Allegory of the replication crisis in algorithmic trading

One of the most interesting ongoing problems in metascience right now is the replication crisis. This a methodological crisis around the difficulty of reproducing or replicating past studies. If we cannot repeat or recreate the results of a previous study then it casts doubt on if those ‘results’ were real or just artefacts of flawed methodology, bad statistics, or publication bias. If we view science as a collection of facts or empirical truths than this can shake the foundations of science.

The replication crisis is most often associated with psychology — a field that seems to be having the most active and self-reflective engagement with the replication crisis — but also extends to fields like general medicine (Ioannidis, 2005a,b; 2016), oncology (Begley & Ellis, 2012), marketing (Hunter, 2001), economics (Camerer et al., 2016), and even hydrology (Stagge et al., 2019).

When I last wrote about the replication crisis back in 2013, I asked what science can learn from the humanities: specifically, what we can learn from memorable characters and fanfiction. From this perspective, a lack of replication was not the disease but the symptom of the deeper malady of poor theoretical foundations. When theories, models, and experiments are individual isolated silos, there is no inherent drive to replicate because the knowledge is not directly cumulative. Instead of forcing replication, we should aim to unify theories, make them more precise and cumulative and thus create a setting where there is an inherent drive to replicate.

More importantly, in a field with well-developed theory and large deductive components, a study can advance the field even if its observed outcome turns out to be incorrect. With a cumulative theory, it is more likely that we will develop new techniques or motivate new challenges or extensions to theory independent of the details of the empirical results. In a field where theory and experiment go hand-in-hand, a single paper can advance both our empirical grounding and our theoretical techniques.

I am certainly not the only one to suggest that a lack of unifying, common, and cumulative theory as the cause for the replication crisis. But how do we act on this?

Can we just start mathematical modelling? In the case of the replicator crisis in cancer research, will mathematical oncology help?

Not necessarily. But I’ll come back to this at the end. First, a story.

Let us look at a case study: algorithmic trading in quantitative finance. This is a field that is heavy in math and light on controlled experiments. In some ways, its methodology is the opposite of the dominant methodology of psychology or cancer research. It is all about doing math and writing code to predict the markets.

Yesterday on /r/algotrading, /u/chiefkul reported on his effort to reproduce 130+ papers about “predicting the stock market”. He coded them from scratch and found that “every single paper was either p-hacked, overfit [or] subsample[d] …OR… had a smidge of Alpha [that disappears with transaction costs]”.

There’s a replication crisis for you. Even the most pessimistic readings of the literature in psychology or medicine produce significantly higher levels of successful replication. So let’s dig in a bit.

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Blogging community of computational and mathematical oncologists

A few weeks ago, David Basanta reached out to me (and many other members of the mathematical oncology community) about building a community blog together. This week, to coincide with the Society for Mathematical Biology meeting in Montreal, we launched the blog. In keeping with the community focus, we have an editorial board of 8 people that includes (in addition to David and me): Christina Curtis, Elana Fertig, Stacey Finley, Jakob Nikolas Kather, Jacob G. Scott, and Jeffrey West. The theme is computational and mathematical oncology, but we welcome contributions from all nearby disciplines.

The behind the scenes discussion building up to this launch was one of the motivators for my post on twitter vs blogs and science advertising versus discussion. And as you might expect, dear reader, it was important to me that this new community blog wouldn’t be just about science outreach and advertising of completed work. For me — and I think many of the editors — it is important that the blog is a place for science engagement and for developing new ideas in the open. A way to peel back the covers that hide how science is done and break the silos that inhibit a collaborative and cooperative atmosphere. A way to not only speak at the public or other scientists, but also an opportunity to listen.

For me, the blog is a challenge to the community. A challenge to engage in more flexible, interactive, and inclusive development of new ideas than is possible with traditional journals. While also allowing for a deeper, more long-form and structured discussion than is possible with twitter. If you’ve ever written a detailed research email, long discussion on Slack, or been part of an exciting journal club, lab meeting, or seminar, you know the amount of useful discussion that is foundational to science but that seldom appears in public. My hope is that we can make these discussions more public and more beneficial to the whole community.

Before pushing for the project, David made sure that he knew the lay of the land. He assembled a list of the existing blogs on computational and mathematical oncology. In our welcome post, I made sure to highlight a few of the examples of our community members developing new ideas, sharing tools and techniques, and pushing beyond outreach and advertising. But since we wanted the welcome post to be short, there was not the opportunity for a more thorough survey of our community.

In this post, I want to provide a more detailed — although never complete nor exhaustive — snapshot of the blogging community of computational and mathematical oncologists. At least the part of it that I am familiar with. If I missed you then please let me know. This is exactly what the comments on this post are for: expanding our community.

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Twitter vs blogs and science advertising vs discussion

I read and write a lot of science outside the traditional medium of papers. Most often on blogs, twitter, and Reddit. And these alternative media are colliding more and more with the ‘mainstream media’ of academic publishing. A particularly visible trend has been the twitter paper thread: a collection of tweets that advertise a new paper and summarize its results. I’ve even written such a thread (5-6 March) for my recent paper on how to use cstheory to think about evolution.

Recently, David Basanta stumbled across an old (19 March) twitter thread by Dan Quintana for why people should use such twitter threads, instead of blog posts, to announce their papers. Given my passion for blogging, I think that David expected me to defend blogs against this assault. But instead of siding with David, I sided with Dan Quintana.

If you are going to be ‘announcing’ a paper via a thread then I think you should use a twitter thread, not a blog. At least, that is what I will try to stick to on TheEGG.

Yesterday, David wrote a blog post to elaborate on his position. So I thought that I would follow suit and write one to elaborate mine. Unlike David’s blog, TheEGG has comments — so I encourage you, dear reader, to use those to disagree with me.

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Hiding behind chaos and error in the double pendulum

If you want a visual intuition for just how unpredictable chaotic dynamics can be then the go-to toy model is the double pendulum. There are lots of great simulations (and some physical implementations) of the double pendulum online. Recently, /u/abraxasknister posted such a simulation on the /r/physics subreddit and quickly attracted a lot of attention.

In their simulation, /u/abraxasknister has a fixed center (block dot) that the first mass (red dot) is attached to (by an invisible rigid massless bar). The second mass (blue dot) is then attached to the first mass (also by an invisible rigid massless bar). They then release these two masses from rest at some initial height and watch what happens.

The resulting dynamics are at right.

It is certainly unpredictable and complicated. Chaotic? Most importantly, it is obviously wrong.

But because the double pendulum is a famous chaotic system, some people did not want to acknowledge that there is an obvious mistake. They wanted to hide behind chaos: they claimed that for a complex system, we cannot possibly have intuitions about how the system should behave.

In this post, I want to discuss the error of hiding behind chaos, and how the distinction between microdynamics and global properties lets us catch /u/abraxasknister’s mistake.
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Software monocultures, imperialism, and weapons of math destruction

This past Friday, Facebook reported that they suffered a security breach that affected at least 50 million users. ‘Security breach’ is a bit of newspeak that is meant to hint at active malice and attribute fault outside the company. But as far as I understand it — and I am no expert on this — it was just a series of three bugs in Facebook’s “View As” feature that together allowed people to get the access tokens of whoever they searched for. This is, of course, bad for your Facebook account. The part of this story that really fascinated me, however, is how this affected other sites. Because that access token would let somebody access not only your Facebook account but also any other website where you use Facebook’s Single Sign On feature.

This means that a bug that some engineers missed at Facebook compromised the security of users on completely unrelated sites like, say, StackExchange (SE) or Disqus — or any site that you can log into using your Facebook account.

A case of software monoculture — a nice metaphor I was introduced to by Jonathan Zittrain.

This could easily have knock-on effects for security. For example, I am one of the moderators for the Theoretical Computer Science SE and also the Psychology and Neuroscience SE. Due to this, I have the potential to access certain non-public information of SE users like their IP addresses and hidden contact details. I can also send communications that look much more official, along-side expected abilities like bans, suspensions, etc. Obviously, part of my responsibility as a moderator is to only use these abilities for proper reasons. But if I had used Facebook — disclosure: I don’t use Facebook — for my SE login then a potential hacker could get access to these abilities and then attempt phishing or other attacks even on SE users that don’t use Facebook.

In other words, the people in charge of security at SE have to worry not only about their own code but also Facebook (and Google, Yahoo!, and other OpenIDs).

Of course, Facebook is not necessarily the worst case of software monoculture or knock-on effects that security experts have to worry about. Exploits in operating systems, browsers, serves, and standard software packages (especially security ones) can be even more devastating to the software ecology.

And exploits of aspects of social media other that login can have more subtle effects than security.

The underlying issue is a lack of diversity in tools and platforms. A case of having all our eggs in one basket. Of minimizing individual risk — by using the best available or most convenient system — at the cost of increasing systemic risk — because everyone else uses the same system.

We see the same issues in human projects outside of software. Compare this to the explanations of the 2008 financial crises that focused on individual vs systemic risk.

But my favourite example is the banana.

In this post, I’ll to sketch the analogy between software monoculture and agricultural monoculture. In particular, I want to focus on a common element between the two domains: the scale of imperial corporations. It is this scale that turns mathematical models into weapons of math destructions. Finally, I’ll close with some questions on if this analogy can be turned into tool transfer: can ecology and evolution help us understand and manage software monoculture?

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Labyrinth: Fitness landscapes as mazes, not mountains

Tonight, I am passing through Toulouse on my way to Montpellier for the 2nd Joint Congress on Evolutionary Biology. If you are also attending then find me on 21 August at poster P-0861 on level 2 to learn about computational complexity as an ultimate constraint on evolution.

During the flight over, I was thinking about fitness landscapes. Unsurprising — I know. A particular point that I try to make about fitness landscapes in my work is that we should imagine them as mazes, not as mountain ranges. Recently, Raoul Wadhwa reminded me that I haven’t written about the maze metaphor on the blog. So now is a good time to write on labyrinths.

On page 356 of The roles of mutation, inbreeding, crossbreeding, and selection in evolution, Sewall Wright tells us that evolution proceeds on a fitness landscape. We are to imagine these landscapes as mountain ranges, and natural selection as a walk uphill. What follows — signed by Dr. Jorge Lednem Beagle, former navigator of the fitness maze — throws unexpected light on this perspective. The first two pages of the record are missing.

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QBIOX: Distinguishing mathematical from verbal models in biology

There is a network at Oxford know as QBIOX that aims to connect researchers in the quantitative biosciences. They try to foster collaborations across the university and organize symposia where people from various departments can share their quantitative approaches to biology. Yesterday was my second or third time attending, and I wanted to share a brief overview of the three talks by Philip Maini, Edward Morrissey, and Heather Harrington. In the process, we’ll get to look at slime molds, colon crypts, neural crests, and glycolysis. And see modeling approaches ranging from ODEs to hybrid automata to STAN to algebraic systems biology. All of this will be in contrast to verbal theories.

Philip Maini started the evening off — and set the theme for my post — with a direct question as the title of his talk.

Does mathematics have anything to do with biology?

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Hackathons and a brief history of mathematical oncology

It was Friday — two in the morning. And I was busy fine-tuning a model in Mathematica and editing slides for our presentation. My team and I had been running on coffee and snacks all week. Most of us had met each other for the first time on Monday, got an inkling of the problem space we’d be working on, brainstormed, and hacked together a number of equations and a few chunks of code to prototype a solution. In seven hours, we would have to submit our presentation to the judges. Fifty thousand dollars in start-up funding was on the line.

A classic hackathon, except for one key difference: my team wasn’t just the usual mathematicians, programmers, computer & physical scientists. Some of the key members were biologists and clinicians specializing in blood cancers. And we weren’t prototyping a new app. We were trying to predict the risk of relapse for patients with chronic myeloid leukemia, who had stopped receiving imatinib. This was 2013 and I was at the 3rd annual integrated mathematical oncology workshop. It was one of my first exposures to using mathematical and computational tools to study cancer; the field of mathematical oncology.

As you can tell from other posts on TheEGG, I’ve continued thinking about and working on mathematical oncology. The workshops have also continued. The 7th annual IMO workshop — focused on stroma this year — is starting right now. If you’re not in Tampa then you can follow #MoffittIMO on twitter.

Since I’m not attending in person this year, I thought I’d provide a broad overview based on an article I wrote for Oxford Computer Science’s InSPIRED Research (see pg. 20-1 of this pdf for the original) and a paper by Helen Byrne (2010).

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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.

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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.

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