[MUD-Dev] Understanding Simulation (was: Point of View)

[John Robert Arras]

On Wed, 25 Sep 2002, Ted L. Chen wrote:
> John Robert Arras wrote

>> I realize that simple systems won't work too well, but I'm saying
>> that you should try to make a system that does what you want,
>> even if it has to be very complex. Thinking about this is fun for
>> me, so I don't mind if it's hard.

> I like ants.  :) They're very localized creatures - in that they
> don't respond to any stimuli other than what is immediately around
> them - which takes most causal loops out of the system, and makes
> them great things to be simulated in numbers.

I wasn't very clear. I'm not talking about how they're
simulated. I'm saying that the kinds of activities they engage in
(fight/gather/settle...) are at about the level of what I would
expect of an ant colony.

> The smarter you try to make your ants by giving them meta
> information (from the vantage point of an ant), the more complex
> their interaction with the environment, and we get back to where
> we started this discussion.  

It's true that you get more complex interactions, but there are
other things that I do. I aggregate knowledge about individuals into
"societies" that compare themselves to each other and use their
members as tools. The individuals are either told what to do or ask
the society what to do whenever they don't have any pressing local
issues to deal with. I've found that this works pretty well. It
saves me the hassle of trying to make the interactions of each
member count. As long as they're doing "the right thing" most of the
time, everything should work out.  Right now, I've found it easier
to design things in this way since I can correct problems at a
higher level.

> So I would personally temper that enthusiasm of stimuli-response
> models (hey, I love them too).  In a MMOG, if you model an aspect
> of the environment using stimulus-response models, where one of
> the stimuli are players, the environment survives at the whim of
> players.  I think "gaming" is the term used.

Even though I'm not going with plain S-R, this is true in any
model. Fortunately, players are fairly predictable. I just assume
that the players are going to kill everything in sight. I don't have
a good answer for how to deal with this except to increase
replacement rates when society populations get low and to have so
many societies and so many members, that hopefully the players won't
be able to kill everything. I'm planning on hundreds of creatures
per player, and some societies that can't be destroyed even if
reduced to 0 population.

> I'm just interested in creating stable, 'life-like' simulations
> here.

Of course. This is MUD-Dev, not SAM-Dev (Scientifically Accurate
Model). :)

> I think what you described was the s-shape growth (if you were
> changing the growth rate of "something's" population).  That's all
> great and stable if you just stop there.  The last reference to
> overshoot and collapse only occurs when you introduce a prey.  But
> then the question is, is the s-growth all that iteresting? It only
> depends on its own population, it doesn't interact with the
> player, and its a one-time deal.  That is, once it reaches
> equilibrium, it stays kaput.  I could hard-script the population
> levels to vary in time and you wouldn't be able to tell the
> difference.

It won't stay in equilibrium. What happens if things raid the city
and kill a bunch~ of members, or this society raids another city and
a lot of the warriors get killed, or if a group splinters off and
forms a new city? Everything is predator and prey.  The point of the
growth rates is just to try to keep the populations from getting too
big and from dying too quickly when they get too small. That's
all. It's so that I know that the societies will get back into the
proper population range for them to do other things that I want them
to do.

> The basic flow of how I personally attack this problem is
 
>   1) model the basic underlying rules
>   2) analysis
>   3) add constraints
>   4) jump to #2

Yep. This is how I solve math problems, and this is how I've been
working on this.

John


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