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In 2011, Hibbard suggested an intelligence measure for agents who compete inan adversarial sequence prediction game. We argue that Hibbard's idea shouldactually be considered as two separate ideas: first, that the intelligence ofsuch agents can be measured based on the growth rates of the runtimes of thecompetitors that they defeat; and second, one specific (somewhat arbitrary)method for measuring said growth rates. Whereas Hibbard's intelligence measureis based on the latter growth-rate-measuring method, we survey other methodsfor measuring function growth rates, and exhibit the resulting Hibbard-likeintelligence measures and taxonomies. Of particular interest, we obtainintelligence taxonomies based on Big-O and Big-Theta notation systems, whichtaxonomies are novel in that they challenge conventional notions of what anintelligence measure should look like. We discuss how intelligence measurementof sequence predictors can indirectly serve as intelligence measurement foragents with Artificial General Intelligence (AGIs).

Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure