The purpose of this research is to investigate the possibility of using aspects of model selection theory to overcome both a logical problem and an epistemic problem that prevents progress towards the truth being measured while maintaining a realist approach to science. Karl Popper began such an investigation into the problem of progress in 1963 with the idea of verisimilitude, but his attempts failed to meet his own criteria, the logical and epistemic problems, for a metric of progress. Although philosophers have attempted to fix Popper’s verisimilitude, none have seemed to overcome both criteria yet. My research analyzes the similarities between Predictive Accuracy (PA) and Akaike’s Information Criterion (AIC), both parts of model selection theory, and Popper’s criteria for progress. I find that, in ideal data situations, it seems that PA and AIC satisfy both criteria; however, in non-ideal data situations, there are issues that appear. These issues present an interesting dilemma for scientific progress if it turns out that our theories are in non-ideal data situations, yet PA and AIC seem to be better overall indicators of scientific progress towards the truth than other attempts at overcoming the problems of Popper’s verisimilitude.
Hanson, K. Raleigh
"Predicting the Truth: Overcoming Problems with Poppers Verisimilitude Through Model Selection Theory,"
Acta Cogitata: A Philosophy Journal: Vol. 4
, Article 5.
Available at: http://commons.emich.edu/ac/vol4/iss1/5