Discrete choice models (DCM) provides a framework with which to exame and predict how people’s choices are influenced by their personal characteristics and by the different attributes of the alternatives available to them.
The models have been used in very different sectors. Some are: transport (which mode of transport -car, bus, rail- to take to work, behavioural responses as a result of tolls), health (patients’preferences for treatment alternatives or choice among alternative healthcare providers), consumer demand(which car to buy o postal service to use). That are also used to examine choices by organizations, such as firms or government agencies.
DCM was developed in the late 1970’s, one parent is Daniel McFadden, who win the 2000 Nobel Prize in Economics for his theories and methods for analyzing discrete choice. In its beginningss, Moshe Ben-Akiva published a Ph.D. dissertation on the subject and Jordan Louviere and David Bunch helped develop original designs for DCM choice experiments. On 90's the DCM was remodeling and got a strong drive.
A discrete choice model is one in which decision makers choose among a set of alternatives or choice set. The decision maker obtains a certain level of utility from each alternatives. DCM are usually derived in a random utility model (RUM) framework in which decision makers are assumed to be utility maximizer. The utility, U, that decision maker, labeled n, obtains from any alternative j is Unj , j = 1...J. The decision maker chooses the alternative with the highest utility: choose alternative i if and only if Uni > Unj ∀j ≠ i and the probability that decision maker n choose alternative is simply: Pni = Prob(Uni > Unj ∀j = i). But the utility is only known to the decision maker but not the analyst. Because there are aspects of utility that the researcher does not or cannot observe, utility is decomposed as Unj =Vnj+εj. Where Vnj, which has some attributes of the alternatives and some attributes of the decision maker, is the systematic component of a decision maker’s utility and εj, wich captures the factors that influence utility but that are not in Vnj, is the stochastic component.
