As in static negotiations, the heuristic approaches in dynamic environments try to translate the complicated reality into relatively simple decision rules that can be used to achieve good (although probably suboptimal) solutions. Here we will discuss one example, a market-driven agent (Sim (2002) and later versions of the model). This is a very interesting approach to negotiation in dynamic environ- ments, because, as the name suggests, it takes the market situation explicitly into consideration when it makes decisions on concessions during the negotiation. When the market situation is tough (a lot of competition, a few opponents, dead- line nearby or a strong need for the service), the concessions are bigger and when the market is more favourable, the concessions are smaller. In addition, market- driven agents consider their reservation prices as being flexible. Thus they are willing to pay more (accept less) if the market situation is hard and expect to pay less (get higher prices) when the market situation is better. We now discuss these two approaches in turn.
First, the concessions the market-driven agents make depend on their view of the market situation. Such an agent will take into consideration four different factors:
• trading opportunity, • competition,
• deadline and
In more detail, trading opportunity is measured with two factors: the number of trading partners and the differences in utilities between the parties’ last offers. Simple heuristics are used to estimate the probability that an agent will obtain a certain utility with at least one of its trading partners. They will then try to get the best possible utility while maintaining a reasonable probability of actually reaching it. Here competition is measured as a probability that the agent is ranked as the most preferred trading partner by at least one of the opponent agents. Again some simple heuristics are used in the estimation (the probability of being the most preferred partner is simply 1 − m
m+1, where m is the number of competitors).
Basically this means that a market-driven agent makes compromises according to the buyer-seller ratio in the market. For the deadline and eagerness, a simple time- dependent strategy is used. The eagerness factor ε determines how the concessions are made.48 Here only two strategies are used: linear (ε = 1) and conservative
(or boulware, 0 < ε < 1), since the conceder strategy was likely to achieve lower utilities (although the lower risk of losing deals was noticed).
Second, market-driven agents in their newer versions are allowed to change their expectations of the outcome. Specifically, an enchanced market-driven agent (EMDA) (Sim and Wang 2004) can decrease its expectations in very tough mar- ket situations. To do this, it uses a fuzzy decision controller, which considers the factors above to guide decision-making. Yet another improvement is to allow the agent to increase its expectations when the market situation seems very favourable (EMDA2) (Sim 2004). To do this, it uses two additional fuzzy decision controllers that allow decision-making about whether or not to postpone reaching an agree- ment and, if so, for how long. The first decision is made by considering both the eagerness and the competition values; the higher these factors are, the higher is the number of good offers required to postpone the acceptance. The second de- cision uses the deadline and the number of opportunities; the further away is the deadline and the higher the number of opponents, the longer the agent is allowed to wait.
This approach is clearly very interesting in our context because the market-driven agents are very flexible and can adapt to many relevant changes in circumstances. However, it is relatively complicated and it is difficult to see, from this structure, if its adaptations are good in all relevant circumstances. Thus, it uses very simple heuristics and combines them into something that is no longer that simple to follow, because the different effects are all considered basically at the same time. We prefer a solution where different decisions are taken clearly in different times
and/or by different components, and where the different factors are combined using a much more explicit analysis. This allows us to consider also cases that are not as clear cut as the cases Sim discusses.
Also the market-driven agents do not consider the opponent’s negotiation tactics or his parameter values (like reservation price or deadline) and we have a market full of diffent kinds of sellers (requirement R3). Moreover, the approach does not take into account the possibility that the buyer agent’s circumstances may change so that it does not need the service any more and should therefore be careful about entering into contracts (requirement R2). In addition, many of the details of the market-driven agents seem a bit ad hoc and no reasonable and consistent theory is offered to explain why these particular heuristics were chosen.
We do not find heuristic approaches satisfactory for our purposes, because the higher levels of our model will need relatively accurate information about the possible outcomes and success probabilities in all situations and heuristic models such as Sim’s will not be able to provide such information.