BizSimThe World of Business—in a Box

THE COMPUTER AS A LABORATORY

T he central process distinguishing science from its competitors—religion, mu-sic, literature, mysticism—in the reality-generation business is the so-calledscientific method. An integral part of this method by which we arrive at scien-

tific “truth” is the ability to do controlled, repeatable laboratory experiments bywhich hypotheses about the phenomenon under investigation can be tested. It isjust such experiments that on a good day lead to the theories and paradigms con-stituting today’s “scientific” worldview. And, more than anything else, it is the in-ability to perform experiments of this type that separate the natural sciences fromthe worlds of social and behavioral phenomena. In the latter, we have no way ofdoing the experiments necessary to create a bona fide scientific theory of processessuch as stock market dynamics, road-traffic flow, and organizational restructuring.

In an earlier, less discerning era, it was often claimed that the realm of humansocial behavior was beyond the bounds of scientific analysis simply because humanbeings “are complex,” “are unpredictable,” “display free will,” “act randomly,” andso on. It’s hard to believe that any modern system theorist would do anything butlaugh at such childish and naive attitudes to the creation of workable and worth-while scientific theories of social and behavioral phenomena. The major barrier tobringing the social beneath the umbrella of science is not the nonexplanations justgiven in quotes, but the fact that until now we have had no way to test hypothesesand, therefore, make use of the scientific method in the creation of theories of socialbehavior. Now we do. And that laboratory in which we do our experiments is thedigital computer. Let me illustrate with an example from the world of finance.

BOOMS AND BUSTS, BUBBLES AND CRASHESIn the fall of 1987, W. Brian Arthur, an economist from Stanford, and John Holland,a computer scientist from the University of Michigan, were sharing a house in SantaFe while visiting the Santa Fe Institute. During hours of evening conversations,Arthur and Holland hit upon the idea of creating an artificial stock market inside acomputer—one that could be used to answer a number of questions that people infinance had wondered and worried about for decades. Among those questions were:

● Does the average price of a stock settle down to its so-called fundamental value—the value determined by the discounted stream of dividends that one can expectto receive by holding the stock indefinitely?

● Is it possible to concoct technical trading schemes that systematically turn a profitgreater than a simple buy-and-hold strategy?

JOHN L. CASTI

John Casti received his Ph.D. inmathematics under Richard Bellmanat the University of SouthernCalifornia in 1970. He worked at theRAND Corporation in Santa Monica,California, and served on the facultiesof the University of Arizona, NYU,and Princeton before becoming one ofthe first members of the research staffat the International Institute forApplied Systems Analysis (IIASA) inVienna, Austria. In 1986 he left IIASAto take up his current post as aProfessor of Operations Research andSystem Theory at the TechnicalUniversity of Vienna. He is also aresident member of the Sante FeInstitute in Santa Fe, New Mexico,USA. In addition to numeroustechnical articles and a number ofresearch monographs, Professor Castiis the author of several volumes oftrade science, including ParadigmsLost, Searching for Certainty,Complexification, and Would-Be-Worlds.

© 1999 John Wiley & Sons, Inc., Vol. 4, No. 4 C O M P L E X I T Y 11CCC 1076-2787/99/04011-04

● Does the market eventually settleinto a fixed pattern of buying andselling? In other words, does it reach“stationarity”?

● Alternately, does a rich “ecology” oftrading rules and price movementsemerge in the market?

A rthur and Holland knew that theconventional wisdom of financeargued that today’s price of a

stock is simply the discounted expecta-tion of tomorrow’s price plus the divi-dend, given the information availableabout the stock today. This theoreticalprice-setting procedure is based on theassumption that there is an objectiveway to use today’s information to formthis expectation. But the informationavailable typically consists of pastprices, trading volumes, economic indi-cators, and the like. So there may bemany perfectly defensible ways basedon many different assumptions to sta-tistically process this information in or-der to forecast tomorrow’s price. For ex-ample, we could say that tomorrow’sprice will equal today’s price. Or wemight predict that the new price will betoday’s price divided by the dividendrate. And so on and so forth.

The simple observation that there isno single, best way to process informa-tion led Arthur and Holland to the not-very-surprising conclusion that deduc-tive methods for forecasting prices are,at best, an academic fiction. As soon asyou admit the possibility that not alltraders in the market arrive at theirforecasts in the same way, the deductiveapproach of classical finance theory,which relies on following a fixed set ofrules to determine tomorrow’s price,begins to break down. So a trader mustmake assumptions about how other in-vestors form expectations and how theybehave. He or she must try to psycheout the market. But this leads to a worldof subjective beliefs and to beliefs aboutthose beliefs. In short, it leads to a worldof induction, in which we generalizerules from specific observations, ratherthan one of deduction.

In order to address these kinds ofquestions, Arthur, Holland, and theircolleagues constructed an electronic

stock market in which they could ma-nipulate traders’ strategies, market pa-rameters, and all the other things thatcannot be done with real exchanges.The traders in this market are each as-sumed to summarize recent market ac-tivity by a collection of descriptors,which involve verbal characterizationlike “the price has gone up every day forthe past week,” or “the price is higherthan the fundamental value,” or “thetrading volume is high.” Let us labelthese descriptors A, B, C, and so on. Interms of the descriptors, the traders de-cide whether to buy or sell by rules ofthe form: “If the market fulfills condi-tions A, B, and C, then buy, but if con-ditions D, G, S, and K are fulfilled, thenhold.” Each trader has a collection ofsuch rules and acts in accordance withonly one rule at any given time period.This rule is the one that the trader viewsas his or her currently most accurate rule.

As buying and selling goes on in themarket, the traders can reevaluate theirdifferent rules by assigning higher prob-ability of triggering a given rule that hasproved profitable in the past and/or byrecombining successful rules to formnew ones that can then be tested in themarket. This latter process is carried outby use of what is called a genetic algo-rithm, which mimics the way naturecombines the genetic pattern of malesand females of a species to form a newgenome that is a combination of thosefrom the two parents.

A run of such a simulation involvesinitially assigning sets of predictors tothe traders at random and then begin-ning the simulation with a particularhistory of stock prices, interest rates,and dividends. The traders then ran-domly choose one of their rules and useit to start the buying-and-selling pro-cess. As a result of what happens in thefirst round of trading, the tradersmodify their estimate of the goodness oftheir collection of rules, generate newrules (possibly), and then choose thebest rule for the next round of trading.And so the process goes, period afterperiod, buying, selling, placing moneyin bonds, modifying and generatingrules, estimating how good the rules areand, in general, acting in the same

way that traders act in real financial

markets.

A typical frozen moment in this arti-

ficial market is displayed in Figure 1.

Moving clockwise from the upper left,

the first window shows the time history

of the stock price and dividend, where

the current price of the stock is the

black line and the top of the grey region

is the current fundamental value. The

region where the black line is much

greater than the height of the grey re-

gion represents a price bubble, whereas

the market has crashed in the region

where the black line sinks far below the

grey. The upper right window is the cur-

rent relative wealth of the various trad-

ers, and the lower right window displays

their current level of stock holdings. The

lower left window shows the trading

volume, where grey is the number of

shares offered for sale and black is the

number of shares that traders have of-

fered to buy. The total number of trades

possible is then the smaller of these two

quantities because for every share pur-

chased there must be one share avail-

able for sale.

After many time periods of trading

and modification of the traders’ deci-

sion rules, what emerges is a kind of

ecology of predictors, with different

traders employing different rules to

make their decisions. Furthermore, it is

observed that the stock price always

settles down to a random fluctuation

about its fundamental value. However,

within these fluctuations a very rich be-

havior is seen: price bubbles and

crashes, market moods, overreactions

to price movements, and all the other

things associated with speculative mar-

kets in the real world.

T he agents in the stock market

simulation are individual traders.

A quite different type of business

simulation emerges when we want to

look at an entire industry, in which case

the agents become the individual firms

constituting that industry. The world’scatastrophe insurance industry servedas the focus for just such a simulationexercise called Insurance World, carriedout by the author and colleagues at the

12 C O M P L E X I T Y © 1999 John Wiley & Sons, Inc.

Santa Fe Institute and Intelligize Inc.over the past couple of years.

INSURANCE WORLDAs a crude first-cut, the insurance in-dustry can be regarded as an interplayamong three components: firms, whichoffer insurance; clients, who buy it; andevents, which determine the outcomesof the “bets” that have been placed be-tween the insurers and their clients. InInsurance World, the agents consist ofprimary casualty insurers and the rein-surers, the firms that insure the insur-ers, so to speak. The events are naturalhazards, such as hurricanes and earth-quakes, as well as various external fac-tors such as government regulators andthe global capital markets. InsuranceWorld is a laboratory for studying ques-tions of the following sort:

● Optimal Uncertainty: While insurersand reinsurers talk about getting abetter handle on uncertainty so as tomore accurately assess their risk andmore profitably price their product,it’s self-evident that perfect fore-knowledge of natural hazards wouldspell the end of the insurance indus-try. On the other hand, complete ig-

norance of hazards is also pretty bad

news, since it means there is no way

to weight the bets the firms make

and price their product. This simple

observation suggests that there is

some optimal level of uncertainty at

which the insurance–but perhaps

not their clients–can operate in the

most profitable and efficient fashion.

What is that level? Does it vary across

firms? Does it vary between reinsur-

ers, primary insurers, and/or end

consumers?

● Industry Structure: In terms of the

standard metaphors used to charac-

terize organizations—a machine, a

brain, an organism, a culture, a po-

litical system, a psychic prison—

which type(s) most accurately repre-

sents the insurance industry? And

how is this picture of the organization

shaped by the specific “routines”

used by the decisionmakers in the

various components making up the

organization?

The simulator calls for the management

of each firm to set a variety of param-

eters having to do with its desired mar-

ket share in certain regions for different

types of hazards and levels of risk it

wants to take on, as well as to provide a

picture of the external economic cli-

mate (interest rates, likelihood of hurri-

canes/earthquakes, inflation rates, and

so forth). The simulation then runs for

10 years in steps of one quarter, at

which time a variety of outputs can be

examined. For instance, Figure 2 shows

the market share for Gulf Coast hurri-

cane insurance of the five primary in-

surers in this toy world, under the as-

sumption that the initial market shares

were almost identical—but not quite. In

this experiment, firm 2 has a little larger

initial market share than any of the

other firms, a differential advantage

that it then uses to squeeze out all the

other firms at the end of the 10-year pe-

riod. This is due to the “brand effect,” in

which buyers tend to purchase insur-

ance from companies that they know

about.

A s a final example of what simula-

tion and business have to say to

each other, consider the move-

ment of shoppers in a typical supermar-

ket. This world is dubbed SimStore by

the author and Ugur Bilge and Mark Ve-

nables of J. Sainsbury in London, who

collaborated with the author on the cre-

ation of this simulator.

SIMSTOREThe starting point for SimStore is a real

supermarket in the Sainsbury chain,

one located in the London region of

South Ruislip. The agents are individual

shoppers who frequent this store. These

electronic shoppers are dropped into

the store and then make their way to the

various locations in the store by rules

such as “wherever you are now, go to

the location of the nearest item on your

shopping list” so as to gather all the

items they want to purchase.

As an example of one of the types of

outputs generated by SimStore, cus-

tomer checkout data are used to calcu-

late customer densities at each location.

Color codes are with descending order:

blue, red, purple, orange, pink, green,

cyan, grey, and nothing. Using the Man-

hattan metric pattern of movement, in

FIGURE 1

A frozen moment in the surrogate stock market

© 1999 John Wiley & Sons, Inc. C O M P L E X I T Y 13

which a customer can move only along

the aisles of the store, all locations

above 30 percent of customer densities

have been linked to form a most popu-

lar customer path. Once this path is

formed a genetic algorithm will mini-

mize (or maximize!) the length of the

overall shopping path.

In the same store, this time each in-dividual customer path has been inter-nally calculated using the simple “near-est neighbor” rule noted above. All cus-tomer paths have been summed foreach aisle, in order to calculate the cus-tomer path densities. These densitiesare displayed in Figure 3 as a relativedensity map using the same color codejust mentioned.

SIMULATION IS GOOD FOR BUSINESS

L arge-scale, agent-based simula-tions of the type discussed here arein their infancy. But even the pre-

liminary exercises outlined here showthe promise of using modern comput-ing technology to provide the basis fordoing experiments that have never beenpossible before. Even better, these ex-periments are exactly the sort called forby the scientific method—controlled andrepeatable—so that for the first time inhistory we have the opportunity to ac-tually create a science of human affairs.If I were placing bets on the matter, I’dguess that the world of business andcommerce will lead the charge into thisnew science that will form during the21st century.

FIGURE 2

Market share distribution for five primary insurers

FIGURE 3

Customer densities along each aisle in the simulated store

14 C O M P L E X I T Y © 1999 John Wiley & Sons, Inc.