Principles of biological computation: from circadian clock to evolution

For the final — third — day of the Santa Fe Institute workshop on “What is Biological Computation?” (11 – 13 September) organized by Albert Kao, Jessica Flack, and David Wolpert, we opened the floor to short impormptu talks from all the participants. The result was 21 presentations organized in 4 sessions. As with my posts on the previous two days of this workshop (Day 1: Elements of biological computation & stochastic thermodynamics of life; Day 2: The science and engineering of biological computation: from process to software to DNA-based neural networks), I want to briefly touch on all the presentations from the closing day in this post and the following. But this time I won’t follow the chronological order, and instead regroup slightly. In this post I’ll cover about half the talks, and save the discussion of collective computation for next week.

If you prefer my completely raw, unedited impressions in a series of chronological tweets, then you can look at the threads for the three days: Wednesday (14 tweets), Thursday (15 tweets), and Friday (31 tweets).

As before, it is important to note that this is the workshop through my eyes. So this retelling is subject to the limits of my understanding, notes, and recollection. This is especially distorting for this final day given the large number of 10 minute talks.

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The science and engineering of biological computation: from process to software to DNA-based neural networks

In the earlier days of TheEGG, I used to write extensively about the themes of some of the smaller conferences and workshops that I attended. One of the first such workshops I blogged about in detail was the 2nd workshop on Natural Algorithms and the Sciences in May 2013. That spawned an eight post series that I closed with a vision for a path toward an algorithmic theory of biology. In the six years since, I’ve been following that path. But I have fallen out of the habit of writing summary posts about the workshops that I attend.

View from the SFISince my recent trip to the Santa Fe Institute for the “What is biological computation?” workshop (11 – 13 September 2019) brought me full circle in thinking about algorithmic biology, I thought I’d rekindle the habit of post-workshop blogging. During this SFI workshop — unlike the 2013 workshop in Princeton — I was live tweeting. So if you prefer my completely raw, unedited impressions in tweet form then you can take a look at those threads for Wednesday (14 tweets), Thursday (15 tweets), and Friday (31 tweets). Last week, I wrote about the first day (Wednesday): Elements of biological computation & stochastic thermodynamics of life.

This week, I want to go through the shorter second day and the presentations by Luca Cardelli, Stephanie Forrest, and Lulu Qian.

As before, it is also important to note that this is the workshop through my eyes. So this retelling is subject to the limits of my understanding, notes, and recollection. And as I procrastinate more and more on writing up the story, that recollection becomes less and less accurate.

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Elements of biological computation & stochastic thermodynamics of life

This week, I was visiting the Santa Fe Institute for a workshop organized by Albert Kao, Jessica Flack, and David Wolpert on “What is biological computation?” (11 – 13 September 2019). It was an ambitious question and I don’t think that we were able to answer it in just three days of discussion, but I think that we all certainly learnt a lot.

At least, I know that I learned a lot of new things.

The workshop had around 34 attendees from across the world, but from the reaction on twitter it seems like many more would have been eager to attend also. Hence, both to help synchronize the memory networks of all the participants and to share with those who couldn’t attend, I want to use this series of blog post to jot down some of the topics that were discussed at the meeting.

During the conference, I was live tweeting. So if you prefer my completely raw, unedited impressions in tweet form then you can take a look at those threads for Wednesday (14 tweets), Thursday (15 tweets), and Friday (31 tweets). The workshop itself was organized around discussion, and the presentations were only seeds. Unfortunately, my live tweeting and this post are primarily limited to just the presentations. But I will follow up with some synthesis and reflection in the future.

Due to the vast amount discussed during the workshop, I will focus this post on just the first day. I’ll follow with posts on the other days later.

It is also important to note that this is the workshop through my eyes. And thus this retelling is subject to the limits of my understanding, notes, and recollection. In particular, I wasn’t able to follow the stochastic thermodynamics that dominated the afternoon of the first day. And although I do provide some retelling, I hope that I can convince one of the experts to provide a more careful blog post on the topic.

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Rationality, the Bayesian mind and their limits

Bayesianism is one of the more popular frameworks in cognitive science. Alongside other similar probalistic models of cognition, it is highly encouraged in the cognitive sciences (Chater, Tenenbaum, & Yuille, 2006). To summarize Bayesianism far too succinctly: it views the human mind as full of beliefs that we view as true with some subjective probability. We then act on these beliefs to maximize expected return (or maybe just satisfice) and update the beliefs according to Bayes’ law. For a better overview, I would recommend the foundations work of Tom Griffiths (in particular, see Griffiths & Yuille, 2008; Perfors et al., 2011).

This use of Bayes’ law has lead to a widespread association of Bayesianism with rationality, especially across the internet in places like LessWrong — Kat Soja has written a good overview of Bayesianism there. I’ve already written a number of posts about the dangers of fetishizing rationality and some approaches to addressing them; including bounded rationality, Baldwin effect, and interface theory. I some of these, I’ve touched on Bayesianism. I’ve also written about how to design Baysian agents for simulations in cognitive science and evolutionary game theory, and even connected it to quasi-magical thinking and Hofstadter’s superrationality for Kaznatcheev, Montrey & Shultz (2010; see also Masel, 2007).

But I haven’t written about Bayesianism itself.

In this post, I want to focus on some of the challenges faced by Bayesianism and the associated view of rationality. And maybe point to some approach to resolving them. This is based in part of three old questions from the Cognitive Sciences StackExhange: What are some of the drawbacks to probabilistic models of cognition?; What tasks does Bayesian decision-making model poorly?; and What are popular rationalist responses to Tversky & Shafir?

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