June 1999
- Languages (slightly offtopic, was Text Parsing) Mark Gritter
- Languages (slightly offtopic, was Text Parsing) Caliban Tiresias Darklock
- Languages (slightly offtopic, was Text Parsing) Travis S. Casey
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Travis Casey
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Travis S. Casey
- Languages (slightly offtopic, was Text Parsing) Jp Calderone
- Languages (slightly offtopic, was Text Parsing) Travis Casey
- Languages (slightly offtopic, was Text Parsing) Greg Miller
- Languages (slightly offtopic, was Text Parsing) Cynbe ru Taren
- Languages (slightly offtopic, was Text Parsing) Koster, Raph
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Koster, Raph
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Caliban Tiresias Darklock
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Ben Greear
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Caliban Tiresias Darklock
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Jon A. Lambert
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Caliban Tiresias Darklock
- Languages (slightly offtopic, was Text Parsing) Greg Miller
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Greg Miller
- Languages (slightly offtopic, was Text Parsing) Caliban Tiresias Darklock
- Languages (slightly offtopic, was Text Parsing) Greg Munt
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Darren Henderson
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Greg Munt
- Languages (slightly offtopic, was Text Parsing) Caliban Tiresias Darklock
- Languages (slightly offtopic, was Text Parsing) Ola Fosheim Grøstad
- Languages (slightly offtopic, was Text Parsing) Greg Miller
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Miroslav Silovic
- Languages (slightly offtopic, was Text Parsing) Alex Oren
- Languages (slightly offtopic, was Text Parsing) Darren Henderson
- Languages (slightly offtopic, was Text Parsing) Greg Miller
- Languages (slightly offtopic, was Text Parsing) David Bennett
- Languages (slightly offtopic, was Text Parsing) Darren Henderson
- Languages (slightly offtopic, was Text Parsing) Ross Nicoll
- Languages (slightly offtopic, was Text Parsing) Caliban Tiresias Darklock
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Greg Miller
- Languages (slightly offtopic, was Text Parsing) Matthew Mihaly
- Languages (slightly offtopic, was Text Parsing) Koster, Raph
- Languages (slightly offtopic, was Text Parsing) Greg Miller
- Languages (slightly offtopic, was Text Parsing) Ross Nicoll
- Languages (slightly offtopic, was Text Parsing) Jon A. Lambert
- Languages (slightly offtopic, was Text Parsing) Greg Miller
- Languages (slightly offtopic, was Text Parsing) Jon A. Lambert
- Languages (slightly offtopic, was Text Parsing) Greg Miller
- Languages (slightly offtopic, was Text Parsing) Jon A. Lambert
- Languages (slightly offtopic, was Text Parsing) Greg Miller
- Languages (slightly offtopic, was Text Parsing) Koster, Raph
- Languages (slightly offtopic, was Text Parsing) Ola Fosheim Grøstad
- Languages (slightly offtopic, was Text Parsing) Ben Greear
- Languages (slightly offtopic, was Text Parsing) Chris Gray
- Languages (slightly offtopic, was Text Parsing) Caliban Tiresias Darklock
- IndustrialMUD J C Lawrence
- IndustrialMUD Greg Miller
- Combat (very long) Matthew Mihaly
- Combat (very long) Caliban Tiresias Darklock
- Combat (very long) Matthew Mihaly
- Combat (very long) Marian Griffith
- What about goal oriented interfaces? (was Text Parsing) Ola Fosheim Grøstad
- What about goal oriented interfaces? (was Text Parsing) Chris Gray
- What about goal oriented interfaces? (was Text Parsing) Ola Fosheim Grøstad
- What about goal oriented interfaces? (was Text Parsing) Matthew Mihaly
- What about goal oriented interfaces? (was Text Parsing) Chris Gray
- What about goal oriented interfaces? (was Text Parsing) Ola Fosheim Grøstad
- What about goal oriented interfaces? (was Text Parsing) Adam Wiggins
- thoughts Matthew Mihaly
- Game Economies Timothy O'Neill Dang
- Game Economies Matthew Mihaly
- Game Economies Caliban Tiresias Darklock
- Game Economies Caliban Tiresias Darklock
- Game Economies Ola Fosheim Grøstad
- Game Economies Marian Griffith
- Game Economies Ola Fosheim Grøstad
- Game Economies Marian Griffith
- Game Economies Ola Fosheim Grøstad
- Game Economies Caliban Tiresias Darklock
- Game Economies Ola Fosheim Grøstad
- Game Economies Matthew Mihaly
- Game Economies J C Lawrence
- Game Economies Timothy O'Neill Dang
- Game Economies Koster, Raph
- Game Economies J C Lawrence
- Game Economies Adam Wiggins
- Game Economies Caliban Tiresias Darklock
- Game Economies Greg Miller
- Game Economies Ross Nicoll
- Game Economies Koster, Raph
- Game Economies Ola Fosheim Grøstad
- Game Economies Jo Dillon
- Game Economies Ross Nicoll
- Game Economies Marian Griffith
- Game Economies Jo Dillon
- Game Economies Hans-Henrik Staerfeldt
- Game Economies Koster, Raph
- Game Economies Timothy O'Neill Dang
- Game Economies Marian Griffith
- Game Economies J C Lawrence
- Game Economies Timothy O'Neill Dang
- Game Economies Ola Fosheim Grøstad
- Game Economies Timothy O'Neill Dang
- Game Economies Timothy O'Neill Dang
- Game Economies Timothy O'Neill Dang
- Game Economies Caliban Tiresias Darklock
- Game Economies Jo Dillon
- Game Economies Timothy O'Neill Dang
- Game Economies Jo Dillon
- Game Economies Matthew Mihaly
- Game Economies Koster, Raph
- Game Economies J C Lawrence
- Game Economies Greg Munt
- Game Economies Matthew Mihaly
- Game Economies Jon A. Lambert
- Game Economies Shawn Halpenny
- Game Economies Brandon J. Rickman
- Game Economies Jon A. Lambert
- Game Economies Shane King
- Game Economies Caliban Tiresias Darklock
- Game Economies Matthew Mihaly
- Game Economies Koster, Raph
- Game Economies Nathan F Yospe
- Game Economies Ola Fosheim Grøstad
- Game Economies J C Lawrence
- Game Economies J C Lawrence
- Game Economies Caliban Tiresias Darklock
- Game Economies Matthew Mihaly
- Game Economies Caliban Tiresias Darklock
- Game Economies Koster, Raph
- Game Economies Brandon J. Rickman
- Game Economies Marian Griffith
- Game Economies Katrina McClelan
- Game Economies Adam Wiggins
- Game Economies Katrina McClelan
- Game Economies Albert
- Game Economies Jp Calderone
- Game Economies Laurel Fan
- Game Economies Albert
- Game Economies Charles Hughes
- Game Economies J C Lawrence
- Game Economies Matthew Mihaly
- Game Economies Mik Clarke
- Game Economies Ola Fosheim Grøstad
- Game Economies Ling
- Game Economies Ling
On Sun, 13 Jun 1999, Ling wrote:
> >
> The following link may prove enlightening (or not):
>
> <URL:http://www.newscientist.com/nsplus/insight/ai/forecast.html>
>
> 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 - Game Economies Mik Clarke
- Game Economies Ling
- Rooms Greg Munt
- Parsing Text Output Jon A. Lambert
- Parsing Text Output Chris Gray
- MUD-Dev request rejected Greg Miller
- Interface/Custom Clients Greg Miller
- Languages (slightly offtopic, was Text Parsing)y Mark Gritter
- Languages (slightly offtopic, was Text Parsing)y Caliban Tiresias Darklock
- Pay Muds & Free Muds - working together, or against each other? Greg Munt
- Pay Muds & Free Muds - working together, or against each other? Caliban Tiresias Darklock
- Pay Muds & Free Muds - working together, or against each other? Matthew Mihaly
- Pay Muds & Free Muds - working together, or against each other? Koster, Raph
- Pay Muds & Free Muds - working together, or against each other? Ross Nicoll
- Pay Muds & Free Muds - working together, or against each other? Richard Bartle
- Pay Muds & Free Muds - working together, or againsteach other? Caliban Tiresias Darklock
- Pay Muds & Free Muds - working together, or against each other? Christopher Allen
- Pay Muds & Free Muds - working together, or against each other? Richard Bartle
- CVS repository for ScryMUD is online at scry.wanfear.com Ben Greear
- Mihaly's Combat Greg Munt
- Mihaly's Combat Matthew Mihaly
- Looking for UML David95037@aol.com
- Looking for UML Greg Munt
- Looking for UML David95037@aol.com
- Slogans (Game Economies) Caliban Tiresias Darklock
- Slogans (Game Economies) Ola Fosheim Grøstad
- A couple Qs, and thanks Ben Greear
- Understanding and "should-ness" Matthew Mihaly
- Understanding and "should-ness" Caliban Tiresias Darklock
- Personalities J C Lawrence
- realism Matthew Mihaly
- Gender and Mud Development Nathan F Yospe
- Gender and Mud Development Greg Miller
- Gender and Mud Development Katrina McClelan
- Gender and Mud Development Jon A. Lambert
- Gender and Mud Development Matthew Mihaly
- Gender and Mud Development Jon A. Lambert
- Gender and Mud Development Caliban Tiresias Darklock
- Gender and Mud Development David Holz
- Gender and Mud Development Jon A. Lambert
- Gender and Mud Development Matthew Mihaly
- Gender and Mud Development Greg Miller
- Gender and Mud Development J C Lawrence
- Gender and Mud Development Koster, Raph
- Gender and Mud Development J C Lawrence
- Gender and Mud Development Ola Fosheim Grøstad
- Gender and Mud Development J C Lawrence
- Gender and Mud Development Koster, Raph
- Gender and Mud Development Greg Munt
- Gender and Mud Development Greg Miller
- Gender and Mud Development Ola Fosheim Grøstad
- Gender and Mud Development Marian Griffith
- Gender and Mud Development Matthew Mihaly
- Gender and Mud Development Caliban Tiresias Darklock
- Gender and Mud Development Ola Fosheim Grøstad
- Gender and Mud Development Adam Wiggins
- Gender and Mud Development Travis S. Casey
- Gender and Mud Development Marian Griffith
- Newbies (was Text Parsing) John Hopson
- Newbies (was Text Parsing) Caliban Tiresias Darklock
- Average play time? Timothy O'Neill Dang
- Average play time? Caliban Tiresias Darklock
- Average play time? Koster, Raph
- {OT} Gender and Mud Development Wendy Winkler
- AOL lawsuit (was text parsing) Koster, Raph
- Sexual archetypes claw@kanga.nu
- Systems and concepts Caliban Tiresias Darklock
- Gender and Mud Development (back on topic, some) Katrina McClelan
- Gender and Mud Development (back on topic, some) Koster, Raph
- Gender and Mud Development (back on topic, some) Greg Miller
- Gender and Mud Development (back on topic, some) J C Lawrence
- Gender and Mud Development (back on topic, some) Jon A. Lambert
- Games within Games [Was: Gender and Mud Development] Kristen Koster
- Games within Games [Was: Gender and Mud Development] Greg Miller
- Games within Games [Was: Gender and Mud Development] Kristen Koster
- Games within Games [Was: Gender and Mud Development] Jon A. Lambert
- Games within Games [Was: Gender and Mud Development] Kristen Koster
- Games within Games [Was: Gender and Mud Development] Marian Griffith
- Tactical Combat and Traps (was the gender thread) Katrina McClelan
- Game construction and a big mistake Caliban Tiresias Darklock
- Game construction and a big mistake Marc Bowden
- Game construction and a big mistake Greg Miller
- Game construction and a big mistake Matthew Mihaly
- Game construction and a big mistake Willowreed@aol.com
- Gender and Mud Development (back on topic, some) Caliban Tiresias Darklock
- Gender and Mud Development (back on topic, some) Koster, Raph
- Gender and Mud Development (back on topic, some) claw@kanga.nu
- Gender and Mud Development (back on topic, some) Marc Bowden
- Client-Server vs. Peer-to-Peer -- Implementing DIS on the Internet claw@kanga.nu
- Goals and directions (was the gender thing) Caliban Tiresias Darklock
- MUD-Dev digest, Vol 1 #93 - 27 msgs Dr. Cat
- MUD-Dev digest, Vol 1 #93 - 27 msgs Greg Miller
- MUD-Dev digest, Vol 1 #93 - 27 msgs Koster, Raph
- MUD-Dev digest, Vol 1 #93 - 27 msgs Ola Fosheim Grøstad
- Gender and Mud Development (drifting off topic again) S. Patrick Gallaty
- Laws of Online World Design (was: realism) Koster, Raph
- Game construction and a big mistake Koster, Raph
- memory and speed Matthew Mihaly
- memory and speed Adam Wiggins
- memory and speed J C Lawrence
- memory and speed J C Lawrence
- memory and speed Matthew Mihaly
- memory and speed Adam Wiggins
- memory and speed Chris Gray
- memory and speed Miroslav Silovic
- memory and speed Caliban Tiresias Darklock
- memory and speed Mark Gritter
- memory and speed Ben Greear
- memory and speed Matthew Mihaly
- memory and speed S. Patrick Gallaty
- memory and speed Adam Wiggins
- memory and speed Brad Leach
- memory and speed Petri Virkkula
- Virtual worlds based on real world history Greg Munt
- Stockmarkets... Ola Fosheim Grøstad
- Games within Games Jesse Farinacci
- ADMIN: Posting authority J C Lawrence
- thoughts on game economies Travis S. Casey
- thoughts on game economies Caliban Tiresias Darklock
- thoughts on game economies Travis Casey
- thoughts on game economies Koster, Raph
- thoughts on game economies Travis S. Casey
- thoughts on game economies Koster, Raph
- thoughts on game economies Hans-Henrik Staerfeldt
- thoughts on game economies Jo Dillon
- thoughts on game economies Greg Miller
- thoughts on game economies Matthew Mihaly
- thoughts on game economies Greg Miller
- thoughts on game economies Matthew Mihaly
- thoughts on game economies Jeremy Music "Sterling"
- thoughts on game economies Robert Brady
- thoughts on game economies Ling
- thoughts on game economies Caliban Tiresias Darklock
- thoughts on game economies Alex Oren
- thoughts on game economies Albert
- thoughts on game economies Adam Wiggins
- thoughts on game economies Travis S. Casey
- thoughts on game economies Travis S. Casey
- memory and speed Matthew Mihaly
- memory and speed Shawn Halpenny
- Game economics Matthew Mihaly
- Game economics Nathan F Yospe
- Playing the monsters David95037@aol.com
- Playing the monsters Katrina McClelan
- Playing the monsters Caliban Tiresias Darklock
- How to debug a method taking a variable list of arguments? Ben Greear
- Twisted Reality claw@kanga.nu
- ADMIN: Searching on MUD-Dev archives claw@kanga.nu
- Object Obsolescence [ WAS: thoughts on game economies ] Shawn Halpenny
- SAR Adam Wiggins
- speed problems update Matthew Mihaly
- speed problems update Quzah [softhome]
- ADMIN: NoMail Subscriptions -- please reconfigure J C Lawrence
- Fwd: Books on Compilers Caliban Tiresias Darklock
- UM2 Greg Munt
- 3D Anarchy Adam Wiggins
- Properties of computer languages Caliban Tiresias Darklock
- Properties of computer languages Jon Leonard
- Properties of computer languages Mark Gritter
- Properties of computer languages Cynbe ru Taren
- Properties of computer languages Travis S. Casey
- Properties of computer languages David95037@aol.com
- Properties of computer languages Travis S. Casey
- Properties of computer languages Hans-Henrik Staerfeldt
- Properties of computer languages Caliban Tiresias Darklock
- Properties of computer languages Joey Hess
- Properties of computer languages Chris Gray
- Properties of computer languages David95037@aol.com
- Properties of computer languages Adam Wiggins
- Properties of computer languages Caliban Tiresias Darklock
- Properties of computer languages Mik Clarke
- Different approaches? Dani Dumitrescu
- Different approaches? Koster, Raph
- i got's a question for yall on the best way to do something.. PartyG2816@aol.com
- i got's a question for yall on the best way to do something.. Marc Hernandez
- i got's a question for yall on the best way to do something.. Jim Clark
- i got's a question for yall on the best way to do something.. PartyG2816@aol.com
- i got's a question for yall on the best way to do something.. PartyG2816@aol.com
- Game Design [Simulation] Justin Lockshaw
- MUD-Dev digest, Vol 1 #105 - 12 msgs Dr. Cat
- MUD-Dev digest, Vol 1 #105 - 12 msgs Caliban Tiresias Darklock
- MUD-Dev digest, Vol 1 #105 - 12 msgs Chris Gray
- MUD-Dev digest, Vol 1 #105 - 12 msgs Mik Clarke
- MUD-Dev digest, Vol 1 #105 - 12 msgs Hans-Henrik Staerfeldt
- A little off topic - anyone receiving multiple copies of list mail? Charles Hughes
- Role playing and Multiple Goals John Bertoglio
- Critiquing Muds Marian Griffith
- Critiquing Muds Koster, Raph
- Critiquing Muds Travis Casey
- Critiquing Muds Marian Griffith
- Critiquing Muds Travis Casey
- Critiquing Muds Caliban Tiresias Darklock
- Critiquing Muds Marian Griffith
- Critiquing Muds Jon A. Lambert
- Critiquing Muds Koster, Raph
- Critiquing Muds Matthew Mihaly
- Critiquing Muds Koster, Raph
- Critiquing Muds Damion Schubert
- Critiquing Muds Ola Fosheim Grøstad
- Critiquing Muds Michael Willey
- Critiquing Muds Jon A. Lambert
- MUD-Dev: An apology J C Lawrence