On Bayesianism, the Many Worlds Interpretation, and personal identity.

Some thoughts worked out in a letter to a friend, which is the kind of thing you do when off Twitter for a glorious week. (🧵)

“Chance is ignorance”—the Bayesian story; all probabilities represent states of mind, not states of the world. One *could* put (some) chances “in the world”, but let’s take Occam’s Razor seriously...
That the probability of a fair coin coming up heads is 50% simply means that marginalizing (tracing, as the physicists say) over the hidden facts leaves you, nearly, maximally ignorant of the outcome.
Quantum uncertainty (access below!) poses an apparent challenge to this story. There seems to be nothing to be ignorant about when it comes to (say) electron spin—there is nothing “inside” the object.
The electron is a simple object, in other words. So where does the uncertainty come from? One could follow David Wallace’s wonderful interpretation in terms of chaotic dynamics and decoherence, but let’s see if we can take another route...
It lies, finally, not in our lack of knowledge of what’s inside the electron (there is nothing there), but rather a lack of self-knowledge—we do not know which branch of the Universe we ended up in!
We lack knowledge, in other words, of the referent of a pronoun—which “I” I am. If you’re Saul Kripke, the rigid designator has detached—you must be re-baptized. For that reason, we should naturally refer to MWI as Born Again Quantum Theory (with the obvious pun).
The world is constantly forking, of course, but the vast majority of those forks are completely meaningless to us; they fall, for all intents and purposes, into the same equivalence class.
It’s only in rare cases (like those electrons at ANU) that we come directly into contact with these forks, where the equivalence class splits. Most classical uncertainty is simply further refining knowledge about the initial quantum state.
(I won’t tell you how my ANU quantum coin came up—ha ha! I know which branch we’re on, you don’t, but that’s just a classical, Bayesian matter.)
This is probably super-obvious to someone like @seanmcarroll, but there are two nice things about it.
First, it’s a nice way to get at the MWI from an unexpected direction. You don’t have to know about the Schrödinger Equation, decoherence, etc to realize the Universe *must* split.
That might help (depending on your philosophy of science) increase your confidence in the underlying metaphysical claims.
Second, it helps me understand why The Universe Splitter is a non-trivial device. It really does split universes... https://t.co/Uar00s1zmN
And it’s kind of cool to realize that it wasn’t until we learned to isolate and pay attention to quantum effects that the universes began to split!
Definitely a (classical) fact of the matter! The radioactive decay couples very quickly to the classical world—which means you’ve already split before you have time to wonder about his state. https://t.co/ffFcngLV3a
In any case, nearly all the forks that matter in human history happened long ago (at inflation, or perhaps reheating). I think. Can anyone think of a pre-twentieth century coupling to novel quantum uncertainty?
Most quantum effects are invisible to us—meaning that our ignorance of which universe we’re in is permanent, and therefore irrelevant. Perhaps when it comes to single-photon events—@DavidDeutschOxf’s frog in _Fabric of Reality_...
But did any decision, or memory, ever hang on the outcome on how a single photon interfered with itself? (Until today, when we basically, being humans, do it for the lulz!)
I’m a little worried I’m missing some massively obvious quantum influence, but one point in my favor is that this kind of world-splitting re-baptism took so long to discover. It’s not obvious at all.
And technically, we couldn’t know about it until Bell’s Inequality proved that there were no (relevant) hidden gears within the electron.
YES! I think this is meaningful. https://t.co/bKiU3y0cAX
It’s (nearly) all classical uncertainty. Those worlds exist, but are separated by a vast gulf—they split billions of years ago, well before any decision we might make.
Until someone uses the ANU device to hang some history on. There’s even a kind of odd utilitarian morality to it—if you are thinking about doing something bad, and hang it off ANU, you reduce the harm by a factor of two.
Nearly all personal identity splits happened (literal) eons ago. Laplace’s demon—we’re just discovering the consequences of the (decohered) initial conditions. https://t.co/pXX0YHIVJy
Those Universes separated so long ago that there’s no real sense in which Classical Simon1 and Classical Simon2 are comparable. You might as well line me up with Alpha Centauri Simon.
Nothing actually forks it. We’re emergent phenomena, our experiences equivalence classes over quantum states. ANU just happens (for the lulz) to split them, which I’m suggesting is actually super-rare in human history. https://t.co/hJ47tyaGIN
Definitely. My lamp tips over. That informs me about a sequence of classical events that goes back to the formation of the Earth, and much further. Now I know which path the atoms took after initial decoherence, but that was my ignorance. https://t.co/ig89rZCbcQ
Kripke semantics would say that a copy of the standard meter doesn’t have to “know” it’s not the standard meter for it to be distinct. https://t.co/Y6EzUTtgtP
This also helps me understand all those bits that ANU gathers but never serves to users. They (eventually) vanish (deleted for space), leaving no trace. Nobody will ever know how they turned out, and so our equivalence class can’t split.
Just realized that one source of universe-splitting started happening with (I think) cosmic ray errors in silicon. Where, in particular, the ray interacts is a quantum effect—causing one error over another, becoming visible to the operator.
You have to buy the Kripke story about how pronouns work. I, personally, think it’s intuitive and how I use them—but you may not! https://t.co/w5kHRVIc4K
I don’t know! I have Meillassoux on my desk (_After Finitude_) and would love any suggestions. https://t.co/JyY1yQsmzF
Oh, I think this works, yes. Sad, but true. Any happier examples? (Although in another sense, Max’s example *is* happy—there are yous that survive.) https://t.co/hErcyUtJ66

More from Simon DeDeo

This is a pretty valiant attempt to defend the "Feminist Glaciology" article, which says conventional wisdom is wrong, and this is a solid piece of scholarship. I'll beg to differ, because I think Jeffery, here, is confusing scholarship with "saying things that seem right".

The article is, at heart, deeply weird, even essentialist. Here, for example, is the claim that proposing climate engineering is a "man" thing. Also a "man" thing: attempting to get distance from a topic, approaching it in a disinterested fashion.

Also a "man" thing—physical courage. (I guess, not quite: physical courage "co-constitutes" masculinist glaciology along with nationalism and colonialism.)

There's criticism of a New York Times article that talks about glaciology adventures, which makes a similar point.

At the heart of this chunk is the claim that glaciology excludes women because of a narrative of scientific objectivity and physical adventure. This is a strong claim! It's not enough to say, hey, sure, sounds good. Is it true?

More from Data science

To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. 🧵👇

https://t.co/EwwOzgfDca : Deep Learning framework in Java that supports the whole cycle: from data loading and preprocessing to building and tuning a variety deep learning networks.

https://t.co/J4qMzPAZ6u Framework for defining machine learning models, including feature generation and transformations, as directed acyclic graphs (DAGs).

https://t.co/9IgKkSxPCq a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification.

https://t.co/EAqn2YngIE : TensorFlow Java API (experimental)
I have always emphasized on the importance of mathematics in machine learning.

Here is a compilation of resources (books, videos & papers) to get you going.

(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)

📘 Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong


Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.

📘 Pattern Recognition and Machine Learning

by Christopher Bishop

Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.

📘 The Elements of Statistical Learning

by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...

📘 Probability Theory: The Logic of Science

by E. T. Jaynes

Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.

You May Also Like

1. Project 1742 (EcoHealth/DTRA)
Risks of bat-borne zoonotic diseases in Western Asia

Duration: 24/10/2018-23 /10/2019

Funding: $71,500

2. Bat Virus Database
Access to the database is limited only to those scientists participating in our ‘Bats and Coronaviruses’ project
Our intention is to eventually open up this database to the larger scientific community

3. EcoHealth Alliance & DTRA Asking for Trouble
One Health research project focused on characterizing bat diversity, bat coronavirus diversity and the risk of bat-borne zoonotic disease emergence in the region.

4. Phelps, Olival, Epstein, Karesh - EcoHealth/DTRA

5, Methods and Expected Outcomes
(Unexpected Outcome = New Coronavirus Pandemic)
1/“What would need to be true for you to….X”

Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?

A thread, co-written by @deanmbrody:

2/ First, “X” could be lots of things. Examples: What would need to be true for you to

- “Feel it's in our best interest for me to be CMO"
- “Feel that we’re in a good place as a company”
- “Feel that we’re on the same page”
- “Feel that we both got what we wanted from this deal

3/ Normally, we aren’t that direct. Example from startup/VC land:

Founders leave VC meetings thinking that every VC will invest, but they rarely do.

Worse over, the founders don’t know what they need to do in order to be fundable.

4/ So why should you ask the magic Q?

To get clarity.

You want to know where you stand, and what it takes to get what you want in a way that also gets them what they want.

It also holds them (mentally) accountable once the thing they need becomes true.

5/ Staying in the context of soliciting investors, the question is “what would need to be true for you to want to invest (or partner with us on this journey, etc)?”

Multiple responses to this question are likely to deliver a positive result.