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The random walk is a somewhat popular mathematical construct that is used in computer science, and now in machine learning. It is described as a "stochastic" process because it works through the application of random variables. The random walk essentially tracks incremental steps by a particular modeled intelligence or digital "rational actor."
Unlike some of the more obscure mathematical concepts used in computer science, the random walk has many direct applications to real-world problems. One of the most famous applications of the random walk is in its application to stock prices – as in Burton Malkiel's 1970s book "A Random Walk down Wall Street." Randomized algorithms using concepts like the random walk can be extremely useful in predicting the motion of stocks or markets.
Many experts describe the classic random walk as an integer walk on a number line. With each turn, the random walk actor either advances or retreats by one integer. Random walks are also much more digestible to human learners on a visual level, and can be modeled in two dimensions or three dimensions. These visual models in real time show randomized bots or other entities moving by integer steps on a two-dimensional or three-dimensional plane.
In machine learning, the random walk represents a classic example of rational actor choices. It applies game theory to machine learning systems to try to predict outcomes. Looking at some of the classic games studied by mathematicians in machine learning research shows how the random walk can be very useful in a wide spectrum of projects.