Location
Tacoma, Washington
Event Website
http://webspace.pugetsound.edu/facultypages/atubert/ConferenceSchedule2016.htm
Start Date
13-2-2016 2:00 PM
End Date
13-2-2016 2:50 PM
Description
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 to be measured while maintaining a realist approach to science. Karl Popper began such an investigation into the problem of progress in 1963 with an 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), 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 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.
Rights
Type
event
Included in
Circling the Truth: Model Selection Criteria as a Metric of Verisimilitude in Theory Selection
Tacoma, Washington
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 to be measured while maintaining a realist approach to science. Karl Popper began such an investigation into the problem of progress in 1963 with an 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), 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 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.
https://soundideas.pugetsound.edu/psupc/psupc2016/Saturday/3