It's infuriating. I've stopped trying to contribute.
This is a tired trope, and it shows you're the one who can't be bothered to understand Wikipedia. Wikipedia is basically an anarchy (most of the time...), meaning that it's unlikely that the person who "rejected" your changes had elevated privileges, it was probably just another editor like you. Incompetent and/or malicious admins do exist on Wikipedia, but its much less likely you encountered one of those as opposed to just a regular user who disagreed with your change.
I'm neither a fan nor a Wikipedia apologist (WP has loads of issues), just trying to explain some basics.
BTW, I assumed you meant "Wikipedia admin" when you said "moderator"; real moderators (Wikimedia employees) do exist, but they're even less likely to encounter.
On the other hand, you say:
> having intimate knowledge of a topic
These kinds of edits sometimes get reverted on Wikipedia, and rightly so. I'm sure you understand that anyone can pretend to have "intimate knowledge of a topic", ideally edits should be backed with good references/sources instead of the editor's (unverifiable) knowledge.
> The anarchy stuff is just marketing nonsense.
With this sentence you're implying that Wikipedians want the public to believe that Wikipedia is anarchic, but this is incorrect, see: https://en.wikipedia.org/wiki/Wikipedia:What_Wikipedia_is_no...
The link is a Wikipedia policy, so pretty much as official as it gets. The sad truth is that Wikipedia policies, guidelines, etc are mostly irrelevant to the functioning of Wikipedia.
The real de facto reality of how Wikipedia operates is that it's a complicated system with most decisions being made being local to only a single or a few articles. Furthermore, nothing is set in stone at Wikipedia, be it content or policy, they all change with the whims of the editors currently active at the relevant place and time. Which is also why policy is mostly powerless.
My references themselves (from impact factor 10+ journals) were attacked and the cause of rejection. I'm not pretending that I know more than someone else; I'm leading the audience toward where they can assess the data for themselves and make their own conclusion.
I do this by referencing high quality, peer-reviewed, reproducible research. I don't post opinion (on Wikipedia). I state facts corroborated by multiple sources.
yup this is a common problem with Wikipedia.
You aren't the only one (albeit in my case, for more mundane/esoteric topics)
Both are hard, especially in the cases I listed.
With a good theory of (e.g.) platforms, you could likely identify a few different fundamental forces which may push in different directions. Hypothetical example: force X would benefit the platform at the expense of sellers, force Y would benefit the sellers at the expense of the platform. A priori it may not be possible in theory to put a magnitude on those effects: there may be nothing that would say "we can show that X > Y in every case."
This is where you need data. (This part is especially hard for platform markets!) You would need data on the platform side AND on the consumer side AND on the supplier side. You write down a model of (generally) consumer demand, supplier response and platform behavior (at least - probably taking a somewhat simplified approach to at least one of those sets of agents). With your estimates, you would try to answer the "Is X > Y?" question posed above.
You can also simulate the impacts of counterfactual policies, among other things.
More specifically, this is where you need data which does not and cannot exist. Specifically, an objective scale which would allow you to compare the cost or benefit of policy to the platform and the sellers. There is no such objective scale, because value is subjective and not comparable between different economic actors, or even for the same actor at different times. Any model which assigns numeric "estimates" for these costs and benefits so they can be compared is substituting the authors' own subjective valuations in place of those of those who will be affected by the policy.
This is wrong. All important work in my field in the past 25 years has been empirical.
> Any model which assigns numeric "estimates" for these costs and benefits so they can be compared is substituting the authors' own subjective valuations in place of those of those who will be affected by the policy.
Very, very wrong. You’ll never get away with picking your own parameter values in modern empirical IO.
If you don’t know the field that’s fine. I probably don’t know your field either. But don’t make these overconfident and totally wrong claims about what’s done in a field you don’t know anything about.
The problem I have with much of economic theory is that it tries to tie things back to what's beneficial or not to societies and tries to do so through a very thick utilitarian lens. There's often a heavy amount of fairly sophisticated math, statistics, and modeling parading as certainty when the fact is, there's a very high amount of uncertainty in many assumptions and in variety of economic theories that are just hand-waved away. It's not to say the problems economists face are simple, they're actually incredibly challenging and I think it's good economist continue to push for a better understanding of these systems.
My gripe arises when economists masquerade or imply current theory with certainty when in many cases, as if it were a mature science. It simply isn't and far too many use the appearance of certainty hidden in complexity of theory as certainty to push a biased personal agenda or perspective. When you overload terms like efficiency, efficiency in what context? Efficient for whom and through what perspective of the world? Very clear definitions need to be made so I don't question the motives of the attempt of quantification and can look at flaws and oversights that may exist in such metrics.
Most economic theory lacks real empirical evidence because it's just to difficult to play the experiments and interplay of micro, meso, and macroscale systems at a global scale without actually running the experiment (changing policies).
It's the politicians and voting public that are the problem.
It's easy to get massive numbers of people to vote for you by giving out is completely wrong as long as you're extremely confident in your presentation of it. It's easy for these political winners to drive the economic systems to collapse while increasing authoritarianism by said false confidence. People want to vote uncertainty away, authoritarianism is one way of doing that.
But there is no link for "socially optimal", and the phrase appears nowhere else in the article, so this definition is incomplete at best and vacuous at worst because its meaning turns entirely on the meaning of "socially optimal" but I can't think of any reasonable way to define it that is not a judgement call.
Technically Pareto optimal. Pareto optimal points are not necessarily socially optimal.
NB I was just comparing the writing style of that page (which seemed a bit odd to me) with other examples.
So where do I find these definitions? Because I am still highly skeptical that they can be defined in ways that do not conceal hidden judgement calls.
You can be skeptical, but the cure for that skepticism is something more akin to taking an undergrad course, or two.
- Lacking time stability means no one tomorrow has any incentive to stick to the policy, and means the policy is unlikely to succeed in its objectives.
- Deadweight is lost efficiency
- The only arguable one is "fairness", and to clarify I blunted the term to avoid the nuance of explaining Pareto optimality. But sure, we can also consider egalitarianism and other moral frameworks.
That's true, but time stability means that a bad policy is going to be maintained even in the face of evidence that it is bad. Deciding which is worse is, as I said, a judgement call.
> Deadweight is lost efficiency
The flip side of increased efficiency is decreased resilience. Without unused capacity (i.e. inefficiency) the slightest contingency will send the entire system into chaos. How much inefficiency you want to maintain as a hedge against contingencies is a judgement call. Different people have different risk postures.
> Pareto optimality
I think it's safe to assume that people on HN understand Pareto optimality (or are capable of looking it up). Do you really think that Pareto optimality is synonymous with "fairness"?
> we can also consider egalitarianism and other moral frameworks.
Of course we can consider these things. But that doesn't answer my question, which is how do we address them in a principled way that doesn't degenerate into a political dispute?
See: CPUs and GPUs in 2020-2021, baby formula in 2022, etc.
It can absolutely be a judgment call.
It has nothing to do with the everyday meaning of the term “deadweight”.
So everyone pays the same charge for IT costs AND market does not have an unfair advantage.
I've talked to several big names that have told me about megaoffers from Amazon. (They probably don't care if people know, since they told me, but I'm not going to name them.)
- Time series forecasting.
- Reduced-form causal inference
- Structural, especially what is called "structural IO".
Among the questions people in the last category will answer are: how should we price this product? How should we decide which new markets to enter?
The subject was "unsolved problems in economics", not "unsolved problems in politics". Regulation is a political topic, not an economic one.
That last point might be economic in nature, but only if Apple is the one making the decision, and is free to choose their own price without fear of regulation. Otherwise it's still politics.
If you don’t care about getting it right, fine. Ask politicians to do whatever they want on the basis of whatever biases they have.
If you want to get it right, then you need to weigh the costs and benefits of different regulatory policies. That requires theory and data.
1) many of the assumptions in economics are culturally specific
2) many of the models in economics, while self consistent and plausible, have not been adequately tested with real world data
3) economic models don’t seem to take constraints into account leading to absurd results like infinite growth
Within the field, are any of those three seen as important problems? Thank you
Which ones and maybe I can comment. I don't think this is true.
> 2) many of the models in economics, while self consistent and plausible, have not been adequately tested with real world data
This is everything that we do. The entire field is all about data and has been for probably 30 years (more actually, but we can point to an improvement in our own standards in the mid-1980's).
> 3) economic models don’t seem to take constraints into account leading to absurd results like infinite growth
A very basic definition of economics in undergrad classes is "the theory of optimizing behavior subject to constraints" so constraints are very much our bread and butter.
Separately I don't think most (or any) macro economists (or growth guys) would say "yeah my model gives great predictions for what the situation will be 1,000 years in the future". Even 50 years in the future is pushing it.
>> at least the way it is popularly understood
This may be your problem. The understanding of the field you find from commenters on this website is a million miles from what it is really about.
The popular understanding of economics has very little to do with economics.
So, just like economists!
I’m glad people like you are at least thinking about it. I have no insights (different degree), but I was wondering about that exact thing a few months ago.
I've studied MITx Microeconomics course. It appears undergrad level Micro course, and a background in machine learning and software engineering.
There is a recent book by Belleflamme and Peitz, which should some day be followed up by a second volume about regulation.
There is also a recent survey of the empirics by Marc Rysman and two coauthors.
The opening line.
This is a partial list. Any specialist in any subfield of the discipline could list a large set of open problems. Nearly all have broader significance.
In my own specialty (industrial organization) unsolved problems which would be of interest to readers here include optimal regulation of platforms and two-sided markets. Theory and empirics are difficult, so regulators are to some extent fumbling in the dark a bit. It is not at all clear how to regulate (for example) Amazon’s offering its own versions of products sold on its marketplace OR what the right tariff Apple should charge for access to the App Store is.
Extremely confident claims that such behavior is permissible or should be forbidden are likely unsupported by any careful analysis one way or the other.