Decision Science News

There's a nice interactive simulation here: https://joshworth.com/jpw/does-this-simulation-explain-why-life-is-unf… And a blog post here: https://www.decisionsciencenews.com/?p=6221

I agree that a nuanced approach is the right way. But many disciplines, in this case psychology, did have the replication crisis coming. Cohen and Gigerenzer have warned about power for literally decades but their warnings were not heeded by a significant part of the field. So a stark message may in fact have been necessary. By the way, Daniel Kahneman basically wrote a "this is a problem, deal with it NOW" letter to some colleagues in 2012 [1]. Very telling and an insightful read. [1] http://www.decisionsciencenews.com/?p=3634

This illogical talk is because of "Thinking, Fast and Slow", Chapter 4, which Daniel Kahneman has since questioned. http://www.decisionsciencenews.com/2012/10/05/kahneman-on-the-storm-of… USCSB has great work place accident round ups (CGI, no gore). Of course Expertise is what makes the workplace safer, it has no dark side. Here's Murphy's law (If it's possible for it to happen, given enough time it will, so don't allow it to physically happen wherever possible over processes) https://www.youtube.com/watch?v=Tflm9mttAAI

This is certainly true, if you're able to do the simulation on your own, you may also play with parameters, remodel the problem, etc. Another problem which is best explained by simulation is the random distribution of wealth [1]. (I played a bit with this one, introducing further parameters like liminal damage, debt, asymmetric amounts depending on investors trust, etc, to see how basic economic assumptions would reshape the model. Answer: only to the worse.) [1] http://www.decisionsciencenews.com/2017/06/19/counterintuitive-problem…

> each match is just +/- 50 points This only happens in the rare cases where you're matching players against (exactly) equally-ranked players. You can mitigate this by always trying to match as "close as possible," but it's only a mitigation. Try simulating random matchmaking with Elo, and you'll get something like this: https://i.imgur.com/1Y08jUB.png (1000 players, 100,000 games). In my simulation, I set k (the Elo constant) = 50. I think it's going to tend to a geometric distribution for reasons discussed here (which is another interesting and non-intuitive result): http://www.decisionsciencenews.com/2017/06/19/counterintuitive-problem…

In a system of exchange, even when the rules of how exchanges take place is completely random, you necessarily end up with wealth inequality: http://www.decisionsciencenews.com/2017/06/19/counterintuitive-problem… Now, in capitalism, where exchanges can be influenced more by those who have more wealth to begin with, it's pretty obvious that it will naturally lead to extreme concentration of wealth. So I don't believe this question "screams for some quantitative analysis" but there you have it anyway. The essence of capitalism is indeed the protection of private property and the resulting dynamics will lead to the aforementioned results. I don't know how you cannot see it. As for inequality throughout history, it is actually a pretty recent phenomenon. Considering humans have been around for 100k+ years, and that we were all pretty equal up until around 6k years ago, inequality is very much an exception in history rather than a norm. And you can make a strong argument that inequality only really took place once humans became sedentary and started to implement a practice of private property. This is also how I think about human nature. The "nature" of hoarding and greedily taking everything and leaving your fellow humans with barely enough to scrape by is not something we have always done. In fact, we only became so successful in nature originally due to our innate capacity for co-operation…

When selecting a model to demonstrate some dynamic behaviour: 1. First select the simplest model that captures some generic and essential. 2. Vary the parameters and introduce additional variables to that model to see how sensitive it is to different parameters values and variations of the model. The general state phase under variation is what builds confidence to the model. Some other results: * Peter Norvig: Simple Economics Simulation: https://github.com/norvig/pytudes/blob/master/ipynb/Economics.ipynb * Uri Wilensky even simpler version http://www.decisionsciencenews.com/2017/06/19/counterintuitive-problem… * Bouchaud JP, Mézard M (2000) Wealth condensation in a simple model of economy. Physica A 282:536–545. https://arxiv.org/abs/cond-mat/0002374 * Colloquium: Statistical mechanics of money, wealth, and income Victor M. Yakovenko and J. Barkley Rosser, Jr. Rev. Mod. Phys. 81, 1703 – Published 2 December 2009 https://link.aps.org/doi/10.1103/RevModPhys.81.1703 The general stable result that emerges from these simulations again and again is the effect of multiplicative randomness. There may be other factors, but it's usually the largest by far and you need to drill deeper if you want to quantify the effect from other factors.

Can you post a picture instead? Right now I can only see empty squares in your comment: https://i.imgur.com/zXAwnbK.png — (I am using Safari 13.0.3 on macOS Catalina 10.15.1) --- Edit: After seeing the image posted by @iruoy — https://i.imgur.com/OH3QTXQ.png — I have to disagree with you because the circle represents a zero (low voltage - FALSE) and the vertical line represents a one (high voltage - TRUE). If you studied computer science, logic, electrical engineering, or attended any basic course on electronics you would know that ones-and-zeroes are the written representation of an electric current, in fact, they are the basics of computing: Bits. Here is more information about the power symbol: • https://en.wikipedia.org/wiki/Bit • https://en.wikipedia.org/wiki/Power_symbol • http://www.decisionsciencenews.com/2016/10/28/off-switch-remember-line…