trylks

[trylks]

The worst type of managers: incompetent managers

Note: crossposting from reddit, because I spent way too long on this to not have a backup.

Honestly, I have had a good number of managers and they all were bad in fairly different ways, but if I have to choose one single complaint, it is sheer incompetence.

Incompetent workers shoot themselves on their feet. When it is a manager*, and they make decisions that impact an entire team, metaphorically is like they are shooting themselves on their feet, making a hole in the boat on which the team is, so everybody drowns. Their bad decisions make work frustrating, unproductive, unrewarding, and sometimes plain impossible with respect to some deadlines.

You may think authoritarian managers will lead to poor decisions more often (not listening to other perspectives, facts, opinions, etc.) Certainly, authoritarianism does not help.

However, sheer incompetence is a greater problem. Off the top of my head, an incompetent and non-authoritarian manager:

Will listen, but will not understand.

Will explain the rationale, strategy, objectives, etc. They may be persuasive, but completely wrong, producing no outcome or the wrong outcome. (Sometimes the

May let people work with autonomy, but without coordination or cooperation. This is fine (paid hobbies!) until their manager fires the entire team due to a lack of alignment with the organization, or not producing anything of value for the organization.

In combination with the previous points, something of value may have been produced but not understood, and what is communicated is something else. Imagine producing a rocket, but the manager communicating it as a car, which is not competitive due to the cost per mile. The result is:

The team fails at being relevant in the organization and producing value for it. (The value was produced, but not realized.)

The organization fails at competing in the market, e.g. failing at competing both with Tesla and SpaceX, while the engineers may have produced something that would beat SpaceX.

Incompetence, especially on a technical level, is often paired with a focus on politics, posing, stealing credit, and shifting blame. They found a way to get the job despite of their incompetence.

The team culture and the way the team works may become "toxic", with:

an abundance of bullshit,

a complete disregard for merit, competence, and

a focus on the form over the content, e.g. PowerPoint with nice stock images, making no sense or being wrong in what is said. Especially considering the previous point, and the first point.

Authoritarianism results in incompetence (not listening means not learning, which means falling behind). But incompetence, even without authoritarianism is a far greater problem in my experience. Not all my bad managers were authoritarian, but all of them could get better at their work (they should, IMHO).

And of course, we have the authoritarians and situations where several of these problems and additional problems may overlap or may be hard to diagnose. I have avoided ambiguous points, e.g. wrongly pairing tasks with the skills of the people responsible. By Hanlon's razor, I would consider that this is due to incompetence. However, occasionally, I have seen the result of not completely wrong pairings, and it is not smooth. In short, if the person responsible for doing something does actually know how to do it, they may make a good point of needing resources (time, data, computation power, access to the opinion of users, experts,...) to do things properly and professionally, and this may be undesirable for some managers that just want employees to produce shit, tick boxes, and not cause trouble. Therefore, I cannot say if this wrong pairing is the result of incompetence or a brilliant and perverse plan.

Nevertheless, authoritarianism would only be 4th in the ranking, which would be:

Incompetence.

Psychopaths.

Delusionists.

Authoritarians and micromanagers.

Absentee managers.

Psychopaths may be competent at taking advantage of the team and the organization. Meanwhile, they destroy everything, look like they are solving every problem, and have the best and most polite manners. They may be technically incompetent, as previously mentioned, but sometimes they may be reasonably competent at a technical level and just find "better" ways to thrive.

Delusionists may be competent at (indefinitely) keeping the delusion of smooth communication between their managers and the people they manage, even the communication with clients, results achieved, etc. However, without any tangible results, you are forced to sell the same delusion to get a new job. Delusionists may get to become billionaires by selling startups to big companies.

(Both previous overlap occasionally. Psychopaths may sometimes be good delusionists because it serves well their ambition. While delusionists may have collateral victims of their reality distortion fields. Nevertheless, I consider them as different groups.)

Authoritarians, micromanagers, and absentee managers are often discussed on the Internet. Therefore, I think this is enough for today's rant. Thank you for the opportunity. I am trying to quit (previously similarly here, here, and here), so... "days since the last rant: 0".

Hopefully, it is useful. HAND.

* and other decision-makers, sometimes they may be system architects, tech leaders, directors, or many other roles, even principal engineers.

[trylks]

[trylks]

Two radically different perspectives about information and markets

Using the Ethereum merge of September 2022 as an example for contextualization.

Perspective 1: it is already priced in

This is the perspective of the efficient market hypothesis. According to this hypothesis:

The information about this event has been available for a while,

actions were taken accordingly, and

any potential price movement has already happened.

i.e. after something has been reported when the information became readily available, there is no reason to write further about it other than new information being readily available, e.g. reporting results after it.

Perspective 2: people need to know about it

This is the perspective of:

every person writing about The Merge these days, some of them professional journalists,

the people sharing links about The Merge in social media like r/CryptoCurrency and Twitter, and

the algorithms recommending you news about The Merge.

Side note

These two perspectives (“efficient markets” vs “people need to know”) will still be around for the foreseeable future and apply well beyond Ethereum and cryptocurrencies.

“Wise people speak because they have something to say. Fools speak because they have to say something.”

Awareness about these two perspectives is useful to everybody, perhaps particularly useful to investors (and gamblers). The Ethereum merge of September 2022 provides a perfect example for easier understanding, but please do not stare at the finger pointing at the Moon.

Posted here after being deleted from r/CryptoCurrency.

[trylks]

Cryptocurrency bad rep problem

There are many problems with the cryptocurrency reputation. Here is a great compilation: Web3 is going just great

Scams, exploits, honest mistakes, lack of regulation attracting bad actors, VCs that put their money where their mouth is but cannot answer simple questions, fundraising for dubious events,... There is no shortage of things that may look bad in crypto, including VC money itself.

The skepticism of blockchain in non-crypto communities is out the charts, and people do not understand what's going on with Reddit user's hate of NFT avatars.

I see only three possible ways from here:

Nuclear cleanse

Quiet value creation

Slow assimilation

Nuclear cleanse

As proposed by u/KAX1107. Fire has purifying capabilities, and so may regulation, both internal and external. It is unclear if something would survive, though. Personally, I believe crypto-skeptics and regulators are doing a good job in this regard, and sometimes it is not indiscriminate.

This is a risky option. It may become a witch hunt or a new type of Spanish Inquisition. This may be the current situation already (see the third option). This is something that may be overdone.

Quiet value creation

Personally, I thought this would be "a better way": Do not tell them, show them.

To do that, start with a problem they have, and solve it. Do not even mention blockchain, let it be some kind of magic in the background. Only after it is solved, they are happy with the solution, and only if they really want to know the secrets of your magic, only then, mention blockchain and crypto.

Create value, solve problems and pain points, without even mentioning cryptocurrencies. In particular, problems that cannot be solved without blockchains and cryptocurrency.

Slow assimilation

I think we may be beyond the point where quiet value creation is possible. The Reddit NFT avatars and NFT card games bring the ownership of the physical items to the digital world. The blockchain and NFT aspects are not "in the face", and still they result in an emotional and negative response.

They are solving problems, not promising Lambos, and they could not be quieter without giving NFTs to people without informing them of having NFTs (which some projects are doing, as a matter of fact), but no value creation is perceivable when there is no rationality left.

In this context, the only suitable option is the Plank's principle, wait for new generations that are open-minded and that are able to benefit from the value creation (quiet or not) from the previous point.

Keep it up, and remember: your opinion matters.

[trylks]

Unrealizable, unthinkable, inconceivable, inapprehensible. How to describe something too far away from comprehension to cause confusion?

Question asked in English Stack Exchange, and then deleted.

tl;dr: I may be trying to distinguish two levels of "unthinkable":

What we cannot think through, to get to a conclusion, and is therefore confusing, puzzling,...

What we cannot comprehend enough to place in our minds, and is therefore not confusing (it is too far from our minds and understanding to have any actual impact on them).

Are there any words for the second group that are not ambiguous from the first group?

More detailed explanation:

Most words (unrealizable, unthinkable, inconceivable) and expressions (beyond one's grasp, over one's head) usually refer to things that:

result in questions that we cannot answer,

cause confusion or puzzlement,

are perceived as complex or enigmatic.

But in a way similar to "cosmic horror", I am trying to find the words to describe what is so far from comprehension as to be beyond any of those, as to go completely unnoticed.

Even after reading a book on the topic, no information is extracted from the book, as it reads like fiction without any connection with reality, written with sentences that make no sense, most of them not even individually.

Examples:

(1) For example, imagine that you have a universal translator and a time machine, and you try to use them to describe the legal structure of modern companies to a group of cavemen, imagine investing that early in any of the largest companies nowadays.

If you describe the company as a tribe, you may have a hard time and find a lot of confusion and ideas flying way over their heads, but through all the confusion and mistakes, they may understand companies as a type of tribe. This is the level of "above someone's head" that I find in most words and descriptions.

However, if you try to describe the more intangible legal structure of a company, the cavemen will just think you are making no sense, there will be no puzzlement about the law. Reciprocally, nobody will make a question about the law that makes sense, if they have any questions. If anything, there may misunderstanding and a new religion may be born, with "retirement" being "the end of the afterlife" (because being part of an intangible entity already feels afterlife enough). There is no confusion about what you said because there is no mapping of your words to any reasonable meaning for them, they may be confused about something like "spirit prisons" but not about companies. Companies are unimaginable.

(2) Or with an even more universal translator, try to explain equality to ants, and how they should not have a queen, but a republic and elections. This concept may be unthinkable for some people, but it is a further unthinkable idea for ants because democracy is a concept too complex for their brains (I think we can agree on that).

(3) Or for a final example, time travel is fairly unthinkable due to the multitude of paradoxes, but we can in fact think about the paradoxes, write fiction, etc. However, try to think about time as a 20-dimensional manifold (call it 20dT), a tesseract is already confusing, but 20dT is as unthinkable for me as democracy may be for an ant (YMMV, some people are very clever).

[trylks]

I have recently discovered that not only Tumblr posts may be embedded in other pages, they allow embedding a lot of things.

I am going to be dumping some Reddit comments here, but if I could find a PKMS that allows arbitrary embeddings, that would be just impressive.

[trylks]

A man is flying in an air balloon and realizes that he is lost. He reduces height and spots a woman down below. He lowers the balloon further and shouts: "Excuse me. Could you help me? I promised a friend I would meet him half an hour ago, but I don't know where I am."

The woman replies: "Yes. You are in a hot air balloon, hovering approximately 10 meters above this field. You are between 40 and 42 degrees latitude, and between 58 and 60 degrees longitude."

"You must be an engineer" says the balloonist. "I am", the woman responds. "How would you know?"

"Well... Everything you told be was technically correct, but I have no idea what to make of your information, which is ultimately useless and void of any benefit. I am as lost as I was before talking with you, I have lost altitude, and I am going to arrive even later, after you wasted my time."

The woman replies: "I see you are a manager." "I am", replies the balloonist. "How did you know?"

"Well..." says the woman. "A huge amount of hot air brought you here. You don't know where you are, nor where you are going. You made a promise and you have not the faintest idea how to keep it. You expect the people below you to solve your problems. And to top all of it, after I answered your question, somehow I am responsible for your situation."

[trylks]

How is the work of a data scientist? — Visual explanation

Your client, boss, supervisor, or whatever person you are working for wants you to use your data scientist skills, and expects results that are either:

explanations about what happened and is happening, i.e. explanatory analysis,

predictions about what is going to happen, i.e. predictive analysis, or

guidelines about how to best proceed to make the best kind of things happen, i.e. prescriptive analysis.

Depending on which one of the three you are working on you may see yourself as the Sherlock Holmes, Patrick Jane, or Dr. Gregory House of the data, respectively. Sounds exciting, right?

Before your ego peaks beyond mere mortals, you have to deliver your data science results. For you to do that, and earn your money, they may provide hardware and other resources, and most importantly: data.

To put it simply, as a metaphor, the data may look like this:

You ask for more data, routinely, because more data is always better, but there is no more data for you, you have already been provided with “a lot of data”, many pixels, in RGB, and in order. You may reasonably conclude that:

What is happening is some kind of celebration, e.g. graduation,

What is going to happen is that this young man is going to enjoy the evening, e.g. dancing,

What you should do depends on your relationship with him, different approaches corresponding to parent, friend, girlfriend, etc.

You congratulate yourself because you noticed some noise in the data between the trees and the shoulder, and you think that you completely nailed it, you are like Holmes, Jane, and House, combined.

Then, the person to who you are reporting your findings gets mad, “your analysis is wrong, useless, and shows a complete lack of knowledge and misunderstanding of the data that you were given and even of the most basic and fundamental principles of data science. You are an impostor, any intern using excel would have provided better results in a shorter time. They did not need any of the 'insight’ that you provided, and you provided absolutely zero information on what they need. You should better work on anything else, as far from computers as possible.”

From their perspective, they want you to use the data that you were given to help with the whole picture. The only problem is, you never received that whole picture of the data:

Of course you may say: “I already knew this meme and the story behind the picture”. That is one way to be a successful data scientist working for pointy haired bosses. If you have a good knowledge of the domain or vertical in which you work, you can make educated guesses for the insights that are needed, and use [otherwise mostly useless] data as an excuse.

Ideally, do not work for pointy haired bosses. But that is not always feasible or affordable.

Good luck!

PD: In large organizations, with large teams dedicated to data science, the models to estimate the age of the person in the photo and the time in the day would take months, and potentially result in several papers, press releases, and/or patents written, after many meetings and an incalculable sunk cost. The gender would not be estimated after the involvement of the ethics committee, papers and press releases may be written about the discrimination that results from inferring the gender of a person just from a photo of them, and the bias that algorithms display in these cases. New datasets with greater diversity in the people represented will be proposed.

[trylks]

[...] further into my natural drift, which was into learning all the big ideas and all the big disciplines. So I wouldn't be a perfect damn fool who was trying to think about one aspect of something that couldn't be removed from the totality of the situation in a constructive fashion. And what I know that, since the really big ideas carry 95% of the freight, there wasn't it all hard for me to pick up all the big ideas in all the disciplines and make them a standard part of my mental routines. Once you have the ideas of course they're no good if you don't practice, if you don't practice you lose it.

So I went through life constantly practicing this multi-disciplinary approach. Well, I can't tell you what that's done for me: it's made life more fun, it's made me more constructive, it's made me more helpful to others, it's made me enormously rich, you name it! That attitude really helps.

Now there are dangers there, because it works so well, that if you do it, you will frequently find you are sitting in the presence of some other expert, maybe even an expert that’s superior to you, supervising you. And you will know more than he does about his own specialty, a lot more. You will see the correct answer when he’s missed it.

That is a very dangerous position to be in. You can cause enormous offense by helpfully being right in a way that causes somebody else to lose face, and I never found the perfect way to to solve that problem. I was a great poker player when I was young, but I wasn't a good enough poker player so the people failed to sense that I thought I knew more than they did about their subjects, and it gave a lot of offense. Now I'm just regarded as eccentric, but there was a difficult period to go through, and my advice to you is to learn sometimes to keep your light under a bushel.

One of my colleagues, also number one of his class and law school of great success in life, clerked for the Supreme Court, etc. But he knew a lot and he tended to show it, as a very young lawyer, and one day the senior partner he was working under called him in and said listen Chuck, says: "I want to explain something to you, this is: your duty under any circumstances is to behave in such a way that the client thinks he's the smartest person in the world. You've got any little energy or insights available after that, use it to make your senior partner look like the smartest person in the world. And only after you've satisfied those two obligations do you want your light to shine at all".

Well that may have a very good advice for rising in a large firm, it wasn't what I did. I always obeyed the drift of my nature, and other people didn't like it. Well, I didn't need to be adored by everybody[...]

— Charlie Munger — USC Law Commencement Speech — May 2007

Shane Parrish on reading:

If you’re a knowledge worker, you’re paid to use your brain, so it’s in your best interest to make that brain as big as possible.

[trylks]

[trylks]

Noam Chomsky - "10 strategies of manipulation" by the media

Renowned critic and always MIT linguist Noam Chomsky, one of the classic voices of  intellectual dissent in the last decade, has compiled a list of the ten most common and effective strategies resorted to by the agendas “hidden” to establish a manipulation of the population through the media.Historically the media have proven highly efficient to mold public opinion. Thanks to the media paraphernalia and propaganda, have been created or destroyed social movements, justified wars, tempered financial crisis, spurred on some other ideological currents, and even given the phenomenon of media as producers of reality within the collective psyche. But how to detect the most common strategies for understanding these psychosocial tools which, surely, we participate? Fortunately Chomsky has been given the task of synthesizing and expose these practices, some more obvious and more sophisticated, but apparently all equally effective and, from a certain point of view, demeaning. Encourage stupidity, promote a sense of guilt, promote distraction, or construct artificial problems and then magically, solve them, are just some of these tactics.

1. The strategy of distraction

The primary element of social control is the strategy of distraction which is to divert public attention from important issues and changes determined by the political and economic elites, by the technique of flood or flooding continuous distractions and insignificant information. distraction strategy is also essential to prevent the public interest in the essential knowledge in the area of the science, economics, psychology, neurobiology and cybernetics. “Maintaining public attention diverted away from the real social problems, captivated by matters of no real importance. Keep the public busy, busy, busy, no time to think, back to farm and other animals (quote from text Silent Weapons for Quiet War ).”

2. Create problems, then offer solutions

This method is also called “problem -reaction- solution. “It creates a problem, a “situation” referred to cause some reaction in the audience, so this is the principal of the steps that you want to accept. For example: let it unfold and intensify urban violence, or arrange for bloody attacks in order that the public is the applicant‟s security laws and policies to the detriment of freedom. Or: create an economic crisis to accept as a necessary evil retreat of social rights and the dismantling of public services.

3. The gradual strategy

acceptance to an unacceptable degree, just apply it gradually, dropper, for consecutive years. That is how they radically new socioeconomic conditions ( neoliberalism ) were imposed during the 1980s and 1990s: the minimal state, privatization, precariousness, flexibility, massive unemployment, wages, and do not guarantee a decent income, so many changes that have brought about a revolution if they had been applied once.

4. The strategy of deferring

Another way to accept an unpopular decision is to present it as “painful and necessary”, gaining public acceptance, at the time for future application. It is easier to accept that a future sacrifice of immediate slaughter. First, because the effort is not used immediately. Then, because the public, masses, is always the tendency to expect naively that “everything will be better tomorrow” and that the sacrifice required may be avoided. This gives the public more time to get used to the idea of change and accept it with resignation when the time comes.

5. Go to the public as a little child

Most of the advertising to the general public uses speech, argument, people and particularly children‟s intonation, often close to the weakness, as if the viewer were a little child or a mentally deficient. The harder one tries to deceive the viewer look, the more it tends to adopt a tone infantilising. Why? “If one goes to a person as if she had the age of 12 years or less, then, because of suggestion, she tends with a certain probability that a response or reaction also devoid of a critical sense as a person 12 years or younger (see Silent Weapons for Quiet War ).”

6. Use the emotional side more than the reflection

Making use of the emotional aspect is a classic technique for causing a short circuit on rational analysis , and finally to the critical sense of the individual. Furthermore, the use of emotional register to open the door to the unconscious for implantation or grafting ideas , desires, fears and anxieties , compulsions, or induce behaviors …

7. Keep the public in ignorance and mediocrity

Making the public incapable of understanding the technologies and methods used to control and enslavement. “The quality of education given to the lower social classes must be the poor and mediocre as possible so that the gap of ignorance it plans among the lower classes and upper classes is and remains impossible to attain for the lower classes (See „ Silent Weapons for Quiet War ).”

8. To encourage the public to be complacent with mediocrity

Promote the public to believe that the fact is fashionable to be stupid, vulgar and uneducated…

9. Self-blame Strengthen

To let individual blame for their misfortune, because of the failure of their intelligence, their abilities, or their efforts. So, instead of rebelling against the economic system, the individual autodesvalida and guilt, which creates a depression, one of whose effects is to inhibit its action. And, without action, there is no revolution!

10. Getting to know the individuals better than they know themselves

Over the past 50 years, advances of accelerated science has generated a growing gap between public knowledge and those owned and operated by dominant elites. Thanks to biology, neurobiology and applied psychology, the “system” has enjoyed a sophisticated understanding of human beings, both physically and psychologically. The system has gotten better acquainted with the common man more than he knows himself. This means that, in most cases, the system exerts greater control and great power over individuals, greater than that of individuals about themselves.

[trylks]

[trylks]

Top ten reasons to not share your code (and why you should anyway)

Copied and pasted from The sustainability institute for reference purposes.

By Randall J. LeVeque, professor in the Department of Applied Mathematics at the University of Washington in Seattle.

This post was originally published as a news item on the Society of Industrial and Applied Mathematics website (SIAM News, Vol. 46, April 2013).

There is no... mathematician so expert in his science, as to place entire confidence in any truth immediately upon his discovery of it... Every time he runs over his proofs, his confidence encreases; but still more by the approbation of his friends; and is raised to its utmost perfection by the universal assent and applauses of the learned world.

---David Hume, 1739*

I am an advocate of sharing the computer code used to produce tables or figures appearing in mathematical and scientific publications, particularly when the results produced by the code are an integral part of the research being presented. I'm not alone, and in fact the number of people thinking this way seems to be rapidly increasing, see for example [1–3, 6–8, 10].

But there is still much resistance to this idea, and in the past several years I have heard many of the same arguments repeated over and over. So I thought it might be useful to write down some of the arguments, along with counter-arguments that may be worth considering.

In this article I am thinking mostly of relatively small-scale codes of the sort that might be developed to test a new algorithmic idea or verify that a new method performs as claimed, the sort of codes that might accompany papers in many SIAM journals. It can be at least as important to share and archive large-scale simulation codes that are used as tools to do science or make policy decisions, but the issues are somewhat different and not all the arguments that follow apply directly. However, as computational mathematics becomes increasingly important outside the ivory tower because of these large simulation codes, it is also worth remembering that the way we present our work can play a role in the ability of other scientists and engineers to do credible and reliable work that may have life-or-death consequences. Reproducibility is a cornerstone of the scientific method, and sharing the computer code used to reach the conclusions of a paper is often the easiest way to ensure that all the details needed to reproduce the results have been provided.

This article grew out of a talk with the same title that I gave in a minisymposium, Verifiable, Reproducible Research and Computational Science, at the 2011 SIAM CSE meeting in Reno, organized by Jarrod Millman. (Slides from my talk and others are available.)

An Alternative Universe

Before discussing computer code, I'd like you to join me in a thought experiment. Suppose we lived in a universe where the standards for publication of mathematical theorems were quite different: Papers would present theorems without proofs, and readers would simply be expected to believe authors who state that their theorems have been proved. (In fact, our own universe was once somewhat like this, but fortunately the idea of writing detailed proofs grew up along with the development of scientific journals. See, for example, Chapter 8 in [9] for a historical discussion of openness in science; I highly recommend the rest of this book as well.)

In this alternative universe, the reputation of the author would play a much larger role in deciding whether a paper containing a theorem could be published. Do we trust the author to have done a good job on the crucial part of the research not shown in the paper? Do we trust the theorem enough to use it in our own work in spite of not seeing the proof? This might be troubling in several respects. However, there are many advantages to not requiring carefully written proofs (in particular, that we don't have to bother writing them up for our own papers, or referee those written by others), and so the system goes on for many years.

Eventually, some agitators might come along and suggest that it would be better if mathematical papers contained proofs. Many arguments would be put forward against the idea. Here are some of them (and, yes, of course I hope you will see how similar they are to arguments against publishing code, mutatis mutandis, and will come up with your own counter-arguments):

The proof is too ugly to show anyone else. It would be too much work to rewrite it neatly so that others could read it. Anyway, it's just a one-off proof for this particular theorem; my intention is not that others will see it or use the ideas for proving other theorems. My time is much better spent proving another result and publishing more papers rather than putting more effort into this theorem, which I've already proved.

I didn't work out all the details. Some tricky cases I didn't want to deal with, but the proof works fine for most cases, such as the ones I used in the examples in the paper. (Well, actually, I discovered that some cases don't work, but they will probably never arise in practice.)

I didn't actually prove the theorem - my student did. And the student has since graduated, moved to Wall Street, and thrown away the proof, because of course dissertations also need not include proofs. But the student was very good, so I'm sure the proof was correct.

Giving the proof to my competitors would be unfair to me. It took years to prove this theorem, and the same idea can be used to prove other theorems. I should be able to publish at least five more papers before sharing the proof. If I share it now, my competitors will be able to use the ideas in it without having to do any work, and perhaps without even giving me credit, since they won't have to reveal their proof technique in their papers.

The proof is valuable intellectual property. The ideas in this proof are so great that I might be able to commercialise them someday, so I'd be crazy to give them away.

Including proofs would make math papers much longer. Journals wouldn't want to publish them, and who would want to read them?

Referees would never agree to check proofs. It would be too hard to determine the correctness of long proofs, and finding referees would become impossible. It's already hard to find enough good referees and get them to submit reviews in finite time. Requiring them to certify the correctness of proofs would bring the whole mathematical publishing business crashing down.

The proof uses sophisticated mathematical machinery that most readers and referees don't know. Their wetware cannot fully execute the proof, so what's the point in making it available to them?

My proof invokes other theorems with unpublished (proprietary) proofs. So it won't help to publish my proof - readers still will not be able to fully verify its correctness.

Readers who have access to my proof will want user support. People who can't figure out all the details will send e-mail requesting that I help them understand it, and asking how to modify the proof to prove their own theorems. I don't have time or staff to provide such support.

Back to the Real World

Of course, sharing code and publishing proofs are different activities. So let's return to the real world and examine some of the arguments in more detail.

It's just a research code, not software designed for others to use. General-purpose software designed to be user-friendly obviously differs from research code developed to test an idea and support a publication. However, most people recognise this difference and do not expect every code found on the web to come with user support. Nor do people expect every code found on the web to be wonderfully well written and documented. The more you clean it up, the better, but people publish far more embarrassing things on the web than ugly code, so perhaps it's best to get over this hangup [2]. Whatever state it is in, the code is an important part of the scientific record and often contains a wealth of details that do not appear in the paper, no matter how well the authors describe the method used. Parameter choices or implementation details are often crucial, and the ability to inspect the code, if necessary, can greatly facilitate efforts of other researchers to confirm the results or to adapt the methods presented to their own research problems.

Moreover, I believe that it is actually extremely valuable to the author to clean up any code used to obtain published results to the point that it is not an embarrassment to display it to others. All too often, I have found bugs in code when going through this process, and I suspect that I am not alone. Almost everyone who has published a theorem and its proof has found that the process of writing up the proof cleanly enough for publication uncovers subtle issues that must be dealt with, and perhaps even major errors in the original working proof. Writing research code is often no easier, so why should we expect to do it right the first time? It's much better for the author to find and fix these bugs before submitting the paper for publication than to have someone else rightfully question the results later.

It's forbidden to publish proprietary code. It is often true that research codes are based on commercial software or proprietary code that cannot be shared for various reasons. However, it is also often true that the part of the code that relates directly to the new research being published has been written by the authors of the paper, and they are free to share this much at least. This is also the part of the code that is generally of most interest to referees or readers who want to understand the ideas or implementation described in the paper, or to obtain details not included in it. The ability to execute the full code and replicate exactly the results in the paper is often of much less interest than the opportunity to examine the most relevant parts of the code.

Some employers may not allow employees to share any code they write. However, if authors are allowed to publish a piece of research in the open literature, then in my view they should be allowed to publish the parts of the code that are essential to the research. After all, employers cannot forbid authors to publish proofs along with their theorems - referees would not put up with it. A change in expectations may lead to a change in what's allowed. Moreover, publishing code can take various forms. Some employers may forbid sharing executables or source code in electronic form, but impose far fewer restrictions on publishing an excerpt of the code (or even the entirety) in a pdf file. It is worth reiterating that, for many readers or referees, being able to inspect the relevant part of the code is often more valuable than being able to run the code.

If you do publish code, in a paper or on the web, it is worth thinking about the type of copyright or licensing agreement you attach to the code. Your choice may affect the ability of others to reuse your code and the extent to which they must give you credit or propagate your license to derivative works [12].

The code may run only on certain systems today, and nowhere tomorrow. Even apart from the question of proprietary software, many codes have certain hardware or software dependencies that may make them impossible for the average reader to run - perhaps a code runs only on a supercomputer or requires a graphics package that's available only on certain operating systems. Moreover, even if everyone can run it today, there is no guarantee that it will run on computers of the future, or with newer versions of operating systems, compilers, graphics packages, etc. In this way a code is quite different from a proof. So what is the value of archiving code? As in the case of proprietary software dependencies, I would argue that being able to examine code is often extremely valuable even if it cannot be run, and it is critical in making research independently reproducible even if the tables or plots in the paper can't be reproduced with the push of a button.

Of course, authors should attempt whenever possible to make it easy to run their code. From a purely selfish standpoint, any effort put into cleaning up code so that it does reproduce all the plots in the paper with a push of the button often pays off for the author down the road - when referees ask for a revision of the way things are plotted, when the author picks up the research project again years later, or, in the worst case, when results in the paper come into question and the co-author who wrote the code has graduated or retired and is no longer available to explain where they actually came from. To minimise difficulties associated with software dependencies and versions, authors might consider using such techniques as a virtual machine to archive the full operating system along with the code [5], or a code-hosting site that simplifies the process of running an archived code without downloading or installing software [4].

Code is valuable intellectual property. It is true that some research groups spend years developing a code base to solve a particular class of scientific problems; their main interest is in doing science with these codes and publishing new results obtained from simulations. Expecting such researchers to freely share their full code might be seen as the computational equivalent of requiring experimentalists publishing a result not only to describe their methods in detail, but also to welcome any reader into their laboratory to use their experimental apparatus. This concern should be respected when advocating reproducibility in computational science, and I don't claim to have a good solution for all such cases.

For many research codes developed by applied mathematicians, however, the goal is to introduce and test new computational methods in the hope that others will use them (and cite their papers). For such codes I see little to be gained by not sharing. The easier it is for readers to understand the details and to implement the method (or even borrow code), the more likely they are to adopt the method and cite the paper. Some people worry that they will not receive proper credit from those who adapt code to their own research. But if everyone were expected to share code in publications, it would be much easier to see what code has been used, and to compare it to the code archived with earlier publications. Citing the original source would then be easy and would become standard operating procedure, leading to more citations for the original author. Readers of mathematics papers can judge for themselves the originality of the ideas in a published proof, and if code development were equally transparent, those developing the original algorithms and code would ultimately receive more credit, not less.

Today, most mathematicians find the idea of publishing a theorem without its proof laughable, even though many great mathematicians of the past apparently found it quite natural. Mathematics has since matured in healthy ways, and it seems inevitable that computational mathematics will follow a similar path, no matter how inconvenient it may seem. I sense growing concern among young people in particular about the way we've been doing things and the difficulty of understanding or building on earlier work. Some funding agencies and journals now require sharing code that is used to obtain published results (see the Science guidelines for authors [11], for example). SIAM journals are not currently contemplating such a requirement, but the capability is now available for accepting and publishing unrefereed supplementary materials (including code) in conjunction with papers for some SIAM journals (see SIAM Journals Introduce Supplementary Materials, SIAM News, March 2013). I believe there is much to be gained, for authors as well as readers and the broader scientific community, from taking advantage of this capability and rethink-ing the way we present our work. We can all help our field mature by making the effort to share the code that supports our research.

References

K.A. Baggerly and D.A. Barry, Reproducible research, 2011

N. Barnes, Publish your computer code: It is good enough, Nature, 467 (2010), 753

S. Fomel and J.F. Claerbout, Guest editors' introduction: Reproducible research, Comput. Sci. Eng., 11 (2009), 5–7

J. Freire, P. Bonnet, and D. Shasha, Exploring the coming repositories of reproducible experiments: Challenges and opportunities, Proc. VLDB Endowment, 4 (2011), 1494–1497

B. Howe, Virtual appliances, cloud computing, and reproducible research, Comput. Sci. Eng., 14 (2012), 36–41

J. Kovačević, How to encourage and publish reproducible research, Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc., IV (2007), 1273–1276

R.J. LeVeque, I.M. Mitchell, and V. Stodden, Reproducible research for scientific computing: Tools and strategies for changing the culture, Comput. Sci. Eng., 14 (2012), 13–17

J. Mesirov, Accessible reproducible research, Science, 327 (2010), 415–416

M. Nielsen, Reinventing Discovery: The New Era of Networked Science, Princeton University Press, Princeton and Oxford, 2012

R.D. Peng, Reproducible research in computational science, Science, 334 (2011), 1226–1227

Science, General information for authors: Data and materials availability, 2012

V.C. Stodden, The legal framework for reproducible scientific research: Licensing and copyright, Comput. Sci. Eng., 11 (2009), 35–40.

*A Treatise of Human Nature, http://www.gutenberg.org/files/4705/4705-h/4705-h.htm.

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