Artificially Intelligent Specification and Analysis of Context-Dependent Attribute Preferences
The IMIS system was given one focus brand from which it selected a competitive set of 4 other brands. The focus brand itself had a market small market share of less than five per cent on average in this competitive set although the brand itself has a significantly larger share worldwide. One issue for analysis is, therefore, why a generally successful brand is exceptionally unsuccessful in this one market.
The ACHE algorithm was set to work on the first 10 dates only in each of two experiments. In the first experiment, the algorithm settled on a model with a single preference state. In the second, we reduced the fitness discount associated with larger numbers of CDAP states and, as a result, identified four CDAP states. Each of these models was used to predict the future shares over the hold-out set. The model with a single CDAP state predicted market shares with an RMSE over all 5 brands of 7.1 per cent. The model with four CDAP states predicted the shares with an RMSE of 5.9 per cent. The graphs below exhibit the comparisons of the simulated and actual market shares of three of the brands. The other two brands accounted for less than 4% of the market and the models both ignored those brands by predicting zero market shares for them.
Increasing the number of CDAP states increases the accuracy of the system's simulation of market shares over whole data set. It also gives a very picture of the demand side of the market. This difference is discussed in section 5.3.
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