During summer hiatus I have promised to look into a data set I have extracted from one particular Facebook contest. While many Facebook contests are not based on any logical reasoning, this contest appeals to me as it appears to require at least some thought or expertise.

Last time I have briefly explored the data set. Now I try to build models for these observations. This time I will consider the day-night cycle.

Being able to forecast is a big thing for scientists, but not always having understanding of the system leads to better forecasting. In physics we have good understanding of celestial dynamics, thus we can make accurate predictions about the movement of planets. Though we also have a decent understanding of how the weather works, weather forecasts are not that reliable. And most likely weather forecasts won't get much better over the time, because we will never be able to precisely predict large number of minor influences, which get amplified and then the system deviates from the forecast (the so-called butterfly effect).

Situation with forecasts in social sciences is even worse, but we can do more to improve them. In this SciShow video Hank Green tells us about some of the interesting intricacies.

In this video I especially liked one aspect - open-minded non-experts outperformed close-minded experts. The non-experts did a better job, because they more often incorporated evidence contradicting their prior beliefs, while the experts more often rejected such evidence.

So, the takeaway lesson? I guess I should not trust an "expert" who shows off his firm beliefs. Nor should I myself be an "expert" who values his beliefs more than the evidence.

During summer hiatus I have promised to look into a data set I have extracted from one particular Facebook contest. While many Facebook contests are not based on any logical reasoning, this contest appeals to me as it appears to require at least some thought or expertise.

Last time I have briefly explored the data set. Now I will try to build models for the event-space observations.

What we are writing about on Physics of Risk might seem a bit abstract. In some cases it actually is, but these seemingly abstract exercises help to make better decision for every day life problems. This time we invite you to watch a SciShow video in which some applications of Game Theory to enhance security are discussed.

During summer hiatus I have promised to look into a data set I have extracted from one particular Facebook contest. While many Facebook contests are not based on any logical reasoning, this contest appeals to me as it appears to require at least some thought or expertise. As far as I am aware, in Lithuanian schools, sets of linear equations are typical 8th grader problem. Even this low threshold allows to reasonably expect to observe herding behavior in the answers, because the ones who lack expertise will simply copy what the others have publicly written.

This contest is particularly interesting to me, because either intentionally or unintentionally there are infinitely many correct answers. It is so as the problem in the contest involves less equations (two) than unknowns (three). Hence there are no reasonable arguments for why to prefer one answer over the others.