How 2 Rob A Bank podcast

42:19

Imagine you and a stranger are paired together for a little game. Now there’s some money up for grabs and you’re both given 2 choices; Share or Snake. If you both share you both win £15 each If one of you shares and one of you snakes, the snake will win £50 leaving the person who chose share with nothing. If you both pick snake, you both leave with nothing. Would you pick ‘snake’ in the hopes of taking a bigger prize for yourself, or would you pick ‘share’ to share a smaller prize!? What would you do? What should you do? And why should we even care? In this episode, which is also the season finale, Bia shares some introductory game theory with Zoey by discussing: The social-media experiment they both conducted through Instagram: "Snake or Share" The Prisoner's dilemma (which is the original problem) The Traveller's dilemma. Acting like a "snake" i.e. picking the "dominant strategy" may give you control leaving you less susceptible to exploitation, but is it always the most profitable strategy? And what about the long-term implications of this? Our Instagram: @how2robabank Our Twitter: @how2robabank Sodosage's Instagram (poet): @sodosage…

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How 2 create a dating app 32:21

4 years ago32:21

32:21

How do dating apps work? And what are your thoughts on them? In this episode, Zoey shares how collaborative filtering works in dating apps such as Tinder, but also in Amazon. Bia shares how Hinge uses the Gale-Shapley algorithm (whilst butchering the pronunciation) to find your most compatible match. They discuss thoughts people shared via Instagram. Further details of the maths and algorithms are shared via Instagram/ their website. Time stamps: 0:47 – Collaborative filtering 13:55 – Gale Shapley algorithm 19:04 – Are dating apps are good/bad thing? Thoughts of Instagram followers 28:48 – Who do dating apps favour/ hinder? Thoughts of Instagram followers & some personal stories Website: www.how2robabank.com Instagram: @how2robabank Twitter: @how2robabank Email: how2robabank@gmail.com Links: Hinge uses Gale-Shapley algorithm: https://www.theverge.com/2018/7/11/17560352/hinge-most-compatible-dating-machine-learning-match-recommendation Judith DuPortail’s article: https://www.theguardian.com/technology/2017/sep/26/tinder-personal-data-dating-app-messages-hacked-sold Interracial dating online: https://psycnet.apa.org/record/2014-02726-002…

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How 2 illustrate a scientist with Nina Chhita 40:58

4 years ago40:58

40:58

In light of International Women's Day 2021, Zoey and Bia interview Nina Chhita, a medical writer based in Canada. Nina brings together art and science by illustrating trailblazers in science who happen to be women. 00:10 Introduction 00:42 What has been the reaction to your work as a science communicator? 02:09 Quick fire quiz 03:15 What does it mean to be a medical writer? 05:31 Did you always want to study biology when you were younger? 08:10 Who were your role models growing up? 11:39 Which blue plaque story led to @science.unhinged and @nina.draws.scientists? 14:59 What was it about Rosalind Franklin that drew you to her story? 16:15 How do you get inspiration for the scientists you illustrate now? 18:26 How easy is it to find misinformation about less well-known women? 20:30 Which scientist you've illustrated has been most fascinating to you? 23:42 Has anything surprised you on this journey of science communication? 26:26 What are ways we can feature women in science to be more mainstream? 32:40 What would you change about the current curriculum to encourage girls to take more STEM-based subjects? 33:40 How much more progress do we need in the future and how do you think we can get there? 35:34 What makes a good scientist in your opinion? 37:45 What is planned next for nina.draws.scientist? Connect with Nina Chhita on Instagram @nina.draws.scientists and on Twitter @Nina_Chhita…

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Q&A 23:19

4 years ago23:19

23:19

In this episode, Zoey and Bia answer questions submitted by listeners. These include what inspired them to make the podcast, Instagram page and blog, why a common person should learn maths, and how hard it is to get good at it. Timestamps: 00:36 What inspired you to make this page and podcast? 02:00 Who are we? 02:49 How did we meet? 03:37 What inspired you to take maths at university? 04:38 What advice would you give yourself if you could go back in time 10 years? 05:25 What advice would you give to young girls who want to pursue STEM (Science, Technology, Engineering and Mathematics)? 06:30 Why do you think a common person should learn maths? 09:39 How hard is it to get good at maths? 12:40 What is missing from elementary school maths syllabus that would make maths more fun? 14:13 Are mathematical models underrated or overrated? 16:37 Do you have any maths book recommendations? 18:10 Does it annoy you that people think you are men online? 20:18 From your experience, what have you learnt about communicating maths ideas online? Useful links: https://www.opendemocracy.net/en/5050/these-women-are-using-instagram-celebrate-unsung-women-science/…

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How 2 lie with Zombie Statistics 38:57

4 years ago38:57

38:57

Zoey and Bia discuss what zombie statistics are, why it's hard for zombie statistics and facts to die and whether it is right for a wrong statistic to be cited even if it produces positive effects. Introduction 00:15 – Lies, damned lies and statistics 01:48 – Zombie statistics definition Quiz and answers discussion 03:52 – Zombie stats or facts quiz 05:45 – Zombie stat/fact #1 – One in four people will suffer from mental illness/ depression in their lifetime 09:18 – Zombie stat/fact #2 – You need to drink eight glasses of water a day 12:18 – Zombie stat/fact #3 – People use only 10% of their brains 14:22 – Zombie stat/fact #4 – You need to walk 10,000 steps a day to stay healthy and fit 19:05 – Zombie stat/fact # 5 – The ban of plastic straws will massively reduce plastic waste in our oceans General discussion 26:26 – Discussion on why it's hard for zombie stats/facts to "die" - beneficial information to people/companies and confirmation bias? 30:37 – Is it okay for a statistic to be wrong even if it has a positive effect? 33:45 – Making sure you understand the entire story of the statistic and taking it with a pinch of salt 35:29 – Conclusions Useful links: Adult Psychiatric Morbidity in England - 2007, Results of a household survey https://digital.nhs.uk/data-and-information/publications/statistical/adult-psychiatric-morbidity-survey/adult-psychiatric-morbidity-in-england-2007-results-of-a-household-survey Medical Myths" https://www.bmj.com/content/335/7633/1288 Dirty Streaming: The Internet's Big Secret https://www.bbc.co.uk/iplayer/episode/p083tb16/dirty-streaming-the-internets-big-secret…

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How 2 rig an election 35:46

4 years ago35:46

35:46

Bia and Zoey discuss some of the key mathematical concepts in voting, focusing on political elections in some Western countries, as well as Brexit. Introduction 0:15 – Introduction on the voting system in the UK, with an example 4:27 – Condorcet’s paradox 6:00 – The French system 6:44 – The Australian system – Preferential/Alternative voting 7:42 – What defines a good voting system? 9:43 – How do we balance a good voting system with one which everyone understands Arrow’s Impossibility Theorem & Instagram poll 11:40 – Arrow’s Impossibility Theorem 13:46 – Independent voting systems where Arrow’s theorem doesn’t apply. 15:00 – Instagram poll discussion: Tactical voting vs Voting for who you want 17:31 – Protest voting vs voting for who you want 20:20 – When it would be worth strategically voting “mathematically” Brexit 21:22 – Zoey exposing Bia as a “Remoaner” 22:50 – How Bia think the referendum should have been done. General discussion 24:05 – Have you ever not voted? 26:12 – Should 16 year olds be allowed to vote? 27:29 – Accessibility in a voting system The future of voting 27:54 – The future of voting 28:52 – The issues with a voting system which takes too long (NP-hard/ NP-complete) 30:00 – Dodgson’s voting method (Lewis Carroll = Charles Dodgson) 32:52 – Final thoughts 80% of voters are strategic: " Counting Votes Right: Strategic Voters versus Strategic Parties, Filippo Mezzanotti and Giovanni Reggiani" http://economics.mit.edu/files/11177…

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How 2 predict grades badly... 29:20

5 years ago29:20

29:20

Zoey and Bia discuss some of the mistakes that Ofqual made in their algorithm, how using “complicated” maths is not necessarily better, and share some anecdotes of their experiences with teachers and dealing with (un)conscious bias. Timestamps 00:20 – Introduction 01:54 – Initial thoughts 02:42 – Mistake #1 – Their approach 04:43 – Mistake #2 – Data leakage 05:15 – Mistake #3 – Emphasis on the rank 06:57 – Mistake #4 – Ignoring outliers 08:31 – Mistake # 5 – No peer review 09:16 – Mistake #6 – Too precise 11:14 - Mistake #7 –Disregarded unconscious bias. 12:53 – Mistake #8: Education system in the UK. 13:30 – Ofqual considered edge cases – (almost a positive thing!) 15:00 – How we might have handled this situation 17:39 – Another example of algorithmic bias – Accounting system the Post Office used. 18:53 – Challenge: “Prison Break”. This based on “Liar’s paradox” attributed to Epimenides (amongst many other philosophers). For more challenges, presented in a more visual manner, check out our Instagram. 25:52 – Anecdotes of experiencing bias from teachers. Useful links: Ofqual’s report Bristol University's study on unconscious bias - http://www.bris.ac.uk/media-library/sites/cmpo/migrated/documents/wp221.pdf Tom SF Haines’ post (Lecturer in Machine Learning at Bath University) - http://thaines.com/post/alevels2020…

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How 2 make a decision 25:34

5 years ago25:34