engineering of markets and artifacts

Electronic Commerce Research and Applications 3 (2004) 369–380

www.elsevier.com/locate/ecra

Engineering of markets and artifacts

Eswaran Subrahmanian a,*, Sarosh N. Talukdar b,1

a ICES and Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USAb Electrical & Computer Engineering and ICES, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Received 3 October 2003; received in revised form 4 June 2004; accepted 17 June 2004

Available online 23 July 2004

Abstract

In this paper, we continue the dialog started by Roth [The Economist as Engineer: Game theory, Experimentation,

and Computation as Tools for Design Economics, Fisher Schultz lecture, Econometrica 70 (2002) (4) 1341–1378]

between economics and engineering in the context of design of markets. We take the position that markets and engi-

neered artifacts are the products of a social process of design formulation. Our perspective is that designing of markets

and artifacts follow the same kind of problem formulation and solution testing process. Further, we show using two

case studies that the design of engineered artifacts and markets are often interdependent. In light of their similarities,

methodologies for modeling and testing using theoretical, analytical, computation and physical models in engineering

can inform the development of methods for testing in the design of markets. We also illustrate an example of devising a

test and results of its execution thorough the use of a simple computational model of an electric power market. We

conclude by summarizing the similarities between the designing of markets and artifacts and call for a continued dia-

logue between engineers and economists.

� 2004 Elsevier B.V. All rights reserved.

Keywords: Engineering design; Market design; Modeling; Testing; Methodologies

1. Introduction

A recent New York Times column by Hal Var-

ian headlined, ‘‘Avoiding the pitfalls when shifting

from science to engineering,’’ reviewed a paper by

1567-4223/$ - see front matter � 2004 Elsevier B.V. All rights reserv

doi:10.1016/j.elerap.2004.06.002

* Corresponding author. Tel.: +1-412-268-5221.

E-mail addresses: [email protected] (E. Subrahmanian), taluk-

[email protected] (S.N. Talukdar).1 Tel.: +1-412-268-8778

Alvin Roth entitled, ‘‘The Economist as an Engi-

neer’’ [19,27]. Why do we suddenly see economists,

purists in their claim to science, starting to see that

engineering is an integral part of economics? Is it a

startling return to often-forgotten Thorstein Veb-len�s view of economists as engineers of the price

system? Is it a renunciation of the claim that eco-

nomics is a pure science? Science without experi-

mentation is no science at all for all the

mathematical devices are not enough to guarantee

ed.

mailto:[email protected]



370 E. Subrahmanian, S.N. Talukdar / Electronic Commerce Research and Applications 3 (2004) 369–380

the validity of the theory if it is not tested in prac-

tice; it is here engineering becomes very important.

While these are fascinating questions, this paper

will focus on a much narrower question. This pa-

per takes as its starting point that the developmentand use of a market is as much a socially designed

mechanism as any other artifact [5]. We take up

this perspective – from the engineering side – in

what we hope will be a dialogue between econo-

mists and engineers about how the experience with

engineering design of artifacts can help inform the

discussion in economics� new focus on design of

markets. Here the lessons from testing from engi-neering become important. Perhaps more impor-

tantly, this debate between engineer and

economist will change the status of designing of

artifacts and markets themselves. While we are

not economists, but more students of design of

engineered artifacts, we want to view economics

with the lens of engineering design.

Our goal is to show that understanding designand engineering can create insightful approaches

to practical economics that have been brought to

fore by the emergence of computing in economics,

experimental economics and e-commerce

[14,17,26,29]. In these areas, the focus has shifted

from being solely on mathematical elegance to also

including as equally important, practical design,

simulation and creation of markets. This shift ineconomics from purely analytical to explore vari-

ous approaches to simulation through computing

has been profound. In this shift, Simon and Von-

Neumann have been most forceful in constructing

a vision of economics as simulated Cyborg Science

[14]. Of course, in this new context the issues of

methodology of research in design of markets have

raised questions that economists, until recently,have not asked themselves. We address some

of these issues by staking the claim that design of

markets has important similarities to design

of engineered artifacts. We further argue that mar-

kets and the nature of engineered artifacts are

more closely inter-related than most economists

tend to acknowledge and engineers dare to state

for fear of trespassing.The paper has five sections. The next section is

on the similarities between design of artifacts in

engineering and markets in economics. Section 3

addresses the interdependence between market

structure and structure of designed artifacts. Sec-

tion 4 then turns to methodologies in modeling

and testing for failures in engineering design and

its relationship to design economics. Section 5 isan example of devising a test for excessive profits

and testing it using a simple model of the electric

power market. Section 6 concludes the paper with

some discussions and conclusions on the relation-

ship between design of markets and artifacts.

2. Markets and artifacts

‘‘Economics is one of the Artificial Sciences’’

[21, p. 83].

Economics is part of the sciences of the artificialas Simon claims in his well-known paper on

bounded rationality and his book on the sciences

of the artificial [20,21]. The mechanisms of eco-

nomics, ‘‘markets (artifacts),’’ are designed to sus-

tain a particular set of desired behaviors (e.g., to

measure and keep time) through constraints on

behavior of the participants (functional/structural

parts) and on transactions (interactions) betweenthem. Determining the set of desired behaviors

and constraints on the artifact (market) is a social

process. In this sense, designing of markets is

something like designing engineered artifacts –

through the social interaction of actors in the for-

mulation of the market design problem and their

acceptance of the solution. We will elaborate on

this claim by first explaining what we mean by for-mulating a design problem and then use a charac-

terization of markets to show how the design of

markets is similar to the design of engineered

artifacts.

2.1. Formulating design problems (artifacts and

markets)

One wants a clock to provide the time without

failing every minute. One wants markets that lead

to innovation while not leading to the abuse of

market power. In designing a market, we want to

achieve a set of competing goals, which is exactly

what we want when we design any engineered arti-

E. Subrahmanian, S.N. Talukdar / Electronic Commerce Research and Applications 3 (2004) 369–380 371

fact. The involved participants, i.e., those whose

interests are involved, set these goals and tests

for the acceptance of the artifact. We will now

elaborate this characterization design problem for-

mulation for engineered artifacts.In our characterization of a design formulation

[24], any design involves a collection of stakehold-

ers all with their own interests who come together

to formulate the design problem in terms of:

(a) Goals – Specification of behavioral and struc-

tural objectives and constraints

(b) Decision variables – Variables where choicesare to be made

(c) Design Space – The space of solutions derived

from the decision variables

(d) Tests – Agreements and metrology used in

testing the goals

(e) Starting points – Existing designed or natural

artifacts fulfilling a similar function as the

problem formulated.

From this viewpoint, we contend that the de-

sign of markets and artifacts are multi-goal (objec-

tives and constraints) problems with competing

and sometimes incommensurate interests. 2 In

both, we are concerned with efficient allocation

of resources, identifying appropriate trade-off

amongst objectives, identifying of constraints onthe structure and behavior, identifying tests that

would allow us to verify that constraints and

expressing behavioral parameters that embody

the different failures and performance criteria to

manage the negative effects and positive effects of

the artifact designed. In both cases, those involved

in the design and those affected have to perceive an

overall value in the artifact individually and collec-tively for it to be accepted.

To provide a starting point for the definition of

markets, we will use the definition of a market by

Guesnerie [9] rephrased by Callon [5],

2 This view does not imply that power relationships among

the stakeholders cannot bias the design and the outcome of

designs. However, we will see in Section 3, a case where the bias

of a set of the stakeholders can have profound impact on the

way both an engineered artifact and the market the stakehold-

ers participate in influencing each other.

‘‘a market is a co-ordination device in which:

(a) the agents pursue their own interests and to

this end perform economic calculations which

can be seen as an operation of maximizationor optimization;

(b) the agents generally have divergent interests

which lead them to engage in

(c) transactions which resolve the conflict by

defining a price.’’

In the elaboration of this definition of markets,

Callon uses prices/contract interchangeably, andwe shall do so as well. The reason for using a con-

tract is that, in the case of engineering design, price

is one of the many factors along with others such

as the use of standards and disciplinary and other

functional specifications. We will not explore these

here, other than to note that contracts are agree-

ments just as prices are.

Even though the above two characterizations(design of artifacts and markets) are not at same

level, the first can be used to elaborate the market

design model by identifying the elements that go in

to formulating this ‘‘co-ordination device’’ just as

one would with any artifact and the calculations

(tests) done by the calculative agencies to achieve

consensus. Resolution of conflict by the calculative

agencies (engineering design and/or markets) isthrough agreements on tests for achieving the

objectives and constraints agreed upon as the set

of goals. Even in the above definition of a market,

the word optimization is used. However, the opti-

mization process by the calculative agencies is lim-

ited by bounded rationality throughout the

process of the resolution and is tamed by the use

of models and methods to reduce time and/or thecosts of resolution. The resolution process in engi-

neering design is through the use of metrology,

analytical formalisms, simulations that embed the

learning and theorizing that is induced over the

experience of the designed artifact as it continu-

ously evolves responding to failures, technological

changes and spillovers [18].

Sustaining the evolution of an artifact in thepractice of engineered artifacts is by the develop-

ment of a range of calculative models and methods

for testing behavior as a means to tame the


limitations of bounded rationality. This kind of

adaptive behavior is not limited to design problem

formulations and engineered artifacts to overcome

bounded rationality. Arthur�s [1] work on the use

of a complex adaptive system metaphor for modelsof markets is that he could account for bounded

rationality by embedding agents with inductive

learning in the market models. These agents in-

clude mental models of the market that evolve

their discriminatory power to eliminate failures

to optimize their performance. The participants

in a market and the designers of the market are

both bounded rationally but both can adapt theirbehavior in light of experience either by optimizing

their interests given a market design or by refor-

mulating the market design problem to accommo-

date new failures and objective.

2.2. Failure in markets and artifact design

A theme that underlies the history of artifactdesign is managing and learning from failure

[16]. One of the most striking things that occurs

when we look at the history of the evolution of

markets and history of design is this uncanny com-

monality in the characterization of the failure

modes in their functioning in society. Economists

tend to call failures in behavior of markets nega-

tive externalities and the benefits positive external-ities. Engineers tend to label failures as any kind of

functional or behavioral breakdowns and to label

positive behavior of the artifact and its second or-

der effects as features or benefits. Often the second

order engineering benefits are economic positive

externalities (creation or facilitation of the func-

tioning of organizations or markets) from an arti-

fact design point of view. To set the context forcomparison, we will use Callon�s definition of mar-

ket failures [6, p. 247]:

‘‘Market failure occurs due to:

(a) Externalities – positive and negative.

(b) Asymmetry of information.

(c) Indivisibility of property rights.

To use a simple example, pollution is a negative

externality, while an increase in the tax base is a

positive externality. In the case of design, the

introduction of a car allowed for the dispersion

of the housing and commercial zones away from

each other while creating pollution and new forms

of accidents. The second kind of failure arising out

of asymmetry of information results in incompleteproblem formulations in artifact design as well as

in market design, often leading to seemingly desir-

able but poor solutions whose failures sometimes

are not visible, let alone testable.

It is not just engineering or economic processes

that are designed and that rely on managing fail-

ures. Simon also mentions the design of the consti-

tution of the US as driven by a desire to avoid theperceived failures of European experiences [20]. In

this case, one of the functional and structural as-

pects of the US Constitution, which often is over-

looked, is the three institution design to prevent

the kind of religious and other persecutions previ-

ously experienced in Europe.

The important lesson here is that a desire to

minimize recognized and anticipated failures whilemaximizing the benefits of all sorts is a fundamen-

tal driver of all human engineered design. How-

ever, one cannot identify and manage failures

without appropriate institutional rules and the de-

sign of appropriate metrology and tests to measure

the achievement of the goals. We will return to this

theme with respect to the use of tests in engineer-

ing and economics.

3. Market design affects artifact design and artifact

design affects market design

We will show two cases to illustrate these rela-

tionships. The first case is the history and emer-

gence of the electric power industry in the US[10]. The electric power industry in the US was

mainly a regulated monopoly until recently when

efforts to introduce markets in this industry began.

The original electric power industry in the US was

decentralized with even small buildings having

their own generators and with small companies

supplying power to small but often disconnected

sets of customers. However, some of the manufac-turers of large power plants felt that, by ensuring a

large number of customers for a regional supplier

of electricity, they could have a captive market


without much competition. At the time they

wanted to push this more centralized model of

production, they constituted less than half of the

US market. 3 However, as Mancur Olson pointed

out in The Logic of Collective Action, majorityinterests are often displaced by well-organized

minorities who more effectively promote their vi-

sion and interests [15]. In this case, the well-organ-

ized large manufacturers of large power plants

dominated, and large-scale centralized electricity

production became the norm. They did this

through a concerted effort with financial interests,

who were behind this centralized production mod-el, convincing government officials and the official

technical forums of the industry about the virtues

of a regulated ‘‘natural’’ monopoly. They con-

vinced key decision makers that it was beneficial

to everybody in this model. This approach lost fa-

vor in the 1980s when free marketers advocated

that markets would solve the problem of providing

power more efficiently. Some claim that a marketoverlaid on an old artifact design meant for a

monopolistic regulated structure was the cause of

the latest blackout in North America [4].

Market design in electric power is constrained

by the physical laws that govern the generation,

transmission, and consumption of electricity. The

supply has to be equal to the demand or else the sys-

tem will be prone to failures at a given geographicallocation at a given point in time. These physical

constraints impose themselves as constraints on

the market design. Clearly design formulation of

the power network and the associated markets have

to reflect this inter-dependence.

The second case is the case of computer indus-

try, where the formulation of problem as a mod-

ular decomposition of the designed artifactcreated its own markets and market structures.

Baldwin and Clark in their book on, ‘‘Design

Rules: Modularity in Design,’’ describe the evolu-

tion of the computer industry by pointing that the

modular design of IBM 360 was a departure from

its own earlier and other competitors vertically

and functionally integrated computers [2]. Modu-

larized design created not only opportunities for

3 [10, pp. 163–164].

IBM to address a larger varied market but also

created additional opportunities for creation of

and entry by firms into markets for modules. Sev-

eral manufacturers started to provide alternative

hardware components to IBM machines andthereby spawned a new industry and market that

turned out to be very beneficial to IBM at least

initially. In the case of the personal computers,

this modularity in design has further separated

hardware processors and components and the

software operating system and applications result-

ing in an industry structure with several suppliers

of components at least on the hardware side ofthe market.

The above two cases illustrate the inter-

dependence between design of artifacts and mar-

kets in the design, manufacture and operation of

the engineered artifact. As Simon argues in his pa-

per on role of organizations and markets, each of

these structures affords a certain economic advan-

tage in terms of reduction of complexities of thetransactions involved and the amount of exchange

of information in the effort to elicit a desired

behavior in context at the desired cost [22]. We

use and structure organizations and hierarchical

decompositions to suit specific purposes and con-

texts in the design of artifacts, similarly we have

to undertake design of markets for specific pur-

poses and contexts. For example, in the case ofmany engineered artifacts, buy or make decisions

involve trading off between buying a part/compo-

nent from the market or from an exclusive sup-

plier, or to internally make the part. The basis of

these decisions are on what a particular firm values

as its competitive advantage (organizational/tech-

nical) over what the market can produce for it to

achieve its goals. This tension may result in alter-nate hierarchical artifact decompositions based

on buy instead of make decision. The development

and use of appropriate standards by firms provides

them the opportunity to decide between buying

and manufacturing. Standards also play a key role

in how these markets develop.

The primary lesson from these examples is that

in designing markets for supply chains and othermarkets that are related to the design of engi-

neered artifacts, one has pay attention to the role

of design in determining where and what type of

Log (cost)

Log (cost)

CF

Analyt

Flight

Log

(er

ror)

Log

(er

ror)

WindTunnel

# of flightconditions Wind

Tunnel

CFD

(a)

(b)

Fig. 1. (a) Log of error vs. log of cost. (b) Log of # of test

conditions vs. log of cost.


markets are the most beneficial to operate. For

example, for standard nuts and bolts one could

use an appropriate existing e-commerce market

model to obtain the parts. However, in the case

of a brand new navigation system you may haveto have exclusive supplier(s). Here the type of mar-

ket one is engaging in is different in the nature and

length of transactions that go beyond price and

quantity measures sufficient in the case of nuts

and bolts markets.

With the understanding of the commonality and

interdependence between artifacts and markets, we

next take up the issue of how engineers deal withthe issues of testing for failures and overflows in

functional and structural behavior of the design

with modeling and experimentation serving as tests

verification and measurement. We then present the

relationship between the engineers approach with

that of design economists. In summary, as cau-

tioned by Varian, ‘‘an understanding and appreci-

ation of existing institutions, good theory, goodcomputational modeling and well designed experi-

ments are critical to a successful design’’ [27].

4. Methodological issues

4.1. Methodological experiences in designing engi-

neered artifacts

The design of aerodynamic shape for aircraft

provides an example of methodological ap-

proaches to testing a designed artifact with respect

to a specified formulation. We draw this example

from a case study of the design of Boeing 777

and the use of computing tools [3]. There are four

types of methods used in the designing of tests.They are (a) analytical, (b) simulation (CFD), (c)

scaled mock up (wind tunnel), and (d) flight tests.

In characterizing these methods and their use the

author presents two approximate graphs, (a) lo-

g(error) vs. log(cost) associated with the methods

and (b) other is of the cost of finding a point in

the design space by a method (Figs. 1(a) and (b)).

While there is a trade-off in costs and error forthese test methods, these tests do not measure ex-

actly the same design parameters or perform the

same tests. For example, wind tunnel testing will

allow for testing of additional effects with an over-

all modeling error while CFD will provide detailed

flow information difficult to obtain experimentally.Even the flight test with its most reliable predic-

tions will still have errors from anticipated condi-

tions. The measure of error is the deviation of

the predicted from the actual behavior. In spite

of advancements in computing, use of CFD mod-

els constitute a tiny fraction of all the aerodynamic

simulations done for an aircraft.

The above aircraft design example illustratesthat in artifact design models serve the purpose

of performing tests on the structural and behavi-

oral goals of the artifact. Similar to the four levels

of testing for just the aerodynamic behavior of the

aircraft, most artifacts have collections and levels

of tests that operate throughout the design proc-

ess. The cost curves for these types of tests vary

from discipline to discipline; nevertheless they dis-play similar trends and shapes. In the case of arti-

fact design, analytical and simulation models

provide only glimpses into the working of the arti-

fact. In practice, use of modeling and simulation

tools seems to be the most dominant in new de-

signs when there is a need to develop insights

[23]. On repetition of similar designs, data induced

design rules, heuristics, tend to dominate the de-sign methods, and computational models tend to

serve mainly as verification.


Informed changes to underlying assumptions in

these models and choices of design parameters re-

main critical in their evolution and utility. Engi-

neers would agree with the economists McAfee

and Milgrom [13] who in reporting on their expe-rience in the design of spectrum auction observe

that simple theoretical and analytical models pro-

vide insights and allow for creating tests that allow

for the exploration of trade-offs. Especially in the

case of complex engineered artifacts, given the

state of understanding and the complexity in creat-

ing and solving an all encompassing model of an

artifact would render the task be impossible. It ishere that specific theories for specific features of

an artifact with simple or few functional forms

are more helpful in developing insights and the

experiments (computational and otherwise) to test

for potential failures.

Engineers often use different levels of incommen-

surable models to test their design formulations

throughout the design process. Improvements inthese methods of testing come from advances in

several areas including theoretical and analytical

modeling, physical testing methods and computa-

tional modeling. New and updated models of the

structure and behavior of the artifact and creation

of corresponding tests challenge the formulation

regularly. This cycle of formulation and reformula-

tion takes place in an evolutionary manner as expe-rience in identification and testing of unanticipated

externalities and failures accumulate.

We have given a brief overview of methodolog-

ical approaches on how engineered artifacts are

tested for the goals of minimizing different types

of failures and maximizing expected performance

that are agreed upon in the design specification.

We contend that the approaches used by engineersare means to manage bounds on rationality that

pervade especially the design solution generation

and testing process.

4.2. The economist as an engineer

Alvin Roth coined the term Design Economics

in his paper on ‘‘The Economist as Engineer:Game Theory, Experimentation, and Computa-

tion as Tools for Design Economics’’ [19]. In this

paper, he argues the case for the use of simple

models in the design of markets, even in the pres-

ence of other complexities, that are augmented

with experience [19]. In making his case for Design

Economics with game theory as one of its theoret-

ical foundations, he illustrates using a simplemodel for auctions for medical interns akin to a ri-

gid beam model used for structural design of

bridges. He complicates the original model by

the use of more elaborate computational models

and the use of real experiments. The results from

these exercises augment the analytical and theoret-

ical to accommodate for complementarities and

other observed failures. These complications arisefrom the potential gaming behavior of the partici-

pants in the market as well as the change in the

strategic environment in which the market oper-

ates. Computational experiments allowed for

modeling different participant behaviors and mar-

ket rules where analytical models were too compli-

cated to solve. Computational models, for example

in Roth�s case, identify that an ‘‘undesirable condi-tion’’ unstable matching of residents to hospitals

does not occur in computer modeling when cou-

ples are considered while the theory predicts the

difficulty in finding a stable match given a set of

market rules. The main goal of computational

experimentation is to test for undesirable or desir-

able behavioral characteristics of the designed

market that lead to their failures or acceptance.As in engineering, design economics will have to

use different models provide different insights in

to the working of a specific market.

The identification of the need for multiple mod-

els of formulating and solving the market design

problem has grown in the literature on e-commerce

and distributed agents that use game theory.

Researchers on distributed agents [11], in evaluat-ing the utility of analytical game theory in the de-

sign of computational markets, are sober in their

assessment of its use for realistic problems. In a re-

cent paper, Wellman and his colleagues, exploring

bidding strategies for market based scheduling,

contend that the problem is not tractable analyti-

cally using game theory and that one may have to

use evolutionary search strategy (computationalmodeling) for specific contexts [28]. Adherents of

game theory and theories of complex adaptive sys-

tems use evolutionary algorithms to model and


simulate social systems such as markets. One can

answer the question of the realism and relevance

of such methods only by empirically testing them

for their relevance and usefulness [7] just as in the

case of CFD in aircraft design.Experiences in market design as reported by

their practitioners reinforce the experiences of

engineers in the design and development of arti-

facts [13,19]. Roth has laid out a program for de-

sign economics that includes computational

modeling, scaled human experiments, and actual

observations of market functioning that very much

have parallels in the engineering world [19]. Multi-level modeling and experimentation have been

used historically and is still very prevalent in the

engineered artifact world [17].

In making the argument that economists have

to pay attention to design for having a pragmatic

impact on society, Roth states the conditions for

the success of Design Economics,

‘‘. . . in the long term, the real test of our successwill be not merely how well we understand the gen-eral principles that govern economic interactions,but how well we can bring this knowledge to bearon practical questions of microeconomic engineer-ing. . . Just as chemical engineers are called uponnot merely to understand the principles that gov-ern chemical plants, but to design them, and justas physicians aim not merely to understand thebiological causes of disease, but their treatmentand prevention, a measure of the success of micro-economics will be the extent to which it becomesthe source of practical advice, solidly groundedin well tested theory, on designing the institutionsthrough which we interact with one another.’’ [19]

We conclude this section by asserting that meth-odological issues for testing the design of aircrafts

shown as an exemplar of engineered artifacts will

hold for design of markets as well.

5. An example in devising a test for a market design

using a simple model for power markets

In a recent paper, we proposed a test to identify

market designs that are prone to excess prices and

profits, whether from learning or capacity concen-

trations [25]. In devising a model for this test, we

use computational models of agents that are evolu-

tionary in terms of learning from experience, sim-

ilar to those in the literature on computational

economics, evolutionary economics and distrib-uted agents. Even without an elaborate model of

the electric power grid, a simple model of the mar-

ket reveals potential market failures in terms of

suppliers being able to learn to achieve monopolis-

tic prices under some demand conditions.

5.1. The problem

Since power systems contain few, if any, facili-

ties for the storage of electric energy, production

must track demand, instant by instant. This de-

mand is quasi-repetitive, meaning that in any long

sequence of consecutive time intervals, there are

sub-sequences of non-consecutive intervals in

which demand changes slowly, if at all. Let {Tn}

be such a sub-sequence, that is, a sequence suchthat the aggregate demand curve for interval Tn

is almost, if not exactly, the same as that for inter-

val Tn + 1. Consider an auction of electric energy

that is repeated for each of the intervals in {Tn}.

Let:

M be the number of suppliers (a supplier may

own several generating plants).

Pn = {pmn} be a set of problems, where pmn is amulti-objective optimization problem representing

the goals of, and constraints on, the mth supplier

in the nth interval. (Both short- and long-term

goals can be converted by techniques, such as the

rolling horizon, into a series of static optimization

problems, one for each interval [30].)

xmn be the final offer (to sell energy) made by

the mth supplier in the nth interval. More specifi-cally, xmn is a (3·J) matrix, each of whose col-

umns represents one of J generating plants. The

first row lists the quantities of energy (in MWh)

from the plants, the second row lists prices in $/

MWh, and the third row contains zeros and ones,

a zero indicating that the energy from the genera-

tor is being withheld, a one indicating that the en-

ergy is being offered for sale.Xn be the aggregate of the xmn for the nth inter-

val, that is Xn represents the supply curve in the

nth interval.


U be the space of all possible values of Xn.

Sn = {Smn} be a set of strategies, where Smn is

the strategy used by the mth supplier to solve its

problem, pmn, and thereby, determine its final of-

fer, xmn, in the nth interval.Umn be the profit made by the mth supplier in

the nth time-interval.

Un = UmUmn be the total profits made by the

sellers in the nth time-interval.

Un be a model of the rest of the system from the

suppliers� viewpoint, that is, a model of the cus-

tomers, the market mechanism, and the network

for the delivery of electric energy.The triple: l = {Sn, Pn, Un} is a dynamic system

whose state variables include the offers and whose

outputs include the profits made by the suppliers.

The offers trace trajectories in the space, U. Thetrajectories converge, if at all, to one of the attrac-

tors of the dynamic system. (These attractors may

be points or more complicated features, such as

limit cycles.) To evaluate a dynamic system, onemust calculate its trajectories, find its attractors

and predict its outputs.

5.2. The importance of learning

One way to simulate trajectories is to pay human

subjects to devise {Sn}, the strategies on which the

trajectories depend. There are two disadvantagesto this approach. First, human subjects make deci-

sions relatively slowly; each experiment can take

hours, and even then, may not have lasted long en-

ough for the trajectory to reach an attractor. Sec-

ond, it is unlikely that any reasonable payments,

or other inducements, can cause the subjects to

faithfully adoptPn, the goals of the actual suppliers.

Our approach is to use software agents instead ofhuman subjects. The agents have automatic learn-

ing capabilities. (Learning is the process of convert-

ing experience and the results of experiments into

added competence.) If the demand is quasi-repeti-

tive and if the suppliers incur few if any penalties

for experimenting with their strategies, then learn-

ing can considerably increase profits. Even simple

evolutionary algorithms can be quite effective. Onesuch algorithm for determining xmn, the offer from

the mth supplier for the nth interval, is

1. Search the seller�s history for the K previ-

ous offers that resulted in the greatest

profits.

2. Apply crossover and mutation operators to

these previous offers to obtain a single offerfor the nth interval.

3. If the probability of mutation is non-zero,

decrease it before using the algorithm for the

next interval.

5.3. An example

Consider a uniform auction with 10 suppliers.

The auction is repeated for the intervals, T1, T2,

T3, . . . The demand in each interval is the same.

The offer by each supplier in each interval is al-

ways at cost, and has the form:

Quantity (MWh)
1 1 1 1 1 1 1 1 1 1
Cost ($/MWh)
1 2 3 4 5 6 7 8 9 10
Withholding
? ? ? ? ? ? ? ? ? ?
These offers differ only in their third rows, that

is, only in the binary vectors that indicate which

blocks of energy are withheld. Let:Ucomp be the competitive clearing price obtained

when no energy is withheld.

Ulearn be the clearing price when each supplier

uses its own previous results and the evolutionary

learning algorithm to determine its withholdings.

Umax be the maximum possible clearing price

obtained when the sellers cooperate (collude) in

determining their withholdings.It was assumed that the effects of the delivery

network could be neglected so the customers could

be connected to the same node as all the suppliers.

Three types of demand were considered:

Constant: the aggregate demand in each interval

was constant at 60 MWh.

Linear: the aggregate demand in each interval

was made to vary linearly with price, specifically(Price in $)=120� (Quantity in MWh)

Nonlinear: the aggregate demand in each

interval was made to vary non-linearly with

price, specifically (Price in $)=360/(Quantity in

MWh)


The calculated clearing prices for these three

types of demand are given below.

Demand
Ucomp Ulearn Umax
Constant
6 10 10
Linear
6 7 8
Nonlinear
6 7 10
Notice that with constant demand, the suppliers

can learn withholding strategies that produce thesame clearing price, as does collusion.

5.4. A test for excess profits

Indicators of a market�s potential for high prices

and excess profits, such as the Herfindahl–

Hirschman Index, Supply Margin Assessment,

and the Residual Supplier Index [31], reflect asym-metries in supplier capacities but do not take the

dynamics of markets, and particularly, the changes

producedby learning, into account. Inwhat follows,

we will define indices that include the effects of

capacity asymmetries, dynamics and learning. Let:

{Tn}, be a series of time intervals, each with the

same aggregate demand curve, D; Amax, be the

maximum possible value of the total profit the sup-pliers can make in a time interval by collusion or

any other means; Alearn, be the asymptotic value

of the total profits the suppliers can make in a time

interval when each supplier adjusts its offer-

strategy with evolutionary learning; Acomp, be the

total profit the suppliers will make in a time inter-

val if they offer all their energy at cost.

L ¼ ðAlearn � AcompÞ=ðAmax � AcompÞ:The larger the value of L, the greater the danger

of high prices and excess profits for demand D,

since real suppliers can be expected to learn at least

as effectively as an evolutionary algorithm.

This small experiment points out that even

using a simple model one can get insights into

the behavior of the sellers once we accountfor the salient characteristics of the market. As in

the experiment, the California experience shows

that suppliers did withhold power as a means to in-

crease prices and hence create undue profits. Using

these simple experiments as starting points, we are

currently examining different market rules, learn-

ing algorithms for buyers and sellers and expect

in the future to conduct joint human and compu-

tational agent experiments. We plan also to in-

clude models of the artifact (power systemsmodels) interconnected with the market model to

understand the effects of the physical constraints

and objectives of the network on the market trans-

actions that are realizable. Many in the field of

power markets have even called for comprehensive

simulation environments that integrate the physi-

cal systems with the market models for effective

training of personnel and management of thepower markets [8].

6. Discussions and conclusions

The important message of this paper is that for-

mulation of engineered artifact design problems

and market design problems are contextual socialprocesses involving multiple goals set by the ac-

tors. In the words of Roth, the importance viewing

economics from a design perspective would be the

following insight [19]:

‘‘The largest lesson in all this is that design isimportant because markets don�t always grow likeweeds – some of them are hothouse orchids. Timeand place have to be established, related goodsneed to be assembled, or related markets linkedso that complementarities can be handled, incen-tive problems have to be overcome, etc.’’

We illustrated that the artifact design and mar-

ket design problems are often inter-dependent and

evolve together making the context even more crit-

ical. Evolution may occur not just in the sense ofthe evolution of market rules and the evolution

of agents in them to create more efficient but more

computationally complex markets [14] but also in

the creation of new inter-linked sets of markets

and industry structures that evolve with the evolu-

tion of the design of the engineered artifact [2].

Failures in engineered artifacts often can be

attributed poor test specification or lack of useof new tests when a reformulation of the problem

is initiated to accommodate the changes in the

external conditions and overflows [13]. Successful


designs in both Roth�s conception of Design Eco-

nomics and in engineering design require that de-

sign outcomes not only have to perform in the

long run but also in the short run. To accommo-

date this form of endurance of the designed arti-fact and its different facets of behavior, use of

multiple modeling and testing mechanisms drawn

from different disciplines are critical to the toolbox

available to both engineers and economists for

contextualizing their designs.

Computing, theories of automata and game

theory have provided a way to characterize mar-

kets and create experimental test beds as in engi-neering. The computational toolboxes for

designers of specialized markets can be found in

the literature for trade networks [26], electricity

networks [26,8] and others. They are not substi-

tutes for real experiments or for a shared memory

of market designs and for validated models and

simulations. Without a shared memory of designs,

there will be no evolution and bounded rationalitywill prevent us from solving problems that are ever

more complex. Categorization and classifications,

of where what works and when, is an important

component of engineering science and design and

we expect that science of design economics will

be no different.

In order to create theories of markets automata,

Mirowski [14] makes a case for taxonomy of mar-kets and their applications. In the e-commerce

world, the paper on ‘‘Parameterization of Auction

Design Space,’’ by Wurman, Wellman and Walsh

is an attempt at creating a taxonomy of auction

mechanisms for automated auction negotiation

in the context of multi-dimensional auctions [29].

We agree with these assessments and efforts on

the need for taxonomy of markets. We believe intheir importance from historical experiences with

engineered artifacts [12]. Taxonomies of markets,

their models and experiences would go a long

way toward helping the design of more effective

and computationally more complex markets. A

well understood market design and its limitations

are always a good starting point for the next de-

sign even if the new design requires a reformula-tion. Good taxonomies help in searching for

good starting points. The contributions to the lit-

erature on market design are from computer scien-

tists, economists and others bringing their own

skills and perspectives. Observing this develop-

ment Roth says,

‘‘But if we want this knowledge to accumulate, ifwe want market design to be better informed andmore reliable in the future, we need to promote sci-entific literature in Design Economics’’ [19].

Maybe there will be a ‘‘Market Engineers

Handbook’’ someday.

In this paper, we have continued the dialogue

started by the economist Roth with design by

describing his vision of Design Economics. We be-lieve this dialog is important as the interconnec-

tions between engineering design and economics

can be strengthened by creating an awareness of

their shared interests in both communities. This

could lead to engineers and economists working

and learning from each other to formulate and

solve design problems.

Acknowledgements

We would like to thank Raghu Arunachalamfor inviting and urging us to write this paper,

and Ira Monarch, Robin King and Arthur Wester-

berg for discussions on this topic and comments.

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