Expected value

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Expected value (EV), also known as expectation, average, mean value, or first moment, is a fundamental concept in probability theory and statistics. It represents the average outcome one can expect from a random variable over a large number of trials. The expected value is a key measure in mathematics, economics, finance, and various fields of science, providing a simple, yet powerful, way of summarizing the long-term behavior of random processes.

Definition

The expected value of a discrete random variable is calculated by summing the products of each possible value the variable can take and its corresponding probability. Mathematically, if \(X\) is a discrete random variable with possible values \(x_1, x_2, ..., x_n\) and \(P(X=x_i)\) is the probability that \(X\) equals \(x_i\), then the expected value of \(X\), denoted as \(E(X)\), is given by:

\[E(X) = \sum_{i=1}^{n} x_i P(X=x_i)\]

For continuous random variables, the expected value is determined by integrating the product of the variable's value and its probability density function over the variable's entire range. If \(f(x)\) is the probability density function of a continuous random variable \(X\), then the expected value is:

\[E(X) = \int_{-\infty}^{\infty} x f(x) dx\]

Properties

Expected value has several important properties that make it a useful summary measure:

• Linearity: The expected value of a sum of random variables is equal to the sum of their expected values, regardless of whether the variables are independent. Mathematically, \(E(aX + bY) = aE(X) + bE(Y)\), where \(X\) and \(Y\) are random variables, and \(a\) and \(b\) are constants.
• Multiplication: For independent random variables, the expected value of the product is the product of their expected values, i.e., \(E(XY) = E(X)E(Y)\) for independent \(X\) and \(Y\).
• It does not necessarily coincide with the most probable value.

Applications

Expected value is widely used across various disciplines:

• In finance, EV is crucial for pricing options, evaluating investment risks, and making decisions under uncertainty.
• In economics, it helps in understanding and modeling consumer behavior, insurance, and market equilibriums.
• In game theory, expected value is used to determine fair games and strategies that maximize players' payoffs.
• In decision theory, it serves as a basis for the expected utility hypothesis, guiding rational choice under uncertainty.

Limitations

While the expected value provides a powerful tool for analyzing random events, it has limitations. It may not always reflect the most likely outcome, especially in distributions with high skewness or in situations involving high stakes and low probabilities (e.g., lottery tickets, catastrophic insurance claims).

See Also

• Variance
• Standard deviation
• Law of large numbers
• Central limit theorem