Most active posters on topics related to systems tend to reject rigorous computer backtesting of lottery systems, for the most part, because they think the "human element" is too complex to be modeled in a computer program. Likewise, suggestions of using computer simulations to augment backtesting efforts tend to be rejected for the same reasons. In addition, there is significant suspicion among system proponents around the concept of randomness in general, and Random Number Generators in particular. For this reason, I suggest the following two recently initiated Topics for anyone who shares the skepticism described here. They are taking a different approach to systems in these threads, one which you might be more comfortable with.
https://www.lotterypost.com/thread/229928
https://www.lotterypost.com/thread/229672
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Here are two articles that illustrate how RNGs have been invaluable in solving some of the worlds toughest problems in Mathematics, Physics, Operations Research, etc. Based on your responses to these, we can decide where to go from here.
--Jimmy4164
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"Monte Carlo methods are especially useful for simulating systems with many
coupled degrees of freedom, such as fluids, disordered materials, strongly coupled solids, and cellular structures (see
cellular Potts model). They are used to model phenomena with significant
uncertainty in inputs, such as the calculation of
risk in business. They are widely used in mathematics, for example to evaluate multidimensional
definite integrals with complicated boundary conditions. When Monte Carlo simulations have been applied in space exploration and oil exploration, their predictions of failures, cost overruns and schedule overruns are
routinely better than human intuition or alternative "soft" methods.[2]"
"You can develop models that capture detailed information about unlikely or worst-case scenarios or obtain approximate solutions to problems that are otherwise intractable or time-consuming to analyze with traditional analytical techniques. Supported capabilities include a wide range of random and quasi-random number generators, parallel computing enabled random number generators, Markov Chain Monte Carlo simulation, and simulation of stochastic differential equations. Financial engineers and actuarial scientists use these capabilities for:
- Incorporating uncertainty into existing models
- Modeling interest rates
- Pricing and valuation of stocks, bonds, options, and derivatives
- Quantifying operational, market, or credit risk
- Valuing financial projects, structured products, and real options
- Assessing insurance and re-insurance risks and value
- Evaluating financial plans and perform what-if studies"
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