Abstract
A chaos dynamics model of inductive inference is proposed. Inductive inference is a process to predict the future from a finite length of a complex sequence of data. Chaos dynamics is able to generate a complex sequence of data governed by a deterministic rule. A certain deterministic rule can be assigned to a finite length of any complex sequence of data in reverse. Once a deterministic rule is assigned to this finite length of complex data, the rule will in turn generate another complex sequence of data in the future. This process of generating data in the future is equivalent to future prediction. The overall process of assigning a deterministic rule and generating data in the future, therefore, is considered to be an inductive inference process. This chaos dynamics model may provide an effective concept for theorizing inductive inference. It is also suggestive of future realization of an inductive inference machine simulating the information processing of the brain.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
