dissertation

machine learning/cs

  • ensemble methods

computer vision

  • maths exam paper marking

economic topics

  • firm lifespan
  • probability/determinants of firm bankruptcy
  • firm quarterly profits

campus topics

  • Quarterly profits
  • Using financial derivatives trading data for financial statistics
  • Anomaly detection, uncertainty and bias in administrative data.

datasets

  • emit trade depository
    • https://www.ons.gov.uk/economy/nationalaccounts/uksectoraccounts/articles/economicstatisticstransformationprogramme/enhancedfinancialaccountsukflowoffundsprogressonfinancialderivativesdata
  •  

project/article ideas

misc learning

  • Rocket textbook
  • Old maths topics
  • Python data analysis
  • R time series
  • Linux
  • Github
  • Maths history
  • Fundamentals of physics
  • agile 
  • leet

articles

  • Classification of phenomena by how easy they are to predict.
  • Properties of industry-related derivatives. 
  • Automation - is it happening?
  • Economic analysis on admin data e.g. VAT data to estimate lifespan of firms.
  • test whether machine learning can 'discover' hidden structure (that is planted)
  • define usefulness & closeness to decision makers.
  • systematic way to uncover secrets knowledge about how things actually work.
  • wage data derivatives

projects

  • booksonnews - multi-disciplinary analysis of news.
  • sig|nal - code to mark mathematics exams.
  • ONS outlier/trend detection - python code.
  • Dissertation - machine learning applied to macroeconomic/micro data.
  • Quantecon - translate Python code into R.
  • OBR - translate winsolve OBR code into Python.
  • Linear algebra udemy course.
  • book review web scraper classified by reviewer
  • guided journalling/
  • G-Fold algorithm
  • Classifying prediction problems
  • r package ons statistics + build in methodology + analysis
  • not big data but small data, outlier/anomaly/weirdness detection
  • youtube video scanning of questions with elon - categorising the questions 
  • model different models competing against each other in fake assets markets and see who wins/ get most returns
  • case studies of where data science has worked

booksonnews

In my whole life, I have known no wise people who didn't read all the time - none, zero.

— Charlie Munger

Problem

  • Human systems are very complex and difficult to understand requiring a multi-disciplinary approach, but mastering all of the subjects takes too much time.

Flawed current solutions

  • Academic expertise: Despite the inter-relatedness of human systems most academic knowledge is silo'ed in disciplines e.g. politics, economics, finance, technology, statistics, international relations, science, business, religion, psychology etc. that don't speak to each other. Also, who has time to read 100 different journals to mine for insight?
  • Journalism: Good job of being relevant but too much focus on breaking news rather than analysis. A lot of articles, even from top journals like the Economist, are very shallow, rarely drawing on the vast academic literature that exists. Good writing distracts from limited content.
  • Blogosphere: Lots of diverse opinions, but finding good content is very difficult: density of (diverse) quality is too low. Who cares what a random blogger thinks? Plus, it is hard to analyse an argument independently from who is doing the arguing - particularly if that person is not actively pointing out potential flaws and limitations in their thinking.
  • Conversation: People are often opinionated but it is hard to really become wiser through conversation as it is very dependent upon who you talk to and is subject to lots of biases like whether you like or relate to who is speaking. Also, it is very easy to talk to be heard, rather than to listen and learn.

booksonnews as a solution

  • Each month booksonnews would analyse a big news issue from many different perspectives/disciplines. The name comes from the idea that lots of different books would be used to analyse a news item.
  • Analysis would be brief, perhaps just 10-20 lines, but look to apply the core ideas to the news item specifically. When combined with perhaps 20-50 other books the content will still be a lot. At first each paragraph of analysis would be independent from the others but perhaps over time we will figure out a way to integrate the ideas as well.
  • For those who are interested, there would be a follow on summary of the theory, book or data that was used to analyse the issue with links to more information and perhaps places to buy books etc.
  • booksonnews may offer an interesting way to invert the learning process. Traditionally, students learn theories and tools without any sense of why they are useful and only after they have learnt them can they look to apply them. bookonnews would invert the process where the news becomes a filter for what is worth learning about.

Inversion: what types of flawed thinking is booksonnews going to try to avoid? 

  • Avoid 'man with a hammer syndrome', where a limited number of causal variables/models are weighted too heavily. Instead, systematically generate a large variety of explanations.
  • Avoid emotional attachment to theories/view points by following Charlie Munger's prescription 'I’m not entitled to have an opinion on this subject unless I can state the arguments against my position better than the people do who are supporting it. I think only when I reach that stage am I qualified to speak.'
  • Avoid silos of insight by forcing integration and comparison of different views and models. Any contradictions? Any compounding effects?
  • Avoid failing to update views and opinions with the changing facts and situation by, up front, citing the conditions which would convince you to change your mind - and then actively seek out such evidence.
  • Avoid the mistake of thinking you understand something when you don't. Try to estimate how well you understand something and where the limits of that understanding is.

Hypotheses/assumptions behind booksonnews

  • A multi-disciplinary approach is more fruitful than a single discipline approach.
  • There is a demand for deeper analysis of news, but admittedly less timely - perhaps even to the point where people would be willing to pay.
  • It is possible to analyse an issue fruitfully from the lens of a specific discipline to an audience without domain expertise in that discipline.
  • Readers will be willing to try a new and unproven news website and promote the website through word of mouth.
  • AI or some cyborg addition (e.g. neural lace) do not completely change human processing power making traditional learning via reading etc. obsolete.