If Only Economics Was as Easy as Rocket Science

Engineering, and it’s use of the physical sciences, is grueling. However, I think humans err in when they attempt to equate planning an “economy” to the physical sciences. An economy comprises humans—all of its participants have varied values, and unique objectives. Attempting to plan out an economy, while using technical tools, can lead to some erroneous outcomes. This does mean to imply the technical tools are not needed in the world of economics, as they are extremely helpful in providing some insight into human behavior. However, the user must understand the limitations.

Professor Gary M Galles explains here:

“Over the years, I have often heard “rocket science” used to describe the hardest things to do. But as an economics professor, I recognize that the questions of social coordination economics addresses are in many ways much more complex and difficult, especially when it comes to controlling results.“

Read the rest here:

https://fee.org/articles/if-only-economics-was-as-easy-as-rocket-science/


Bias with Big Data

Statistics is a extremely valuable tool in research and in business today. It helps in forecasting sales, market analysis, elections, and so on. Since we are moving into the world of big data, everything can be quantified…so it seems. Well, that is the attempt: To quantify everything.

In this zeal to quantify everything, it can be helpful, and provide some benefit, this quest does come with some huge drawbacks. The end result can potentially lead the analysis down some fallacious conclusions. Does this mean stats analysis should not be used? No. It just means that the analysis is one aspect of the story.

Key excerpt:

“Among experts it’s well understood that “big data” doesn’t solve problems of bias. But how much should one trust an estimate from a big but possibly biased data set compared to a much smaller random sample? In Statistical paradises and paradoxes in big data, Xiao-Li Meng provides some answers which are shocking, even to experts.”

Read more here: https://marginalrevolution.com/marginalrevolution/2020/01/big-datasmall-bias.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+marginalrevolution%2Ffeed+%28Marginal+Revolution%29