Posts

Sep 17, 2015
A Summer of game theory software development
This Summer has seen 3 undergraduates carry out 8 week placements with me developing further game theoretic code in Sagemath:
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Sep 5, 2015
Picking a good Vainglory jungler with game theory and sagemath
I’ve recently been playing a really cool video game: Vainglory. This is described as a MOBA which I must admit I had never heard off until this year when my students mentioned it to me, but basically it’s an online multi player game in which players form two teams of 6 heroes and fight each other. The choice of the heroes is very important as the composition of a team can make or break a match. This seems to have a bit of a cult following (so no doubt just like for my post about clash of clans I might annoy people again) and there is a great wiki that gives guides for the play of each player. In this post I’ll describe using Python to scrape that wiki to get data that feeds in to a game theoretic model which I then analyse using Sagemath to give some insight about the choice of hero.
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Aug 28, 2015
Natural language processing of new jokes from 2015
This is a brief update to a previous post: “Python, natural language processing and predicting funny”. In that post I carried out some basic natural language processing with Python to predict whether or not a joke is funny. In this post I just update that with some more data from this year’s Edinburgh Fringe festival.
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Aug 9, 2015
Why I am a paying member of cloud.sagemath
If you are not familiar with Sagemath it is a free open source mathematics package that does simple things like expand algebraic expressions as well as far more complex things (optimisation, graph theory, combinatorics, game theory etc…). Cloud.sagemath is a truly amazing tool not just for Sage bu for scientific computation in general and it’s free. Completely 100% free. In this post I’ll explain why I pay for it.
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Aug 1, 2015
Simulating continuous Markov chains
In a blog post I wrote in 2013, I showed how to simulate a discrete Markov chain. In this post we’ll (written with a bit of help from Geraint Palmer) show how to do the same with a continuous chain which can be used to speedily obtain steady state distributions for models of queueing processes for example.
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