Main Sequence:
- An Introduction To Prisoner’s Dilemma
- Nash Equilibria
- Evolutionary Game Theory
- [Excerpt] The Tragedy of Commonsense Morality
Decision Theory extensions:
Final Report for UW Applied Algorithms class
Category sequence
[Sequence] Causal Inference
Background Material
Rubin’s Causal Framework:
Pearl’s Framework:
- d-separation
- Causal Inference in R
Applications
External Resources
- For an intro to Pearlian causality, I recommend this powerpoint and this talk.
- This SEP section explores the philosophical underpinnings in more depth.
- Contrast Pearl vs Rubin’s causal model here: ([1] [2] [3] [4]).
- SWIGs: Richardson’s attempt to unite Pearl & Rubin’s contrasting traditions.
[Sequence] Graphical Models
Hi! So, I’m trying out something new. It’s no longer summer, which means I have much less time to follow my own research paths (because class). But this quarter, I want to “live blog” my insights as I go along. The class is:
- Class Site: http://j.ee.washington.edu/~bilmes/classes/ee512a_fall_2014/
- Video Lectures: https://www.youtube.com/channel/UCvPnLF7oUh4p-m575fZcUxg
Lecture Posts
Exact Inference On Approximate Problem:
- Chordal Graphs [Graphic]. Trees aren’t the only thing supporting perfect elimination orderings (PEOs). An infographic describing the chain of mathematical proofs which ultimately establish chordal graphs as supporting PEOs via the concept of minimal separators.
- Family vs. Universe. Graphical models conjoin graphs with a set of rules, which sample & thereby constrain the entire universe U (solution-space). The constrained solution-space is called a family F.
- Designer Families. A concrete walkthrough of family design, including which heuristics are important to bear in mind.
- Murderous Cliques. How cliques give us insight into making good variable-elimination-ordering choices.
- Murderous Messages. Graphs with no cycles (trees) allow us to re-interpret variable elimination epistemically, as a form of message passing.
- Tree, Generalized. Introduces k-trees, and discusses the relationship between trees, k-trees, and chordal graphs.
Approximate Inference on Exact Problems:
- Exponential Families [Graphic]. From conjugate duality to variational inference.
Final Project Posts
[Sequence] Breakdown Of Will
In this sequence, we will be exploring this précis of this book. Specifically, we will be exploring the implications of akrasia (the act of behaving against one’s own desires).
Preliminary Posts
- An Introduction To Prisoner’s Dilemma. Sketches game theory’s most well-known result, the Prisoner’s Dilemma.
Content Summary
- An Introduction To Hyperbolic Discounting. Based on Chapter 1-3. Introduces the concepts of akrasia and utility, proceeds to model akrasia as a symptom of discount curves shaped like hyperbolas.
- Willpower As Preference Bundling. Based on Chapter 5. Discusses how willpower (a therapy against akrasia) comes to make our successive selves consistent with one another. Willpower is presented as the brain subtly manipulating how it instantiates hyperbolic discount functions.
- Personal Rule Feedback Loops. Based on Chapter 6. Builds a mental model of preference bundling, and explores the recursive nature of personal rules.
- Iterated Schizophrenic’s Dilemma. Based on Chapter 6. Grounds Ainslee’s account of willpower (and preference bundling) in a modified form of Iterated Prisoner’s Dilemma.
- Against Willpower. Based on Chapter 9. If willpower is preference bundling, then its mechanisms become available for scrutiny. Ainslee here locates four surprising implications of his theory of willpower, which suggest that it is not the unilaterally-beneficial tool that we might suspect.
[Sequence] Bayesian Statistics
Prelminaries
Core Sequence
- An Introduction To Bayesian Inference.
- Bayesian Statistics [6 presentations]
- Designing a Plausibility Calculus
- Surveys this overview of Cox’s theorem.
[Sequence] Deserialized Cognition
[Sequence] Hiding Data From Ourselves
This series discusses a startling, and some would say anti-democratic, idea: hiding data from ourselves may be an effective way to move faster than science.
This sequence is composed of four articles.
- About A Noise. Discusses the origins of noise within data.
- Data Partitioning: Bias vs Variance. Covers a core idea in the machine learning community: building fences around data sources protects machines from underestimating noise.
- Overfitting: Failure Mode of Meat Science. Surveys evidence of overfitting within human scientific communities (“meat science”). Connects to philosophy of science, and motivates the application of data partitioning into the human realm.
- [Planned] Reconstructing The Shoulders Of Giants. A look at what data partitioning would look like in practice, including how I am applying it on this blog.
[Sequence] Decision Making In Chess
Designed for people who know little more than how the chess pieces move, this series introduces the game, and scans its results for lessons we can apply to how we understand life more generally.
- An Introduction To Chess. Surveys chess culture and illustrates chess evaluation, bringing attention to the often-subconscious nature of the latter.
- Decision Trees In Chess. Explores how the decision trees and the minimax algorithm can capture the entirety of chess gameplay.
- The Chess SuperTree. Having exploring single-move decisions, this post zooms out to consider the game of chess as a whole – its complete game tree.
- The Psychology Of Chess. Compares these computer science & game theoretic approaches to chess with hints on how the brain uses somatic markers & heuristics to decide more efficiently.
[Sequence] Cognitive Modularity
Context
Main Sequence
- The Argument From Design
- The Argument From Animals
- The Argument From Computational Tractability
- The Argument From Explananda Diversity
- The Argument From Lesions
- The Argument From Small-World Network Topography
[Sequence] C.S. Peirce & Pragmatism
Charles Sanders Peirce (1839-1914) has been called “the father of pragmatism”, “America’s greatest logician”, and “the most original thinker of his time”. He founded the field of semiotics (the study of signs, which I touch on here), invented abduction (inference to the best explanation), and anticipated the work of geniuses like Georg Cantor (mathematics of infinity), Claude Shannon (information theory), and Ernst Zermelo (set theory) by decades.
Peirce met with a fate not unusual for thinkers of caliber: much of his work only came to be fully appreciated posthumously. His writings were never consolidated in book form, and remained largely disorganized until collated into various anthologies.
An autobiographical snippet from a paper entitled Concerning The Author:
My book will have no instruction to impart to anybody. Like a mathematical treatise, it will suggest certain ideas and certain reasons for holding them true; but then, if you accept them, it must be because you like my reasons, and the responsibility lies with you. Man is essentially a social animal: but to be social is one thing, to be gregarious is another: I decline to serve as shepherd. My book is meant for people who want to find out; people who want philosophy ladled out to them can go elsewhere. There are philosophy soup shops at every corner, thank God!
The development of my ideas has been the industry of thirty years. I did not know as I ever should get to publish them, their ripening seemed so slow. But the harvest time has come, at last, and to me that harvest seems a wild one, but of course it is not I who have to pass judgment. It is not quite you, either, individual reader; it is experience and history.
For years in the course of this ripening process, I used to collect my ideas under the designation fallibilism; and indeed the first step toward finding out is to acknowledge you do not satisfactorily know already; so that no blight can so surely arrest all intellectual growth as the blight of cocksureness; and ninety-nine out of every hundred good heads are reduced to impotence by that malady – of whose inroads they are most strangely unaware!
Indeed, out of a contrite fallibilism, combined with a high faith in the reality of knowledge, and an intense desire to find things out, all my philosophy has always seemed to me to grow ….
In many ways, Peirce and I march to the beat of the same drum…
Reviewed essays:
Fewer Lacunae 