Why is the term "Monte Carlo simulation" used instead of "Random simulation"? I always read/hear "Monte Carlo" simulations. I have done "Monte Carlo" simulations before to calculate the odds in certain gambling games as part of my job and it was nothing more than basically using an RNG to simulate random results (slot machines spinning wheels) and build upon them to get the final result of the game, repeat and get an estimated average outcome.
Anyone would be doing this without ever knowing that what they're doing is called "Monte Carlo" X.
My question is, is there any reason to call a random simulation a "Monte Carlo" simulation other than sounding sophisticated/clever? Any technical/legitimate reasons?
EDIT: I suggest people read this question's answers. It represents what I wanted to ask about better. I was trying to find out if there are technical reasons to distinguish between a "Monte Carlo" and a "random" simulation, disregarding any historical reasons.
 A: I have sometimes heard of people who distinguish between Monte Carlo algorithms and Las Vegas algorithms. Unlike a Monte Carlo algorithm—which will always terminate, but has a chance of giving wildly inaccurate results—a Las Vegas algorithm has a chance of running for an arbitrarily long time, but will always give accurate results. I suspect that most people don't make the distinction often, since (as you note) most people use "Monte Carlo" and "random" interchangeably. (Wikipedia says that there are also Atlantic City algorithms, but I had never heard of that term until now.)
A: There are simulations that are not Monte Carlo:

Basically, all Monte Carlo methods use the (weak) law of large numbers: The mean converges to its expectation.
Then there are Quasi Monte Carlo methods. These are simulated with a compromise of random numbers and equally spaced grids to yield faster convergece.
Simulations that are not Monte Carlo are e.g. used in computational fluid dynamics. It is easy to model fluid dynamics on a "micro scale" of single portions of the fluid. These portions have an initial speed, pressure and size and are affected by forces from the neighbouring portions or by solid bodies. Simulations compute the whole behaviour of the fluid by calculating all the portions and their interaction. Doing this efficiently makes this a science. No random numbers are needed there.
In meteorology or climate research, things are done similarly. But now, the initial values are not exactly known: You only have the meteorological data at some points where they have been measured. A lot of data has to be guessed.

Further, Monte Carlo Simulations are expected to help researchers obtain results close to reality, they are random simulations meant to mimic reality.  If your random simulation doesn't have anything to do with reality or predicting some actual event, then it would not be correct to call your random simulation a Monte Carlo Simulation.
A: Nicholas Metropolis claimed in 1987 that

It  was  at  that
time  that  I  suggested  an  obvious  name
for  the  statistical  method - a  suggestion
not  unrelated  to  the  fact  that  Stan[islaw Ulam] had  an
uncle  who  would  borrow  money  from  relatives because he “just had to go to Monte
Carlo.”

"Monte Carlo" refers to a casino in Monaco. Of course, as you note, casinos have a connection to random number generation. (And to - potentially ruinous - results from generating many random numbers.)
This nomenclature needs to be seen in the context of a group of physicists and mathematicians that amuse themselves playing small-stakes poker. Relatedly, Stanislaw Ulam wrote in his memoirs that

Metropolis once described what a triumph it was to win ten dollars from John von Neumann, author of a famous treatise on game theory. He then bought his book for five dollars and pasted the other five inside the cover as a symbol of his victory.

This may give you an idea of the intellectual environment that gives birth to technical terms patterned after places of gambling.
Edit: you ask

is there any reason to call a random simulation a "Monte Carlo" simulation other than sounding sophisticated/clever?

I don't see or know of any other reason other than it's the commonly accepted term for a random simulation. This may not be a "technical" reason, but I would say that using an accepted term for a technical issue is quite a sufficient reason to minimize misunderstandings.
A: You can read about the history of the Monte Carlo name in the other answers and comments. So this answer will provide a complementary perspective.
In sophisticated company, it's referred to as stochastic simulation.  See for example, the book "Stochastic Simulation: Algorithms and Analysis", Asmussen and Glynn. http://www.springer.com/us/book/9780387306797 . 
Monte Carlo simulation is a rather down-market term (pardon my snobbery). In my workplace, I usually refer to Monte Carlo simulation, because many people wouldn't have a clue what I was talking about if I said stochastic simulation. I don't usually find myself in upscale company there, ha ha.
A: This is actually a really good question, and it has provoked some fine answers. I'm adding this because I wondered about this too, and believe that Monte Carlo was used and became popular, because the process employed by gambling casinos, and the statistical process of estimation have specific, similar characteristics. The methods employ randomness, but themselves are not random, as scientists typically use the term.  (Eg. "I expected to see some evidence of xxxxxx, but the results looks completely random.").  Both the Monte Carlo casino operators, and those using statistical techniques are seeking a specific outcome, and they are using similar methods to achieve a desired outcome.  Random typically implies no evident pattern or unpredictable.  
The methods used by gambling casinos are very well thought out.  The unpredictability of the specific outcome of specific events is established (else it would not be gambling, would it?), but the nature and distribution of the range of outcomes is fully understood - and this key fact, both in casino gambling, and in the use of statistical estimation techniques - makes all the difference.  
An example: A Monte Carlo roulette wheel will have 1 to 18 numbers in one colour, and 19 to 36 in another, if I remember correctly, so red or black have equal probability of appearing. You can wager a specific number, or just bet on red or black appearing.  The players are playing against each other, for each other's money.  How does the casino operator make any money from this?  
The wheel has a "0" position, and if and when the ball lands there, the casino operator rakes in all the bets - the house wins.  Each time the ball lands on the zero, the house wins.  So each trial - each spin of the wheel - has a random outcome, but the house has (assuming the wheel is not rigged, say by putting a little magnet under the "0" number), then the house still can expect to - on average - sweep all the bets off the table with a 1/37 (or 0.027027) probability.  And if the house wants to improve its outcome?  It can add a second number to the wheel - typically "00", or double-zero.  Now, the probability that the house will win is almost (but not quite) doubled, to 2/38 (or 0.0526316).  That's over 5%, or a serious take.  Suppose the average amount of money wagered at the table each night, by the high-rollers, is $ 170,000.  With one zero, the house can be expected to make 170000 * 0.027027 = $4594.59, but adding the extra zero, and the expected take for the house is now 170000 * 0.0526316 = $8947.37.  
See, the amount gambled each night will be random.  We won't know what it will be. But assuming the wheel is fair and true (and smart gamblers are always watching to see if a game is "rigged" - just like the house detectives are always watching to see if players are cheating), we can say with close to certainty, that by adding the extra zero to the wheel, the casino can almost double it's take from the roulette game.  If the casino operators can add that extra zero to the wheel, and not drive away players, and reduce the nightly amount bet, then they will do it.  And just using simple probability, the improvement in the cash-take can be predicted.  If the casino operator adds free drinks, to attract more players, then the cost of such extra attractions can be subtracted from the expected improved take.  It may well be that adding the extra double-zero to the wheel and adding free alcohol, may improve the bet-flow.  As the casino operator, you would run some experiments, and assess the outcomes.  And since the operation of the process is filling your pocket with money, you are willing to focus on how it works, with some serious attention to detail.  
And that, lastly is the key point. Although randomness is employed, the operation of any casino is a very analysis-intensive business, where statistical techniques and an understanding of probability are key to obtaining a successful outcome.  Casino operators know the expected outcome on each and every game they offer, and because the randomness is limited to activity within a known distribution of possible results, the expected cash take on each game can be estimated quite accurately.  This is how cheaters are caught.  One particular game experiences a big divergence from expected outcome, right?  As the casino operator, you know something is wrong.
Monte Carlo methods, or the techniques of statistical and probabilistic estimation, can be very effective at predicting the outcomes of processes where the distribution of possible results is known.  And if either side can obtain an "edge", or shift the distribution of random outcomes in such a way as to alter the long-term expected value of the outcome even slightly, such an "edge" can make a person (or more often, the casino operator), very rich.
Monte Carlo methods employ randomness, but the methods - and the outcomes they can provide - are not random at all.  The methods themselves can be as well-engineered and finely-tuned as the engines of the Porsche automobiles in the casino parking lots.  And that is why the term is used.
