Abstract: For modeling consumer activity, a plurality of processor computational resources generates potential model types. In addition, the plurality of processor computational resources divides activity data into a training data set, a test data set, and a validation data set. The plurality of processor computational resources further trains the potential model types with the training set data. In addition, the plurality of processor computational resources selects a model type with the test data set. The plurality of processor computational resources calculates algorithmic parameters with the validation data set.

Abstract: For modeling consumer activity, a plurality of processor computational resources generates potential model types. In addition, the plurality of processor computational resources divides activity data into a training data set, a test data set, and a validation data set. The plurality of processor computational resources further trains the potential model types with the training set data. In addition, the plurality of processor computational resources selects a model type with the test data set. The plurality of processor computational resources calculates algorithmic parameters with the validation data set.

Abstract: For cheat likelihood calculation, a method records client activity for a client. The client activity includes components of a game economy including burns calculated as a function of measurable client sacrifices to a game and earns calculated as a function of measurable game rewards from the game. The method calculates the cheat likelihood from the probability of the client activity being anomalous.

Abstract: For modeling consumer activity, code generates potential model types. In addition, the code divides activity data into a training data set, a test data set, and a validation data set. The code further trains the potential model types with the training set data. In addition, the code selects a model type with the test data set. The code calculates algorithmic parameters with the validation data set.

Abstract: For cheat likelihood calculation, a method records client activity for a client. The client activity includes components of a game economy including burns calculated as a function of measurable client sacrifices to a game and earns calculated as a function of measurable game rewards from the game. The method calculates the cheat likelihood from the probability of the client activity being anomalous.