Information Technology Reference
In-Depth Information
Impact of bid modifiers and learning rates
1.6
rampant, high alpha
rampant
overbid, high alpha
overbid
myopic
1.4
Profits for 10 agents and 7 strategic bidders, 2 extra
1.2
2
1
0.8
1.5
0.6
1
0.4
0.5
0.2
0
0
0
500
1000 1500 2000
1
2
3
4
5
6
7
8
9 10
agents
epochs
Fig. 5.
More speculative bidders (a) and better learning (b)
outcome for smart agents competing in scenarios where resources are scarce. The
perfect tuning of the strategy will however depend on how scarce the resources
currently are and how tactical the opponent agents are. Furthermore, an agent
that is more quickly able to adapt to changing circumstances, i.e. a change in the
number of sold goods in the auctions, or has a better learning algorithm/model,
can likely temporarily exploit the other bidders until the next equilibrium is
reached.
7
Discussion and Conclusion
The application of software agents bidding in online auctions is of increasing im-
portance. In this work, we contributed to the understanding of bidding strategies
for domains where bundles of items are bought in a unknown sequence of auc-
tions where there are complementarities between the items of the bundle. We
show, through experiments and a game theoretical analysis link to the
nIP D
,
that agents in competitive settings converge to near marginal utility as they at-
tempt to exploit the super additive value in their bids for the individual items.
This is beneficial from the viewpoint of the auctioneers.
References
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Information Sciences
, 170(1):101-119, 2005.
2. P. Anthony, W. Hall, V. D. Dang, and N. R. Jennings. Autonomous agents for
participating in multiple on-line auctions. In
Proceedings of the International Joint
Conference on Artificial Intelligence (IJCAI) Workshop on E-Business and the
Intelligent Web
, pages 54-64, 2001.
3. R. Axelrod.
The Evolution of Co-operation
. Basic Books, Inc., New York, 1984.
