[e2e] end2end-interest Digest, Vol 19, Issue 11
David P. Reed
dpreed at reed.com
Thu Sep 15 03:16:02 PDT 2005
Yogesh Prem Swami wrote:
>I have a somewhat general question about simulations. My question is
>that is there any scientific reason why simulations should be able to
>predict the behavior of a real packet transmission phenomena? Unless you
>make the assumption that packet transmission/interaction is a
>non-chaotic phenomenon (chaotic, as used in physics), there is no reason
>to believe why a simulation would be able to model real world events.
This is a crucial question. Far too often, when forced to settle for
simulations, we convince students that improving the behavior in
simulations is the proper goal of network science.
Of course it isn't. But simulation is incredibly important for a
variety of reasons.
First, simulations provide much more efficient experimental
environments. It's very hard to construct repeatable experiments in
vivo (so to speak) and in many cases the most important in vivo
environments are inaccessible to the researchers who have the time and
insight to explore the range of possibilities.
Second, simulations provide a key way to express our understanding of
reality. A scientific theory *is* a simulation itself - Newton's Laws
or Maxwell's equations are nothing more than a simulation program in a
mathematical programming language for simulating experiential reality.
We validate theories by testing key executions of those programs on our
"math computers" against measured experiments.
But simulations don't prove anything. Even close matches of simulation
runs against real experiments don't prove that the simulation code is
Doing good work with simulation is no different than doing good work in
symbolic mathematical analysis.
Bad simulations are probably equally common as bad foundations in formal
analysis of systems - the assumption of Poisson arrival in queueing
theory analyses, or the assumption of gaussian noise in information
theory channel analyses, or the assumption that physical systems are
linear or near-equilibrium.
The solution to these problems is to understand simulation tools at
least as well as we understand the mathematical foundations of Analysis
(the branch of mathematics that is used to express models and formalisms
symbolically), and ALSO to understand the limits of modeling.
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