4 Towards a Structured Market Engineering
4.1 Introduction to Market Engineering
4.1.3 Holistic Market Engineering
4.1.3.1 Market Engineering as Design Problem
Both market engineering definitions are apparently behavioral definitions, as they describe the activities of a market engineer (Lewis and Samuel 1989). Engineering design of institutional rules is, in essence, a complex problem solving activity. In the following, it is envisioned to pinpoint the market-engineering problem, i.e. the problem that market engineering tries to solve.
The term problem is one of those basic and all-embracing words whose meaning is generally accepted without close examination. There are, nevertheless, a variety of different problem types. Problems in general do not exist in a vacuum, but arise when agents perceive an objec-tive, but not the means of attaining it (George 1970; Lewis and Samuel 1989). In the attempt of achieve this goal and to maintain conditions at a desired level of performance, the problem-solving engineer basically has to conduct the three major activities of engineering design, operation and research.
The emphasis of market engineering is, however, on design as a form of engineering problem solving. It follows that the market-engineering problem entangles with the design of the pre-requisites for an electronic market service in a way that a specific value proposition is offered.
This problem description is, nonetheless, still very vague and thus inapplicable. Again a look back to the microeconomic system framework and the associated mechanism design theory may help to clarify the market-engineering problem.
Principally, the market-engineering problem resembles the mechanism design problem. While mechanism design is concerned with the design of trading rules, market engineering pertains to the design of the composite institutional rules. Both approaches set the rules in a way that the design objectives are satisfied. The design objectives of market engineering and mecha-nism design may, nevertheless, not be congruent.
140 It should be noted that McCabe, Rassenti and Smith use the term Market Engineering for the study of in-stitutions by means of laboratory experiments. As such it covered by the holistic definition of market en-gineering (McCabe, Rassenti et al. 1993).
To recapitulate, the mechanism design problem can be formulated as the identification of a mechanism such that the equilibrium outcome satisfies the given objectives or desiderata, which are expressed by the evaluation function U(X,e). The evaluation function values the outcome attained by the mechanism in a specific environment e. This formulation is more general (and abstract) than the previous one given in chapter 2.2.1.1, as it not only regards the preference profile but also all other arguments of the environment.141 Now mechanism design seeks to describe mechanisms (M,y) that maximize the evaluation function U subject to three constraints. The first constraint, the incentive compatibility constraint, requires that the agents truthfully report their information about their local environment. In such a case, the outcome X is the same as if a benevolent arbitrator would have chosen the outcome on the basis of the full information about the environment. The second constraint, computational compatibility, refers to the complexity of the outcome function y. Outcome functions can be very demanding concerning computational tractability (Ledyard 1993; Rothkopf, Pekec et al.
1998; Kalagnanam and Parkes 2003). The computational compatibility constraint assures the feasibility of the applied outcome function. The third constraint, the participation constraint, requires that the agents voluntarily take part in the mechanism. The agents participate if the benefit they draw out of participation is higher than participation in an alternative mechanism.
The abstract mechanism design problem can thus be expressed in the Ledyardian form (Ledyard 1993):
] mechanism
e alternativ an
is y' and e in X outcomes the
of evaluation
own s i' is v where i all for ) e ), m ( ' y ( v ) e ), m ( y ( v ., e . i [ int constra ion
Participat
] m all for computed be
can y ., e . i [ ity compatibil nal
Computatio
)]
m ( y ) e ( X ., e . i [ ity
compatibil Incentive
. t.
s
) e ), e ( X ( U max
i i
i )
y , M (
≥
=
As the electronic market framework generalizes the institution definition of the microeco-nomic system framework, it is analogously attempted to extend the mechanism design prob-lem to the market-engineering probprob-lem. Recall that market engineering can take on the two extreme goals: cost coverage or profit maximization reflecting the cooperative and entrepre-neurial spirit of the market firm (recall chapter 3.2.3.1).
Cost-Coverage
For a fixed time period t, it is straightforward to formulate the cost-coverage problem in refer-ence to the mechanism design problem. The cost-coverage-oriented market-engineering prob-lem is to identify a set of institutional rules – including the trading rules but also the media and business rules and so forth – such that the sum of the individual utilities is maximized. In terms of the mechanism formulation variations in the trading object definition are expressed by a change in the socio-economic environment142. Apparently, the action space comprising
141 Furthermore one can account for the fact that mechanisms can work differently in different environ-ments. The general design formulization, following a Bayesian approach, adopts prior beliefs about the probability of any economic environment e in the space of all possible environments E and ranks mecha-nisms according to their expected valuation (Ledyard 1993).
142 In chapter 3.1.1.3 a Lancastrian utilization of the utility function was motivated. The argument was that the economic environment has to be stable by definition. In the formulization of the market-engineering problem this definition is implicitly dropped. The reason for this relaxation lies in convenient way to
all feasible design decisions is tremendously augmented. The maximization problem is, how-ever, constrained by four additional requirements. Firstly, the demand for cost-coverage of the market firm, i.e. the market firm’s profit must at least be zero or greater. This constraint be-comes relevant as market engineering relaxes the assumption of costless provision of the elec-tronic market service. Accordingly, the market firm must recoup these costs through fees, which subsequently diminish the benefit of the participating agents. Secondly, in order to at-tain a maximization of the sum of individual utilities, it must be guaranteed that the agents only truthfully provide the necessary private information for maximization. Thirdly, the agents must have an incentive to participate. They participate only in the case that taking part yields higher utility than any alternative. Fourthly, as in mechanism design this more technical constraint applies requiring the outcome function to be computationally feasible.
To summarize, market engineering that focuses cost-coverage extends the notion of a mecha-nism by introducing the environment as a variable through the determination of the trading object definition, including costs and fees and introducing the additional constraint of a cost-coverage compatibility (i.e. costs are not higher than the accrued fees). In analogy to the mechanism design problem, the cost-coverage-oriented market-engineering problem can be formulated as follows:
] i all for P ) e ), m ( y ( v P ) e ), m ( y ( v ., e . i [ int
constra ion
Participat
] m all for computed be
can y ., e . i [ ity
compatibil nal
Computatio
)]
m ( y ) e ( X ., e . i [ ity
compatibil Incentive
] 0 C F P ., e . i [ ity
compatibil erage
cov Cost . t.
s
P ) e , X ( v max
' i
* i i i
N 1 i
i N
1 i
i N
1 i
i e i
), y , ' M (
−
≥
−
=
≥
⎟⎠
⎜ ⎞
⎝
⎛ +
−
−
−
∑
∑
∑
=
=
=
where
M’ = Extended Mechanism including not only the trading rules but also the media and business rules,
Pi = Fee charged from the i-th agent for the electronic market service, F = Fixed costs for the electronic market service,
Ci = Variable costs per agent for the electronic market service.
Individual utility is again formulated as a quasi-linear utility function as the fees are merely subtracted from the utility drawn from participating in the trade. The action space not only includes the extended mechanism components but also the environment, which accounts for the fact that the market firm can affect the environment through the trading object definition.
The costs incurred by running the system can be distinguished into a variable and a fixed por-tion, whereas it is assumed that the variable costs are induced by the participation of the i-th agent.
The similarity between the mechanism design problem and this extended market-engineering problem may not conceal that the latter problem is much more complex as it widens the ac-tion space.
formulate the market-engineering problem with designable trading object definition rules and its struc-tural resemblance with the mechanism design problem.
Profit-Maximization
The entrepreneurial formulation of the market-engineering problem looks, in contrast, differ-ent. The goal of the market firm in time period t is to identify a set of institutional rules – in-cluding the trading rules but also the media and business rules and so forth – such that the profit of the market firm is maximized. All extensions that were introduced for the cost-coverage version also apply to the profit-maximization problem. Nevertheless the maximiza-tion problem is different, as some constraints become meaningless. Rather than making entry into the system, a probabilistic function of the net value, the probability that agent i partici-pates in the electronic market provides maximum utility.
} y
\ S ' y ' P ) e ), m ( ' y ( v P ) e ), m ( y ( v {
prob i i i i
it = − ≥ − ∀ ∈
π
In other words, the i-th agent chooses to trade in the electronic market if the expected utility associated with trading in this market is higher than trading in any existing alternative (elec-tronic) market described by the set S.143 The maximization problem of the market firm is then as follows:
] m all for computed be
can y ., e . i [ ity
compatibil nal
Computatio .
t.
s
C F
P ofit
Pr
max N
1
i it i
N
1
i it i
e t ), y , ' M
( ⎟
⎠
⎜ ⎞
⎝
⎛ +
−
=
∑ ∑
=
=
π π
The market firm just strives for maximizing their own profit. As the utility of the participating agents is no longer subject to maximize, incentive compatibility is no longer a binding con-straint. From the market firm’s point of view, it is secondary whether the agents report their true private information. The main objective is rather to promote participation. The participa-tion constraint is, furthermore, incorporated into the objective funcparticipa-tion via the participaparticipa-tion probability. The contribution of agent i in time period t to the market firm’s profit is weighted by the probability that this agent does not find a more attractive trading venue. The maximiza-tion is of course also constraint by the feasibility of mechanisms.
Remark 4.1-1: Biased electronic market
The previous market-engineering problem definition refers to a neutral market firm, meaning the market operator is not actively engaging trade on its own platform. In many business-to-business electronic markets, the operator is the decisive or even the only par-ticipant on the buy or sell-side. In those cases the market firm is suspected to fleece on the other participating agents. In those cases the objective function would also incorporate the net utility of the market operator as trader. The choice of the institutional rules would also depend on the trading interest of the market operator.
It is rather obvious that the profit-oriented formulization of the market-engineering prob-lem differs from the traditional mechanism design probprob-lem. The numbers of the decision variables as well as the objective functions are different. And there is another – though not yet mentioned – difference. Market engineering is concerned with multiple resource allo-cations. Hitherto market engineering was defined as a one-shot allocation at time period t.
To view the market-engineering problem in its totality one has to integrate the profit func-tion over time.
143 This formulization adopted here assumes a quasi-linear form that is dependent on the outcome and the fees only. This assumption is only for convenience; an extension to other arguments such as preferences concerning employed mechanisms, inclusion of time-dependency or effects such as lock-ins is possible.
Remark 4.1-2: Multi-product electronic markets
The term multi-product electronic markets points at the fact that electronic markets are not restricted to a single product for which a trading facility is offered. Instead, the market firm usually provides trading venues for many products. The market-engineering problem is in such cases clearly affected, as there may be substantial economies of scope. In fact, the provision of an additional electronic market service is in essence not as expensive as the establishment of the first service, because the trading platform can be reused (Bakos 1991). Thus, trading in more than one product also affects the market-engineering prob-lem.
The profit-oriented market-engineering problem reflects a simplified optimal service design problem (Karmarkar and Pitbladdo 1995; Pullman and Moore 1999). Extensions concerning capacity, capacity improvement, non-linear pricing, marketing and so forth are also possible (Dewan and Mendelson 1990). As profit-orientation and cost-coverage mark two extremes, the actual market-engineering problem is approximately somewhere between optimal service design and mechanism design.