probability to reach an agreement – and the efficiency of agreements – i.e. the probability that reached agreements are Pareto-optimal. Aspects increasing the prospects of a favorable agreement – if an agree- ment is reached –, like for instance a though approach to negotiation, high opening offers, or small concession rates, simultaneously decrease the prospects to reach an agreement at all (e.g. Pruitt, 1981; Zartmann, 2002). We call this fundamental trade-off between outcome dimensions in negotiations – as sometimes done in literature – the negotiation dilemma.

After having presented and discussed outcomes for the different components of automated ne- gotiation systems and the interactions of these components in sections 6.2 to 6.5 this section changes the perspective and primarily focuses on the outcome measures. Given the prevalence of the negotiation dilemma in traditional negotiations, it is obvious to assume it also for auto-

mated negotiation, and not much surprising that we also found design trade-offs concerning the configuration of an automated negotiation system with respect to different outcome dimensions in negotiations.

In general – as can be seen from Section 6.4 where we compared the interaction of components and the effect sizes – components of the automated negotiation system influencing the proportion of agreements – and thereby also the proportion of Pareto-optimal agreements as they are a share of the total agreements –, which were above all the interaction protocol and the concession strategy, only have minor or no influence on the quality of the agreement reached, measured on various dimensions – minimal distance to the Pareto frontier, individual utilities of the agreement, and contract imbalance –,, which is mainly influenced by the offer generation strategies the software agents follow.

For a high proportion of agreements the configuration of the automated negotiation system should consist of protocol 1, which neither allows quit nor reject messages of the software agents and therefore results in an agreement in each simulation run irrespective of the software agents applied. If protocols other than protocol 1 are used, however, it is possible that automated negotiations end without agreement, if a quit message is sent by one software agents in protocol 2, or if two subsequent messages of the software agents were of the type reject in protocol 3, which negatively impacts the proportion of agreements reached especially for protocol 2. In this case the software agents should follow active concession making strategies, making first concession steps if the opponent reciprocated previous ones, which obviously increases the prospects of reaching an agreement when this is not mandatory. The offer generation strategies were found to be of minor influence on the proportion of agreements only, but last-cost-issue concession MUM and full reciprocation of perceived concessions of the opponent TFT, both making more generous offers to the opponent, demonstrated to reach more agreements than the other offer generation mechanisms, which explore the set of possible agreements more systematically and resist in making too large concessions.

Given an agreement is reached the quality of this agreement can be studied for various aspects of the outcome. The components of the automated negotiation system influencing the quality of an agreement, however, are quite different from those influencing the existence of an agreement. For the minimal distance to the Pareto frontier, the individual utility of the agreement to the parties, and contract imbalance it was found that the offer generation strategy of the software agents has major influence, followed by the interaction protocol, and only little effects were found for concession strategies of the software agents. Moreover, within these components also the options to chose for achieving high-quality agreements considerably differ from those to be chosen for a high proportion of agreements. These two observations imply that just like for traditional negotiation also for the configuration and design of automated negotiation systems major trade-offs between outcome aspects of the negotiation – i.e. the negotiation dilemma – exist.

protocol 2, which accounts for the lowest proportion of agreements, achieves agreements closest to the Pareto frontier, of highest utility to the parties, and lowest contract imbalance. In these outcome measures it is followed by protocol 3, which achieves nearly the same results, which indicates that the possibility to interrupt the offering sequence, which is possible in these proto- cols by sending quit and reject messages, increases the quality of agreements reached in these

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outcome measures. However at the cost of less agreements, especially for protocol 2, where the rigid interruption rule causes a break off of negotiations often and leads to agreements only in about 20% of the simulation runs. protocol 1, on the other hand, achieves agreements in all simulation runs but of inferior quality in these outcome measures. The concession strategy of the software agents has not so much influence on the quality of the agreement concerning its Pareto optimality, utility to the parties, and contract balance. In most of the cases where it was found to have significant influence – albeit the lower effect sizes – passively conceding software agents achieve outcomes of higher quality than actively conceding software agents, while it is opposite for the proportion of reached agreements.

Finally concerning the offer generation strategies, we already mentioned, that they have the major influence on the quality of the agreement. For the minimal distance to the Pareto frontier the offer generation strategies that propose offers in a more systematic fashion (MOC and SMC) achieve better performance than those found to reach a high proportion of agreements (MUM and TFT). Furthermore, concerning the other quality measures of an agreement – individual utility to the parties and contract imbalance – additional trade-offs emerge. In case an agreement is reached it is higher for the focal party (lower for the opponent party) if the focal party is represented by – in this order – MOC, SMC, LEX, MUM, and finally TFT. As the ordering is exactly inverse for the opponent’s utility of an agreement a major trade-off can be identified for the individual utilities of the parties. Moreover, as those offer generation strategy lying in the middle of these two rankings (LEX and SMC) of agents for agreements of high utility to the own party and the opponent, achieve agreements of lowest contract imbalance the optimal software agent to use if fairness is of importance also differs from those to use if the party’s utility of an agreement is important (MOC) or if the opponent’s utility of an agreement is important (TFT).

Summarizing, no system configuration is superior in all aspects of the outcome of negotiations, but the optimal system configuration has to be determined according to the intentions of the users and the purpose of the system. However, if we are to suggest a system configuration in general, we opt for systems consisting of protocol 3 and MUM agents – MUMact if reaching an agreement is critical or MUMpas if the quality of agreements reached is of higher importance. Though such a system not achieves best performance in all outcome measures, in our opinion the lower proportion of agreements is outweighed by the higher quality of the agreements, and at least in comparison to human negotiation these systems achieve higher performance in all outcome dimensions as discussed in the previous section.