## Poor reasons for preprints & post-publication peer-review

Last week, I revived the blog with some reflections on open science. In particular, I went into the case for pre-prints and the problem with the academic publishing system. This week, I want to continue this thread by examining three common arguments for preprints: speed, feedback, and public access. I think that these arguments are often motivated in the wrong way. In their standard presentation, they are bad arguments for a good idea. By pointing out these perceived shortcoming, I hope that we can develop more convincing arguments for preprints. Or maybe methods of publication that are even better than the current approach to preprints.

These thoughts are not completely formed, and I am eager to refine them in follow up posts. As it stand, this is more of a hastily written rant.

## Preprints and a problem with academic publishing

This is the 250th post on the Theory, Evolutionary, and Games Group Blog. And although my posting pace has slowed in recent months, I see this as a milestone along the continuing road of open science. And I want to take this post as an opportunity to make some comments on open science.

To get this far, I’ve relied on a lot of help and encouragement. Both directly from all the wonderful guest posts and comments, and indirectly from general recognition. Most recently, this has taken the form of the Canadian blogging and science outreach network Science Borealis recognized us as one of the top 12 science blogs in Canada.

Given this connection, it is natural to also view me as an ally of other movements associated with open science; like, (1) preprints and (2) post-publication peer-review (PPPR). To some extent, I do support both of these activities. First, I regularly post my papers to ArXiv & BioRxiv. Just in the two preceeding months, I’ve put out a paper on the complexity of evolutionary equilibria and joint work on how fibroblasts and alectinib switch the games that cancers play. Another will follow later this month based on our project during the 2016 IMO Workshop. And I’ve been doing this for a while: the first draft of my evolutionary equilibria paper, for example, is older than BioRxiv — which only launched in November 2013. More than 20 years after physicists, mathematicians, and computer scientists started using ArXiv.

Second, some might think of my blog posts as PPPRs. For example. occasionally I try to write detailed comments on preprints and published papers. For example, my post on fusion and sex in proto-cells commenting on a preprint by Sam Sinai, Jason Olejarz and their colleagues. Finally, I am impressed and made happy by the now iconic graphic on the growth of preprints in biology.

But that doesn’t mean I find these ideas to be beyond criticism, and — more importantly — it doesn’t mean that there aren’t poor reasons for supporting preprints and PPPR.

Recently, I’ve seen a number of articles and tweets written on this topic both for and against (or neutral toward) pre-prints and for PPPR. Even Nature is telling us to embrace preprints. In the coming series of posts, I want to share some of my reflections on the case for preprints, and also argue that there isn’t anything all that revolutionary or transformative in them. If we want progress then we should instead think in terms of working papers. And as for post-publications peer review — instead, we should promote a culture of commentaries, glosses, and literature review/synthesis.

Currently, we do not publish papers to share ideas. We have ideas just to publish papers. And we need to change this aspect academic culture.

In this post, I will sketch some of the problems with academic publishing. Problems that I think any model of sharing results will have to address.

## Cataloging a year of blogging: complexity in evolution, general models, and philosophy

Last month, with just hours to spare in January, I shared a linkdex of the 14 cancer-related posts from TheEGG in 2016. Now, as February runs out, it’s time to reflect on the 15 non cancer-specific posts from last year. Although, as we’ll see, some of them are still related to mathematical oncology. With a nice number like 15, I feel that I am obliged to divide them into three categories of five articles each. Which does make for a stretch in narrowing down themes.

The three themes were: (1) complexity, supply driven evolution, and abiogenesis, (2) general models and their features, (3) algorithmic philosophy and the social good.

And yes, two months have passed and all I’ve posted to the blog are two 2016-in-review posts. Even those were rushed and misshapen. But I promise there is more and better coming; hopefully with a regular schedule.

## Social algorithms and the Weapons of Math Destruction

Cathy O’Neil holding her new book: Weapons of Math Destruction at a Barnes & Noble in New York city.

In reference to intelligent robots taking over the world, Andrew Ng once said: “I don’t work on preventing AI from turning evil for the same reason that I don’t work on combating overpopulation on the planet Mars.” Sure, it will be an important issue to think about when the time comes. But for now, there is no productive way to think seriously about it. Today there are more concrete problems to worry about and more basic questions that need to be answered. More importantly, there are already problems to deal with. Problems that don’t involve super intelligent tin-men, killer robots, nor sentient machine overlords. Focusing on distant speculation obscures the fact that algorithms — and not necessarily very intelligent ones — already reign over our lives. And for many this reign is far from benevolent.

I owe much of my knowledge about the (negative) effects of algorithms on society to the writings of Cathy O’Neil. I highly recommend her blog mathbabe.org. A couple of months ago, she shared the proofs of her book Weapons of Math Destruction with me, and given that the book came out last week, I wanted to share some of my impressions. In this post, I want to summarize what makes a social algorithm into a weapon of math destruction, and share the example of predictive policing.

## Computational kindness and the revelation principle

In EWD1300, Edsger W. Dijkstra wrote:

even if you have only 60 readers, it pays to spend an hour if by doing so you can save your average reader a minute.

He wrote this as the justification for the mathematical notations that he introduced and as an ode to the art of definition. But any writer should heed this aphorism.[1] Recently, I finished reading Algorithms to Live By by Brian Christian and Tom Griffiths.[2] In the conclusion of their book, they gave a unifying name to the sentiment that Dijkstra expresses above: computational kindness.

As computer scientists, we recognise that computation is costly. Processing time is a limited resource. Whenever we interact with others, we are sharing in a joint computational process, and we need to be mindful of when we are not carrying our part of the processing burden. Or worse yet, when we are needlessly increasing that burden and imposing it on our interlocutor. If you are computationally kind then you will be respectful of the cognitive problems that you force others to solve.

I think this is a great observation by Christian and Griffiths. In this post, I want to share with you some examples of how certain systems — at the level of the individual, small group, and society — are computationally kind. And how some are cruel. I will draw on examples from their book, and some of my own. They will include, language, bus stops, and the revelation principle in algorithmic game theory.

## Systemic change, effective altruism and philanthropy

The topics of effective altruism and social (in)justice have weighed heavy on my mind for several years. I’ve even touched on the latter occasionally on TheEGG, but usually in specific domains closer to my expertise, such as in my post on the ethics of big data. Recently, I started reading more thoroughly about effective altruism. I had known about the movement[1] for some time, but had conflicting feelings towards it. My mind is still in disarray on the topic, but I thought I would share an analytic linkdex of some texts that have caught my attention. This is motivated by a hope to get some guidance from you, dear reader. Below are three videos, two articles, two book reviews and one paper alongside my summaries and comments. The methods range from philosophy to comedy and from critical theory to social psychology. I reach no conclusions.

## Diversity and persistence of group tags under replicator dynamics

Everyday I walk to the Stabile Research Building to drink espresso and sit in my cozy — although oversaturated with screens — office. Oh, and to chat about research with great people like Arturo Araujo, David Basanta, Jill Gallaher, Jacob Scott, Robert Vander Velde and other Moffitters. This walk to the office takes about 30 minutes each way, so I spend it listening to podcasts. For the past few weeks, upon recommendation from a friend, I’ve started listing to the archive of the Very Bad Wizards. This is a casual — although oversaturated with rude jokes — conversation between David Pizarro and Tamler Sommers on various aspects of the psychology and philosophy of morality. They aim at an atmosphere of two researchers chatting at the bar; although their conversation is over Skype and drinks. It is similar to the atmosphere that I want to promote here at TheEGG. Except they are funny.

While walking this Wednesday, I listed to episode 39 of Very Bad Wizards. Here the duo opens with a Wilson & Haidt’s TIME quiz meant to quantify to what extent you are liberal or conservative.[1] They are 63% liberal.[2]

To do the quiz, you are asked to rate 12 statements (well, 11 and one question about browsers) on a six point Likert scale from strongly disagree to strongly agree. Here are the three that caught my attention:

1. If I heard that a new restaurant in my neighborhood blended the cuisines of two very different cultures, that would make me want to try it.
2. My government should treat lives of its citizens as being much more valuable than lives in other countries.[3]
3. I wish the world did not have nations or borders and we were all part of one big group.[4]

Do you strongly agree? Strongly disagree? What was your overall place on the liberal-conservative scale?

Regardless of your answers, the statements probably remind you of an important aspect of your daily experience. The world is divided into a diversity of groups, and they coexist in a tension between their arbitrary, often artificial, nature and the important meaning that they hold to both their own members and others. Often this division is accompanied by ethnocentrism — a favoring of the in-group at the expensive of, or sometimes with direct hostility toward, the out-group — that seems difficult to circumvent through simply expanding our moral in-group. These statements also confront you with the image of what a world without group lines might look like; would it be more cooperative or would it succumb to the egalitarian dilemma?[5]

As you know, dear reader, here at TheEGG we’ve grappled with some of these questions. Mostly by playing with the Hammond & Axelrod model of ethnocentrism (2006; also see: Hartshorn, Kaznatcheev & Shultz, 2012). Recently, Jansson’s (2015) extension of my early work on the robustness of ethnocentrism (Kaznatcheev, 2010) has motivated me to continue this thread. A couple of weeks ago I sketched how to reduce the dimensionality of the replicator equations governing tag-based games. Today, I will use this representation to look at how properties of the game affect the persistence and diversity of tags.

## Radicalization, expertise, and skepticism among doctors & engineers: the value of philosophy in education

This past Friday was a busy day for a lot of the folks in Integrated Mathematical Oncology here at the Moffitt Cancer Center. Everybody was rushing around to put the final touches on a multi-million dollar research center grant application to submit to the National Cancer Institute. Although the time was not busy for me, I still stopped by Jacob Scott’s office towards the end of the day to celebrate. Let me set the scene for you: it is a corner office down the hall from me; its many windows are scribbled over with graphs, equations, and biological interaction networks; two giant screens crowd a standing desk, and another screen is hidden in the corner; the only non-glass wall has scribbles in pencil for the carpenters: paint blackboard here. There are too many chairs — Jake is a connector, so his office is always open to guests.

A different celerbation in Jake’s office. The view is from his desk towards the wall that needs to be replaced by a blackboard.

In addition to the scientific and administrative stress of grant-writing, Jake was also covering for his friend as the doc-of-the-day for radiation oncology. So as I rambled on: “If we consider nodes of degree three or higher in this model, we would break up contingent blocks of mutants and result in the domain of our probability distribution going from $n^2$ to $2^n$“, scribbling more math on his wall, we would get interrupted by phone calls. His resident calling to tell him that the neurosurgeons have scheduled a consultation for an acute myeloid leukemia patient who is recovering from surgery earlier that day.

“Only on a Friday afternoon do you get this kind of consult!” Jake fires off, “He’s still in surgery! We can’t do anything for at least a few days – schedule him for Monday.”

The call was on speakerphone, but I could not keep up with the conversation. After years of training and experience, this was an effortless context-shift for Jake. He went from the heavy skepticism of a scientist staring at a blackboard to the certainty of a doctor that needed to get shit done, and back, in moments. I couldn’t imagine having this sort of confidence in my judgements, mostly because I have no training in medicine, but also because I am not expected to be certain. That is why I lean towards using abductive models versus insilications for clinial research; I have more confidence in machine learning than in my own physical and biological intuitions about cancer. Even if that approach might produce less understanding.

In recent weeks, I’ve noticed a theme in some of the (news and blog) articles I’ve been reading. In this post, I wanted to provide an annotated collection of some of these links, along with my reflections on what they say about the tension between expertise and skepticism and how that can radicalize us, both in mundane ways and in drastic ones. And what role philosophy can play in helping us cope. I will end up touching on recent events and politics as a source context, but hopefully we can keep the overall conversation more or less detached from current events.

## Emotional contagion and rational argument in philosophical texts

Last week I returned to blogging with some reflections on reading and the written word more generally. Originally, I was aiming to write a response to Roger Schank’s stance that “reading is no way to learn”, but I wandered off on too many tangents for an a single post or for a coherent argument. The tangent that I left for this post is the role of emotion and personality in philosophical texts.

In my last entry, I focused on the medium independent aspects of Schank’s argument, and identified two dimensions along which a piece of media and our engagement with it can vary: (1) passive consumption versus active participation, and (2) the level of personalization. The first continuum has a clearly better end on the side of more active engagement. If we are comparing mediums then we should prefer ones that foster more active engagement from the participants. The second dimension is more ambiguous: sometimes a more general piece of media is better than a bespoke piece. What is better becomes particularly ambiguous when being forced to adapt a general approach to your special circumstances encourages more active engagement.

In this post, I will shift focus from comparing mediums to a particular aspect of text and arguments: emotional engagement. Of course, this also shows up in other mediums, but my goal this time is not to argue across mediums.

## False memories and journalism

We like to think of ourselves as a collection of our memories, and of each memory as a snapshot of an event in our lives. Sure, we all know that our minds aren’t as sturdy as our computer’s hard-drive, so these snapshots decay over time, especially the boring ones — that’s why most of us can’t remember what we had for breakfast 12 years ago. We are even familiar with old snapshots rearranging their order and losing context, but we don’t expect to generate vivid and certain memories of events that didn’t occur. How could we have a snapshot of something that didn’t happen?

This view of memory is what makes Brian Williams’ recent fib about being on board a helicopter that was hit by two rockets and small arms fire in Iraq 12 years ago, so hard to believe. There was indeed a helicopter that was forced to land on that day, but the downed aircraft’s crew reports that Williams was actually on a helicopter about an hour behind the three that came under fire. Williams has apologized for his story, saying he conflated his helicopter with the downed one. To this, Erik Wemple voices the popular skepticism that “‘conflating’ the experience of taking incoming fire with the experience of not taking incoming fire seems verily impossible.”

But research into false memories suggests that such constructed memories as Williams’ do occur. In this post, I want to discuss these sort of false memories, share a particularly interesting example, and then discuss what this might mean for journalism.