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<article-title>Searching for Joint Gains in Automated Negotiations<br/>
Based on Multi-criteria Decision Making Theory</article-title>
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<author><a href="mailto:vqbao@cs.rmit.edu.au"><name>Quoc Bao Vo</name></a></author>
<aff>School of Computer Science and IT RMIT University, Australia</aff>

<author><a href="mailto:linpa@cs.rmit.edu.au"><name>Lin Padgham</name></a></author>
<aff>School of Computer Science and IT RMIT University, Australia</aff>

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<title>ABSTRACT</title>
<p>It is well established by conflict theorists and others that successful
negotiation should incorporate "creating value" as well as "claiming
value." Joint improvements that bring benefits to all parties
can be realised by (i) identifying attributes that are not of direct
conflict between the parties, (ii) tradeoffs on attributes that are valued
differently by different parties, and (iii) searching for values
within attributes that could bring more gains to one party while not
incurring too much loss on the other party. In this paper we propose
an approach for maximising joint gains in automated negotiations
by formulating the negotiation problem as a multi-criteria
decision making problem and taking advantage of several optimisation
techniques introduced by operations researchers and conflict
theorists. We use a mediator to protect the negotiating parties from
unnecessary disclosure of information to their opponent, while also
allowing an objective calculation of maximum joint gains. We separate
out attributes that take a finite set of values (<italic>simple attributes</italic>)
from those with continuous values, and we show that for simple
attributes, the mediator can determine the Pareto-optimal values.
In addition we show that if none of the simple attributes strongly
dominates the other simple attributes, then truth telling is an equilibrium
strategy for negotiators during the optimisation of simple
attributes. We also describe an approach for improving joint gains
on non-simple attributes, by moving the parties in a series of steps,
towards the Pareto-optimal frontier.</p>
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