<!-- MHonArc v2.4.4 --> <!--X-Subject: Re: [MUD-Dev] Game Economies --> <!--X-From-R13: Zvat <Y.Z.Zb-94Nfghqrag.yobeb.np.hx> --> <!--X-Date: Thu, 17 Jun 1999 08:39:05 -0700 --> <!--X-Message-Id: Pine.SOL.3.96.990617131816.23280E-100000@sun-cc203 --> <!--X-Content-Type: text/plain --> <!--X-Reference: Pine.SOL.3.96.990613201803.15305A-100000@sun-cc203 --> <!--X-Head-End--> <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2//EN"> <html> <head> <title>MUD-Dev message, Re: [MUD-Dev] Game Economies</title> <!-- meta name="robots" content="noindex,nofollow" --> <link rev="made" href="mailto:K.L.Lo-94#student,lboro.ac.uk"> </head> <body background="/backgrounds/paperback.gif" bgcolor="#ffffff" text="#000000" link="#0000FF" alink="#FF0000" vlink="#006000"> <font size="+4" color="#804040"> <strong><em>MUD-Dev<br>mailing list archive</em></strong> </font> <br> [ <a href="../">Other Periods</a> | <a href="../../">Other mailing lists</a> | <a href="/search.php3">Search</a> ] <br clear=all><hr> <!--X-Body-Begin--> <!--X-User-Header--> <!--X-User-Header-End--> <!--X-TopPNI--> Date: [ <a href="msg00828.html">Previous</a> | <a href="msg00833.html">Next</a> ] Thread: [ <a href="msg00799.html">Previous</a> | <a href="msg00857.html">Next</a> ] Index: [ <A HREF="author.html#00832">Author</A> | <A HREF="#00832">Date</A> | <A HREF="thread.html#00832">Thread</A> ] <!--X-TopPNI-End--> <!--X-MsgBody--> <!--X-Subject-Header-Begin--> <H1>Re: [MUD-Dev] Game Economies</H1> <HR> <!--X-Subject-Header-End--> <!--X-Head-of-Message--> <UL> <LI><em>To</em>: <A HREF="mailto:mud-dev#kanga,nu">mud-dev#kanga,nu</A></LI> <LI><em>Subject</em>: Re: [MUD-Dev] Game Economies</LI> <LI><em>From</em>: Ling <<A HREF="mailto:K.L.Lo-94#student,lboro.ac.uk">K.L.Lo-94#student,lboro.ac.uk</A>></LI> <LI><em>Date</em>: Thu, 17 Jun 1999 13:30:14 +0100 (BST)</LI> <LI><em>Reply-To</em>: <A HREF="mailto:mud-dev#kanga,nu">mud-dev#kanga,nu</A></LI> <LI><em>Sender</em>: <A HREF="mailto:mud-dev-admin#kanga,nu">mud-dev-admin#kanga,nu</A></LI> </UL> <!--X-Head-of-Message-End--> <!--X-Head-Body-Sep-Begin--> <HR> <!--X-Head-Body-Sep-End--> <!--X-Body-of-Message--> <PRE> On Sun, 13 Jun 1999, Ling wrote: > > > The following link may prove enlightening (or not): > > <URL:<A HREF="http://www.newscientist.com/nsplus/insight/ai/forecast.html">http://www.newscientist.com/nsplus/insight/ai/forecast.html</A>> > > Skip the first ten odd paragraphs to get straight to the stock market > simulator using moderately complex agents. It's even in simple English. > > Considering the simulation was conducted in 1987, I doubt the method > described above is beyond most muds these days. With much caution, a copy of the text in that link is attached below. I suspect the John Holland mentioned below is the same Holland who wrote quite an influential paper on genetic algorithms way back. Unfortunately, pretty diagrams will have to be obtained from the original webpage itself. ---<cut>--- Firm forecast Managing the vagaries of everyday life has always been a nightmare for chiefs of disaster insurance companies and senior stock brokers. Now, a generation of virtual humans could put the prediction business on a sound footing, says John Casti WHEN HURRICANE ANDREW scythed through Florida and Louisiana in 1992, it left behind a cruel trail of destruction. But people living along the storm track were not the only ones to take a beating. The financial waves triggered by this most costly hurricane ever spread around the world, battering many insurance companies and sinking others. Now, given that seers, soothsayers and scientists have all failed to find a reliable way to forecast truly devastating storms, a prudent insurance company chief might at least hope for some idea of how to offset the firm's risk against such disasters. And a company with a more aggressive style might even want to know what impact another costly storm would have on its rivals, and be ready to exploit any business opportunities that might arise. Today, such business decisions are based largely on rules of thumb gained from long experience. But company chiefs long to do better. They hunger for some way to replace hunches and experience with a systematic approach to making decisions. They want to be able to develop theories about how their business works which they can put to the test in rigorous, repeatable experiments. In short, they want business to become a science. As recently as the early 1980s, the idea of subjecting social and behavioural systems to scientific study was shunned because humans were thought to be "too complex" and "unpredictable". But, in reality, these have never been the main barriers. The real problem has been the lack of laboratories in which to conduct experiments. Today, we have these laboratories. They come in the form of powerful computers running simulations of real-life behaviour. And, while these simulations are still in their infancy, their promise is hard to miss. Already, people from such diverse fields as stock market trading, supermarket design and the insurance business are starting to explore their money-making possibilities. In the autumn of 1987, W. Brian Arthur, an economist from Stanford University, California, and John Holland, a computer scientist from the University of Michigan, shared a house while they visited the Santa Fe Institute in New Mexico. During hours of evening conversations over numerous beers, Arthur and Holland hit upon the idea of creating a virtual stock market inside a computer-one that could be used to answer questions that people in finance have pondered for decades. Economists, for example, refer to a quantity called the fundamental value of a share. This is simply the sum of all the dividends that a person can expect to receive by holding on to the share indefinitely-but adjusted, or "discounted", to take account of factors such as inflation which make a dollar today worth more than a dollar in future. Arthur and Holland wanted to know if the average price of a share settles down to its fundamental value. This conjecture forms the basis of one of the most cherished tenets of finance theory, which academics use to understand market behaviour. Another common question is whether a market eventually settles into a fixed pattern of buying and selling, or whether a rich "ecology" of trading rules emerges instead. But how should Arthur and Holland go about creating a model exchange capable of giving answers that are relevant to real life? Finance theory was one option open to them. Its virtue is that it provides a set of rules on which deductions can be based. Take the prediction of the price of a share. Conventional wisdom has it that tomorrow's share price is simply the discounted expectation of today's price plus a factor taking into account one day's worth of the share's dividend. This calculation assumes that other factors, such as how fast the share is trading and economic indicators such as the interest rate remain the same. But in real life, of course, they don't. So there may actually be many perfectly reasonable ways to predict tomorrow's price, based on different ways of combining all or some of these variables. For example, we could say that tomorrow's price will equal today's price. Or we might predict that the new price will be today's price divided by the dividend rate. And so on. Finance theory really doesn't give any help in choosing which to use. The simple observation that there is no single, best way to process information sets deductive logic on a slippery slope. In the real world, a trader has not only to decide which forecasting method to use, but must also make assumptions about how other investors are going to make the same decision. Ultimately the reasoning chases its own tail. If I am a trader, I have to base my decisions partly on what I think other traders will do, knowing that they are basing their decisions on what they think I will do. All this led Arthur and Holland to the not very surprising conclusion that deductive methods based on grand laws are, at best, an oversimplified academic fiction. Instead, they decided to build their model stock market from the bottom up, starting with individual traders. Their model includes 60 software "agents" representing the traders. Each one is assumed to summarise recent market activity by a collection of descriptors (labelled A, B, C and so on), which are statements about the state of the market, such as "the price has gone up every day for the past week," or "the price is higher than the fundamental value", or "the trading volume is high". The traders then decide whether to buy or sell by invoking rules of the form: "If the market fulfils conditions A, B, and C, then buy, but if conditions D, G, S, and K are fulfilled, then hold." Each trader has a collection of such rules, but uses only one of them at a time. This rule is the one the trader views as its current, most accurate rule. As buying and selling proceeds, traders can re-evaluate their rules and bring another into play if it has proved profitable in the past. Suppose I'm an agent using one rule, but I know that another is useful when the inflation rate rises. When inflation does goes up, I will abandon the existing rule in favour of the other. Traders can also recombine successful rules to form new ones that they can then test in the market. This is carried out using what is called a genetic algorithm, an invention of Holland's that mimics the way the genes of two parents are mixed in a fertilised egg. The genetic algorithm generates new rules by combining elements from two "parent" rules. This simulated market, which trades just one company's shares, runs on a desktop computer. Before trading begins, the traders are fed a particular history of stock prices, interest rates and dividends, and are assigned a set of rules. The traders then randomly choose one of their rules and use it to start buying and selling. Adapting to the market After the first round of trading, each agent assesses how good its current rule is by comparing it with the way all its other rules would have performed. It then generates a new rule, and chooses the best rule for the next round of trading. And so the process goes, period after period, buying, selling, placing money in bonds, modifying and generating rules, estimating how good the rules are, and, in general, acting in the same way that traders act in real financial markets. Diagram: On the virtual exchange, share price moves just as in the real market. Black areas are where investors are willing to pay more for a share than its fundamental value (see text). Pink areas are where the market crashes. A frozen moment in this artificial market is displayed in the Diagram. It shows the time history of the share price and the fundamental value of the stock, where the price of the share is the white line and the top of the red region is the fundamental value. The black region, where the white line is higher than the top of the red region, represents a speculative bubble in which investors are willing to pay more for the share than it is truly worth (as measured by the fundamental value). In the pink region, where the white line sinks far below the top of the red, the market has crashed. So did this simulated market answer any of Arthur and Holland's questions? After many periods of trading and modification of trading rules, what emerges is a kind of ecology of predictors, with different traders employing different rules to make their decisions. Furthermore, the price of the share always settles down to a random fluctuation about its fundamental value. However, within these fluctuations a very rich behaviour is seen: market moods, overreactions to price movements and all the other things associated with real speculative markets. Indeed, the model appears to be very realistic. The bubbles and crashes resemble closely those seen in real life. And variants of the model are now being tested by both investment houses and finance theorists to study the dynamics of price movements, and to look at how traders move from one rule to another in the face of what their colleagues are doing. Arthur and Holland's agent-based approach can be used for simulating more than just the stock market. If you picture the agents on the virtual trading floor sporting sharp clothes and cell phones, the agents in another simulation, called SimStore, would have shopping trolleys and wire baskets. SimStore is a model of a real British supermarket-the Sainsbury's store at South Ruislip in West London. It is the result of a collaboration between Ugur Bilge of SimWorld, which is based in London, Mark Venables of Sainsbury's and me. The agents in SimStore are software shoppers, armed with shopping lists. They make their way round the silicon store, picking goods off the shelves according to rules such as the nearest neighbour principle: "Wherever you are now, go to the location of the nearest item on your shopping list." Using these rules, SimStore generates the paths taken by customers, from which it can calculate customer densities at each location. The diagram shows customer densities around the store with blue as the highest density and white the lowest. Diagram: By modelling people's shopping habits, SimStore can predict which parts of a supermarket will be most popular. It is also possible to link all points visited by, say, at least 30 per cent of customers to form a most popular path. A genetic algorithm can then change where in the supermarket different goods are stacked and so minimise, or maximise, the length of the average shopping path. Shoppers, of course, don't want to waste time, so they want the shortest path. But the store manager would like to have them pass by almost every shelf, to encourage impulse buying. So there is a dynamic tension between the minimal and maximal shopping paths that needs further exploration. Among other uses, this model is aimed at helping Sainsbury's to redesign its stores so as to generate greater customer throughput, reduce inventories and shorten the time that products are on the shelves. In both stock market and supermarket, the agents represent individual people. A quite different type of business simulation emerges when the agents are companies and the model is one of an entire industry-which brings us back to insurance. Over the past couple of years, I and colleagues at the Santa Fe Institute and Complexica, also in Santa Fe, have designed an agent-based model of the world's catastrophe insurance industry. As a crude first cut, the insurance industry can be regarded as an interplay between three components: firms which offer insurance, clients who buy it, and events which determine the outcomes of the "bets" placed between the insurers and their clients. In "Insurance World", the agents are primary insurers and reinsurers, the firms that insure the insurers, so to speak. This world can be perturbed by natural events, such as hurricanes and earthquakes, as well as factors such as changes in government regulations, which alter the ground rules of the insurance game, and global capital markets, which govern the availability of funds. So what is Insurance World good for? Insurers and reinsurers talk incessantly about getting a better handle on uncertainty, so they can assess their risk more accurately and price their products more profitably. Yet it's self-evident that if everyone had perfect foreknowledge of natural hazards, this would spell the end of the insurance industry. On the other hand, complete ignorance 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 suggests that there is some optimal level of uncertainty at which the insurance companies (though perhaps not their clients) can operate in the most profitable and efficient fashion. With Insurance World, we hope to be able to find this optimal level, and whether it varies between firms. Does it, for example, vary between reinsurers, primary insurers and/or customers? Another question to ask is which of the standard metaphors used to characterise organisations-a machine, a brain or an organism, for example-most accurately represents the insurance industry. And how is this picture of the organisation shaped by the "rules" used in the boardrooms of the companies that make up the industry? Understanding which metaphor works best should help to uncover good rules for operating those firms. The simulator calls for the decision makers of each firm to set a variety of parameters, such as their desired market share in certain geographical and/or commercial sectors and the level of risk they want to take on. They also have to estimate economic parameters such as future interest and inflation rates, and assess the likelihood of hurricanes and earthquakes. The simulation then runs for 10 years in steps of three months, at which time a variety of outputs can be examined. Diagram: Five virtual insurance companies with not quite equal market shares fight it out. Within six years, the leading firm (in blue) has all but squeezed the others out. For instance, the Diagram shows how the market for hurricane insurance around the Gulf of Mexico is split between five primary insurers in this toy world. The initial market shares were almost identical-but not quite. In this experiment, firm 4 has a slightly larger initial market share than any of the others, an advantage that it uses to squeeze out the other firms. This is not a result of a better premium-setting strategy or any other business tactic, but is solely down to the "brand effect", in which buyers tend to purchase insurance from companies they know about. Large-scale, agent-based simulations like the three described here are in their infancy. But they clearly show how computers can create laboratories for doing experiments that have never been possible before. These experiments are exactly the sort called for by the scientific method: they are controlled and repeatable. So, for the first time in history, we have the opportunity to create a true science of human affairs. The consequence could be studies of, say, the mechanisms underlying revolution or how racism takes hold of an institution. If I were placing bets on the matter, I'd guess that the world of business and commerce will lead the charge into this entirely new science. From New Scientist, 24 April 1999 ---<cut>--- | Ling Lo (living life in mono) _O_O_ kllo#iee,org _______________________________________________ MUD-Dev maillist - MUD-Dev#kanga,nu <A HREF="http://www.kanga.nu/lists/listinfo/mud-dev">http://www.kanga.nu/lists/listinfo/mud-dev</A> </PRE> <!--X-Body-of-Message-End--> <!--X-MsgBody-End--> <!--X-Follow-Ups--> <HR> <ul compact><li><strong>Follow-Ups</strong>: <ul> <li><strong><A NAME="00857" HREF="msg00857.html">Re: [MUD-Dev] Game Economies</A></strong> <ul compact><li><em>From:</em> Mik Clarke <mikclrk#ibm,net></li></ul> </UL></LI></UL> <!--X-Follow-Ups-End--> <!--X-References--> <UL><LI><STRONG>References</STRONG>: <UL> <LI><STRONG><A NAME="00799" HREF="msg00799.html">Re: [MUD-Dev] Game Economies</A></STRONG> <UL><LI><EM>From:</EM> Ling <K.L.Lo-94#student,lboro.ac.uk></LI></UL></LI> </UL></LI></UL> <!--X-References-End--> <!--X-BotPNI--> <UL> <LI>Prev by Date: <STRONG><A HREF="msg00828.html">Re: [MUD-Dev] Properties of computer languages</A></STRONG> </LI> <LI>Next by Date: <STRONG><A HREF="msg00833.html">Re: [MUD-Dev] Properties of computer languages</A></STRONG> </LI> <LI>Prev by thread: <STRONG><A HREF="msg00799.html">Re: [MUD-Dev] Game Economies</A></STRONG> </LI> <LI>Next by thread: <STRONG><A HREF="msg00857.html">Re: [MUD-Dev] Game Economies</A></STRONG> </LI> <LI>Index(es): <UL> <LI><A HREF="index.html#00832"><STRONG>Date</STRONG></A></LI> <LI><A HREF="thread.html#00832"><STRONG>Thread</STRONG></A></LI> </UL> </LI> </UL> <!--X-BotPNI-End--> <!--X-User-Footer--> <!--X-User-Footer-End--> <ul><li>Thread context: <BLOCKQUOTE><UL> <LI><STRONG>Re: [MUD-Dev] Game Economies</STRONG>, <EM>(continued)</EM> <ul compact> <ul compact> <LI><strong><A NAME="00738" HREF="msg00738.html">Re: [MUD-Dev] Game Economies</A></strong>, J C Lawrence <a href="mailto:claw#varesearch,com">claw#varesearch,com</a>, Fri 11 Jun 1999, 03:17 GMT </LI> </ul> <LI><strong><A NAME="00749" HREF="msg00749.html">RE: [MUD-Dev] Game Economies</A></strong>, Koster, Raph <a href="mailto:rkoster#origin,ea.com">rkoster#origin,ea.com</a>, Fri 11 Jun 1999, 14:38 GMT </LI> <LI><strong><A NAME="00788" HREF="msg00788.html">RE: [MUD-Dev] Game Economies</A></strong>, Charles Hughes <a href="mailto:charles.hughes#bigfoot,com">charles.hughes#bigfoot,com</a>, Sat 12 Jun 1999, 02:38 GMT </LI> <LI><strong><A NAME="00799" HREF="msg00799.html">Re: [MUD-Dev] Game Economies</A></strong>, Ling <a href="mailto:K.L.Lo-94#student,lboro.ac.uk">K.L.Lo-94#student,lboro.ac.uk</a>, Sun 13 Jun 1999, 20:12 GMT <UL> <LI><strong><A NAME="00832" HREF="msg00832.html">Re: [MUD-Dev] Game Economies</A></strong>, Ling <a href="mailto:K.L.Lo-94#student,lboro.ac.uk">K.L.Lo-94#student,lboro.ac.uk</a>, Thu 17 Jun 1999, 15:39 GMT <UL> <LI><strong><A NAME="00857" HREF="msg00857.html">Re: [MUD-Dev] Game Economies</A></strong>, Mik Clarke <a href="mailto:mikclrk#ibm,net">mikclrk#ibm,net</a>, Fri 18 Jun 1999, 20:43 GMT </LI> </UL> </LI> </UL> </LI> </ul> </LI> <LI><strong><A NAME="00481" HREF="msg00481.html">[MUD-Dev] thoughts</A></strong>, Matthew Mihaly <a href="mailto:diablo#best,com">diablo#best,com</a>, Fri 04 Jun 1999, 15:40 GMT <UL> <LI><strong><A NAME="00486" HREF="msg00486.html">Re: [MUD-Dev] thoughts</A></strong>, Jon A. Lambert <a href="mailto:jlsysinc#ix,netcom.com">jlsysinc#ix,netcom.com</a>, Fri 04 Jun 1999, 17:56 GMT <UL> <LI><strong><A NAME="00496" HREF="msg00496.html">Re: [MUD-Dev] thoughts</A></strong>, Matthew Mihaly <a href="mailto:diablo#best,com">diablo#best,com</a>, Fri 04 Jun 1999, 21:24 GMT </LI> </UL> </LI> </UL> </LI> </UL></BLOCKQUOTE> </ul> <hr> <center> [ <a href="../">Other Periods</a> | <a href="../../">Other mailing lists</a> | <a href="/search.php3">Search</a> ] </center> <hr> </body> </html>