Methods and apparatus to model consumer choice sourcing

An apparatus to calculate a choice probability, comprising:

• a multinomial logit (MNL) engine to add a set of products having respondent choice data to a base MNL model, the MNL engine to include an item utility parameter and a price utility parameter associated with corresponding ones of products in the set of products;
• an aggregate building engine to generate a number of copies of the base MNL model to form an aggregate model based on a number of the corresponding ones of products in the set of products, each one of the number of copies of the base MNL model exhibiting an effect of an independence or irrelevant alternatives (IIA) property;
• a sourcing modifier to proportionally affect interrelationships between dissimilar ones of the number of products in the set by inserting sourcing effect values in the aggregate model to be subtracted from respective ones of the item utility parameters;
• an estimator to estimate the item utility parameters of the aggregate model based on the number of copies of the base MNL model and the respondent choice data;
• and
• a simulation engine to calculate the choice probability for the corresponding ones of the products in the set of products based on the estimated item utility parameters and the price utility parameters.

A tangible machine-readable storage medium comprising instructions that, when executed, cause a sourcing engine to, at least:

• add a set of products having respondent choice data to a base multinomial logit (MNL) model, the base MNL model including an item utility parameter and a price utility parameter associated with corresponding ones of products in the set of products;
• generate a number of copies of the base MNL model to form an aggregate model based on a number of the corresponding ones of products in the set of products, each one of the number of copies of the base MNL model exhibiting an effect of an independence of irrelevant alternatives (IIA) property;
• proportionally affect interrelationships between dissimilar ones of the number of products in the set by inserting sourcing effect values in the aggregate model to be subtracted from respective ones of the item utility parameters;
• estimate the item utility parameters of the aggregate model based on the number of copies of the base MNL model and the respondent choice data;
• and
• calculate the choice probability for the corresponding ones of the products in the set of products based on the estimated item utility parameters and the price utility parameters.