Lecture

Random Number Generation: Deterministic Sequences in C++

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Description

This lecture discusses the implementation of random number generation in C++. The instructor emphasizes the importance of having a permanent variable for randomness, which is crucial for simulations. A static variable is declared to ensure that the random number generator maintains its state across multiple calls. The program compares sequences of 0s and 1s over a defined duration, allowing for the observation of identical sequences. This is achieved by resetting the random number generator with the same seed before generating each sequence. The instructor explains that using the same seed guarantees the same sequence of random numbers, which is essential for debugging purposes. The lecture highlights the paradox of randomness in computing, where deterministic behavior is desired despite the inherent unpredictability of random number generation. The program is compiled and executed, demonstrating the generation of consistent sequences based on user input. The instructor concludes by addressing the need for reinitialization of the random number generator during simulation updates, ensuring that the generated sequences remain consistent.

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Ontological neighbourhood

Theoretical computer science

Programming language theory: High-level programming languages

Computer engineering

Computer security: Pseudorandomness

Related concepts (32)

Random number generation

Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols that cannot be reasonably predicted better than by random chance is generated. This means that the particular outcome sequence will contain some patterns detectable in hindsight but unpredictable to foresight. True random number generators can be hardware random-number generators (HRNGs), wherein each generation is a function of the current value of a physical environment's attribute that is constantly changing in a manner that is practically impossible to model.

Simulation

A simulation is the imitation of the operation of a real-world process or system over time. Simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time. Often, computers are used to execute the simulation. Simulation is used in many contexts, such as simulation of technology for performance tuning or optimizing, safety engineering, testing, training, education, and video games.

Hardware random number generator

In computing, a hardware random number generator (HRNG), true random number generator (TRNG) or non-deterministic random bit generator (NRBG) is a device that generates random numbers from a physical process capable of producing entropy (in other words, the device always has access to a physical entropy source), rather than by means of an algorithm. Such devices are often based on microscopic phenomena that generate low-level, statistically random "noise" signals, such as thermal noise, the photoelectric effect, involving a beam splitter, and other quantum phenomena.

Simulation video game

Simulation video games are a diverse super-category of video games, generally designed to closely simulate real world activities. A simulation game attempts to copy various activities from real life in the form of a game for various purposes such as training, analysis, prediction, or entertainment. Usually there are no strictly defined goals in the game, and the player is allowed to control a character or environment freely. Well-known examples are war games, business games, and role play simulation.

Randomness

In common usage, randomness is the apparent or actual lack of definite pattern or predictability in information. A random sequence of events, symbols or steps often has no order and does not follow an intelligible pattern or combination. Individual random events are, by definition, unpredictable, but if the probability distribution is known, the frequency of different outcomes over repeated events (or "trials") is predictable. For example, when throwing two dice, the outcome of any particular roll is unpredictable, but a sum of 7 will tend to occur twice as often as 4.

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