Is there data on the quality of management decisions?
danluu.com
danluu.com
0: https://equilibriabook.com/toc/ 1: https://www.scottaaronson.com/blog/?p=3535
Why wouldn't those who have had exposure to the concept of free energy prefer that metaphor to yours? You may be biased having exposure to more game theory than free energy. One person's conceptual diarrhea is another person's cleanest explanation.
Take five minutes and read the wikipedia page on the prisoner's dilemma then.
Even in a two player game, it's clear why individually optimizing for personal gain leads to poor outcomes for everyone, yet if there was a way to force people to act in a particular way, the total outcome for everyone would be better for all.
There's equivocation in that statement; what market efficiency is and what people generally mean by efficiency are not the same thing. This is not an accusation of danluu, though, as I believe it is an accurate statement of an error made by a lot of people.
Market efficiency is really a rather boring statement about the difficulty of performing arbitrage in the long term. Plus, given that the market is not absolutely efficient we should expect errors to be made.
But we can do some numerical analysis here. Consider the range of possibilities. Suppose population X is paid 0% of what everyone else is paid in the market, for the same output. It would take an astonishingly-large, an implausibly large, countervailing force to prevent some company from swooping in and claiming that free labor. (Remember, even if 99.9% of the firms on the market choose not to, it just means that the .1% is going have that much easier of a time.) However, for group X to be paid 99.9% of what everyone else is paid on average doesn't take much at all; indeed, mere noise could produce that result. Rather than it being a question of "is or is not discrimination", this sort of analysis can put numbers on how much, from which you can start analyzing things like the value of certain changes vs. their costs.
No conclusion; I'm just musing here. Except that I think that people generally do a lot of binary thinking here when quantitative might be more useful.
True, but: it's no coincidence that the terms are used for different things. What we really care about as humans is "market efficiency" in the ordinary English sense of the word. That's really hard to define, though, so economists redefine it in a very precise, technical, and yes, 'boring' statement. Now it is easier to see if we have it, but also not very useful. However, it can still be used to argue that any intervention in the market will be a mistake. The confusion about what the term "market efficiency" means is not just a coincidence. Redefine it as something easily proven, and then let people mistake that for a proof of what they actually care about, and you get the same political/lobbying result as the (rather more difficult, perhaps impossible) task of proving that markets are efficient in the way we actually care about as humans, rather than just economists.
And right now, the sense is that we're spending a lot of our time in jobs that overwhelmingly benefit a rich minority, which is not time spent well it.
When our ability to focus on self-preservation is being expropriated, people revolt
This interpretation isn't even up for debate, IMO. It's been investigated by biologists, neuroscientists, psychologists, medical doctors, philosophers, logicians, mathematicians, physicists, computer scientists.
A curious omission: economists. Very few of re-known willfully admit their own field is being twisted into undermining the majority for the minority.
Whether something will be subject to arbitrage is sensible to a ton of factors (barriers to entry, market structure, political environment/incentives, behavioral biases) that if something is to be arbitraged or not is purely an empirical question for any instance of the problem.
On the other hand, "no one had ever tried it" is an unbelievably strong force.
At the risk of sounding like an old fogey, I believe a similar statement can be made for stats-utopian circles and their belief in ability to quantify every thing.
As the post says:
> One nice thing about sports is that they often have detailed play-by-play data and well-defined win criteria which lets us tell, on average, what the expected value of a decision is.
So, using sports as a proxy is not the same as management decisions where there are no well-defined criteria or expected value.
Exactly. The biggest problem with quantifying efficiency in particular is that you have to define the inputs and outputs. Your biases are embedded in that definition rather than in the calculation per se. You can make some pretty awful processes "efficient" if you assign zero value to some of the inputs, and conversely some pretty great processes "inefficient" if you assign zero value to some of the outputs.
I'm halfway convinced that time-motion studies are some kind of demonic invention for exactly this reason. We can get 3% more productivity out of an assembly line, just by doubling the accident rate and giving every worker RSI!
Goodhart's Law is dangerous even when we're making management and business decisions in good faith, but when ideas like "long term injuries are irrelevant" are implicitly added it becomes monstrous.
Small aside but some including me would have a problem with even using sports as a proxy. There's a school of thought in soccer that maintains that the best match is a 0-0 draw [1][2][3]. While the data reflects a completely unproductive match, a 0-0 draw means that both sides performed admirably in defending and in countering the efforts of the other side. There is very detailed stats in soccer too now, but, that doesn't weaken the point.
[1] http://lastwordonfootball.com/2017/09/09/five-famous-0-0-dra...
[2] https://www.theguardian.com/football/2007/aug/14/newsstory.s...
[3] http://worldsoccertalk.com/2010/06/22/explaining-a-draw-to-t...
Wouldn't it make his result even stronger? If the thesis fails to hold even in better conditions (like when there is a well-defined expected value), it should also fail to hold in worse conditions.
That’s really not true at all — key decisions like whether to relieve a pitcher are not made within the context of a game. At a higher level, decisions to trade players are made that will affect outcome as well.
As someone who manages projects, there are parallels from a context point of view to be drawn to baseball. Each project is a measure of success or failure, but my effectiveness isn’t defined by individual projects. I may choose to do a project less efficiently to control cash flow or prioritize something else.
> If we want to look at the quality of decision making, it’s too simplistic to say that we expect a firm to make good decisions because they’re exposed to markets and there’s economic value in making good decisions and people within the firm will probably be rewarded greatly if they make good decisions.
Is there actually anything a reader here can take from this writing other than the assertion I quoted above (which is the author's point of view and isn't proven in nearly as much rigour as the baseball analysis is.)
I don't think the post proves that the market fails to incentivize good decisions. That's a hard thing to prove. It does certainly give us serious reason to doubt the common claim that the market succeeds in incentivizing good decisions on a short timescale. (It might still be true, but we should have much less confidence in that statement being true than before we read this post.)
How does the article suggest this?
For example: The members of the defending team adopt fielding positions based on a maximum potential for fielding a hit ball. But if there is even a threat of a stolen base attempt, the defense must change their positioning accordingly, affecting their ability to field a ball put into play.
If a player does make an attempt to steal a base, at least one defender must begin to change their positioning before the hitter has even had a chance to hit the ball. If they do not, they will not make it to the base in time to receive the throw from the catcher and either the throw will not occur or it will go out into the outfield and the runner may advance another base. Alternatively, the hitter may actually hit the ball and the ball may go to an area that it is statistically likely to go, where a defender would have been but for the stolen base attempt. What was to be an out is now a hit and the runner, having a head start because of their stolen base attempt, is able to advance further than they normally would have been able to. Because the hitter hit the ball, this situation does not get recorded in the statistics as a stolen base attempt!
Baseball is really a fascinating game to think about!
Yeah, that was the biggest flaw I spotted in the analysis of base-stealing: it doesn't seem to capture effect of the threat of base stealing on the defense's behavior, which is very probably non-zero and in favor of the offense. If you never, ever steal, and the defense knows that (someone'll notice before long), they can play better defense against the batter. It may still work out that a never-steal rule still provides better outcomes (by a smaller margin), but I'm guessing "rarely steal" ends up being the better guideline, overall.
If trying to steal loses bases, then the defense could spend less effort preventing steals, and more effort fielding.
This principle comes up a lot: if you get into every college/job you applied to, then you probably didn't apply to enough schools/jobs, unless 1) you didn't want to go to Harvard/AppAmaFaceGooSoft, 2) you got in to Harvard/AppAmaFaceGooSoft, 3) you couldn't afford the applications.
"Another reason to sac bunt (or bunt in general) is that the tendency to sometimes do this induces changes in defense which make non-bunt plays work better."
It apparently changes the probabilities slightly, but not enough to change the overall conclusion.
Looking at stats from 2000-2017, total errors underwent a steady drop from ~3400 in 2000 to ~2800 in 2017.
On the other hand, it is not entirely clear that teams stopped stealing bases starting in 2012. Yes, 2015-2017 were far below average, but so were 2003-2005.
The offense is permitted three "outs" in their half of each inning (after which the teams switch roles, or the next inning commences).
There are four bases, the first three of which may be occupied by a member of the offense. Bases are ordered. Players may advance directly from one base to the next by "stealing". This comes at the risk of an out, which results in losing occupation of a base entirely.
A player which reaches the fourth base scores a "run" for the offense and no longer occupies a base. Runs (and only runs) count toward the final score.
One player from offense is always "at bat". That player has a chance to occupy a base (not necessarily the first); failure to do so results in an out. During this process, the other base occupants may (be forced to) advance bases, possibly scoring runs or losing bases and gaining outs in the process. (The particulars of this are not relevant to the article, but there is a fun story about the process here [1].)
Rather than permit the player at bat the chance to advance potentially several bases, the defense can "walk" the batter by granting them uncontested occupation of first base.
Similarly, rather than permit the defense to prevent any base advancement, the player at bat can choose a "sacrificial bunt" ("sac" bunt) which, properly executed, nearly guarantees that players currently occupying bases advance one base (or even score), while the player at bat (and only that player) is called out (and thus does not advance). SPOILER ALERT. This action is key to the plot in a particular episode of Deep Space 9 [2].
[1] http://www.espn.com/mlb/story/_/id/20294816/skunk-outfield-h...
[2] http://memory-alpha.wikia.com/wiki/Take_Me_Out_to_the_Holosu...
I see a lot of (B). Hilarity ensues.
And that's assuming you want to optimize total wins. Increasing your odds of winning the playoffs may reduce your odds of getting into them by having more higher paid people, but a lower average player. Further, maximizing making money over time is yet another option, which may promote showmanship over success.
"But there are situations where the approximation isn’t very good, such as when it’s the 9th inning and the game is tied. In that case, a decision that increases the probability of scoring 1 run but decreases the probability of scoring multiple runs is actually the right choice."
I understood that the "full" model includes these considerations.
Bottom of the 9th bases are loaded and the guy at bat hit's a home run, that's the kind of thing that sticks with people and makes more money in the long run than a bunt that get 1 run.
When this happens, the last bad decision usually takes the blame, however it is rarely the true cause.
This pretty much sums up a lot of tech news and the associated comments on such news.
Now, it may be that their motivations are all messed up and they're acting the way they are because sabotaging their relationships reaffirms their core belief that they're unlovable. But it does make sense once you understand the framework they're working in.
1. "Quality" for management decisions are notoriously ill defined for the same reason that we never know if we did the right thing with the economy: the experiments can never be repeated.
2. The example serves to explain what a decision is and what a good decision would be, but that is not the concept that needs explanation!
3. "some seeming inefficiency can’t actually be inefficient because the market is efficient and inefficiencies will quickly be eliminated." is not the right statement of efficient markets. Rather, the act of trying to make it more efficient is exactly what makes it efficient. So if you see an opportunity, someone exploits it and thus makes it more efficient.
Eventually, every company wants to own the market, and convincing government to do so is a key part of that strategy. The invisible hand sometimes is holding a visible club.
It is subsumed in this work that good for baseball is winning games, and if you look at some of the references, it is taken for granted that there are ways to rank the possible results on some scale of efficiency or effectiveness. I would certainly question this.
Some of the references study the huge money-eating monster: healthcare. Here is a simple non-controversial problem -- look at all the healthcare systems in the world and rank the 10 best in order from best to worst by efficiency, effectiveness or ???
Even in baseball, there is no way to rank results. Some years, some teams have had very low winning percentages but much higher profitability than in other years. When I studied economics, some very good economists like Vernon Smith and Roger Noll took an interest in sports and particularly baseball. A standard assumption then was that there was an optimum winning percentage at which game attendance would be maximized, around 60% to 70%.
Baseball is a good example of the difference between short-term and long-term objectives, too. The winning vs losing equation is viciously zero-sum, and almost any innovation will be copied if it succeeds in the short-run. This leaves about zero long-term incentive to innovate. The real questions are: What kind of game do you want to play? What kind of game do people want to watch? What kind of job do you want to have? What kind of relationships do managers want to have with their unwashed workers? Which stakeholders will the firm respect?
First clause should be "average caught stealing"
For reasons why, see Thinking, Fast and Slow (just reading it now; it's quite good) and Thaler's Misbehaving.
You could replace that entire post with "No. Unlike baseball."
Not that the post wasn't interesting, of course. To me, it leads to the inevitable conclusion that we need to record failures as well as wins in order to get accurate data on management decisions.
0: https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...