In this post we present a high level introduction to evolution and to how we can use mathematical tools such as dynamical systems and Markov chains to model it. Questions about evolution then translate to questions about dynamical systems and Markov chains – some are easy to answer while others point to gaping holes in current techniques in algorithms and optimization.
What is a the difference between a random and a pseudorandom number? And what can pseudo random numbers allow us to do that random numbers can’t?
Supertasks allow you to accomplish an infinite number of tasks in a finite amount of time. Find out how these paradoxical feats get even stranger once randomness is introduced. What happens when you try to empty an urn full of infinite balls? It turns out that whether the vase is empty or full at the end of an infinite amount of time depends on what order you try to empty it in.
Throughout much of human history, people consciously and intentionally produced randomness. They frequently used dice – or dice-shaped animal bones and other random objects – to gamble, for entertainment, predict the future and communicate with deities. Despite all this engagement with controlled random processes, people didn’t really think of probability in mathematical terms prior to 1600.
Random Walks are used in finance, computer science, psychology, biology and dozens of other scientific fields. They’re one of the most frequently used mathematical processes. So exactly what are Random Walks and how do they work?
There are some pretty out-there explanations for the processes at work behind the incredibly successful mathematics of quantum mechanics – things are both waves and particles at the same time, the act of observation defines reality, cats are alive and dead, or even: the universe is constantly splitting into infinite alternate realities. The weird results of quantum experiments seem to demand weird explanations of the nature of reality.