In economics, discrete choice problems involve choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed to be a continuous variable. In the continuous case, calculus methods (e.g. first-order conditions) can be used to determine the optimum, and demand can be modeled using regression analysis. On the other hand, discrete choice analysis examines situations in which the potential outcomes are discrete, such that the optimum is not characterized by standard first-order conditions. Loosely, regression analysis examines “how much” while discrete choice analysis examines “which.” However, discrete choice analysis can be and has been used to examine the chosen quantity in particular situations, such as the number of vehicles a household chooses to own and the number of minutes of telecommunications service a customer decides to use.
Discrete choice models are statistical procedures that model choices made by people among a finite set of alternatives. The models have been used to examine, e.g., the choice of which car to buy, where to go to college, which mode of transport (car, bus, rail) to take to work among numerous other applications. Discrete choice models are also used to examine choices by organizations, such as firms or government agencies. In the discussion below, the decision-making unit is assumed to be a person, though the concepts are applicable more generally. Daniel McFadden won the Nobel prize in 2000 for his pioneering work in developing the theoretical basis for discrete choice.
Discrete choice models statistically relate the choice made by each person to the attributes of the person and the attributes of the alternatives available to the person. For example, the choice of which car a person buys is statistically related to the person’s income and age as well as to price, fuel efficiency, size, and other attributes of each available car. The models estimate the probability that a person chooses a particular alternative. The models are often used to forecast how people’s choices will change under changes in demographics and/or attributes of the alternatives.
Read more about Discrete Choice: Applications, Common Features of Discrete Choice Models, Prominent Types of Discrete Choice Models, Textbooks For Further Reading
Famous quotes containing the words discrete and/or choice:
“One can describe a landscape in many different words and sentences, but one would not normally cut up a picture of a landscape and rearrange it in different patterns in order to describe it in different ways. Because a photograph is not composed of discrete units strung out in a linear row of meaningful pieces, we do not understand it by looking at one element after another in a set sequence. The photograph is understood in one act of seeing; it is perceived in a gestalt.”
—Joshua Meyrowitz, U.S. educator, media critic. “The Blurring of Public and Private Behaviors,” No Sense of Place: The Impact of Electronic Media on Social Behavior, Oxford University Press (1985)
“It was a real treat when he’d read me Daisy Miller out loud. But we’d reached the point in our relationship when, in a straight choice between him and Henry James, I’d have taken Henry James any day even if Henry James were dead and not much of a one for the girls when living, either.”
—Angela Carter (1940–1992)