jupyter book

.gitignore

@@ -3,3 +3,13 @@
 /results.txt
 /paper/llm-cooperation.pdf
 /notebooks/Results.ipynb
+/jupyter-book/Results.ipynb
+/jupyter-book/_build/.doctrees/environment.pickle
+/jupyter-book/_build/.doctrees/intro.doctree
+/jupyter-book/_build/.doctrees/llm-cooperation.doctree
+/jupyter-book/_build/.doctrees/markdown.doctree
+/jupyter-book/_build/.doctrees/markdown-notebooks.doctree
+/jupyter-book/_build/.doctrees/notebooks.doctree
+/jupyter-book/_build/.doctrees/Results.doctree
+/jupyter-book/.ipynb_checkpoints/Results-checkpoint.ipynb
+/jupytext.toml

notebooks/Results.py → jupyter-book/Results.py

@@ -12,6 +12,9 @@
 #     name: python3
 # ---
 
+# %% [markdown]
+# # Results
+
 # %%
 import pandas as pd
 # %%

jupyter-book/_config.yml

@@ -0,0 +1,31 @@
+# Book settings
+# Learn more at https://jupyterbook.org/customize/config.html
+
+title: Investigating Emergent Goal-Like Behavior in Large Language Models using Experimental Economics
+author: Steve Phelps
+
+# Force re-execution of notebooks on each build.
+# See https://jupyterbook.org/content/execute.html
+execute:
+  execute_notebooks: force
+
+# Define the name of the latex output file for PDF builds
+latex:
+  latex_documents:
+    targetname: llm-cooperation.tex
+
+# Add a bibtex file so that we can create citations
+bibtex_bibfiles:
+  - llm-cooperation.bib
+
+# Information about where the book exists on the web
+repository:
+  url: https://github.com/executablebooks/jupyter-book  # Online location of your book
+  path_to_book: docs  # Optional path to your book, relative to the repository root
+  branch: master  # Which branch of the repository should be used when creating links (optional)
+
+# Add GitHub buttons to your book
+# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
+html:
+  use_issues_button: true
+  use_repository_button: true

jupyter-book/_toc.yml

@@ -0,0 +1,8 @@
+# Table of contents
+# Learn more at https://jupyterbook.org/customize/toc.html
+
+format: jb-book
+root: intro
+chapters:
+- file: llm-cooperation
+- file: Results

jupyter-book/intro.md

@@ -0,0 +1,23 @@
+As large language models (LLMs) continue to advance, understanding the emergent
+goal-like behaviors and cooperative tendencies of artificially generated agents
+becomes crucial for AI alignment research. This paper outlines an
+interdisciplinary research agenda that combines insights from cognitive
+science, artificial intelligence, and experimental economics to explore the
+cooperative and competitive dynamics of simulacra instantiated by LLM prompts.
+
+We propose a series of experimental economics simulations, including the
+Prisoner's Dilemma, public goods games, and other well-established frameworks,
+to evaluate the propensity of LLM-generated simulacra to cooperate under
+various conditions. These experiments will enable us to assess the goal-like
+behaviors that emerge, and whether these behaviors align with human values and
+cooperation norms.
+
+In addition to detailing the experimental design, we will discuss the potential
+implications of our findings for the development of AI alignment strategies and
+safe AGI. By elucidating the factors that govern cooperative behavior in
+LLM-generated simulacra, this research aims to contribute to the broader
+understanding of the emergent properties of AI systems and inform the design of
+models that better align with human values and societal goals.
+
+```{tableofcontents}
+```

paper/llm-cooperation.md → jupyter-book/llm-cooperation.md

@@ -1,35 +1,6 @@
----
-title: Investigating Emergent Goal-Like Behavior in Large Language Models using Experimental Economics
-author: Steve Phelps and Yvan Russell
-geometry: margin=2cm
-fontsize: 12pt
-date: April 6, 2023
-output: pdf_document
-bibliography: llm-cooperation.bib
----
 
 ## Abstract
 
-As large language models (LLMs) continue to advance, understanding the emergent
-goal-like behaviors and cooperative tendencies of artificially generated agents
-becomes crucial for AI alignment research. This paper outlines an
-interdisciplinary research agenda that combines insights from cognitive
-science, artificial intelligence, and experimental economics to explore the
-cooperative and competitive dynamics of simulacra instantiated by LLM prompts.
-
-We propose a series of experimental economics simulations, including the
-Prisoner's Dilemma, public goods games, and other well-established frameworks,
-to evaluate the propensity of LLM-generated simulacra to cooperate under
-various conditions. These experiments will enable us to assess the goal-like
-behaviors that emerge, and whether these behaviors align with human values and
-cooperation norms.
-
-In addition to detailing the experimental design, we will discuss the potential
-implications of our findings for the development of AI alignment strategies and
-safe AGI. By elucidating the factors that govern cooperative behavior in
-LLM-generated simulacra, this research aims to contribute to the broader
-understanding of the emergent properties of AI systems and inform the design of
-models that better align with human values and societal goals.
 
 ## Motivation and background
 
@@ -266,5 +237,4 @@ Payoff structure: The experiment's payoff structure incentivizes cooperation, co
 
 Although the specific payoffs in the experiment differ from the classic Prisoner's Dilemma, the structure and the strategic decision-making process are quite similar. The experiment can be seen as a variant of the Prisoner's Dilemma, allowing researchers to study trust, cooperation, and competition in a controlled setting.
 
-## Bibliography
 

jupyter-book/markdown-notebooks.md

@@ -0,0 +1,53 @@
+---
+jupytext:
+  formats: md:myst
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.11.5
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
+# Notebooks with MyST Markdown
+
+Jupyter Book also lets you write text-based notebooks using MyST Markdown.
+See [the Notebooks with MyST Markdown documentation](https://jupyterbook.org/file-types/myst-notebooks.html) for more detailed instructions.
+This page shows off a notebook written in MyST Markdown.
+
+## An example cell
+
+With MyST Markdown, you can define code cells with a directive like so:
+
+```{code-cell}
+print(2 + 2)
+```
+
+When your book is built, the contents of any `{code-cell}` blocks will be
+executed with your default Jupyter kernel, and their outputs will be displayed
+in-line with the rest of your content.
+
+```{seealso}
+Jupyter Book uses [Jupytext](https://jupytext.readthedocs.io/en/latest/) to convert text-based files to notebooks, and can support [many other text-based notebook files](https://jupyterbook.org/file-types/jupytext.html).
+```
+
+## Create a notebook with MyST Markdown
+
+MyST Markdown notebooks are defined by two things:
+
+1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed).
+   See the YAML at the top of this page for example.
+2. The presence of `{code-cell}` directives, which will be executed with your book.
+
+That's all that is needed to get started!
+
+## Quickly add YAML metadata for MyST Notebooks
+
+If you have a markdown file and you'd like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command:
+
+```
+jupyter-book myst init path/to/markdownfile.md
+```

jupyter-book/markdown.md

@@ -0,0 +1,55 @@
+# Markdown Files
+
+Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or
+in regular markdown files (`.md`), you'll write in the same flavor of markdown
+called **MyST Markdown**.
+This is a simple file to help you get started and show off some syntax.
+
+## What is MyST?
+
+MyST stands for "Markedly Structured Text". It
+is a slight variation on a flavor of markdown called "CommonMark" markdown,
+with small syntax extensions to allow you to write **roles** and **directives**
+in the Sphinx ecosystem.
+
+For more about MyST, see [the MyST Markdown Overview](https://jupyterbook.org/content/myst.html).
+
+## Sample Roles and Directives
+
+Roles and directives are two of the most powerful tools in Jupyter Book. They
+are kind of like functions, but written in a markup language. They both
+serve a similar purpose, but **roles are written in one line**, whereas
+**directives span many lines**. They both accept different kinds of inputs,
+and what they do with those inputs depends on the specific role or directive
+that is being called.
+
+Here is a "note" directive:
+
+```{note}
+Here is a note
+```
+
+It will be rendered in a special box when you build your book.
+
+Here is an inline directive to refer to a document: {doc}`markdown-notebooks`.
+
+
+## Citations
+
+You can also cite references that are stored in a `bibtex` file. For example,
+the following syntax: `` {cite}`holdgraf_evidence_2014` `` will render like
+this: {cite}`holdgraf_evidence_2014`.
+
+Moreover, you can insert a bibliography into your page with this syntax:
+The `{bibliography}` directive must be used for all the `{cite}` roles to
+render properly.
+For example, if the references for your book are stored in `references.bib`,
+then the bibliography is inserted with:
+
+```{bibliography}
+```
+
+## Learn more
+
+This is just a simple starter to get you started.
+You can learn a lot more at [jupyterbook.org](https://jupyterbook.org).

jupyter-book/references.bib

@@ -0,0 +1,56 @@
+---
+---
+
+@inproceedings{holdgraf_evidence_2014,
+	address = {Brisbane, Australia, Australia},
+	title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}},
+	booktitle = {International {Conference} on {Cognitive} {Neuroscience}},
+	publisher = {Frontiers in Neuroscience},
+	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.},
+	year = {2014}
+}
+
+@article{holdgraf_rapid_2016,
+	title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility},
+	volume = {7},
+	issn = {2041-1723},
+	url = {http://www.nature.com/doifinder/10.1038/ncomms13654},
+	doi = {10.1038/ncomms13654},
+	number = {May},
+	journal = {Nature Communications},
+	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.},
+	year = {2016},
+	pages = {13654},
+	file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf}
+}
+
+@inproceedings{holdgraf_portable_2017,
+	title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science},
+	volume = {Part F1287},
+	isbn = {978-1-4503-5272-7},
+	doi = {10.1145/3093338.3093370},
+	abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.},
+	booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}},
+	author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.},
+	year = {2017},
+	keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy}
+}
+
+@article{holdgraf_encoding_2017,
+	title = {Encoding and decoding models in cognitive electrophysiology},
+	volume = {11},
+	issn = {16625137},
+	doi = {10.3389/fnsys.2017.00061},
+	abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.},
+	journal = {Frontiers in Systems Neuroscience},
+	author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.},
+	year = {2017},
+	keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials}
+}
+
+@book{ruby,
+  title     = {The Ruby Programming Language},
+  author    = {Flanagan, David and Matsumoto, Yukihiro},
+  year      = {2008},
+  publisher = {O'Reilly Media}
+}

jupyter-book/requirements.txt

@@ -0,0 +1,3 @@
+jupyter-book
+matplotlib
+numpy