nikoleta-v3/thesis

A reposity for my PhD thesis

Understanding responses to environments for the Prisoner's Dilemma: A machine learning approach

This thesis contains the source code and the Latex documents for the thesis titled ``Understanding responses to environments for the Prisoner's Dilemma: A machine learning approach'' submitted in fulfillment of the requirements for the degree of Doctor of Philosophy.

Cloning

To clone the repository locally run the following command:

$ git clone  --recurse-submodules https://github.com/Nikoleta-v3/Thesis.git

Note that command includes the option --recurse-submodules. This is because there are several submodules to this repository.

Environment and requirements

All code for this thesis is in Python with all versions specified in environment.yml.

To create and activate the environment run:

$ conda env create -f environment.yml
$ source activate thesis

Compiling the thesis document

Once the repository and the submodules have been cloned locally, and the conda environment is activated run the following command to compile the written document:

$ inv compile

thesis's Issues

Young to OR 20 Conference

It would be interesting to give a talk here. I could talk about:

• Game Theory
• IPD
• Axelrod. Give an example of the spatial tournaments
• The db I made

Link : http://www.theorsociety.com/Pages/Conferences/YOR20/YOR20.aspx

Status Report 16-03-2017

Meta Tournament Analysis

Here is the pull request: Nikoleta-v3/meta-analysis-of-prisoners-dilemma-tournaments#4
I addressed the code efficiency comments you gave me.

Concerning the analysis:

• Bins and Violin plots: https://github.com/Nikoleta-v3/axlml/blob/nick/src/Bins%20at%20Strategies%20Level.ipynb
  Not much information is given from here. We have to play around with the number of bins a bit.

• Hexbins (Geraint and his great ideas for plotting): https://github.com/Nikoleta-v3/axlml/blob/nick/src/Hexbin%20at%20Strategies%20Level.ipynb

• The scatter plot you have seen before: https://github.com/Nikoleta-v3/axlml/blob/nick/src/Scatter%20Plots%20at%20Strategies%20Level.ipynb

• Some visualisation at the tournament level: https://github.com/Nikoleta-v3/axlml/blob/nick/src/Tournament%20Level.ipynb

The alpha go paper

https://www.dropbox.com/s/sbgbuz2sdzwuib9/2016%20-%20Silver%20et%20al.%20-%20Mastering%20the%20game%20of%20Go%20with%20deep%20neural%20networks%20and%20tree%20search.pdf?dl=0

Agenda 09-21-2017

Literature Review.

Following our discussion and the comment on the PR:

• I have address the trivial ones and push in working_article: Nikoleta-v3/bibliometric-study-of-the-prisoners-dilemma#11
• I have created a new branch re-structure where I am working on the new structure as we have discussed. https://github.com/Nikoleta-v3/Literature-Article/tree/re-structure
  I will work you through the new structure tomorrow and I have started working sections for the analysis.

Optimisation of memory one

Working on Resultant Theory. I have looked at several articles including,

• http://comet.lehman.cuny.edu/vpan/pdf/DEKPalg.pdf
• http://www.ams.org/journals/mcom/2005-74-249/S0025-5718-04-01722-3/S0025-5718-04-01722-3.pdf
• http://www4.ncsu.edu/~kaltofen/bibliography/89/CKL89.pdf
• https://ac.els-cdn.com/S0747717108800120/1-s2.0-S0747717108800120-main.pdf?_tid=89bb205e-cdfd-11e7-b246-00000aacb361&acdnat=1511187628_65d30f142e9fc8d6e9266e2887256208
• http://isc.tamu.edu/resources/preprints/1996/1996-02.pdf

So far I have read about Sylvester's resultant, Bezout's, Dixon's, Macaulay u -resultant and GCP.
Macaulay u -resultant is the most common used in literature.

Newsletter

For the FaceTAV event: https://docs.google.com/document/d/1CXIcxGCFHUA54PKJicVlAFebfDFAfW2i_GqbYaD2FGA/edit?usp=sharing

To discuss

• the poster (for the Southampton one).
• my OR talk.
• A PGR day event I am helping with SIAM.

List of papers about structured strategies

Take a look at: https://www.dropbox.com/s/nmdq3elsy1h974k/2006%20-%20Ashlock%2C%20Ashlock%20-%20Changes%20in%20Prisoner%20%E2%80%99%20s%20Dilemma%20Strategies%20Over%20Evolutionary%20Time%20With%20Different%20Population%20Sizes.pdf?dl=0

Came to this paper after reading that paper for James' work. In this paper they give a nice list of papers that train strategies in different representations:

• Neural nets
• Lookup tables
• Finite state machines
• Probabilities

I think this is a good place to start your literature review. :)

Natcor

Did we discuss NATCOR.

Here are the dates for 2017:

Stochastic Modelling
3rd - 7th April 2017

Simulation
10th - 14th July 2017

Combinatorial Optimisation
4th - 8th September 2017

There is a discussion about funding at the moment, I am thinking that I'd like you to attend all of them (they're as useful/less as anything else...). If you were to only do 2, which one would you want to drop?

Potential conferences

#2016-2017

Keep an eye on PyData London: london.pydata.org/

• Lanaster game theory conference ~ November 2016:
  www.lancaster.ac.uk/lums/economics/research/seminars-and-conferences/game-theory-conference/
• PyCon Namibia ~ February
• Euroscipy ~ August: www.euroscipy.org/
• PyCon UK ~ September
• Maybe - Summer School on Network Theory ~ June:
  sites.google.com/site/cigneworkshop2017/
• Maybe - Research Workshop in honor of Ehud Kalai ~ July:
  sites.google.com/site/igtconf2017/
• Stony brook conference for game theory ~ July:
  www.gtcenter.org/

Keep an eye on www.gametheorysociety.org/
(possibly become member).

Agenda 03-05-2017

Optimisation of Memory One Strategies

We are now moving to tex. 1. move all the introduction stuff to text. structure can be found here: https://github.com/drvinceknight/Optimisation-of-Memory-One-strategies/issues/5
2. Look at simulation vs analytical. All the work that we have done, defining the utility function etc, has been done analytically following the work of Press and Dyson. Because we have in hands Axelrod, a simulation tool, we will test our results comparing to the simulation. Thus, 3. create plots and tables with the difference.

Once we have shown that our formulation holds, we move to the next chapter. Because our function is non convex/concave, in order to understand it we are looking at the following cases:

• purely random
• reactive
• other 1/2 memory
• memory one.

Now we are focusing on the purely random players utility. 4. identify the cases that the plots indicate, do that by summarising everything we have talked with Vince and the carry the same work to the quadratic.

Try to prove that for our limits [0, 1] the cost function is always quadratic ?

Reading & Writing

Things need to be read: Everything

Things need to be written:

• Literature article
• Marc article
• report

*Let's impress Vince 🎉 *

For my ``free time''

• Rhino work
• Game theory revision

Start building literature review dataset

• Start with early papers
• Keep a data base:

title, authors, year, abstract, keywords, subject, etc

Work on: Axelrod-Python/Axelrod#225

Agenda 12-04-2017

Optimisation of Memory One Strategies

Run parameter sweep.

Results with the following parameters (number of q, p 5, lambda 20) ended up being enormous. Currently, the script is being run again with the parameters (number of q, p 4, lambda 5).

From the results, which functions (give p) are convex should be identified. To do that writing a
1. function that returns certain, correct, plots.

The functions that are convex should be save in a different 2. convex data frame.

---

p	convex	min_diff

Once we know which functions are convex investigate 4. why are they convex.
Run the parameters sweep 5. for only the give p that the function is convex.

Write first draft.

Create a 1. new main notebook which will be the draft of the article. Thus, the old one
should be 2. rename. The rest of the notebooks (apart from the new main) are Appendices.
3. Rename to A.0.1 why symbolic. Continue writing.

Meta tournament Analysis

No all issues have been addressed from previous Agenda #26. (1. close issue)

Moving forward, the same plots should be looked at 2. four tournament type level.

Literature Review Article

1. Continue reading and writing.

Perform 2. supervised machine learning. I have identified 3 trends within the list of articles:

• social behaviour
• strategic rules
• strategies robustness

Status Report 20-01-2017

Research

1. Cleaning Data

---

In order to clean the data I have done the following:

• For fields Author, Abstract and Title if values were None were replaced manually.
• Syntax Errors. Symbols in Titles mainly
• Springer Author. The first author's name and surname were splitting. That was a bug in the Arcas which is now fixed with PR (https://github.com/Nikoleta-v3/Arcas/pull/17)
• I have read the titles and some abstracts and irrelevant articles have been removed (I might have missed a few). The keywords at fault were mainly Tit for Tat and Memory One

2. Sql to json

---

Keeping the structured we agreed on. Here is an notebook which pings the rest frame work and spits the
json files into a pandas data frame. (https://github.com/Nikoleta-v3/Axelbib/blob/scraping/scraping/Analysis%20of%20Results/Retrieving%20Data.ipynb)

3. Initial Analysis

---

A notebook containing the initial analysis. I do have some inline comments. Notebook: https://github.com/Nikoleta-v3/Axelbib/blob/scraping/scraping/Analysis%20of%20Results/Initial%20Analysis.ipynb
Furthermore the things that stand out to me are:

• The words that stand out in the titles are: Social, Network, Spatial, Evolution. These seem to be the most focused topics of the field
• 1111 articles
• We got articles from 1966-2016
• The keywords are not that trustworthy. A lot of them are missing and a few a wrong. I note in the notebook that the labels (the ones we assign to an article) could be way more useful. By reading a few titles and abstracts you can get enough information. Thus I am currently reading about nature language process because I believe we could assign labels and base our research on those
• It seems interesting to investigating the trends for labels and authors. The idea is that topics that died out and topics that are still strongly going will stand out. As for authors, we can see both the productivity of an author (plots) and whether authors jumped around topics (table).
• There are a lot of articles for the applications of the strategy tit for tat apart from an IPD tournament (more of general knowledge)

SSI

If and only if you have time at any point. My slides for the irregular meeting:

• pr: Nikoleta-v3/talks#6
• pdf: https://github.com/Nikoleta-v3/Talks/blob/ir-meeting/2017-01-26-SSI/main.pdf

On a happy note

• I assigned for the Machine learning course. Start date 23-01-2017
• J. Thompson asked to do MSc tutorials, thus I have MSc for the first 5 weeks.

Currently

• Really looking into nature language process
• A bit of literature

Agenda 5-10-2017

Axelrod-dojo

I have re factored the dojo in such way that now an algorithms folder exists.

• pso algorithm is now a file within the folder and does not contain any of
  the script code.
• gambler was implemented as an archetype.
• pso can now run for the fsm archetype.

The code can be found here: https://github.com/Nikoleta-v3/axelrod-dojo/tree/creating-algorithms

An update since our last meeting:

• I have tested the pso at it works the way we expected. https://github.com/Nikoleta-v3/axelrod-dojo/blob/creating-algorithms/tests/algorithms/test_particle_swarm_optimization.py
• I have added the initial move for the fsm to be a decision variable as well (we forgot about that).

Problems

Multiprocessing is failing with the following error:

AttributeError: Can't pickle local object 'PSO.swarm.<locals>.objective_function'.

I tried a few things but I am not sure what is causing the error.

Rhino

• Look at Tamsin's work: https://www.overleaf.com/11362503rrgmkctrpnpc#/42899082/
• Address the issues: https://github.com/drvinceknight/Evolutionary-game-theoretic-Model-of-Rhino-poaching/issues

looking at the chances through overleaf is a huge pain. I will cheat and use tig.

Fix virtual enviroments

Fix the virtual environments and python that seems to be having an issue in Batman.

Agenda 24-10-2017

Meta tournament analysis

We now have 12.265 tournaments

Blog post

Tit for Tat 1987 post: https://docs.google.com/document/d/1yFzzNHXMx-lDISUakobcXY4iV2Hh2u1P9trFFVRUDkE/edit

I will publish it on Tuesday.

Trivial Stuff

• I have spoken to Scott about the workshop.
• References on the Moran paper.

Happy note

• done with the rhino work 🎉

Literature Review

I have worked on,

• the Era of strategies section,
• and the Software section.

I did a lot of reading regarding the spatial tournaments and the concept of evolution. I believe I
am seeing how Nowak's work evolved. (I think I will buy his textbook)

I found an article that spoke about artificial neural networks in 1996!

The branch I am working on. https://github.com/Nikoleta-v3/Literature-Article/tree/working_article

Reading list

From links they come my way.

• https://arxiv.org/abs/1709.04326
• https://blog.openai.com/learning-to-model-other-minds/
• https://twitter.com/randal_olson/status/895057154293542913
• https://rd.springer.com/article/10.1140/epjb/e2017-80219-0
• https://phys.org/news/2007-10-snowdrift-game-tops-prisoner-dilemma.html
• https://twitter.com/NatureHumBehav/status/897926945324752896
• https://twitter.com/eLife/status/885138006050570240

Status update for 12-01-2016

A test issue for Henry

Here is a list of things we've discussed:

• The first thing.
• The second thing.

@Nikoleta-v3 (ignore this)

Agenda 31-03-2017

Optimisation of Memory One Strategies

Additions to script:

• Keep track of the stable states. In particular the stable states of p, q1, q2, lambda*q1, (1 - lambda)*q2.
  This will be implemented by writing a function for each stable state and then added the required ones to the list with what we are writing to file.

• Do not run the same parameters. If files exists then read the csv in (read_in() function).
  Make the lists to sets and before passing arguments to worker check that they are not in the sets.

One the script has been implemented run the file and get as much data as possible. Plot for each give p
the mean, max and min diff.

Meta tournament Analysis

3 Sections need to be implemented here. Notebook Relationship between rank & cooperation rating (link once pushed) contains information for each section.

Literature Review Article

The literature review is broken into 3 parts.

• Reading
• Writing the literature review
• Implementing and writing the analysis

Arcas Clean up

Two main fixes before the CW.

• Make a basic good documentation
• Do not pass arguments as a dictionary any more.

Look in to natural language processing

?

Agenda 19 -04-17

Meta Tournament Analysis

Perform an analysis based on the four tournament types. Initially, 1.create separate data frames with a line for each tournament. The data will include information such:

• seed
• normalised rank of high cooperator
• normalised rank of low cooperator
• highest coop
• lowest coop
• mean coop
• median coop
• size

To perform the analysis also write *3. plotting functions.
Write a little 2. note, where we are, what have we done and what we expect to do once we get back to this analysis.

Memory One Optimisation

Turn the huge 1. csv - hdf5 file. 2. Re-run with float.
3. perform the analysis, meaning get the plots, identify convex function and do a deep search. 4. Make sense from a game theory perspective and importantly look at 5. literature on quadratic forms optimisation (see issue for list: #28).

Literature Article

Keep reading, labelling and writing.

Once I have reached 200 articles perform as a supervised machine learning algorithm.

Other stuff

• Abstract for Gregynog
• Look at 3 mins thesis
• Expensive form
• Slides for meeting with rhino woman

Create repo for Literature Review paper

Structure:

• The IPD
• A time line that will cover all until Axelrod Python Library
• The library
• Axelbib and statistical comparison of the articles.

Agreed courses

• Game Theory (VK course): Spring 2017, to be examined. http://vknight.org/Year_3_game_theory_course/
• Simulation 10th - 14th July 2017
• Combinatorial Optimisation 4th - 8th September 2017

Agenda 2-01-2017

Literature Review

• Close all the issues.
• Address restructure.
• Literature on network analysis of papers (citation).
• Hopefully finish off.

Resultant

• write tests
• finish documentation
• talk to sympy people?

Poster

• draft poster! priority

Blog

• resultants
• rhino

Lancaster game theory conference ~ November 2016

Opening an issue for this conference to discuss/take notes/etc...

Agenda 27-11-2017

Literature Review

Following our last discussion:

• Restructured the paper ones again. Now very simple going throw highlight events that took place in
  a chronological order. The current sections (of the timeline) are:
  • Origin and (1961-1972)
  • Axelrod’s Tournaments (1981-1984)
  • Further work and Criticism on Computer Tournaments (1984-1993)
  • Evolutionary Dynamics (1987-1999)
  • Modern Approaches (1995-2011)
  • Zero Determinant (2012 - 2015)
  • Current Area (2015 - 2017)
• Working on the analysis and addressing all the ideas we had.

Resultant Theory

Only a few things on notebook looking at the Sylvester's and Bezout's resultants. I found the definition
of resultants 🕶️.

Summary 04-07-2017

Coming back from France

France was a great experience and I seriously believe it will benefit my research. I summarised
the notes I had and put everything together in a notebook. Also I wrote a blog post: https://docs.google.com/document/d/1qKM7PNrEZfdmn4bzebO2i_o-Nfw1hk2f_qLFOO4S-ps/edit?usp=sharing

Website

Following the blog. My website is ready but I have not launched it yet. (I might need your help with something).

The main is here: https://github.com/Nikoleta-v3/Nikoleta-v3.github.io
and then I transformed by Talks repo to a site itself: Nikoleta-v3/talks#15

Arcas

Not so much progress was done with Arcas. I made this pull request but there are several
things I still need to hack before the new release: ArcasProject/Arcas#31

Corrections

I did the corrections to the Annual Report.

Optimisation

Now that the corrections were made the Optimisation report was also updated. Now I started working on
the reactive strategies. Yeeey

This mean:

• write the new formulation
• update the script with the new functions and test them
• try to solve the optimisation problem

Evolutionary stuff

Lets chat about this. But the meetings with Tamsin were really good!
She is currently working on the stability of $s=0$ and me in the
evolutionary stability of $s=s^*$.

I cleaned the overleaf project a bit.

On a happy note

I moved! 🎆 🎆 👏

This doesn't look very good but might be nice to reference

(Not sure they consider iteration, have only scan read)

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1743922/pdf/v013p00461.pdf

Status update for November

Agenda 17-10-2017

Rhino work

Draft is with Tamsin now. I replied to hear email she just got confused with the
marked-diff.pdf but I replied her and did not have to chance anything.

Writing

Have written two blog posts:

• Another side of Tit For Tat. Verifying an article from the 1980s.: https://docs.google.com/document/d/1yFzzNHXMx-lDISUakobcXY4iV2Hh2u1P9trFFVRUDkE/edit?usp=sharing.
• Python, Graphs and Game of Thrones.: https://docs.google.com/document/d/1JQZhNe-2fEdklNGZvf1Bg39VxyouFlXHGTP72vl7ypY/edit?usp=sharing.

and the justification for the Facebook conference: https://docs.google.com/document/d/14jsYlBvGXiPTtqDlEFd1xD4Q-GhEHzgvLHHCCwzmNzc/edit?usp=sharing.

Talk for PyDIff

Slides: https://github.com/Nikoleta-v3/talks/blob/earth_science_talk/talks/2017-10-29-PyConUK/main.pdf

Literature Review

currently writing ...

Machine Learning Algorithms

https://www.opendatascience.com/blog/implementing-a-neural-network-from-scratch-in-python-an-introduction/

Agenda 27-09-2017

Dojo-work

The structure I aim for is this:

.
|---  src/axelrod-dojo
       |--- archetypes
             |--- ann_evolve.py
             |--- fsm.py
             |--- fsm_evolve.py
             |--- hmm_evolve.py
             |--- lookup_evolve.py
             |--- pso_evovle.py
             |--- init.py
       |---optimisation_algorithms
             |--- genetic_algorithm.py
             |--- swarm_algorithm.py
       |--- analyze_data.py
       |--- archetype.py # contains the class for the archetypes
       |--- __init__.py
       |--- utils # contains several function which are being used by the evolutionary algorithms
       |--- version.py
|---  tests
       |--- test_archetypes
             |--- test_ann_evolve.py
             |--- test_fsm.py
             |--- test_fsm_evolve.py
             |--- test_hmm_evolve.py
             |--- test_lookup_evolve.py
             |--- test_pso_evovle.py
       |--- test_optimisation_algorithms
             |--- test_genetic_algorithm.py
             |--- test_swarm_algorithm.py
       |--- unit
             |--- test_archetype.py
             |--- test_utils.py
       |--- integration
|--- LICENSE.txt
|--- lookup_tables.csv
|--- README.md
|--- requirements.txt
|--- setup.py

Note that fsm and fms_evolve will be squashed together at the end.

I moved everything and made sure that the old testes were running. For tomorrow
all the archetypes are tested.

This to discuss:

• make sure we are both happy with the structure.
• look into the parse_repr functions.
• the pso_algorithm
• what to do with the integration tests you have there?

Cooperative Game Theory

Reading the paper on the truck scheduling and your work to familiarise myself with
cooperative game theory. me doing literature review

Talks

I have 4 talks/lightning talks coming up:

• OR society in the department, 20 mins. Optimisation Talk
• SWORDS, 5 min. Rhino Talk
• First years, for Rob. 3min.
• PGR lightning talks, 5 min. Network Theory + Got.

I worked on my talks and made some slides that were needed.

Blogs

• Drawing the strategies is out.
• Euroscipy, finished Raniere's corrections waiting for the publisher to contact me.

Happy note

• Rhino work is done.

Need to

• book a hack day

Recurrent neural network

The idea is to use recurrent neural networks to train strategies. This will build
upon Toby's work.

Useful links:

• a podcast about machine learning libraries in Python : http://pca.st/kVRU
• an introduction to using Keras for recurrent nn: https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html

Catch up with work 16-08-2017

Four sections

Optimisation of memory

Purely random study has finished and I am currently working on reactive strategies.
The reactive strategies study lead to be looking at the differentiation of quadratic forms to get create our $S_q$ set.

Notes one the topic: https://goo.gl/photos/LyriaHJJuhsUqRG6A

Links for differentiating quadratics and the possible connection with ring theory:

• http://michael.orlitzky.com/articles/the_derivative_of_a_quadratic_form.xhtml
• http://www.atmos.washington.edu/~dennis/MatrixCalculus.pdf
• http://www.sciencedirect.com/science/article/pii/0022314X73900140
• http://www.jhuapl.edu/techdigest/TD/td2804/Williams.pdf#

It was also decided to look at the problem from numerical solutions. Just for the sake of argument.

• get equations for $p_1. p_2$. Because from the diff by part we have 3 equations.
• write a simple optimisation problem. Look at the neighbours and decide on local minimum.

Tournament data

Keep collecting data from Raven.

• Run analysis we what we have now to see any difference that stands out.
• Get ideas on data analysis to perform on the data set.
• In order to do the above we need questions to answer to.

Literature Review

Literature review goes hand to hand with Arcas. When hacking on Arcas new examples for Arcas examples will be ideas to implement on the data set I have for the IP.

The data set has been updated for 2017, re-run script when I get back.

Get the implementation for analysis ideas for both literature and tournament data

On a happy note

Get my talks together:

• Scipy?!
• Pycon UK

Finishing touches on blog:

• Possible change the menu bar?
• Fix my cv to be auto generating.

Axelrod hackday

• to do

Rhino work:

• When I get back most of these will be done but still. Need to make sure the new model is correct, and that I am okay with the new interpolation of the results. Re run all the notebook analysis and comment on it. Collect data and see if we get stability and evolutionary stability.

Agenda 18-09-2017

Meta tournament analysis

We now have almost 9000 results. I re-run the initial analysis and fix the notebooks a bit.
The to_C_rates are interesting. Nikoleta-v3/meta-analysis-of-prisoners-dilemma-tournaments#7

Blog posts

Euroscipy: https://docs.google.com/document/d/1j-eYBuNEH9-0pie57c9IHAbJWwaR6kDD50FYsLDLGpM/edit?usp=sharing

Papers: https://docs.google.com/document/d/1AxycZBffMVLd25OueDGahv149IDLWlqK1dM4ZAHX_B4/edit?usp=sharing

SSI

Preparations for my Webinar talk were made!

Rhino work

Two things we need to discuss about the rhino work.

• The evolutionary stuff from your notes
• The analysis does not make sense anymore. I can explain what I mean.

Collaborations

• My talk with Martina and a conference to look out for (she will be attending): https://www.esmtb.org/news/140-ecmtb-conference-2018

• Natcor Collaboration with lecturer from Hamburg? I need to read this: http://www.sciencedirect.com/science/article/pii/S1366554516300801

The dojo

• work on the dojo on Friday

17-06-2017

Rhino Work

• Good read up to the rhino work
• Make some progress on the evolutionary stuff

France Trip

• Make sure I now where/when/how
• Make a 10 min talk

Annual Report Corrections

• Read through Jonathan's tips
• Minor errors we discovered

My Awesome Web

• Work on my website

Proposals

• https://github.com/Nikoleta-v3/Applications/issues/21

Meta Tournament Script

• Fix the script to paralyse and run tournaments with the new release

Scraping Google Scholar

Scholar is the winner.

List:

• create a csv
• read csv
• create object dict
• post to json object

Agenda 12-12-2017

Rhino work

Fix branches: Nikoleta-v3/Evolutionary-game-theoretic-Model-of-Rhino-poaching#30.

Discuss next step.

• email to Tamsin
• arXiv

Resultant

Finished implementing the resultant, not the two methods of extracting the solutions. These are:

• GCP

• RSC

• Talk to you about issue with determinant of big tables.

Literature Review

Re-writing the network analysis chapter. The structure I have decided to go with is:

• The prisoner's Dilemma Network.
  • ...
• Comparison with other networks
  • ...
• The sub networks of people we are looking for
• Conclusion
  • the network compared to other fields.
  • the work of Matjaz, how it stand out from the analysis, the nice connection to Nowak
  • the centrality of the other sub graphs. As a guidance to the reader

I will walk you through this.

Happy note

• ask about setting up meeting group.

Status Report 07-03-2017

Status Report

(My road to suicide in tribute of team 6)

Meta Tournaments (axlml)

I have created a script for reading in the files (Nikoleta-v3/meta-analysis-of-prisoners-dilemma-tournaments#4), I opened
PR.

After timing the script and looking at the memory usage I believe csv is a suitable file format.

Memory One Optimisation

The were a few things to do for mo-opt. I have clean the main.py and have written some of the
theory. I went as far as verifying the claim of Press and Dyson about A & B. (https://github.com/Nikoleta-v3/Optimisation-of-Memory-One-strategies/blob/nick/main.ipynb)

For checking the difference for the cost and the normalised score I have worked in the following notebook: https://github.com/Nikoleta-v3/Optimisation-of-Memory-One-strategies/blob/nick/cost.ipynb
, also verify the results using Axelrod.

For checking whether the cost function is convex I have written the following script: https://github.com/Nikoleta-v3/Optimisation-of-Memory-One-strategies/blob/nick/convex.py
which is currently running on raven.

For all the above a tools.py was written to handle functions between notebooks. PR is here: https://github.com/drvinceknight/Optimisation-of-Memory-One-strategies/pull/1

Moran Processes

I am studying Nowak's text book and I have put a little something (pdf) together.

Literature Review Article

Looking at minimising the propriety measure for k- means.
To finally achieve the clusters and see the keywords for each.

Udacity ML course

https://www.udacity.com/course/deep-learning--ud730

How many hours is it?

Might be worth doing after the coursera one...

Status Report 13-01-2017

Applications

Links for all the PRs.

• Euro: https://github.com/Nikoleta-v3/Applications/pull/11
  Also you have to address this issue 😄 : https://github.com/Nikoleta-v3/Applications/issues/13
• Network Camp: https://github.com/Nikoleta-v3/Applications/pull/12
• YoungOR: https://github.com/Nikoleta-v3/Applications/pull/14

Proposals

I have opened this issue for PyCon Namibia(https://github.com/Nikoleta-v3/Applications/issues/15) and I am currently writing the proposals.

Do the coursera course

https://www.coursera.org/learn/machine-learning?utm_source=gg&utm_medium=sem&campaignid=426787857&adgroupid=22532597457&device=c&keyword=coursera%20machine%20learning&matchtype=e&network=g&devicemodel=&adpostion=1t1&creativeid=142192908855&hide_mobile_promo&gclid=Cj0KEQjwg8i_BRCT9dHt5ZSGi90BEiQAItdjpL1M8U9TKlrg736wP9seKW7X-xD8pB0RetqpJTZFYdgaAk2b8P8HAQ

Mathematical and Theoretical Biology

Keeping an eye on the society.

This the Europian Society: https://www.esmtb.org/
Maybe become a member? Lets discuss about the money.

Look into this journal: https://twitter.com/natureecoevo?lang=en

and keep an eye on Mathematical Models in Ecology and Evolution. It happens every 2 years. Last on
was in London 2017: Mathematical Models in Ecology and Evolution

I followed everything on twitter.

Agenda 9-11-2017

Literature Review

Working on the literature review article. This branch is up to date: https://github.com/Nikoleta-v3/Literature-Article/tree/working_article. Apart from the Stability Section the rest are ''well'' written.

As for the data collection. I will rerun the script to collect 2017 articles and do the preliminary analysis for the new data set. Then start moving on with the analysis and writing.

Other

Workshop proposal: https://docs.google.com/document/d/1OFITgrJYDvqDKqASFq0wzH2Ox1EmYlqHdB5dgzYgWhY/edit?usp=sharing

Poster. There are two poster competitions that are going on this year. The SIAM I told you and the STEM. Here is a proposal: https://docs.google.com/document/d/1UYBBVNkIt8ukPwtvX7PjefeRnOrojaYIuk0k2dg_zp0/edit?usp=sharing
Let's talk over this. See if I should submit or not etc.

Blog

SSI accepted my funding request for FaceTAV.

• need to write a blog post.

never again will I mention the rhino work

Agenda 17-07-2017

Optimisation

Currently working on optimisation for reactive. Anything I come across is implemented
as a function. Solving the equation for the partial derivatives is kind of tricky after all (needs discussion).

I am now looking at the bounds of the solution set.

Rhino work

I did write the section for the mixed population.
For the evolutionary part, none of the strategies seems to be evolutionary stable.

Moving forward we all need to agree on example that we will perform and write down the results.
Also I need to write down the evolutionary section.

Data

Data are coming down nicely. Currently at 1,300.

Topics for discussion

• ml-paper. I read over it. I do have some comments we can discuss if you don't mind me raising my humble opinion. Overall is a really nice piece of work. Personally, I would love to have such summary of strategies and tournaments when I started.

• testing. I had a very interesting conversation with a fellow PhD student here at Natcor. I have a few questions I would like to clarify with you.

On a different note

• My meeting with Marc
• Finance issues

Agree on projects over my break

• Arcas
• Pick up the history class. Implementing misperception will be useful once I start looking at the network ideas gathered from the network summer school
• Rhino work

Optimising quadratic forms

A list of articles/blog posts to look at:

• https://web.stanford.edu/~boyd/books.html
• http://sourcedb.igg.cas.cn/cn/zjrck/200907/W020121023597227321340.pdf
• https://ie.technion.ac.il/~becka/papers/RQfinal.pdf
• https://pdfs.semanticscholar.org/817d/22496edae9f61cd3b9c33d2b2795898b7386.pdf
• https://en.wikipedia.org/wiki/Rayleigh_quotient
• http://math.stackexchange.com/questions/2022290/optimization-of-a-sum-of-ratios-of-quadratic-forms

Links

A list of links to go through, either for simple visualization or data analysis.

All using python:

• Visualization Landscape

(old ones)

• https://blog.modeanalytics.com/python-data-visualization-libraries/

• http://www.datadependence.com/2016/05/scientific-python-pandas/

Status Report 22-03-2017

Meta tournament analysis

Looking at the overall data set for any interesting results and taking the
analysis on the strategy level a bit further down: https://github.com/Nikoleta-v3/axlml/tree/nick/src

Optimisation

• convexity
• re writing code
• Nan's

Travelling

• CW17
  • feedback on lighting talk
• Natcor 3rd - 7th April 2017

Time managing

• Discuss about the CW
• A few tips for managing my time

Rhino work

• Vince needs to fill me up with the details of the visit.

Status Report 09-12-2016

Arcas

The syntax of all APIs has been doubled checked. All APIs apart from PLOS (we will discuss about that) have been implemented and tested. Tests contain hypothesis testing now as well.
Pull request: https://github.com/Nikoleta-v3/Arcas/pull/12

I have set up the read docs. https://screencloud.net/v/6tC5
and I am working locally for now: https://screencloud.net/v/s8l1

Axelbib

Axelbib now has a scraping folder. Within the folder a script to get articles and push to data base has been
implemented. The script spits out a report file (with the keys, url and status error). A notebook to check that each articles has been successfully pushed at least ones is here: https://github.com/Nikoleta-v3/Axelbib/blob/arcas-axelbi/scraping/Analysis%20of%20Results/Status%20Error.ipynb
And a validation fail file. Which write's down the url of the articles that did not pass the validation.

Furthermore, some analysis is located in this notebook (the one I showed you): https://github.com/Nikoleta-v3/Axelbib/blob/arcas-axelbi/scraping/Analysis%20of%20Results/Analysis%20%26%20Ploting.ipynb

Things I am working on

I am currently writing the documentation and I want to do some writing (applications etc) for the next hours. I will make the script run when I get to my office from that computer and will do the analysis again for the data for tomorrow.

Agenda 4-12-2017

Rhino work

Restructuring the paper for submission to The American Naturalist. The guidelines are here: http://www.journals.uchicago.edu/journals/an/instruct

The branch with the new structure is here: https://github.com/drvinceknight/Evolutionary-game-theoretic-Model-of-Rhino-poaching/tree/american-naturalist

I will walk you through the changes. Though I have a very detailed commit message: Nikoleta-v3/Evolutionary-game-theoretic-Model-of-Rhino-poaching@fa7dfdc.

Also created a markdown file for the submission: https://github.com/drvinceknight/Evolutionary-game-theoretic-Model-of-Rhino-poaching/blob/american-naturalist/submission.md

On the same note.

• discuss about the summer project on the rhino work and add stuff to your grant proposal.

Literature Review

Here is the most updated version of the paper: https://github.com/Nikoleta-v3/Literature-Article/tree/re-structure

It's almost ready for you to read (and tear apart 'jk') but I would like to finish some plots tonight before our meeting tomorrow. I need to add legends to the network graphs and I got some example code from Geraint today.

Resultant

I am working on the resultants. I am currently reading and codding over this paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.39.3370&rep=rep1&type=pdf

which is a very good overview of the multivariate resultants and also compares the methods. 👍

Agenda 25-04-2017

Memory One Optimisation

We have discovered that writing to file is breaking due to the multiprocessing. This can be fixed by queuing before writing to file (axelrod does something similar: https://github.com/Axelrod-Python/Axelrod/tree/master/axelrod).

For now the data have been cleaned (the broken rows have been removed) and everything is in a huge HDF5 file. Still, the multi processing could be fixed and I can re - run the script.

The following analysis has to be held now:

• 1. plot the differences and see if for any function (given q) the utility is convex

Because the numerator and denominator and the utility function itself is not convex we will move towards reactive strategies. (2. prove and write all the above).

Now we have the reactive strategies because there are only two variables we can 3. plot stuff.
reactive reference: Comparing reactive and memory-one strategies of direct reciprocity 2016

Literature Review

30 % of my point so be focus on this. Reading and writing.

Fill in literature review for Moran article. Mainly I will focus on ZD beginning, Nowak's memory, The art of war.

Restructure

Restructure my work similar to Vince's : https://github.com/Axelrod-Python/axelrod-moran