2011 International Conference on Information and Intelligent Computing
IPCSIT vol.18 (2011) © (2011) IACSIT Press, Singapore
Empirical Study on the Market Economy Analysis for Online
Auctions with Homogeneous and Heterogeneous Bidder Agents
Jacob Sow 1, Patricia Anthony 2 and Chong Mun Ho 3
1,2
School of Engineering and Information Technology, Universiti Malaysia Sabah, Locked Bag 2073, 88999,
Kota Kinabalu, Sabah, Malaysia
3
School of Science and Technology, Universiti Malaysia Sabah, Locked Bag 2073, 88999, Kota Kinabalu,
Sabah, Malaysia
1
2
jacob07.my@gmail.com, panthony@ums.edu.my, 3 cmho@ums.edu.my
Abstract. Online auctions have changed the traditional trading methods in the business world today.
Sellers and buyers are not required to meet before any transaction is made. Moreover, due to the furtherance
of computer agent technology, both sellers and buyers are usually represented by their seller and buyer agents.
In this paper, the market economy of utilizing bidder agents is considered. A comparison is drawn between
agents of the same type and agents of different types from the bidders’ and sellers’ point of view. Based on
the empirical results, deploying different types of bidders agents with different preferences may produce
various auction outcomes which can be quantified and measured in terms of average consumer surplus ratio,
the number of auctions that are closed successfully and sellers’ utility. Having different types of bidder
agents with different bidding strategies allow the human buyers to select the best bidding agents to bid on
their behalf and promotes healthy competition among the bidders in any online auction. From the sellers’
perspectives, they welcome heterogeneous bidder agents than homogeneous bidder agents since their sales
and profits are increased.
Keywords: bidder agents, bidding strategies, market economy, English auction.
1. Introduction
Online auctions have become increasingly popular and accepted by trading community over the last few
years. Businesses that are previously conducted in the bricks and mortars are now available in a global
environment without geographical limitation. By using the Internet, sellers and buyers are free to trade their
goods from around the world.
Currently, there are hundreds of thousands of online auctions that are conducted from various auction
marketplaces such as Yahoo! Auction and eBay where bidders may obtain their desired items. Choosing an
auction from the available auctions’ pool may be risky. Since bidders may not know which auction to select,
they are exposed with the risk of losing an auction or paying too high for the item being auctioned. Therefore,
intelligent bidder agents that are equipped with various bidding strategies can assist in solving these problems.
These agents can be applied to determine which auction that they participate and the appropriate bid that they
can use when bidding in the auctions.
There are a few literatures that focus on the online auction market economy such as [1],[2],[3],[4]. They
focused on the human bidders or sellers with different risk behaviors and their preferences in auctioning and
bidding processes. As the agent technology in auction marketplace is becoming a more dominant trend, it
would be interesting to explore the market economy when agents are implemented. In [5], it is found that
human bidders tend to overbid in the auction participated. This would indicate that sellers obtain higher
revenues when such scenarios are found in their auctions. When intelligent agents are used, sellers may
assume that their revenues are reduced since these bidder agents do not overbid and always make a wider
survey such as the available auctions than human bidders can participate before making decision on which
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auction to join and the bid amount to be submitted. In this paper, we would like to investigate the market
economic effects from the perspective of bidders when they are represented by bidder agents. Moreover,
sellers’ reaction when bidder agents are introduced is also examined briefly especially when they are
confronted with bidder agents of single type as well as bidder agents of multiple types.
In this paper, a simulated marketplace is used to generate multiple English auctions and participants. The
surplus ratio, the average closing price of auctions won, the quantity of the desired items procured by winners
of various types and seller’s utility are examined. In Section 2, we discuss some related works, followed by
the discussion of the market setup in Section 3. In Section 4, the results of the experiment are elaborated.
Finally, in Section 5 we conclude and discuss avenues for future works.
2. Literature Review
2.1. English Auctions
In English auction, a price is successively raised until there is only one bidder who is willing to buy the
item being auctioned [3]. In this auction, all bids submitted are made known to every participant immediately.
Therefore, interested bidders can submit their new bids to outbid the current highest bidder. Besides that,
before an auction is started, an item may have a reserved price in which the item will only be sold if the
closing price of that auction is greater than that. This price is not known to the bidders.
One of the interesting scenarios found in English auctions is overbidding. Overbidding is a phenomenon
in which the winner of an auction finds himself paying too much for the item being auctioned after the auction
closes such as the finding in [5]. Lee and Malmendier explained that this phenomenon may be caused by the
cost of switching, the limited attention and limited memory of bidders and the extra winning utility enjoyed by
the winners. Also, auctions that attract overbidders would in turn increase the sellers’ revenue.
In another research [6], Hu and Bolivar investigated and analyzed multiple online auction properties
including consumer surplus and their cross-relationships. They found that the rareness of the item makes the
valuation process difficult, therefore bidders’ valuation vary widely leading to high surplus ratios.
2.2. Intelligent Agents
According to [7],[8], an intelligent agent is a computer system that is capable of flexible autonomous
action in order to meet its design objectives. The term flexible means that an intelligent agent must be
responsive, proactive and social. It should perceive its environment and respond in a timely fashion to changes
that occur in it. Meanwhile, it must not simply act in response to its environment. It should be able to exhibit
opportunistic, goal-directed behaviors and take the initiative where appropriate. Moreover, it must be able to
interact with its environment with other artificial agents and humans in order to complete its own problems
and to help others with their problems.
Due to these properties, agents have been implemented in many areas such as the electricity distribution
management system, CIDIM [9] and online auctions. By using bidding agents in online auctions, these agents
would reduce the burden of their users by searching and monitoring different auctions according to the
specifications provided by their users.
2.3.
Bidding Strategies of Intelligent Agents
Since the auction protocols are well-defined, users can trust their bidder agents easily to accomplish the
tasks delegated and communication among agents and with auction houses is simplified [10]. Hence, many
bidding agents and their strategies are studied by researchers.
The works of [11],[12] were studied from the heuristic approach. He et al. [13],[14] contributed their
results from the approach of neuro-fuzzy technique. Lim et al. [15],[16] studied the use of the grey prediction
model in forecasting the closing price of an online auction which would help bidders in making a better bid.
Furthermore, some of the bidding strategies are evolved from the human bidders’ behaviors such as bid
sniping as studied in [17].
3. Auction Marketplace
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3.1. Simulated Marketplace
In this work, a simulated marketplace is used to model a real auction house where multiple English
auctions are conducted. All auctions in this marketplace are based on the symmetric independent private
values (SIPV) auctions [4],[18]. It is assumed that:
a. Each auction is selling a single indivisible object (single-unit auction).
b. Bidders know their own private valuations only. Even if he knows about other bidders’ valuation, this
would not contribute to a change of his valuation.
c. All bidders are indistinguishable (symmetry).
d. Unknown valuations are independently and identically (iid) distributed and using continuous random
variables (independence, symmetry and continuity).
In this simulated marketplace, the auction parameters such as the number of auctions, the number of
bidders and the bidding strategies are determined. There are three different bidding strategies, namely the
Greedy agents [19], the Heuristic agents [11] and the Sniping agents [17]. The greedy strategy is selected
because of its bidding attribute. Some bidders may use their agents to look for auctions with the lowest current
bids with the hope to pay minimally to win an auction. Next, Heuristic agents would represent another group
of bidders who are well prepared before participating in any auction. They do not only consider the current bid,
but also other factors such as the timeline and auctions available. Lastly, the sniping strategy is taken into
account because it avoids bidding war [17]. These are the group of bidders who are keen to obtain the items
desired but are impatient with longer auction closing time.
Initially, the system prepares the marketplace by generating auctions, sellers and bidder agents based on
some predefined settings. Both sellers/bidders are assigned a reserve price/private valuation randomly based
on a normal distribution which is generated by providing a mean value and a standard deviation value of the
actual closing prices collected from online auctions. Then, bidder agents are free to submit their bids
according to their preferences. A higher bid dominates the auction. A universal time is used in this
marketplace and the time steps are discrete and indivisible. Throughout the whole bidding process of different
auctions, bidding histories are recorded for data analyses.
3.2. Bidder Agents and Bidding Strategies
There are three types of bidder agents considered in this marketplace which are the Greedy agent, the
Heuristic agent and the Sniping agent. A Greedy agent would always look for an auction with the lowest
current bid as its target auction (Fig. 1). Then it would increase the current bid randomly with an increment
from a predefined range. The agent’s objective is to pay minimally to win an auction. In the case where
multiple auctions are found to have the lowest current price, the first auction found is to be selected as the
target auction for an agent.
Next, a Heuristic agent considers four tactics in its bidding process. Firstly, the remaining time it has in
the market. Secondly, it considers the remaining auctions in the market. Thirdly, it considers the bidder’s
willingness of bargaining. Lastly, it considers the desperateness of obtaining the item. By combining these
four tactics, a new suggested bid is proposed. If the difference between the current price and the suggested bid
is greater than a preset threshold, it is further modified based on the current highest bid to avoid submitting a
high bid in an auction (Fig. 2).
Lastly, a Sniping agent would hold its bid until the last time step of an auction with the hope of outbidding
others while giving them insufficient time to react. The sniped bid is a sum of the current bid of an auction
with an increment from a predetermined range (Fig. 3). Similarly, when there is more than one auction that
closes on the last possible time step, the first auction found by an agent would be selected as its target auction.
49
while (
t < t max ) and (item not obtained = true)
Build active auctions list
List all auctions that are active before t max .
Select potential auctions from active auctions list to bid in.
Select target auction as one that has the lowest current bid.
Calculate the new bid, current bid + randomize bid increment.
Bid in the target auction with the new bid.
End while
where
t is the current universal time across all auctions;
t max
is the agent’s allocated bidding time by when it must obtain
the goods or leave the auctions.
Figure 1. The Top-level Algorithm for the Greedy Agent.
while (
t < t max ) and (item not obtained = true)
Build active auctions list
List all auctions that are active before t max .
Calculate the new suggested bid using the agent’s strategy.
If the difference (suggested bid, current bid) > a preset threshold,
new suggested bid = current bid + a portion of difference
Select potential auctions from active auctions list to bid in.
Select target auction as one that maximizes agent’s expected
utility.
Bid in the target auction with the new suggested bid.
End while
where
t is the current universal time across all auctions;
t max
is the agent’s allocated bidding time by when it must obtain
the goods or leave the auctions.
Figure 2. The Top-level Algorithm for the Heuristic Bidding Agent.
while (
t < t max ) and (item not obtained = true)
Build active auctions list
List all auctions that are active before t max .
Select potential auctions from active auctions list to bid in.
Select target auction as one that has t = end time - 1.
Calculate the new bid, current bid + randomize bid increment.
Bid in the target auction with the new bid.
End while
where
t is the current universal time across all auctions;
t max
is the agent’s allocated bidding time by when it must obtain
the goods or leave the auctions.
Figure 3. The Top-level Algorithm for the Sniping Agent.
4. Experimental Analysis
4.1.
Experimental Setup
In this experiment, markets fully populated by agents of the same type and agents of various types are
simulated separately. Here, we are interested to study the competition that occurs among homogeneous bidder
agents as well as the competition that occurs among heterogeneous bidder agents. In the first experiment, a
marketplace fully occupied by Heuristic agents is considered. 90 auctions are generated and the performances
of 900 Heuristic agents are evaluated (homogeneous environment). In the second experiment, 900 bidder
agents with various bidding strategies participate in 90 auctions generated (heterogeneous environment). In
both experiments, the same market is conducted repeatedly 10 times. After that, the performances of these
bidder agents are analysed and a comparison between the performances obtained in both experiments is
conducted.
In the first measurement, the consumer surplus ratio (CSR) introduced in [6] is calculated as follow:
50
⎛ (V − VFi ) ⋅ ( Ni + N m ) ⎞
CSR j = Median∀ j ⎜ H i
⎟
⎝ VFi ⋅ Ni + VHi ⋅ N m ⎠
(1)
where j is the type of winners, VHi is the winner’s private valuation in auction i, V is the final winning bid in
Fi
auction i, Ni is the number of bids of item iand Nm is the median number of bids across all the auctions
conducted. By considering the number of bids found in an auction and also the median number of bids across
all the auctions conducted, the factor of number of bids received is minimized as in various auctions; some
auctions may only receive a small number of bids compared to others which in turn would affect the closing
price of that auction.
Secondly, auctions won by different groups of agents are counted by using the following equation:
Wi =
∑
n
2
number of auctions won by typei
n
(2)
where Wi is the average number of auctions won by winners of type i and n is the number of runs conducted
in the experiment.
On the other hand, the average seller’s utility is calculated. As suggested by [20], sellers extract more
revenues from auctions with higher number of bidders. In this market, bidder agents may submit their bids in
any auction at their will. Some of the auctions may receive more bids compared to other auctions. Therefore,
by using the ratio instead of the surplus, the factor of having different values of winning bids in different
auctions (due to the number of bids received in an auction) is minimized. The equation is given as below:
R j (v) =
V j − RPj
Vj
and
∑
R (v ) =
n
j =1
(3)
R j (V )
n
(4)
where
is the seller’s utility of auction j, V is the winning bid of auction j, RPj is the seller’s reserved price
j
of auctionj, R (v) is the average seller’s utility and n is the number of auctions that are closed with winner.
R j (V )
4.2. Results and Discussions
Table I shows the results obtained from both of the experiments. On average, winners of type Heuristic
received 0.1315 as their average CSR in homogeneous environment. On the other hand, Greedy agents,
Heuristic agents and Sniping agents obtained 0.0817, 0.1013 and 0.0110 respectively as their average CSR in
the heterogeneous environment. A high value in this CSR would indicate that the winner receives a high
surplus compared to his own private valuation after minimizing the influence of the number of bids received
in an auction. The Sniping agents received the lowest utility because they ignored the auctions that are not
closing soon but may return to them a better utility if they were winning in these auctions. When comparing
the average CSR between Heuristic agents in Env 1 and Env 2, they received lower ratio as competition arises
among groups of various agents.
Next, 22.6 and 87.8 auctions on average are closed with winners in Env 1 and Env 2 respectively. In Env
1, since all of these Heuristic agents are equipped with the same strategy and are free to join any auction, they
may end up bidding in similar auctions while neglecting the less promising auctions. Consequently, 74.89% of
the auctions are closed without winners. Conversely, when various types of agents are populated in the market,
they may select different auctions to join based on their strategies and thus encourage more successful trades.
From Table I, Heuristic agents obtained 53.56% of the items being auctioned (in Env 2) in the market. This
can be credited to their auction selection strategy that always bid in the most promising auctions based on the
expected utility calculated and their bidding strategy that considers the available auction information. It is
worth noticing that Sniping agents obtained more items than Greedy agents due to their sniping capability of
giving insufficient time to their counterparts to react. Besides that, simply looking at the lowest current bid
may lead the agents to jump from an auction with higher probability of winning to other more risky auctions.
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From Table I also, other information is retrieved. On average, each auction that is closed with a winner
has a closing price of 373.1478 (in Env 1) and 344.6544 (in Env 2). In addition, from the sellers’ perspective,
they received 0.3088 and 0.2497 as their average utility in Env 1 and Env 2 respectively. This utility indicates
the gross margin of the sellers. In other words, generally sellers of the completed auctions gained 30.88% and
24.97% (after multiplying with 100%) from their payments received as their profit in both environments.
Lastly, on average, there are 26.8 bids (Env 1) and 14.7 bids (Env 2) received in every completed auction
(those that were closed with winner).
By comparing the results from both experiments, when homogeneous agents are competing with one
another in a market, their average auction closing price is higher than the average auction closing price in a
market of heterogeneous agents. Those homogeneous agents with similar bidding strategy would most likely
participate in few auctions and neglect the rest of the auctions unattended. Therefore, the final closing price
increases as the competition among buyers increases. It is supported by the average median number of bids
submitted in a completed auction (26.8 bids and 14.7 bids in Env 1 and Env 2 respectively). This finding is
also consistent with the suggestion in [20].
It is observed that sellers achieved 30.88% and 24.97% profit margin from markets fully populated by
homogeneous and heterogeneous agents respectively. Even though a market with single type of agents
produced a higher returned margin, in terms of the number of successful auctions, the market fully resided by
homogeneous agents is worse than the market fully occupied by agents of various types. With a total of 90
English auctions generated, on average, 22.6 items were successfully sold in the former market compared to
87.8 items traded successfully in the latter market. Hence, if the average seller’s profit is calculated based on
the average auction closing price, the average seller’s utility and the average number of auctions that are
traded successfully, the former market would generate 2604.1537 compared to the latter market that would
produce 7556.0859. Based on the profit calculated, sellers may prefer heterogeneous bidder agents instead of
homogeneous bidder agents as their auctions’ participants since they bring more profit to sellers.
5. Conclusion and Future Works
In conclusion, the performances of using agents in bidding auctions are tested in a simulated marketplace.
From the experiment, market economy is studied when it is populated by homogeneous agents and
heterogeneous agents respectively. By analyzing the empirical results obtained, in a market that is fully
populated by homogeneous bidder agents, even though they obtained the highest average CSR (0.1315)
compared to their value (0.1013) found in a market fully occupied by various types of agents, they performed
badly in terms of the number of winning auctions (25.11%).
TABLE I.
PERFORMANCES OF HOMOGENEOUS AND HETEROGENEOUS AGENTS IN A MARKET OF 90 ENGLISH
AUCTIONS GENERATED.
Performances
Average CSR
Average Winning Auction
Average Auction Closing Price
Average Seller’s Utility
Average Median Number of Bids Received in a
Completed Auction
Homogeneous (Env 1)
Heterogeneous (Env 2)
A Market populated by Homogeneous Agents
A Market populated by Heterogeneous Agents
Heuristic
0.1315
Greedy
0.0817
22.6
16.5
Heuristic
0.1013
Sniping
0.0110
48.2
23.1
373.1478
344.6544
0.3088
0.2497
26.8
14.7
This finding is similar to the results obtained in [21] which stated that multiple strategic buyers with the
same strategy performed worse than their performance in situation where other types of bidders are present.
Meanwhile, a market fully populated by heterogeneous bidder agents may achieve higher closing rate of
97.56% due to their various bidding strategies that led them to different auctions. Eventually, a healthier
competitive environment was created. On the other hand, as the online auction marketplaces are evolving
towards the implementation of agent technology, sellers would prefer the participation of heterogeneous
bidder agents compared to homogeneous bidder agents since it guarantees more sales and profit.
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There are still many interesting aspects which are not included in this work, one of them would be the
capability of prediction. It would be useful if agents with prediction capability are implemented in
participating auctions.
6. Acknowledgment
This project is currently funded by the Ministry of Science, Technology and Innovation (MOSTI) of
Malaysia.
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