## Limits of prediction: stochasticity, chaos, and computation

Some of my favorite conversations are about prediction and its limits. For some, this is purely a practical topic, but for me it is a deeply philosophical discussion. Understanding the limits of prediction can inform the philosophies of science and mind, and even questions of free-will. As such, I wanted to share with you a World Science Festival video that THEREALDLB recently posted on /r/math. This is a selected five minute clip called “What Can’t We Predict With Math?” from a longer one and a half hour discussion called “Your Life By The Numbers: ‘Go Figure'” between Steven Strogatz, Seth Lloyd, Andrew Lo, and James Fowler. My post can be read without watching the panel discussion or even the clip, but watching the clip does make my writing slightly less incoherent.

I want to give you a summary of the clip that focuses on some specific points, bring in some of discussions from elsewhere in the panel, and add some of my commentary. My intention is to be relevant to metamodeling and the philosophy of science, but I will touch on the philosophy of mind and free-will in the last two paragraphs. This is not meant as a comprehensive overview of the limits of prediction, but just some points to get you as excited as I am about this conversation.

## Should we be astonished by the Principle of “Least” Action?

As one goes through more advanced expositions of quantum physics, the concept of action is gradually given more importance, with it being considered a fundamental piece in some introductions to Quantum Field Theory (Zee, 2003) through the use of the path integral approach. The basic idea behind using the action is to assign a number to each possible state of a system. The function that does so is named the Lagrangian function, and it encodes the physics of the system (i.e. how do different parts of the system affect each other). Then, to a trajectory of a system we associate the integral of this number over all the states in the trajectory. This contrasts with the classical Newtonian approach, where we study a system by specifying all the possible ways in which parts of the system exercise forces on each other (i.e. affect each other’s acceleration). Using the action usually results in nicer mathematics, while I’d argue that the Newtonian approach requires less training to feel intuitive.

In many of the expositions of the use of action in physics (see e.g. this one), I perceive an attempt at transmitting wonder about the world being such that it minimizes a function on its trajectory. This has indeed been the case historically, with Maupertuis supposed to have considered action minimization (and the corresponding unification of minimization principles between optics and mechanics) as the most definite proof available to him of the existence of God. However, along the spirit of this stack exchange question, I never really understood why such a wonder should be felt, even setting aside the fact that it assumes that our equations “are” the world, a perspective that Artem has criticized at length before.

## Philosophy of Science and an analytic index for Feyerabend

Throughout my formal education, the history of science has been presented as a series of anecdotes and asides. The philosophy of science, encountered even less, was passed down not as a rich debate and on-going inquiry but as a set of rules that best be followed. To paraphrase Gregory Radick, this presentation is mere propaganda; it is akin to learning the history of a nation from its travel brochures. Thankfully, my schooling did not completely derail my learning, and I’ve had an opportunity to make up for some of the lost time since.

One of the philosophers of science that I’ve enjoyed reading the most has been Paul Feyerabend. His provocative writing in Against Method and advocation for what others have called epistemological anarchism — the rejection of any rules of scientific methodology — has been influential to my conception of the role of theorists. Although I’ve been meaning to write down my thoughts on Feyerabend for a while, now, I doubt that I will bring myself to do it anytime soon. In the meantime, dear reader, I will leave you with an analytic index consisting of links to the thoughts of others (interspersed with my typical self-links) that discuss Feyerabend, Galileo (his preferred historic case study), and consistency in science.

## Experimental and comparative oncology: zebrafish, dogs, elephants

One of the exciting things about mathematical oncology is that thinking about cancer often forces me to leave my comfortable arm-chair and look at some actually data. No matter how much I advocate for the merits of heuristic modeling, when it comes to cancer, data-agnostic models take second stage to data-rich modeling. This close relationship between theory and experiment is of great importance to the health of a discipline, and the MBI Workshop on the Ecology and Evolution of Cancer highlights the health of mathematical oncology: mathematicians are sitting side-by-side with clinicians, biologists with computer scientists, and physicists next to ecologists. This means that the most novel talks for me have been the ones highlighting the great variety of experiments that are being done and how they inform theory.In this post I want to highlight some of these talks, with a particular emphasis on using the study of cancer in non-humans to inform human medicine.

## Colon cancer, mathematical time travel, and questioning the sequential mutation model.

On Saturday, I arrived in Columbus, Ohio for the the MBI Workshop on the Ecology and Evolution of Cancer. Today, our second day started. The meeting is an exciting combination of biology-minded mathematicians and computer scientists, and math-friendly biologist and clinicians. As is typical of workshops, the speakers of the first day had an agenda of setting the scope. In this case, the common theme was to question and refine the established model as embodied by Hannah & Weinberg’s (2000) hallmarks of cancer outlined. For an accessible overview of these hallmarks, I recommend Buddhini Samarasinghe’s series of posts. I won’t provide a full overview of the standard model, but only focus on the aspects at issue for the workshop participants. In the case of the first two speakers, the standard picture in question was the sequential mutation model. In the textbook model of cancer, a tumour acquires the hallmark mutations one at a time, with each subsequent mutation sweeping to fixation. Trevor Graham and Darryl Shibata presented their work on colon cancer, emphasizing tumour heterogeneity, and suggesting that we might have to rewrite the sequential mutation page of our Cancer 101 textbooks to better discuss the punctuated model.

## Defining empathy, sympathy, and compassion

When discussing the evolution of cooperation, questions about empathy, sympathy, and compassion are often close to mind. In my computational work, I used to operationalize-away these emotive concepts and replace them with a simple number like the proportion of cooperative interactions. This is all well and good if I want to confine myself to a behaviorist perspective, but my colleagues and I have been trying to move to a richer cognitive science viewpoint on cooperation. This has confronted me with the need to think seriously about empathy, sympathy, and compassion. In particular, Paul Bloom‘s article against empathy, and a Reddit discussion on the usefulness of empathy as a word has reminded me that my understanding of the topic is not very clear or critical. As such, I was hoping to use this opportunity to write down definitions for these three concepts and at the end of the post sketch a brief idea of how to approach some of them with evolutionary modeling. My hope is that you, dear reader, would point out any confusion or disagreement that lingers.

## Transcendental idealism and Post’s variant of the Church-Turing thesis

One of the exciting things in reading philosophy, its history in particular, is experiencing the tension between different schools of thought. This excitement turns to beauty if a clear synthesis emerges to reconcile the conflicting ideas. In the middle to late 18th century, as the Age of Enlightenment was giving way to the Romantic era, the tension was between rationalism and empiricism and the synthesis came from Immanuel Kant. His thought went on to influence or directly shape much of modern philosophy, and if you browse the table of contents of philosophical journals today then you will regularly encounter hermeneutic titles like “Kant on <semi-obscure modern topic>”. In this regard, my post is in keeping with modern practice because it could have very well been titled “Kant on computability”.

As stressed before, I think that it is productive to look at important concepts from multiple philosophical perspectives. The exercise can provide us with an increased insight into both the school of thought that is our eyes, and the concept that we behold. In this case, the concept is the Church-Turing thesis that states that anything that is computable is computable by a Turing machine. The perspective will be of (a kind of) cognitivism — thought consists of algorithmic manipulation of mental states. This perspective that can often be read directly into Turing, although Copeland & Shagrir (2013) better described him as a pragmatic noncognitivist. Hence, I prefer to attribute this view to Emil Post. Also, it would be simply too much of a mouthful to call it the Post-Turing variant of the Church-Turing thesis.

## Weapons of math destruction and the ethics of Big Data

I don’t know about you, dear reader, but during my formal education I was never taught ethics or social consciousness. I even remember sitting around with my engineering friends that had to take a class in ethics and laughing at the irrelevance and futility of it. To this day, I have a strained relationship with ethics as a branch of philosophy. However, despite this villainous background, I ended up spending a lot of time thinking about cooperation, empathy, and social justice. With time and experience, I started to climb out of the Dunning-Kruger hole and realize how little I understood about being a useful member of society.

One of the important lessons I’ve learnt is that models and algorithms are not neutral, and come with important ethical considerations that we as computer scientists, physics, and mathematicians are often ill-equipped to see. For exploring the consequences of this in the context of the ever-present ‘big data’, Cathy O’Neil’s blog and alter ego mathbabe has been extremely important. This morning I had the opportunity to meet Cathy for coffee near her secret lair on the edge of Lower Manhattan. From this writing lair, she is working on her new book Weapons of Math Destruction and “arguing that mathematical modeling has become a pervasive and destructive force in society—in finance, education, medicine, politics, and the workplace—and showing how current models exacerbate inequality and endanger democracy and how we might rein them in”.

I can’t wait to read it!

In case you are impatient like me, I wanted to use this post to share a selection of Cathy’s articles along with my brief summaries for your browsing enjoyment: