Methodologies for the design of negotiation protocols on E-markets

Computer Networks 37 (2001) 137±152

www.elsevier.com/locate/comnet

Methodologies for the design of negotiation protocols on E-markets Martin Bichler a,*, Arie Segev b b

a IBM T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY 10598, USA Fisher Center for IT and Marketplace Transformation, Walter A. Haas School of Business, University of California, Berkeley, CA 94720-1930, USA

Abstract Markets play a central role in the economy, facilitating the exchange of information, goods, services, and payments. In recent years, there has been an enormous increase in the role of information technology, culminating in the emergence of electronic marketplaces. Negotiations are at the core of each negotiation. Economists, game theorists and computer scientists have started to take a direct role and designed di€erent kinds of negotiation protocols for computer products, travel, insurances, or electric power. The design of negotiation protocols is a challenging research direction and involves a number of disciplines including information systems development, game theory, mechanism design theory, simulation and laboratory experimentation. In this paper we survey the key techniques for the design of negotiation protocols and describe the main steps in the design of a multi-attribute auction protocol for a ®nancial market. The case illustrates the interplay of the various methodologies. Ó 2001 Elsevier Science B.V. All rights reserved. Keywords: Electronic market; Negotiation protocol; Experimental economics; Mechanism design

1. Introduction The emergence of the Internet and the World Wide Web has led to the creation of new electronic market places. An electronic market system can reduce customers' costs of obtaining information about the prices and product o€erings of alternative suppliers as well as suppliers' costs of communicating information and negotiating about the prices and product characteristics [4]. The past few years have shown an enormous growth in the number of Internet marketplaces. Electronic catalogs were the ®rst step in this direction. Many

companies are now moving beyond simple price setting and online order taking to creating entirely new electronic marketplaces. These companies are setting up marketplaces for trading products such as phone minutes, gas supplies, and electronic components, a ®eld that is expected to grow enormously over the next few years [10]. Like stock exchanges, these electronic markets must set up mechanisms for clearing transactions and for making sure that both buyers and sellers are satis®ed. The most widely used form of market mechanism today is online auctions. Companies like Onsale 1 or eBay 2 run live auctions where

*

Corresponding author. E-mail addresses: [email protected] (M. Bichler), segev@ haas.berkeley.edu (A. Segev).

1 2

http://www.onsale.com http://www.ebay.com

1389-1286/01/$ - see front matter Ó 2001 Elsevier Science B.V. All rights reserved. PII: S 1 3 8 9 - 1 2 8 6 ( 0 1 ) 0 0 2 1 2 - 2

138

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

people outbid one another for computer gear, electronic components, and sports equipment. Di€erent market mechanisms are appropriate in di€erent situations and there is not a single solution for the various negotiation situations. What is so special about the design of ``electronic'' market mechanisms is the fact that a designer has many more possibilities to design a mechanism than in physical markets. Computer networks make it easy to communicate large amounts of information relevant to a market transaction, not just price quotes. Using decision support software it is possible to analyze this information quickly, in order to make more informed, and hopefully, better decisions. The basic question is, what is a good market mechanism for a given marketplace? Unfortunately, to date there is no general, computational theory of negotiations and the design of market mechanisms. The design of electronic markets is a challenging task and involves a number of disciplines. This paper is an approach towards establishing a toolset for the design of negotiation protocols on electronic markets. This ®eld is also referred to as ``market design'' by many economists [30], although, the scope of a market transaction in general is much wider including tasks such as information gathering as well as settlement. In the next section, we survey the most important methods including game theory, mechanism design theory, simulation and laboratory experimentation. Then, Section 3 describes the design of a multi-attribute auction mechanism for the trading over-the-counter (OTC) ®nancial derivatives. Although, this describes a very particular market, it illustrates, how multiple techniques can help in answering the strategic questions of a certain negotiation situation. Finally, in Section 4 we summarize the ®ndings and outline open research questions in this ®eld. Throughout the paper we will use the terms ``negotiation protocol'' and ``market mechanism'' interchangeably. 2. Design methodologies Market design creates a meeting place for buyers and sellers and a format for transactions.

Recently economists, game theorists and computer scientists have started to take a direct role and designed di€erent kinds of market mechanisms for electronic markets, e.g., auction markets for electric power, railroad schedules, and procurement markets for electric components. A recent example for successful market design was the case of radio spectrum auctions by the US Federal Communications Commission (FCC). Between 1994 and early 1997, the FCC raised US$ 23 billion from 13 auctions. The diculty was to design an auction procedure to promote price discovery of complicated, inter-related packages of spectrum rights in di€erent regions. Because licenses may be more valuable in combination than separately, it was necessary to allow bidders to bid on bundles, in a way that allowed them to change packages in response to price changes during the auction. For this reason, multi-round auctions were adopted, and much of the design focus was on rules intended to promote ecient price discovery, by preventing bidders from concealing their interest in certain licenses and then bidding at the last minute. Economists successfully deployed game-theoretical analysis in order to design the bidding process in the case of the FCC spectrum auctions. Game theory is important, but it is by far not the only technique needed for successful market design. Electronic market design practice is in its early stages and comprises di€erent methodologies from economics and computer science. 2.1. Game-theoretic analysis of negotiations The classic microeconomic theory of general equilibrium as formulated by Leon Walras and re®ned by his successors Samuleson [34], Arrow [1] and others depicts the outcome of competition, but not the activity of competing. Game-theoretic models, by contrast, view competition as a process of strategic decision making under uncertainty. It has only been relatively recently that the depth and breadth of robust game-theoretic knowledge has been sucient so that game theorists could o€er practical advice on institutional design. The amount of literature in this ®eld is huge and in this section we can only introduce the most basic

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

concepts. For a more rigorous discussion see [2,11]. Nash [27,28] initiated two related, in¯uential approaches. He proposed a model which predicted an outcome of bargaining based only on information about each bargainer's preferences, as modeled by an expected utility function over the set of feasible agreements and the outcome which would result in case of disagreement. Nash described a two-person multi-item bargaining problem with complete information and used the utility theory of von Neumann and Morgenstern [42]. Nash's approach has in¯uenced many researchers and initiated extensions like the analysis of repeated or sequential bargaining games. In sequential games the players do not bid at the same time, but one player moves and then the other player responds. These dynamic games are more dicult to solve than static ones. Rubinstein [32] calculated perfect equilibrium in a bargaining model that involved a pie, two players, and sequential alternating o€ers of how to split the pie. Each player has his own di€erent cost per time period. Rubinstein shows that if player one has a lower time cost than player two, the entire pie will go to player one. Harsanyi and Selten [13] extended Nash's theory of two-person bargaining games with complete information to bargaining situations with incomplete information and found that there are several equilibria; a shortcoming is due to the fact that these models have little predictive power. Besides bargaining, single-sided auction mechanisms such as the English, Vickrey, Dutch and ®rst-price sealed-bid auctions have been another very active area of game-theoretical analysis (see, for example, [23,26]). The most thoroughly researched auction model is the symmetric independent private values (SIPV) model. In this model all bidders are symmetric/indistinguishable and all bidders have a private evaluation for the good, which is independent and identically distributed. The bidders are risk neutral concerning their chance of winning the auction, and so is the seller. Under these assumptions, the bidders' behavior can be modeled as a non-cooperative game under incomplete information. An example of an object which ®ts the SIPV model would be a work of art purchased purely for enjoyment. It is interesting to

139

®nd out under these assumptions, whether the auctions achieve the same equilibrium price, or if we can rank the di€erent auction formats in any order. The surprising outcome of the SIPV model is that with risk neutral bidders all four auction formats are payo€ equivalent. This is also known as the revenue equivalence theorem (see [29] or [43], p. 372 €), which does not hold if we remove some of the basic assumptions of the SIPV (such as risk neutrality of bidders). Another approach to analyze auctions is the common value model, which assumes that valuation of an object to a bidder is determined both by the private signal mentioned above and also by one or more external factors, such as its resale value or the opinions of other bidders. A frequently observed phenomenon in these auctions is the so-called winner's curse, where the winner bids more than the good's true value and su€ers a loss. The main lesson learned from the common value model is that bidders should shade their bids, as the auction always selects the winning bidder as the one who received the most optimistic estimate of the item's value. Although some of their qualitative predictions have received some support, the existing models have performed poorly as point predictors in laboratory experiments [17]. For example, Balakrishnan et al. [5] points to several empirical studies, some of which suggest that fundamental concepts in game theory fail. There has also been criticism concerning many of the basic assumptions of game-theoretical auction models and their validity for real-world environments [31]. Nevertheless, game theory, together with experimental economics, has lead to a considerable knowledge about auction mechanisms. Moreover, game theory is often seen as a basic guideline for implementing negotiation strategies in agent-based environments where software agents behave in a more rational way and have greater computational abilities than human agents [40]. 2.2. Mechanism design theory Hurwicz traditional analysis to institutions

[15] was one of the ®rst to go beyond equilibrium and game-theoretical actively focus on the design of new and resource allocation mechanisms.

140

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

Mechanism design theory di€ers from game theory in that game theory takes the rules of the game as given, while mechanism design theory asks about the consequences of di€erent types of rules. Mechanism design theory provides helpful guidelines for electronic market design. Formally, a mechanism M maps messages or signals from the agents, S ˆ fs1 ; . . . ; sm g, into a solution as a function M : S ! f of the information that is known by the individuals. An important type of mechanisms in this context is a direct revelation mechanism in which agents are asked to report their true private information con®dentially. Hurwicz mentions a number of criteria for a new mechanism, which he calls (Pareto-) satisfactoriness. First, the outcome of the mechanism should be feasible. In addition, one would wish that the mechanism possesses some equilibrium for every class of environments it is designed to cover and that it produces a uniquely determined allocation, i.e., only a single equilibrium price. Finally, a mechanism should be non-wasteful, i.e., (Pareto-) ecient. Formally, a solution, f, is Pareto-ecient if there is no other solution, g, such that there is some agent j for which Uj …rjg † > Uj …rjf † and for all agents k; Uk …rkg † P Uk …rkf †. Hurwicz has also stressed that incentive constraints should be considered coequally with resource (and budget) constraints that are the focus of classic microeconomic models. The need to give people an incentive to share private information and exert e€orts may impose constraints on economic systems just as much as the limited availability of resources. Incentive compatibility is the concept introduced by Hurwicz [15] to characterize those mechanisms for which participants in the process would not ®nd it advantageous to violate the rules of the mechanism. If a direct mechanism is incentive compatible then each agent knows that his best strategy is to follow the rules, no matter what the other agents will do. Such a strategic structure is referred to as a dominant strategy game and has the property that no agent needs to know or predict anything about the others' behavior. Gibbard [12] made a helpful observation that is now called the revelation principle. In order to ®nd the maximum ecient mechanism, it is sucient to

consider only direct revelation mechanisms. In other words, for any equilibrium of any arbitrary mechanism, there is an incentive-compatible direct-revelation mechanism that is essentially equivalent. Therefore, by analyzing incentivecompatible direct-revelation mechanisms, one can characterize what can be accomplished in all possible equilibria of all possible mechanisms. In the following we summarize some of the most important guidelines one can derive from game theory and mechanism design, relevant to the design of electronic markets. · The solution of a mechanism is in equilibrium, if no agent wishes to change its message given the information it has about other agents. When designing a mechanism, one would like to know, if it converges towards equilibrium and if it produces a uniquely determined allocation. In a Nash equilibrium each agent maximizes his expected utility, given the strategy of the other agent. · A general criterion for evaluating a mechanism is Pareto eciency, meaning that no agent could improve its allocation without making another agent worse o€. In the Prisoner's dilemma, for example, the Nash equilibrium, in which both players defect, is not a Pareto ecient solution. · The solution of a mechanism is stable, or in the core [41, p. 388], if there is no subset of agents that could have done better by coming to an agreement outside the mechanism. If a mechanism is stable, then it is Pareto ecient, although the reverse is not true [39]. · A direct auction is incentive compatible if honest reporting of valuations is a Nash-equilibrium. A particularly strong and strategically simple case is an auction where truth telling is a dominant strategy. This is a desirable feature because an agent's decision depends only on its local information, and it gains no advantage by expending e€ort to model other agents [22,26]. Mechanisms that require agents to learn or estimate other's private information do not respect privacy. It is important to keep these guidelines in mind, when designing a new mechanism. However, the designer of an electronic marketplace has to solve numerous ®ner grained problems. One has to

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

de®ne the closing conditions such as the elapsed time in an open-cry auction and the deadline for sealed-bid auctions. In all auctions one can specify minimum starting bids and minimum bid increments. In addition, one has to decide about participation fees, the possibility of multiple rounds, etc. Prototyping and laboratory experiments can be a valuable aid. 2.3. Computational economics and simulation Computational methods have spread across the broad front of economics to the point that there is now almost no area of economic research that is not deeply a€ected. Although computational exploration of markets is relatively new to economic research, there are several examples where researchers successfully deployed computational methods as a tool to study complex environments. In their 1991 research report on computational economics Kendrick [19] mentions that ``. . . simulation studies for devising institutions that improve markets such as varieties of electronic market making and looking at search techniques of market participants and their results are necessary''. The traditional economic models are based on a top±down view of markets or transactions. In general equilibrium theory, for example, solutions depend on an omnipotent auctioneer who brings all production and consumption plans in the economy into agreement. The mathematical modeling of dynamic systems such as arti®cial societies and markets often requires too many simpli®cations, and the resulting models may not be therefore valid. Operations research and system sciences often use simulation methods for the purpose of analyzing stochastic problems, which would require very complex mathematical models. Advances in information technology have made it possible to study representations of complex dynamic systems that are far too complex for analytical methods, such as weather forecasting. Also, a number of economists have started to explore di€erent approaches to economic modeling. Here the model-maker has to specify in detail how agents evaluate information, form expectations, evolve strategies and execute their plans.

141

Simulation is an appropriate methodology in all of these cases. This newly developing ®eld, also called agent-based computational economics (ACE), is roughly de®ned as the computational study of economies modeled as evolving decentralized systems of autonomous interacting agents [36], and is a specialization of the economics of the basic arti®cial life paradigm [37]. The models address questions that are often ignored in analytical theory, such as the role of learning, institutions and organization. A central problem for ACE is to understand the apparently spontaneous appearance of regularity in economic processes, such as the unplanned coordination of trading activities in decentralized market economies that economists associate with Adam Smith's invisible hand. Agents in ACE models are typically modeled as heterogeneous entities that determine their interactions with other agents and with their environment on the basis of internalized data and behavioral rules. That is, they tend to have a great deal more internal cognitive structure and autonomy than what can be represented in mathematical models. Evolution and a broader range of agent interactions are typically permitted in ACE models. A good example is the trade network game (TNG) developed by Tesfatsion [38] for studying the formation and evolution of trade networks. TNG consists of successive generations of resource-constrained traders who choose and refuse trade partners on the basis of continually updated expected payo€s, and evolve their trade strategies over time. Each agent is instantiated as an autonomous, endogenously interacting software agent with internally stored state information and with internal behavioral rules. The agents can therefore engage in anticipatory behavior. Moreover, they can communicate with each other at event-triggered times. Experimentation with alternative speci®cations for market structure, search and matching among traders, expectation formation, and evolution of trade site strategies can easily be undertaken. Roth [30] used computational experiments and simulations in order to test the design for professional labor markets: ``Computational methods will help us analyze games that may be too

142

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

complex to solve analytically''. When game theory is used primarily as a conceptual tool, it is a great virtue to concentrate on very simple games. Computation can play di€erent roles, from explorations of alternative design choices, to data exploration, to theoretical computation (i.e., from using computational experiments to test alternative designs, to directly exploring complex market data, to exploring related simple models in ways that nevertheless elude simple analytical solutions). When analyzing the economic behavior of complex matching mechanisms simulation can be an excellent supplement to the tool set of game theory. 2.4. Experimental economics Laboratory experiments are an important complement to the set of methods we described so far. They help to inform us about how people behave, not only in environments too complex to analyze analytically, but also in simple environments (in which economists' customary assumptions about behavior may not always be such good approximations). For market design it is useful to study new market mechanisms in the laboratory before introducing them in the ®eld. Laboratory experimentation can facilitate the interplay between the evolution and modi®cation of proposed new exchange institutions. Experimenters can repeat testing to understand and improve the features of new market mechanisms. When analyzing mechanisms such as auctions or one-to-one bargaining the experimental literature is particularly large. Many experimental observations of the outcomes of various types of auctions examine game-theoretic hypothesis such as the revenue equivalence theorem [16]. Others are designed not to test mathematically precise theories, but rather to test proposed new market mechanisms. McCabe, Rassenti and Smith [24], for example, compare the properties of several new market institutions whose theoretical properties are as yet poorly understood. Banks et al. [6] tested innovative mechanisms for allocating and pricing a planned space station. Although the results of laboratory experiments are interesting, there is still a question, if one can

generalize the ®ndings of experimental tests. Experimental sciences use ``induction'' as an underlying principle and assume that regularities observed will persist as long as the relevant underlying conditions remain substantially unchanged. What makes experiments so di€erent from other methods microeconomists use is the presence of human subjects. Vernon Smith [35] refers to this question as the ``parallelism precept'': ``Propositions about the behavior of individuals and the performance of institutions that have been tested in laboratory microeconomies apply also to non-laboratory microeconomies where similar ceteris paribus conditions hold.'' Nowadays, experiments are commonplace in game theory, ®nance, electronic commerce and many other ®elds. 3. Design of a matching mechanism for OTC derivatives In the previous section we outlined some of the basic methodologies for electronic market design. Here we describe the design of a particular market mechanism for OTC ®nancial derivatives (although the mechanism itself might well be suited for other domains). The section summarizes the result of a research project, which we have conducted during the past two years. It describes how methods from various disciplines can help gain an understanding about a new market mechanism. We will concentrate on auction mechanisms and omit other multi-lateral negotiation protocols such as unstructured bidding, following the standard view among economists that an auction is an e€ective way of resolving the one-to-many or many-to-many negotiation problem. For example, Milgrom [25] shows that of a wide variety of feasible selling mechanisms, conducting an auction without a reserve price is an expected-revenuemaximizing mechanism. Auctions use the power of competition to drive the negotiation, and their simple procedural rules for resolving multi-lateral bargaining enjoys wide popularity. Traditionally, auctions are a means for automating price-only negotiations. In the ®eld of OTC derivatives, however, it is important to support negotiations on a wider variety of attributes.

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

3.1. Trading ®nancial derivatives Automating negotiations in OTC ®nancial markets is much harder than traditional ®nancial markets because products are not standardized and therefore one has to negotiate on more than just the price. In the following we will provide a brief introduction to OTC derivatives trading, in order to illustrate the special needs of a market mechanism in this domain. Financial derivative instruments comprise a wide variety of ®nancial contracts and securities, including forwards, options, futures, swaps and warrants. Banks, securities ®rms, or other ®nancial institutions are intermediaries who principally enable end-users to enter into derivative contracts. An option is the right to buy (call) or sell (put) an underlying instrument at a ®xed point in time at a strike price. It is bought by paying the option premium/price upon conclusion of the contract and restricts the risk of the buyer to this premium. For example, the holder of a call purchases from the seller of the call the right to demand delivery of the underlying contract at the agreed price any time upon (American style) or exactly at (European style) the expiration of the option contract. The strategies of market participants as well as their valuations for various product attributes depend on the investor's market expectations, the investor's objective and risk tolerance and the chosen market. Whereas standardized options are traded on an exchange, OTC options are traded o€-¯oor. In general, option contracts are based on a number of preset terms and criteria: · type of option (call or put), · style (American or European), · underlying instrument and price, · contract size or number of underlying instruments, · maturity, and · strike price. All these criteria in¯uence the option premium no matter whether the options are traded on an exchange or OTC. For example, every change in the price of the underlying asset is re¯ected by a change in the option premium. In order to set a certain option premium in the context of its strike price and other parameters, traders often use the

143

so-called implied volatility which indicates the volatility implied by a certain market price. Thus the value of a certain price can be measured independent of the strike price. For many traders it has become common practice to quote an option's market price in terms of implied volatility [21]. On an exchange all of these attributes are speci®ed in advance and the only negotiable attribute is the price. This makes trading much easier, however, it reduces the number of derivatives traded to a small set of possible products. Financial engineers created a whole bunch of di€erent ®nancial OTC products tailored for speci®c purposes, ranging from plain vanilla options where all important attributes are negotiated during the bargaining process, to exotic derivatives with certain prede®ned properties (see [20] for di€erent types of options and details of option pricing). Trading OTC options is not bound to an organizational structure in that supply and demand are concentrated in a centralized trading ¯oor. Potential buyers of OTC options bargain with a number of investment brokers or banks on attributes such as the strike price, the style, the maturity and the premium of an option. Terms and conditions are usually not determined by auction, but by way of bargaining. On the one hand, bargaining with banks or investment brokers is conducted over the phone, leading to high transaction costs for a deal. Unlike electronic exchanges, investors lose their anonymity and also have to bear the contracting risk. On the other hand, negotiating on several attributes gives a participant many degrees of freedom during the negotiation and has the potential to achieve a better deal for both parties. 3.2. Multi-attribute auctions It would be useful in this context to have a mechanism that takes multiple attributes of a deal into account when allocating it to a bidder. In other words, the mechanism should automate multi-lateral negotiations on multiple attributes of a deal. We have taken a heuristic approach and proposed a set of multi-attribute auction mechanisms. The buyer ®rst has to de®ne his preferences for a certain product in the form of a utility

144

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

function. The buyer has to reveal this utility function to suppliers whereas the suppliers do not have to disclose their private values. Then the mechanism designates the contract to the supplier who best ful®lls the buyer's preferences, i.e., who provides the highest overall utility for the buyer. This way, traditional auction mechanisms are extended to supporting multi-attribute negotiations. Formally, a bid received by the auctioneer can be described as a vector Q of n relevant attributes indexed by i. We have a set B of bids and index the m bids by j. A vector xj ˆ …x1j . . . xnj † can be speci®ed, where xij is the level of attribute i in bid bj . In the case of an additive scoring function S…xj † the buyer evaluates each relevant attribute xij through a scoring function Si …xij †. Under the assumption that an additive scoring function corresponds to the buyer's true utility function U …xj †, the individual scoring function S : Q ! R, translates the value of an attribute into ``utility units''. The overall utility S…xj † for a bid bj is then given by the sum of all individual scorings of the attributes. For a bid bj that has values x1j . . . xnj on the n relevant attributes, the overall utility for a bid is given by S…xj † ˆ

n X iˆ1

wi Si …xji † and

n X

wi ˆ 1:

…1†

iˆ1

In this scoring function we use weights wi in order to express the importance of the various attributes. A reasonable objective in allocating the deal to the suppliers is to allocate them in a way that maximizes the utility for the buyer, i.e., selecting the supplier's bid with the highest overall utility for the buyer. This function max S…xj † (and 1 < j < m) gives us the utility of the winning bid and can be determined through various auction schemes. In a so-called ®rst-score sealed bid auction the winner gets a contract awarded containing the attributes xj of the winning bid. The multi-attribute English auction (also ®rst-score open-cry auction) works in the same way, except that all bids are made available to the participants during an auction period. In a second-score sealed-bid auction we take the overall utility achieved by the second highest bid Smax 1 and transform the gap to the highest overall utility …Smax Smax 1 † into implied volatility. Consequently, the winning bidder can

charge a higher option price in the contract. In the ®rst-score and second-score sealed bid schemes the auction closes after a certain pre-announced deadline. In a multi-attribute English auction, bids are made public and the auction closes after a certain elapsed time in which no further bids are submitted. 3.3. An internet-based marketplace for OTC derivatives Based on the above ideas we implemented an Internet-based marketplace for OTC derivatives. The electronic market system implements three multi-attribute auction mechanisms through the use of a buyer's client and a bidder's client. In a ®rst step, a buyer speci®es his utility, i.e., scoring function for the bidders using a Java applet which can be downloaded over the Web (see Fig. 1). Eliciting the buyers' preferences is one of the key problems that needs to be addressed by the graphical user interface of the applet. We then need to map the buyer's preferences, from input by the applet into coherent utility functions. Researchers in the ®eld of operations research and business administration attempted to utilize utility theory in order to actively make decisions. In our work, we adopt those concepts of classic utility theory and decision analysis in order to determine the buyer's utility function. Nowadays, decision analysis techniques such as the multi-attribute utility theory (MAUT) [9,18], the analytic hierarchy process (AHP) [33] and conjoint analysis [3] are used in a broad range of software packages for decision making and can also be used to determine the utility function of a buyer. In our current implementation we use MAUT with an additive utility function. Fig. 1 shows a screenshot of the Java applet we use in our implementation on the buyer side. The user interface consists of several areas. In the upper left ®eld the buyer supplies a unique identi®er, which he gets upon registration through a WWW form. Below we ®nd a list of relevant attributes for the auction. The negotiable attributes in this case are the strike price and the implied volatility. ``Duration'', i.e., maturity and ``style'' are ®xed in advance. In the lower left panel users can de®ne

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

145

Fig. 1. Buyer client.

the individual utility functions for the negotiable attributes, which can be either a continuous or discrete functions. From the input of the buyer the applet compiles a request for bids (RFB) in XML format and sends the RFB via HTTP to an electronic brokerage service. The RFB contains the bidder ID, the product description and the parameters for the additive utility function. The brokerage service parses the RFB, retains all the important data in a database and informs potential bidders via e-mail. After the auction begins the buyer can query a list of bids submitted on the right hand side of the applet, ranked by overall utility (third column). By clicking on a certain bid the buyer can see the details of every bid in the form of green numbers on the left-hand side of the applet. Bidders, on the other hand, download the RFB from the URL

they received via e-mail to a bidder client, allowing them to enter parameters for all negotiable attributes and to upload an XML-formatted bid via HTTP to the brokerage service. 3.4. Research questions The implementation of the electronic marketplace is helpful in obtaining a detailed understanding of the procedure. However, for the deployment of a new market mechanism in the ®eld it is very important to understand the economic behavior of this new allocation scheme. The following is a list of selected questions, which are important for the e€ective deployment of multiattribute auctions in an electronic market: · Do all multi-attribute auction formats achieve the same results?

146

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

As we have seen, similar to conventional auction theory there are various multi-attribute auction formats, namely the English, the ®rst score, the second score and the Dutch multi-attribute auction. The question is if one of these formats is better than the other ones in terms of seller revenue. · Are the equilibrium values achieved in a multi-attribute auction higher compared to single-attribute auctions with respect to the underlying utility function of the bid taker? A basic question of auction design is which auction format maximizes the bid takers pro®t. In a multi-attribute auction, the bidder has several possibilities to improve the value of a bid for the bid taker, sometimes even without increasing her costs and thereby creating joint gains for all parties. A more speci®c question is, how the number of negotiable attributes impacts the results. · Are multi-attribute and single-attribute auctions ecient? Allocation's eciency can be measured in terms of the percentage of auctions where the high value holder wins the item. We want to learn about the eciency of multi-attribute auctions compared to single-attribute auctions. Of course there are many more interesting research questions. However, in this paper we want to focus on these most important ones. In the next section we will show how di€erent methodologies can be used to tackle the various questions. 4. Game-theoretic analyses One approach is game theory. Only little gametheoretical work has been done in the ®eld of multi-attribute auctions so far. A thorough analysis of the design of multi-attribute auctions has been provided by Che [8]. Che studied design competition in government procurement by a model of two-dimensional auctions, where ®rms bid on price and quality. He focuses on an optimal mechanism in cases where bids are evaluated by a scoring rule designed by the procurer. Each bid contains a quality, q, and a price, p, and quantity in this model is normalized to one. The buyer in

this model derives a utility from a contract comprising q and p U …q; p† ˆ V …q†

p;

…2†

where V is the individual utility function of quality. On the other hand a winning ®rm earns pro®ts from a contract …q; p† pi …q; p† ˆ p

c…q; hi †

…3†

In the cost function c the unit cost is expressed as h which is private information. h is assumed to be independently and identically distributed. Losing ®rms earn zero pro®ts and trade always takes place, even with a very high h: In Che's model, an optimal multi-attribute auction selects the ®rm with the lowest h. The winning ®rm is induced to choose quality q which maximizes V …q† considering the costs. Che considers three auction rules: In a so-called ``®rst-score'' auction ± a simple generalization of the ®rst-price auction, each ®rm submits a sealed bid and, upon winning, produces the o€ered quality at the o€ered price. In other auction rules, labeled ``second-score'' and ``second-preferred-offer'' auctions, the winner is required to match the highest rejected score in the contract. The secondscore auction di€ers from the second-preferredo€er auction in that the latter requires the winner to match the exact quality-price combination of the highest rejected bid while the former has no such constraint. A contract is awarded to the ®rm whose bid achieves the highest score in a scoring rule S ˆ S…q; p†. In the model it can be shown, that the equilibrium in the ®rst-score auction is reduced to the equilibrium in the ®rst price auction if the quality is ®xed. An important question analyzed by Che [8] tries to discover the optimal scoring rule for the buyer. He showed that if the scoring function under-rewards quality compared to the utility function, ®rst- and second-score auctions implement an optimal mechanism. This is true, because the true utility function fails to internalize the informational costs associated with increasing quality. Che also shows that if the buyer's scoring function re¯ects the buyer's preference ordering, i.e., equals his utility function, all three auction schemes yield the same expected utility to the

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

buyer. This is an initial answer to our ®rst research question in Section 3.4 and a two-dimensional extension of the revenue equivalence theorem. The costs in Che's model are assumed to be independent across ®rms. In the context of procurement auctions one might expect the costs of the several bidders not to be independent. Branco [7] derives an optimal auction mechanism for the case when the bidding ®rms' costs are correlated, but the initial information of ®rms is independent. He shows that when the quality of the item is an issue, the existence of correlations among the costs has signi®cant e€ects on the design of optimal multi-attribute auctions. Under these conditions the multi-attribute auctions analyzed by Che are not optimal. Unlike in the independent-cost model of Che, optimal quality cannot be achieved just through the bidding process. As a result, the procurer has to use a two-stage mechanism: a ®rstscore or second-score auction, followed by a stage of bargaining over quality between the procurer and the winner of the ®rst stage. 5. Computational exploration and simulation The diculty of multi-attribute auctions is the variety of di€erent scoring functions and parameter settings one can deploy. This is a reason why the basic assumptions of game-theoretical models are kept relatively simple. The models in the previous section describe two-dimensional negotiations (price and quality) and also the bidders' behavior is modeled in a rather simple way. Nevertheless, the analytic complexity of these models poses tight constraints for the modeler. In this section we describe the results of two simulation models exploring the economic behavior of multiattribute auctions. In this model we assume a generic good, which can be described by its price and a certain amount of qualitative attributes. The buyer's scoring function has the form shown in Eq. (1). Every bidder in this model has a pro®t function p in the form of p…x; p† ˆ p

n 1 X 1

hi x i

…4†

147

where p is the price of the good, hi is the private cost parameter for an attribute xi and n the number of attributes including the price. The vector (x,p) corresponds to the qualitative attributes x1 . . . xn 1 and the price p ˆ xn from the buyer's scoring function in Eq. (1). The cost parameter h is uniformly distributed between [0,1] and describes the eciency of the bidder in producing the good. The minimum pro®t a bidder wants to achieve can also be modeled as uniformly distributed random variable ‰pmin ; pmax Š, where pmin bzw. pmax is a lower and upper bound for the minimum expected pro®t. This parameter is a proxy for the risk aversion of the bidder. For reasons of simplicity we assume the individual utility of all qualitative attributes in the scoring function to be continuous, ascending and convex. Not all bidders in our model are able to provide the maximum quality for the attributes x1 . . . xn 1 . Therefore, we assume the maximum values xi a bidder is able to provide to be uniformly distributed between ‰xi;min ; xi;max Š. The price p can now be determined for every combination of attribute values n 1 X   hi f …xi ; wi † : …5† p ˆp‡ iˆ1

During the simulation bidders perform an optimization f …xi ; wi † of their bids in that they consider the weights wi from the buyer's scoring function Eq. (1), when determining the level of a qualitative attribute xi . We implemented this model in Java using the simjava package [14], which includes several classes for discrete event simulation. Fig. 2 depicts the key actors in this simulation. In our analysis we have assumed ten relevant attributes of the good, i.e., nine qualitative attributes and the price. Fig. 3 depicts the average results of 60 auction periods. In every auction period we had 12 virtual bidders and every bidder posted 10 bids. In the ®rst bid the bidder assumed that all 10 relevant attributes were negotiable. In the second bid she assumed that nine attributes (including the price) were negotiable and one attribute was pre-speci®ed at a level of (xi;max /2) by the buyer, and so forth. Finally, she assumes all qualitative attributes to be pre-speci®ed and only negotiates on the

148

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

Auctioneer

S(x ) x j

...

Bidder (1..m)

Fig. 2. Simulation of multi-attribute auctions.

price. We used six di€erent scoring functions in which we altered the values of the weights wi . In addition, we had to draw from a number of uniquely distributed random variables for the bidders in every new auction period, namely the cost parameter hi as well as the upper bounds for all qualitative attributes xi . From these initial endowments bidders calculated their optimal bids. The total number of bids evaluated was 43,200. Fig. 3 shows the results of the simulation using di€erent weights for price and qualitative attributes in the scoring function S(x) and a di€erent number of negotiable attributes. The scores dimension in the ®gure shows the utility values that correspond to winning bids for a given number of negotiable variables and parameter values. A single line is always the result of the same scoring function using a di€erent number of negotiable

attributes. For example, in line 1 the price is of high importance to the buyer …wprice ˆ 91†, whereas all the qualitative attributes have a weight of 1. In contrast, all attributes including price have a weight of 10 in line 6. It can be easily seen that in cases where all attributes are of equal importance (i.e., line 6), it is useful to deploy multi-attribute auctions since the multi-attribute auctions value high achievements in all attributes. If the scoring function correctly mirrors the buyer's utility function she can expect to be better o€ at the end. The more relevant attributes come into play, the higher is the di€erence in the achieved utility values. If the buyer's scoring function puts a high emphasis on the price, there is only little di€erence in the outcome of a multiattribute vs. a single-attribute auction. If all 10 attributes are of equal importance, the bidder has many more possibilities to comply with the buyer's preferences. She cannot only lower the price, but she can improve on several qualitative attributes. The simulation is sensitive to changes in the basic assumptions such as the initial distributions of attribute values or the cost parameters. However, in all other settings there was a positive correlation between the achieved utility values and the number of negotiable attributes in the auction. The simulation provides a number of ideas on how the number of attributes impacts the results and therefore an answer to the second research question in Section 3.4.

Fig. 3. Simulation results assuming di€erent scoring functions.

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

6. Laboratory experimentation Game-theoretic analyses and computational exploration helped us gain an insight into the economic behavior of multi-attribute auctions. Game theory tells us that under certain assumptions there is revenue equivalence between several multi-attribute auction formats. The simulation showed that multi-attribute auctions achieve better utility values whenever multiple attributes are of interest to the buyer. In this section we want to test these results in the laboratory and we want to learn whether multi-attribute auctions are ecient in real-world environments. For the laboratory experiments we have used the electronic marketplace described in Section 3.3. This section provides a brief summary of the experimental results. 6.1. Experimental design During the May and October 1999 we conducted 16 experimental sessions with MBA students at the Vienna University of Economics and Business Administration. In every session a group of four subjects conducted six di€erent trials, namely a ®rst-price sealed bid auction, a Vickrey and an English auction, all of them in their singleattribute and their multi-attribute form. Before the experiment we introduced the scenario in a 40-min lecture to all students, and provided them with examples of valuations and bids along with pro®t calculations to illustrate how the auction works. Before each session (approximately 112 h) we conducted two dry runs in order to familiarize the students with multi-attribute bidding and the bidder applet. Before a session began we asked all participants to provide us with a list of valuations, i.e., a minimum implicit volatility value for each strike price. These valuations were used afterwards to analyze eciency and strategic equivalence of the di€erent auction schemes. In order to give the MBA students an incentive to bid reasonably during all auction periods, we introduced a reward mechanism. In our trials we wanted the subjects to bid consistently with their risk attitude and market expectations. After the option expired (after a month) we took the actual data of the Vienna Stock Exchange and computed

149

the pro®ts and losses for all winners of an auction. Students gained credit for participating in the experiment in the following way. We ranked the students by their pro®ts and gave them additional credit points towards the ®nal grade depending on their pro®t. If a student incurred a loss, he also lost part of his credit towards the ®nal grade. The following summary of results is centered around the research questions outlined in Section 3.4. We provide a comparison of equilibrium values achieved in conventional and multi-attribute auctions. 6.2. Comparison of equilibrium values In a ®rst step, we computed the utility score of the winning bid as a percentage of the highest valuation given by the participants at the beginning of each session. This allowed us to compare trials under di€erent conditions (e.g., stock market prices). In our experiment the utility scores achieved in multi-attribute auctions were signi®cantly above those of single-attribute auctions for groups of size n ˆ 4. Multi-attribute auctions in our experiment achieved, on average, 4.27% higher utility than single-attribute formats. In 72.92% of all trials the overall utility achieved in multi-attribute auctions was higher than in single-attribute auctions. An explanation for this result is that in a multi-attribute auction, a bidder has more possibilities for improving the value of a bid for the bidtaker, sometimes even without increasing her own costs. As can be seen in Fig. 4, we could not ®nd evidence for the hypothesis of revenue equivalence among the various auction formats. 6.3. Eciency of multi-attribute auctions In single-attribute private value auctions eciency is measured in terms of the percentage of auctions where the high value holder wins the item. Eciency has to be computed slightly differently in the case of multi-attribute auctions. Here, the high value holder is the one where one of her valuations (containing strike price and volatility) provides the highest overall utility score for the buyer. Subjects had to report these valuations before each session to the experimenters, based on

150

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

Fig. 4. Di€erence between multi-attribute and single-attribute auctions.

their market expectations. In all trails 79.17% of the single-attribute auctions and 74.47% of the multi-attribute auctions were ecient. The slightly lower eciency achieved in multi-attribute auctions is a possible consequence of the diculty for the bidder to determine the ``best'' bid, i.e., the combination of values providing the highest utility for the buyer. 7. Conclusions Multi-attribute auctions are a very useful addition to conventional negotiation protocols, which can be used in a number of contexts. We utilize competitive bidding on multiple attributes, in order to achieve ecient results in complex, multi-lateral negotiation situations. However, it is important to consider a few issues, when applying multi-attribute auctions. Bidding is more complex in multi-attribute auctions, as it is not obvious for the bidder right from the start which combination of attributes provides the highest overall utility for the bid taker. This is a minor issue in the case of two negotiable attributes; however, in the case of many negotiable attributes, this can lead to outcomes that are not ecient. Appropriate decision support tools for the bidder play a crucial role in overcoming this problem. In addition, buyers also have to get used to the new tool and learn about

the consequences of di€erent parameter settings in their scoring function. As we have learned from the simulation, it is important to have a good knowledge about market conditions in order to de®ne a ``good'' scoring function. We believe that in a professional environment like corporate procurement buyers will adapt quickly to the new tool. Less experienced buyers, however, do not know the market conditions that well and face the danger that their scoring functions, and consequently the results of the auction are biased. Based on the results of this research a multiattribute auction market has been developed in cooperation with a mayor European destination management system. Using this software in a realworld environment, we plan to collect and analyze ®eld data as a next step in our research. This data contains a wealth of information about buyers' preferences and suppliers' capabilities. A thorough analysis of this data can result in a deeper understanding of the economic issues involved with multi-attribute auctions. Currently, many companies introduce new negotiation protocols without or with only little theoretical or empirical validation. The danger is, that the resulting outcomes are far from ecient in an economic sense. Only a thorough analysis of the various aspects of a new negotiation protocol can prevent such situations. There is not a single technique for the design and evaluation of new negotiation protocol. However, as we have illustrated, a combination of methods from economics and computer science can gain relevant insight into the economic behavior of a new mechanism. References [1] K.J. Arrow, H.D. Block, L. Hurwicz, On the stability of competitive equilibrium II, Econometrica 27 (1959) 82±109. [2] R.J. Aumann, S. Hart, Handbook of Game Theory, vol. I, North-Holland, Amsterdam, 1992, p. 733. [3] K. Backhaus, B. Erichson, W. Plinke, R. Weiber, Multivariate Analysemethoden, Springer, Berlin, 1996. [4] Y. Bakos, A strategic analysis of electronic marketplaces, MIS Quarterly 15 (1991) 295±310. [5] P. Balakrishnan, V. Sundar, J. Eliashberg, An analytical process model of two-party negotiations, Management Science 41 (1995) 226±243.

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152 [6] J.S. Banks, J.O. Ledyard, D.P. Porter, Allocating uncertain and unresponsive resources: an experimental approach, RAND Journal of Economics 20 (1989) 1±23. [7] F. Branco, The design of multidimensional auctions, RAND Journal of Economics 28 (1997) 63±81. [8] Y.-K. Che, Design competition through multidimensional auctions, RAND Journal of Economics 24 (1993) 668±680. [9] R.T. Clemen, Making Hard Decisions: An Introduction to Decision Analysis, Wadsworth, Belmont, CA, 1996. [10] A.E. Cortese, M. Stepanek, Good-bye to Fixed Pricing, in: Business Week, 1998. [11] D. Fudenberg, J. Tirole, Game Theory, MIT Press, Boston, 1991. [12] A. Gibbard, Manipulation of voting schemes: a general result, Econometrica 41 (1973) 587±602. [13] J.C. Harsanyi, R. Selten, A generalized Nash solution for two-person bargaining games with incomplete information, Management Science 18 (1972) 80. [14] F. Howell, R. McNam, Simjava: a discrete event simulation package for Java with applications in computer systems modelling, in: First International Conference on Web-based Modelling and Simulation, vol. HoMc, Society for Computer Simulation, San Diego, CA, USA, 1998. [15] L. Hurwicz, The design of mechanisms for resource allocation, American Economic Review 63 (1973) 1±30. [16] J.H. Kagel, Auctions: a survey of experimental research, in: J.H. Kagel, A.E. Roth (Eds.), The Handbook of Experimental Economics, Princeton University Press, Princeton, NJ, 1995, pp. 501±587. [17] J.H. Kagel, A.E. Roth, The Handbook of Experimental Economics, Princeton University Press, Princeton, NJ, 1995. [18] R.L. Keeny, H. Rai€a, Decision Making with Multiple Objectives: Preferences and Value Tradeo€s, Cambridge University Press, Cambridge, 1993. [19] D. Kendrick, Research opportunities in computational economics, Center for Economic Research, UT Austin, TX, 1991. [20] R.W. Kolb, Financial Derivatives, New York Institute of Finance, Englewood Cli€s, NJ, 1993. [21] Y.-K. Kwok, Mathematical Models of Financial Derivatives, Springer, Singapore, 1998. [22] C. Ma, J. Moore, S. Turnbull, Stopping agents from ``cheating'', Journal of Economic Theory 46 (1988) 355± 372. [23] R. McAfee, P.J. McMillan, Auctions and bidding, Journal of Economic Literature 25 (1987) 699±738. [24] K.A. McCabe, S.J. Rassenti, V.L. Smith, Designing a uniform price double auction an experimental evaluation, in: D. Friedman, J. Rust (Eds.), The Double Auction Market Theories and Evidence, Addison-Wesley, Reading, MA, 1993. [25] P.R. Milgrom, Auction theory, in: T. Bewley (Ed.), Advances in Economic Theory: Fifth World Congress, Cambridge University Press, Cambridge, 1987. [26] P.R. Milgrom, R.J. Weber, A theory of auctions and competitive bidding, Econometrica 50 (1982) 1089±1122.

151

[27] J. Nash, The bargaining problem, Econometrica 18 (1950) 155±162. [28] J. Nash, Two-person cooperative games, Econometrica 21 (1953) 128±140. [29] J.G. Riley, J.G. Samuleson, Optimal auctions, American Economic Review 71 (1981) 381±392. [30] A.E. Roth, Game Theory as a Tool for Market Design, vol. 1999, 1999. [31] M.H. Rothkopf, R.M. Harstad, Modeling competitive bidding: a critical essay, Management Science 40 (1994) 364±384. [32] A. Rubinstein, Perfect equilibrium in a bargaining model, Econometrica 50 (1982) 97±109. [33] T.L. Saaty, The Analytic Hierarchy Process, McGraw-Hill, New York, 1980. [34] P.A. Samuelson, Foundations of Economic Analysis, Harvard University Press, Cambridge, MA, 1947. [35] V.L. Smith, Microeconomic systems as an experimental science, American Economic Review 72 (1982) 923±955. [36] L. Tesfatsion, Agent-based computational economics: a brief guide to the literature, in: Reader's Guide to the Social Sciences, Fitzroy-Dearborn, London, UK, 1998. [37] L. Tesfatsion, How economists can get a life, in: B. Arthur, S. Drulauf, D. Lane (Eds.), The Economy as an Evolving Complex System, Addison-Wesley, Reading, MA, 1997. [38] L. Tesfatsion, A trade network game with endogenous partner selection, in: H. Amman, B. Rustem, A. Whinston (Eds.), Computational Approaches to Economic Problems, Kluwer Academic Publishers, Dordrecht, 1997. [39] L.G. Tesler, The usefulness of core theory in economics, Journal of Economic Perspecitives 8 (1994) 151±164. [40] H. Varian, Economic mechanism design for computerized agents, in: Usenix Workshop on Electronic Commerce, New York, 1995. [41] H. Varian, Microeconomic Analysis, Norton, New York, 1992. [42] J. von Neumann, O. Morgenstern, Theory of Games and Economic Behavior, Princeton University Press, Princeton, NJ, 1944. [43] E. Wolfstetter, Auctions: an introduction, Journal of Economic Surveys 10 (1996) 367±420. Martin Bichler is Associate Professor at the Vienna University of Economics and Business Administration and visiting scientist at the IBM T.J. Watson Research Center. He received his M.Sc. from the University of Vienna, Austria and his Ph.D. in Information Systems from the Vienna University of Economics and Business Administration. His research focuses on electronic commerce infrastructures and advanced negotiation protocols. Dr. Bichler has published papers in journals such as Decision Support Systems, Journal of Distributed and Parallel Databases, International Journal of Electronic Markets, International Journal of Cooperative Information Systems, and Journal of End User Computing.

152

M. Bichler, A. Segev / Computer Networks 37 (2001) 137±152

Arie Segev is Professor at the Haas School of Business and Director of Fisher Center for Information Technology and Marketplace Transformation. His research interests span elec-

tronic commerce, corporate data management and distributed and client±server computer systems.