
The Good Employee, a story about how you can explain companies with graph theory - made2591
https://madeddu.xyz/posts/im-still-learning/
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circles_for-day
this post will benefit from heavy editing. Remove sentences unrelated to the
main story arc. Introspection and reflection are only interesting to your
self. You can remove anything resembling those too. Seems like a cool concept.
I look forward to reading it.

~~~
Nevermark
I agree, the prioritization of humor over clarity creates a wall of
distraction and frustration for me.

If you have something important to say, say it as clearly as you can. Use
humor sparingly and only when it emphasizes a real point that naturally
contains some humor or irony, not as the main ingredient.

The rare exception is when someone has an absolutely wickedly clever sense of
humor that is so funny and enlightening you can't help reading, no matter how
random the exposition. But that is high art.

~~~
TimTheTinker
> an absolutely wickedly clever sense of humor that is so funny and
> enlightening you can't help reading

That describes G. K. Chesterton to a T.

~~~
monkeydreams
Yes, but he used humor as an integral part of the explanation, not as an
aside. "Dear Professor Whirlwind" is my favourite example of this.

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dvt
This is the kind of project that is going to get upvoted on HN because it's
technically "interesting," but not super-deep. Unfortunately it's, in
practice, completely worthless. Long post ahead, so apologies in advance.

1) First, let's look at the premise:

> But this time, let’s analyze the things from a mathematical perspective -
> because today if you don’t speak about data and models, nobody hears you.
> Let’s give the theoretical machine learning definition of a modern AI
> company: a modern AI company is a WUG - a.k.a. an Weighted Undirected Graph
> - that links at least three different families of nodes - Employees,
> Processes and Projects - in a chain of skills, troubles, goals, beers,
> prizes, achievements, careers, promotions, pizza, parties, lies and God only
> knows what else, with the overall goal to solve (or introduce) dependencies,
> investigate (or ignore) consequences but, by the end of the day, feed the so
> hungry desire to “change something” - also referred to as “bring something
> in production”.

This is nonsense, not to mention _completely_ arbitrary. If you're going to do
this kind of thing (which game theorists actually do routinely), you need to
very carefully define your terms. I have no idea what a process is; I have no
idea what a project is; I have no idea what an employee even is (are
contractors employees? they naturally have different incentives than full-time
or part-time employees). Etc. I could make a just-as-valid post arguing that a
company is actually a n-dimensional topological space (and then using vectors
instead of edges/nodes, while getting totally different "results"), but what
the hell does that mean?

2) Next, we have some (yet again) arbitrary definitions:

I'm especially confused about why things are proportional to other things,
namely budget, cost, or headcount. Again, if you're going to try to provide a
mathematical model for social behaviors, you better have some solid
justification behind your definitions.

3) The assumption that humans are robots:

> Ok, so in a digital Forest like the one just described, you cannot move with
> weapons to defend yourself against pumas - or whatever else lives inside a
> real forest with the desire of eating you and your backpack full of energy
> bars. Instead, the good Employee inside the WUG. Sorry, the Forest. Sorry
> again, in the Company, uses his ability and applies the Prim’s algorithm.
> Now, before going ahead, it could be useful to remind some concepts about
> graph exploration.

On the face of it, this _seems_ (?) right, I guess. But when you think about
it for more than 30 seconds, you realize what an insane claim it actually is.
Humans are _famously_ non-rational agents. The idea that humans subconsciously
apply Prim's theorem is experimentally wrong... in fact, people are
notoriously awful long-term decision makers, but I digress.

4) The weights table

Listen, I get it. Sometimes, you need to model social behavior. And to do
that, you need to assign weight values to decisions. Again, game theorists do
this all the time. But there's a few differences from a GT paper you'd read
and this blog post. First of all, dude... like 15 weights? Really? This goes
beyond speculative.

And second of all, we need justifications! Why is "pizza" -15, but "parties"
are -25? These systems are actually pretty sensitive to initial conditions; in
fact, the more weights/nodes, the _more sensitive_ we'll be to initial
conditions.

> Of course, you can add as many weights as you want.

Yeah, you could, but you _don 't_. In fact, the idea when creating these kinds
of models is to try to _minimize_ these kinds of arbitrary weights.

5) People are robots.. again

Author claims "...yes, some of them apply Djistrka[sic]..." \-- again, this is
simply experimentally wrong. And hearing it out loud just sound so awkward. I
don't even think I "apply Dijkstra" when I walk back to my car.

6) Making policy based on math is stupid

> This state of confusion leads to the fear of hiring people who are not
> expert in something, that is not able to solve company’s actual problem:
> that problem arose yesterday, come out months before, without nobody looking
> in the right direction, because the minimum_spanning_tree rules them all

No, the minimum_spanning_tree does _not_ rule them all. Making company (or
worse, political) policy based on these kinds of analyses is a plague. And
sadly, these kinds of reductive and abstract models (often hailed as "data-
driven") are tone-deaf and completely wrong.

~~~
iciac
Best to read this blog as an example of economic behaviour explained through a
graph - valuable in itself, as even basic graph theory concepts rarely make it
onto an economics curriculum. Which is a shame, given how neatly algorithmic
thinking and complexity costs improve a standard rational agent model (e.g. a
few behavioural economic concepts, such as myopic discounting, pop out
naturally if you assume mental costs to imagining future states).

Conceptually, it's interesting and potentially foreign to the target reader -
more formal definitions can wait for an academic paper rather than a casual
blog.

~~~
dvt
> Best to read this blog as an example of economic behaviour explained through
> a graph

Not sure if this is good company to keep. Generally speaking, behavioural
economic models aren't worth the paper they're printed on.

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santoshalper
Fucking terrible. Bad ideas multiplied by bad writing.

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made2591
Hi, all :-) I'm the author. First of all, thank you, everyone, for your
comments and interests: they are really appreciated and I think everyone
touched somehow different things I thought as well before publishing it.

Excluding the fact that it could be written differently, more concise, more
clear, etc - by the way, thank you for links and suggestions as well - the
overall idea behind this post was to provide a 50-50 footprint (both funny and
serious) around the human behavior in a complex organization. I think the
first sentence that gives the reader an idea of the context in which the post
will be placed is in the "[..], because today if you don’t speak about data
and models, nobody hears you." But... I didn't think a lot about this
sentence, I think it was just for laziness, and I don't wanna give my opinion
over this topic.

Back to the Prim behavior, I think this is all human and, by the way, shared
across all people in every kind of organization, not only inside companies. I
mean, not the algorithm, but - bringing it to the extremes - the human nature
of being selfish. The idea of providing a high-level formalism to imagine a
common scenario we all live every day came spontaneously to my mind, without
being so convinced in the beginning. So I tried to figure out if the metaphor
would have been a good fit for this idea I had in my mind. And this is it.

When I finished, I read it many times, and I know the feeling like "what the
hell is saying? I'm confused" because I felt the same reading my old posts in
the past... I'm not 100% sure, but I think I learned from myself that
sometimes I feel good in thinking something simply are weird or difficult to
understand. I think it has been, as many other times in the past, an
unconscious part of my writing process: I like the idea of being a bit wrong,
confused, misleading, just a bit, to give more freedom and let the people
change their mind while reading. Being distracted, somehow, arise doubts.

I don't know if I could be able to formalize it in a better way like suggested
by iciac, definitely, it wasn't my intention to provide scientific proof or
anything mathematically correct as suggested by dvt - and I hoped that
incompleteness feeling had explained this better than how much it actually had
done - but... again, this is it :-)

And I hope you at least enjoyed this flight of fancy. For any questions, feel
free to reach me here, by email, twitter, linkedin, etc.

Have a nice day :-)

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perlgeek
This sounds like it contains some good ideas, but could presented clearer and
more concise.

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powerball
i lose the author at the decision to arbitrarily weight the dataset on weak
foundations

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lifeisstillgood
I love this - I am convinced this can approach can tie to Coase's theory of
firm and give us ways to evaluate an ideal size of firm (i think you choose
the weights that determine the size of the MST)

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teamski
Anyone has a tldr? Topic sounds super interesting and I don't have time to
read now.

