The Saad Truth with Dr. Saad - May 18, 2024


Using Markov Chains to Predict the Consequences of Immigration (The Saad Truth with Dr. Saad_672)


Episode Stats


Length

3 minutes

Words per minute

140.75714

Word count

466

Sentence count

14

Harmful content

Hate speech

1

sentences flagged


Summary

Summaries generated with gmurro/bart-large-finetuned-filtered-spotify-podcast-summ .

In this episode, Ghatam Saad uses the weight example to describe the three possible states of the world that can result from a decision you make on a given day as relating to your weight. He then applies this framework to immigration in general.

Transcript

Transcript generated with Whisper (turbo).
Hate speech classifications generated with facebook/roberta-hate-speech-dynabench-r4-target .
00:00:00.160 Hi everybody, this is Ghat Saad. Some of you may have heard me in the past use the following weight example in describing the three possible states of the world that can result after you decide to implement certain decisions and behaviors in a given day as relating to your weight, right?
00:00:23.180 So depending on what I eat that day, depending on how much I exercise that day will result in a net calculation of my calories which will then be computed against my basal caloric needs for the day and then at the end of the day only one of three events can happen.
00:00:44.300 I can either gain weight that day, my weight could stay exactly the same, or my weight can go down.
00:00:50.900 If you want to put it in more formal mathematical terms for those of you who are mathematically inclined, there is something in applied mathematics called Markov change which basically says that the probability of all states of events at t plus one depend on wherever you were at time, you know, what happened at time t.
00:01:12.220 And so there is a stochastic process that allows you to calculate in a Markov chain what is likely to unfold.
00:01:20.080 So in the case of the weight example, we may not know what is the probability of my gaining weight, staying the same weight or losing weight, but there really are only three possible states of the world.
00:01:32.780 I often use this example because it's a beautiful and elegant way to try to understand how a particular decision, whether it be at the individual level, at the group level, at the family level, at the societal level, will affect some dependent variable.
00:01:51.340 So for example, if you create a society where every incoming immigrant to that society is chosen to only be a vegan, we are only going to allow in our host countries, people who are vegan.
00:02:12.760 Then you could say, well, okay, if we make that particular decision, how likely is it that there will be a greater, an increase of animals killed in the society, no change in animals killed or a reduction of animals killed.
00:02:28.760 Okay, so now let's apply it to immigration in general.
00:02:32.760 If you let in people into your society that come from cultures that have very, very high Jew hatred as an endemic feature of their culture, will the host society see an increase in Jew hatred, no change in Jew hatred or a decrease in Jew hatred? 0.96
00:02:53.440 And so you can use that framework to clarify your thinking.
00:03:00.060 You're not attacking individuals.
00:03:02.260 You're making a probabilistic statement regarding the likely outcome of a particular decision that you make and how that decision might affect some metric of interest.
00:03:17.100 Have a good day, everybody.