Choice modelling system and method

A computational system for performing an online choice model, the system comprising:

• at least one processor;
• at least one memory operatively coupled to the at least one processor;
• and
• a plurality of modules, each of the modules comprising instructions for execution by the at least one processor, the plurality of modules comprising:
• a problem definition module comprising a problem definition user interface for receiving a plurality of attributes from a user wherein each attribute has an associated plurality of attribute levels;
• an online choice model survey module comprising:
• an experimental design generator module for generating a survey experimental design and an associated plurality of treatments;
• comprising a library of experimental designs;
• wherein the experimental design generator module determines the signature of the attribute space from the received plurality of attributes and associated attribute levels;
• and
• for one or more experimental designs in the library of experimental designs, performs one or more transformations until the signature of the transformed experimental design matches the signature of the attribute space to obtain one or more matching transformed experimental designs;
• wherein each transformation preserves the information properties of the untransformed experimental design;
• wherein the experimental design generator module selects the survey experimental design from the one or more matching transformed experimental designs;
• and
• wherein the experimental design generator module obtains a set of treatments from the selected survey experimental design;
• an online survey assembly module which receives the plurality of treatments and assembles an online survey from one or more survey templates pages, and a plurality of treatment representations created using the plurality of treatments received from the experimental design module;
• a data collection and sampling module for conducting the assembled online survey;
• wherein the data collection and sampling module allocates treatments to survey respondents and collects responses using the assembled online survey;
• a model generation module for receiving the data collected by the data collection and sampling module and building a model to obtain a plurality of model parameter estimates and errors from which a utility estimate can be obtained for each attribute level;
• and
• a model explorer module comprising a model explorer user interface for allowing the user to enter one or more attribute levels and obtain a model prediction of the expected utility;
• wherein the data collection and sampling module comprises a treatment allocation module, the treatment allocation module comprising:
• an allocation frequency counter for counting the allocation of each treatment in the set of treatments, wherein the allocation frequency counter is initialized to be zero;
• a receiver for receiving a request for a treatment to be provided to a survey respondent;
• and
• an allocator for allocating a treatment to a survey respondent from the set of treatments;
• wherein the allocated treatment is selected from the subset of treatments which have not previously been allocated to the survey respondent and whose allocation frequency differs by no more than a predetermined amount from the most allocated treatment in the set of treatments;
• the computational system further comprising:
• a length N global binary Deck Vector D which is initialized as a string of N ones indicating that all treatments are available where N is the number of treatments;
• respondent allocation vector R of length N for each respondent for indicating whether a given treatments has been delivered to the respondent and is initialized as string of N ones;
• a treatment for a respondent is selected by performing a binary AND between the Deck Vector D and respondent allocation vector R to obtain an availability vector A in which a one represents an available treatment,
• the respondent is allocated a treatment by randomly selecting one of the positions in the availability vector having a value of one;
• and
• then updating the global binary Deck Vector D and the respondent vector R by changing the state of the allocated position to a zero in both vectors.

A method for computationally performing an online choice model, the method comprising:

• receiving a plurality of attributes from a user wherein each attribute has an associated plurality of attribute levels;
• generating a survey experimental design and an associated set of treatments, comprising the steps of:
• determining the signature of the attribute space from the received plurality of attributes and associated attribute levels;
• selecting one or more experimental designs from a library of experimental designs;
• for each selected experimental design;
• performing one or more transformations until the signature of the transformed experimental design matches the signature of the attribute space to obtain one or more matching transformed experimental designs;
• wherein each transformation preserves the information properties of the untransformed experimental design;
• selecting a survey experimental design from the one or more matching transformed experimental designs;
• obtaining a set of treatments from the selected survey experimental design;
• assembling an online survey, comprising the steps of:
• creating a plurality of survey templates pages;
• creating a plurality of treatment representations based on the set of treatments associated with the survey experimental design;
• assembling the plurality of survey templates pages and plurality of treatment representations to form an online survey;
• conducting an online survey, the online survey comprising
• allocating each treatment to one or more respondents;
• providing a plurality of combinations of treatments to the one or more respondents;
• receiving the responses of the one or more respondents;
• generating a model based upon the received responses to obtain a plurality of model parameter estimates and errors from which a utility estimate can be obtained for each attribute level;
• and
• providing a model explorer user interface for allowing the user to enter one or more attribute levels and obtain a model prediction of the expected utility;
• wherein the one or more transformations comprise one or more of the following group of transformations:
• factorial splitting of a factor F into two sub factors A, B where A×B=F and A or B match at least one unmatched factor in the signature;
• factorial expansion of a factor F into a new factor A×F where A×F matches at least one unmatched factor in the signature;
• factor truncation of a factor F into a new factor F−A where F−A matches at least one unmatched factor in the signature;
• full factorization by generation of a new factor F where F matches at least one unmatched factor in the signature;
• and
• deleting a factor F when all the other factors in the signature are matched.

A method for generating a survey experimental design and an associated set of treatments for use in an online choice model, comprising the steps of:

• determining the signature of the attribute space from the received plurality of attributes and associated attribute levels;
• selecting one or more experimental designs from a library of experimental designs;
• for each selected experimental design;
• performing one or more transformations until the signature of the transformed experimental design matches the signature of the attribute space to obtain one or more matching transformed experimental designs;
• selecting a survey experimental design from the one or more matching transformed experimental designs;
• and
• obtaining a set of treatments from the selected survey experimental design;
• wherein the one or more transformations comprise one or more of the following group of transformations:
• factorial splitting of a factor F into two sub factors A, B where A×B=F and A or B match at least one unmatched factor in the signature;
• factorial expansion of a factor F into a new factor A×F where A×F matches at least one unmatched factor in the signature;
• factor truncation of a factor F into a new factor F−A where F−A matches at least one unmatched factor in the signature;
• full factorization by generation of a new factor F where F matches at least one unmatched factor in the signature;
• and
• deleting a factor F when all the other factors in the signature are matched.

A method for allocating a treatment to a survey respondent from a set of treatments in an online choice model survey, comprising the steps of:

• receiving a set of treatments for use in an online choice model survey;
• initializing an allocation frequency counter for each treatment in the set of treatments;
• receiving a request for a treatment to be provided to a survey respondent;
• creating a length N global binary Deck Vector D and initializing as a string of N ones indicating that all treatments are available where N is the number of treatments;
• creating a respondent allocation vector R of length N for each respondent for indicating whether a given treatments has been delivered to the respondent and initializing each respondent vector as string of N ones;
• and
• allocating a treatment to a survey respondent from the set of treatments;
• wherein the allocated treatment is selected from the subset of treatments which have not previously been allocated to the survey respondent and whose allocation frequency differs by no more than a predefined maximum difference amount from the most allocated treatment in the set of treatments;
• wherein allocating a treatment to a survey respondent from the set of treatments comprises:
• generating an availability vector A by performing a binary AND between the global binary Deck Vector D and respondent allocation vector R to obtain an availability vector A in which a one represents an available treatment;
• randomly selecting one of the available positions in the availability vector A;
• allocating the selected treatment to the respondent;
• and
• updating the global binary Deck Vector D and the respondent vector R by changing the state of the allocated position to a zero in both vectors.

A method for allocating a treatment to a survey respondent from a set of treatments in an online choice model survey, comprising the steps of:

• receiving a set of treatments for use in an online choice model survey;
• initializing an allocation frequency counter for each treatment in the set of treatments;
• receiving a request for a treatment to be provided to a survey respondent;
• and
• allocating a treatment to a survey respondent from the set of treatments;
• wherein the allocated treatment is selected from the subset of treatments which have not previously been allocated to the survey respondent and whose allocation frequency differs by no more than a predefined maximum difference amount from the most allocated treatment in the set of treatments;
• wherein allocating a treatment to a survey respondent from the set of treatments comprises:
• creating an N length overflow vector O which is initialized as a string of N ones;
• creating a new availability vector if there are no available positions in the availability vector A by performing a binary AND between the respondent vector and the overflow vector;
• randomly selecting one of the available positions in the new availability vector A;
• allocating the selected treatment to the respondent;
• and
• updating the overflow vector O and the respondent vector R by changing the state of the allocated position to a zero in both vectors.

A non-transitory computer readable medium, comprising computer executable instructions for causing a processor to perform a method comprising:

• receiving a plurality of attributes from a user wherein each attribute has an associated plurality of attribute levels;
• generating a survey experimental design and an associated set of treatments, comprising the steps of:
• determining the signature of the attribute space from the received plurality of attributes and associated attribute levels;
• selecting one or more experimental designs from a library of experimental designs;
• for each selected experimental design:
• performing one or more transformations until the signature of the transformed experimental design matches the signature of the attribute space to obtain one or more matching transformed experimental designs;
• selecting a survey experimental design from the one or more matching transformed experimental designs;
• obtaining a set of treatments from the selected survey experimental design;
• assembling an online survey, comprising the steps of:
• creating a plurality of survey templates pages;
• creating a plurality of treatment representations based on the set of treatments associated with the survey experimental design;
• assembling the plurality of survey templates pages and plurality of treatment representations to form an online survey;
• conducting an online survey, the online survey comprising
• allocating each treatment to one or more respondents;
• providing a plurality of combinations of treatments to the one or more respondents;
• receiving the responses of the one or more respondents;
• generating a model based upon the received responses to obtain a plurality of model parameter estimates and errors from which a utility estimate can be obtained for each attribute level;
• and
• providing a model explorer user interface for allowing the user to enter one or more attribute levels and obtain a model prediction of the expected utility,
• wherein the one or more transformations comprise one or more of the following group of transformations:
• factorial splitting of a factor F into two sub factors A, B where A×B=F and A or B match at least one unmatched factor in the signature;
• factorial expansion of a factor F into a new factor A×F where A×F matches at least one unmatched factor in the signature;
• factor truncation of a factor F into a new factor F−A where F−A matches at least one unmatched factor in the signature;
• full factorization by generation of a new factor F where F matches at least one unmatched factor in the signature;
• and
• deleting a factor F when all the other factors in the signature are matched.