TORONTO — CryptoLogic Inc., (NASDAQ:CRYP; TSE:CRY) a leading supplier of software to the Internet gaming and e-commerce industries, has been ranked #1 in the PROFIT 100, Canada’s authoritative ranking of high-growth companies. Results of the annual survey, with stories profiling CryptoLogic and the nation’s other growth leaders, appear today in the June issue of PROFIT magazine.
“It’s a great honour for CryptoLogic to head such an impressive list of high-growth companies,” said Jean Noelting, CryptoLogic’s president and CEO. “As a world leader in software and services for Internet gaming we’re a profitable, fast-growing company in a dynamic industry – and it’s exciting to see independent verification of our success.”
The PROFIT 100 ranks firms on percentage sales growth over five years, and the 2002 rankings are based on the change in gross revenue between the 1996 and 2001 fiscal years. CryptoLogic’s revenue grew by 26,181% during that period to US$43,550,000 in fiscal 2001. The PROFIT 100 is open to Canadian-owned private and publicly-traded companies headquartered in Canada. To qualify, companies must report a minimum of Cdn$100,000 gross revenues in the base year.
In addition to being a fast-growing firm, CryptoLogic has also demonstrated the consistent profitability that eludes many “new economy” companies. For 2002, the Company is focused on sustaining profitable growth by expanding its presence worldwide with brand-name licensees, remaining a leader in regulatory compliance and adding products through development or acquisition to drive growth of licensees’ sites and enter new vertical gaming markets.
Are Random Number Generators Really Random?
Random Number Generators (aka RNGs) often come up when we online gaming people start musing over the nature of our universe. For the most part the questions boil down to this: What are they? How do they work? How are they used in casino software? Are they reliable? Are they honest?
I wrote my first RNG early in my university days. I’d found a text with the basic RNG algorithms so it wasn’t hard. As I recall it took me about two hours to write and debug the program. I was pretty pleased until I decided to test the randomness of my creation. That took about a week and in the end I learned that my RNG was basically useless. It took me two years to learn and understand why.
RNGs basically come in three flavors. The first and simplest produce quasi-random results. This is what I’d written and in my case that meant that the results of the RNG were reasonably well distributed but were not sufficiently independent. In other words patterns would emerge that prohibit this type of RNGs practical use.
At the other end of the spectrum are the ideal, or truly random RNGs. Unfortunately these are equally unrealistic in everyday use because truly random results are only found in naturally occuring phenomenon such as the radioactive decay of isotopes or particle detection in solar radiation detectors. These 168 events can be recorded and used to drive software based RNGs but this is typically too cumbersome for practical applications. And so, as ever in the software world, we are left with a compromise. By far the most popular and widespread RNGs are called pseudo-random in that for all practical purposes they appear to produce results that are both statistically independent and uniformly distributed. In other words as far as normal statistical analysis is concered they are good enough for practical use.
There is a forth solution to the RNG problem and that is a random number producing piece of hardware such as the RNG microchips Intel is including in their new security enhanced computer systems. However, since such chips are typically just a silicon-encoded version of a computer program, there is insufficient evidence to recommend this solution over the far cheaper software-based RNGs.
Whatever flavor of RNG is employed, actually using it in a software program — such as your casino software download — is straightforward. The software simply asks the RNG for a new result within a specified range and the RNG module coughs up a number. If we’re talking dice the number would be between 1 and 6. If it was Blackjack it would be between 1 and 52 where each possibility represented a given card in the standard deck. In the end the reliability of a given system’s RNG depends on (a) the tests to which that RNG has been subjected to determine it’s statistically random performance, and (b) the software’s use of the RNG’s results. In the first case we’re talking about some kind of (hopefully routine) audit. In the second case it’s simply a matter of trust.
Just in case we’ve missed the forest for the trees let me be perfectly clear: finding and using a reliable RNG is one of the least complicated tasks involved in offering honest gaming system. Excellent RNGs aren’t literally a-dime-a-dozen but they’re pretty close to it. But even the best RNGs can only produce raw numbers that the gaming software must then take and translate into the dice face, or card, or roulette number, or whatever the game at hand may be. The question of your faith that the casino is using the RNG fairly and properly is quite beyond the scope of this article or any similar discussion of RNG-based systems.