Game Theory

George Price was a rather interesting fellow. A few months ago, I was reading a rather interesting piece about his life from HN. If you follow my blog posts (hello to the two of you), you’ll note that altruism and cooperative games is one of the things I like to blog about.

Following that article, I discovered the Price equation* Funny story. I was quite surprised I hadn't heard of the Price equation, so I hit the books. I found the equation being referenced very very very very briefly in Martin Nowak's Evolutionary Dynamics, and that was all . While grokking the equation, it had suddenly occurred to me that kin selection and group selection were indeed the same thing. It was a gut feeling, and I couldn’t prove otherwise.

I recently had a lot of time on hand, so I thought I’d sit down and try to make sense of my gut feel that kin selection and group selection were in fact the same thing. Bear in mind I’m neither a professional mathematician nor am I a professional biologist. I’m not even an academic and my interest in the Price equation came from an armchair economist/philosopher point of view. And so, while I grasp a lot of concepts, I may actually have understood them wrongly. In fact, just be forewarned that this entire post was a result of me stumbling around.

So, let’s recap what the Price equations look like (per Wikipedia):

Simply put, $latex \Delta z$ is the difference in phenotype between a parent population and the child population. And that difference is a function of two things:

1. The covariance of fitness and phenotype — $latex \frac{1}{w} cov(w_i, z_i) $ where $latex w $ is the average fitness of the population, $latex w_i $ is the individual fitness of $latex i $, and $latex z_i $ is the phenotype shared in the group.
2. The expected value of the fitness of the difference between the group’s phenotype and the parent group’s phenotype.

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Carina Zona gave the Sunday keynote for PyConAU 2015. It was a very interesting talk about the ethics of insight mining from data, and algorithms. She gave examples of data mining fails – situations where Target discovered a teenage girl was pregnant before her parents even knew; or like machine learned Google search matches that implied black people were more likely to be arrested. It was her last few points that I got interested in the ethical dilemmas that may occur. And it is these last few points that I want to focus the discussion on.

One of the key points that I took away* not necessarily the key points she was trying to communicate – it could just be I have shitty comprehension, hence rendering this entire blogpost moot was that the newer and more powerful machine learning algorithms out there are inadvertantly discriminate along the various power axes out there (think race, social economic background, gender, sexual orientation etc). There was an implicit notion that we should be designing better algorithms to deal with these sorts of biases.

I have experience designing these things and I quite disagree with that notion. I noted on Twitter that the examples were basically the machine learning algorithms were exposing/mirroring what is learned from the data.

The Google example is merely algorithms exposing the inherent bias in the genpop #pyconau

— Chewxy (@chewxy) August 1, 2015

Carina did indeed point out that the data is indeed biased – she did indeed point out that for example, film stock in the 1950s were tuned for fairer skin, and therefore the amount of photographic data for darker skinned peole were lacking * This NPR article seems to be the closest reference I have, which by the way is fascinating as hell.

But before we dive in deeper, I would like to bring up some caveats:

• I very much agree with Carina that we have a problem. The points I’m disagreeing upon is the way we should go about to fix it
• I’m not a professional ethicist, nor am I a philosopher. I’m really more of an armchair expert
• I’m not an academic dealing with the topics – I consider myself fairly well read, but I am by no means an expert.
• I am moderately interested in inequality, inequity and injustice, but I am absolutely disinterested with the squabbles of identity politics, and I only have a passing familiarity of the field.
• I like to think of myself as fairly rational. It is from this point of view that I’m making my arguments. However, in my experience I have been told that this can be quite alienating/uncaring/insensitive.
• I will bring my biases to this argument, and I will disclose my known biases whereever possible. However, it may be possible that I have missed, and so please tell me.

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Preamble: I have not blogged in a while. I have quite a few things to say, and have started at least 7 blog posts but never found the steam to complete them. Last Friday, I was having a rather interesting conversation with my colleagues, and that was cut short by a prior dinner arrangement. Having left the conversation topic unended, I decided that it’s a good point to jump off and continue blogging.

I lean slightly left towards Marxism, and I made it clear what it is that appeals to me. What appeals to me about Marxism is that it is most sci-fi in nature. “From each according to his ability, to each according to his need” is probably one of the most Star Trek-esque thing you can say. Indeed, I dream about a future where society functions like this, and I am actively working towards making such a change in society.

Of course there are other bits of Marxism that are I think outdated - the concept of class warfare, and proleteriats needing to seize control of Das Kapital* by that I mean, means of production. I think this is a very good pun is in my opinion, a very 19th century view. I do however, note a similarity between today’s society and the society that Marx lived in, one on the verge of a technological revolution* Das Kapital was published just as the dust of the Industrial Revolution was settling. Its observations of course, were made by Marx DURING what we call now the Industrial Revolution . Just as I note that the philosophy of Marx’s time was that people find meaning of life through work, we are similarly in a period where the same has happened. Think of how you would introduce yourself to other people - it’s your name, followed by what you do.

And there we were, seated at the table. Me, J and P were discussing my Marxist leanings. J posited a very interesting question, which I have paraphrased to omit the amount of obscenities that are wont to come about around groups of male humans speaking:

Imagine if there were two students, A and B. A is super hardworking, and does all the work during the semester. A even does extra work to understand the subject deeply. B on the other hand is a party animal, preferring to skip classes and not study, and would rather spend his time partying.

Then comes exam time. Obviously A does better than B. But here’s the twist. The lecturer for whatever reason, approaches A and proposes that A averages out his grade with B.

If you were A would you do that?

This is obviously a variant of the legendary socialism classroom experiment story that has been floating around the Internet for some time now.

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I had this thought the other day: latte art is signalling. To pour a rosetta in a cappuccino, you would need perfectly brewed espresso, and perfectly steamed and textured milk. If you do not pull enough crema in your espresso shot, or if the crema dissipates too quickly, you cannot pour a rosetta. If the milk isn’t properly textured, and there is too much air in the milk (or too little), you won’t be able to pour a rosetta because the milk wouldn’t drag across the surface of the crema easily. [Read More]

I read this from Dan Crow, about how Apple has hit its peak this morning:

Steve was famous for his “reality distortion field”. I saw it up close and personal, and it was amazing. But Steve knew that when he turned on the hype, he needed an outstanding product to back it up. The reason he could seemingly bend reality to his will was that products like the iMac, the iPod, the iPhone and the iPad really were exceptional, breakthrough products. Steve’s showmanship was justified

I followed with a discussion with Simon about this issue. He agrees with that statement whereas I mentioned that all Apple has to be is be outstanding enough, where as other companies won’t do well with just being outstanding enough as they lack Apple’s reality distortion field. He then brings up the fact that ‘outstanding’ means a different thing to different people. Which was what I disagreed on.

Coincidentally I was reading Dustin Curtis’ blog post about seeking the best. In the HN discussions, he too brought up that ‘the best’ means a different thing to different people. Again, on this, I disagree.

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It was the longest long weekend in Australia last week – a total of 5 days holiday – it’s a combination of Good Friday, the weekend, Anzac Day and Easter Monday. There won’t be another 5 day long weekend in Australia until 2038. I had spent the long weekend in the Central Coast of New South Wales with my partner and made some very interesting observations.

We had left the house at about 8 a.m on Friday, to try to beat the traffic jam to the Central Coast. Feeling a little peckish, we decided to have some yum cha for brekkie. Alas, there were no shops within a 7 km radius that was open for yum cha. I live in a suburb with a fairly high concentration of Chinese food places, and none of the shops were open. That perhaps, would have been indicator of what was to come next.

We arrived at our destination at about 11 a.m, and feeling extremely hungry, decided to look for brunch. The whole town had only one eating place open. Like the yum cha places, most shops had decided to close for the long weekend. As I munched on my $40 lunch (yes, it was a case of supply and demand – but that’s not the point of this article), I began to ponder upon the shops being closed.

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