## From linear to nonlinear payoffs in the double public goods game

If you recall, dear reader, around this time last year, Robert Vander Velde, David Basanta, Jacob Scott and I got excited about the Archetti (2013,2014) approach to modeling non-linear public goods in cancer. We’ve been working on this intermittently for the last year, but aim to focus now that I have settled in here at Moffitt. This means there will be a lot more cancer posts as I resume thinking careful about mathematical oncology. Although I didn’t update the blog in the summer, it doesn’t mean that nothing was written. The work below is mostly from when I visited Tampa in late July. As are these two blackboards:

In this project, we are combining growth factor production (Archetti, 2013) and acidity (2014) as a pair of anti-correlated public goods. The resulting dynamics cannot be understood by studying just one or the other good. The goal is to explore the richer behaviors that are possible with coupled social dilemmas. At the start of the year — in my first analysis of the double public goods game — as a sanity check I considered the linear public goods $f(q) = b_f q$ and $m(p) = b_m p$. After a long meeting with Robert a few month ago, I think that these were misleading payoffs to consider. I jotted these notes after the meeting, but forgot to release them on the blog. Instead, you get to enjoy them now while I refresh my memory.

## 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.

## Cytokine storms during CAR T-cell therapy for lymphoblastic leukemia

For most of the last 70 years or so, treating cancer meant one of three things: surgery, radiation, or chemotherapy. In most cases, some combination of these remains the standard of care. But cancer research does not stand still. More recent developments have included a focus on immunotherapy: using, modifying, or augmenting the patient’s natural immune system to combat cancer. Last week, we pushed the boundaries of this approach forward at the 5th annual Integrated Mathematical Oncology Workshop. Divided into four teams of around 15 people each — mathematicians, biologists, and clinicians — we competed for a \$50k start-up grant. This was my 3rd time participating,[1] and this year — under the leadership of Arturo Araujo, Marco Davila, and Sungjune Kim — we worked on chimeric antigen receptor T-cell therapy for acute lymphoblastic leukemia. CARs for ALL.

Team Red busy at work in the collaboratorium. Photo by team leader Arturo Araujo.

In this post I will describe the basics of acute lymphoblastic leukemia, CAR T-cell therapy, and one of its main side-effects: cytokine release syndrome. I will also provide a brief sketch of a machine learning approach to and justification for modeling the immune response during therapy. However, the mathematical details will come in future posts. This will serve as a gentle introduction.

## Symmetry in tag-based games & invariants under replicator dynamics

Mathematicians and physicists love finding symmetries. The reason is simple: symmetries make life easier. The situation is no different when studying the evolutionary dynamics of life. If the fitness functions of your organisms have some symmetry or other nice structure then you can usually exploit it to make analyzing your replicator equations easier. In this post, I want to show an example of this in tag-based models. This analysis is an essential base case when building more complicated models of ethnocentrism — like our work in the Hammond and Axelrod model — and I have been meaning to write about it for a while. This will give me a chance to show a concrete example where my method for factoring the replicator equation is useful, and how observing a straighforward symmetry can reduce the dimensionality of a problem. Maybe this exercise will also teach us something about the evolution of ethnocentrism.

## Abusing numbers and the importance of type checking

What would you say if I told you that I could count to infinity on my hands? Infinity is large, and I have a typical number of fingers. Surely, I must be joking. Well, let me guide you through my process. Since you can’t see me right now, you will have to imagine my hands. When I hold out the thumb on my left hand, that’s one, and when I hold up the thumb and the index finger, that’s two. Actually, we should be more rigorous, since you are imagining my fingers, it actually isn’t one and two, but i and 2i. This is why they call them imaginary numbers.

Let’s continue the process of extending my (imaginary) fingers from the leftmost digits towards the right. When I hold out my whole left hand and the pinky, ring, and middle fingers on my right hand, I have reached 8i.

But this doesn’t look like what I promised. For the final step, we need to remember the geometric interpretation of complex numbers. Multiplying by i is the same thing as rotating counter-clockwise by 90 degrees in the plane. So, let’s rotate our number by 90 degrees and arrive at $\infty$.

I just counted to infinity on my hands.

Of course, I can’t stop at a joke. I need to overanalyze it. There is something for scientists to learn from the error that makes this joke. The disregard for the type of objects and jumping between two different — and usually incompatible — ways of interpreting the same symbol is something that scientists, both modelers and experimentalists, have to worry about it.

If you want an actually funny joke of this type then I recommend the image of a ‘rigorous proof’ above that was tweeted by Moshe Vardi. My writen version was inspired by a variant on this theme mentioned on Reddit by jagr2808.

I will focus this post on the use of types from my experience with stoichiometry in physics. Units in physics allow us to perform sanity checks after long derivations, imagine idealized experiments, and can even suggest refinements of theory. These are all features that evolutionary game theory, and mathematical biology more broadly, could benefit from. And something to keep in mind as clinicians, biologists, and modelers join forces this week during the 5th annual IMO Workshop at the Moffitt Cancer Center.

## 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.

## Passive vs. active reading and personalization

As you can probably tell, dear reader, recently I have been spending too much time reading and not enough time writing. The blog has been silent. What better way to break this silence than to write a defense of reading? Well, sort of. It would not be much of an eye-opener for you — nor a challenge for me — to simply argue for reading. Given how you are consuming this content, you probably already think that the written word is a worthwhile medium. Given how I am presenting myself, I probably think the same. But are our actions really an endorsement of reading or just the form of communication we begrudgingly resort to because of a lack of better alternatives?

Ostensibly this post will be a qualified defense against an attack on reading by Roger Schank at Education Outrage. Although it is probably best to read it as just a series of reflections on my own experience.[1]

I will focus on the medium-independent aspects of learning that I think give weight to Schank’s argument: the distinction between passive and active learning, and the level of personalization. This will be followed next week by a tangent discussion on the importance of emotional aspects of the text, and close with some reflections on the role of literary value, historic context, and fiction in philosophical arguments. This last point is prompted more by my recent readings of Plato than by Schank. In other words, much like last year, I will rely on Socrates to help get me out of a writing slump.

Among the highlights of my recent visit to IMO were several stimulating discussions with Artem Kaznatcheev. I’m still thinking over my response to his recent post about reductionist versus operationalist approaches in math biology, which is very relevant to some of my current research. Meanwhile, at Artem’s suggestion, this post will discuss a reanalysis of the “cancer and bad luck” paper that spurred so many headlines at the start of this year. Whereas many others have written critiques of that paper’s statistical methods and interpretations, my colleagues and I instead tried fitting alternative models to the underlying data. We thus found ourselves revisiting a couple of celebrated scientific paradoxes.

To start this post, I will introduce you to Simpson’s paradox and Peto’s paradox. With these pair of paradoxes in mind, we’ll turn a critical eye to Tomasetti & Vogelstein (2015), and I will explain our reanalysis of their data set.

## Operationalizing the local environment for replicator dynamics

Recently, Jake Taylor-King arrived in Tampa and last week we were brainstorming some projects to work on together. In the process, I dug up an old idea I’ve been playing with as my understanding of the Ohtsuki-Nowak transform matured. The basic goal is to work towards an operational account of spatial structure without having to commit ourselves to a specific model of space. I will take replicator dynamics and work backwards from them, making sure that each term we use can be directly measured in a single system or abducted from the other measurements. The hope is that if we start making such measurements then we might see some empirical regularities which will allow us to link experimental and theoretical models more closely without having to make too many arbitrary assumptions. In this post, I will sketch the basic framework and then give an example of how some of the spatial features can be measured from a sample histology.