The Many Models Mindset | Scott E. Page

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08/31/20 • 62 min

On this episode of The Artists of Data Science, we get a chance to hear from Scott Page, a professor who studies complex systems and collective intelligence teams and political and economic institutions. He's known for his research on and modeling of diversity and complexity in the social sciences with a particular interest in the roles that diversity plays in complex systems. His book, “The Model Thinker”, stresses the application of ensembles of models to make sense of complex phenomena.

Scott shares with us his predictions into the future of machine learning, the importance of using a simple model, and how diversity impacts productivity. This episode is packed with amazing content that all data scientists and machine learning practitioners can apply in their lives. It was an absolute pleasure chatting with Scott!

WHAT YOU'LL LEARN

[12:41] Scariest applications of machine learning we might see

[24:56] What is a model, and why must they be simple?

[33:30] Many model thinking and it’s advantages

[47:07] How diversity impacts productivity

[49:46] How creativity impacts success, and how to be more creative

QUOTES

[6:31] “...you have to separate achievement from purpose.”

[35:45] “...if you really want to understand a complex phenomena, you've got to look at it with lots of lenses...”

[45:02] “...what you really want...is people who are acquiring different ways of thinking and understanding different tools, because then the whole is going to be so much more than the sum of the parts.”

[46:36] “Creativity is the union of sets. Getting at the truth is the intersection of sets.”

SHOW NOTES

[00:01:15] Introduction for our guest

[00:02:45] What drew you to the field of modeling in general and specifically game theory and complexity?

[00:03:49] So what were some of the challenges you faced while you're paving your own lane in the field?

[00:05:34] Separate achievement from purpose

[00:06:53] The synergy of ideas

[00:10:24] The biggest positive of machine learning on society in the next two to five years.

[00:12:35] The scariest applications of machine learning in the next two to five years?

[00:14:00] The online echo chamber

[00:15:12] Big data versus thick data

[00:17:05] Is thick data like longitudinal data?

[00:19:23] As practitioners of data science and machine learning, what do you think will be some of our biggest areas of concern?

[00:21:34] The “Scott Page Canned Beets” argument

[00:24:49] What is a model and why must they be simple?

[00:26:10] What are the three classes of models?

[00:26:50] What are the seven uses of models, aka the REDCAPE?

[00:29:00] The wisdom hierarchy

[00:31:14] The importance of assumptions while constructing a model

[00:33:20] Many model thinking vs single model thinking

[00:35:53] The difficulties of modelling human behavior

[00:39:02] Identity diversity versus cognitive diversity

[00:42:42] Cognitive diversity and mental models

[00:44:43] Cognitive diversity for knowledge workers

[00:45:14] Diversity and creativity

[00:47:04] In what ways does diversity make systems more productive?

[00:48:28] Is Data science machine learning to be an art or purely a hard science?

[00:49:31] Success and creativity

[00:51:32] What's the one thing you want people to learn from your story?

[00:53:41] The lightning round

Special Guest: Scott E. Page.

WHAT YOU'LL LEARN

[12:41] Scariest applications of machine learning we might see

[24:56] What is a model, and why must they be simple?

[33:30] Many model thinking and it’s advantages

[47:07] How diversity impacts productivity

[49:46] How creativity impacts success, and how to be more creative

QUOTES

[6:31] “...you have to separate achievement from purpose.”

[35:45] “...if you really want to understand a complex phenomena, you've got to look at it with lots of lenses...”

[46:36] “Creativity is the union of sets. Getting at the truth is the intersection of sets.”

SHOW NOTES

[00:01:15] Introduction for our guest

[00:02:45] What drew you to the field of modeling in general and specifically game theory and complexity?

[00:03:49] So what were some of the challenges you faced while you're paving your own lane in the field?

[00:05:34] Separate achievement from purpose

[00:06:53] The synergy of ideas

[00:10:24] The biggest positive of machine learning on society in the next two to five years.

[00:12:35] The scariest applications of machine learning in the next two to five years?

[00:14:00] The online echo chamber

[00:15:12] Big data versus thick data

[00:17:05] Is thick data like longitudinal data?

[00:19:23] As practitioners of data science and machine learning, what do you think will be some of our biggest areas of concern?

[00:21:34] The “Scott Page Canned Beets” argument

[00:24:49] What is a model and why must they be simple?

[00:26:10] What are the three classes of models?

[00:26:50] What are the seven uses of models, aka the REDCAPE?

[00:29:00] The wisdom hierarchy

[00:31:14] The importance of assumptions while constructing a model

[00:33:20] Many model thinking vs single model thinking

[00:35:53] The difficulties of modelling human behavior

[00:39:02] Identity diversity versus cognitive diversity

[00:42:42] Cognitive diversity and mental models

[00:44:43] Cognitive diversity for knowledge workers

[00:45:14] Diversity and creativity

[00:47:04] In what ways does diversity make systems more productive?

[00:48:28] Is Data science machine learning to be an art or purely a hard science?

[00:49:31] Success and creativity

[00:51:32] What's the one thing you want people to learn from your story?

[00:53:41] The lightning round

Special Guest: Scott E. Page.

Previous Episode

Naked Data Science | Charles Wheelan

On this episode of The Artists of Data Science, we get a chance to hear from Charles Wheelan, a professor, journalist, speaker and author, who holds a PHD in public policy from the University Chicago. He's currently a senior lecturer and policy fellow at the Rockefeller Center at Dartmouth College, and the author of Naked Economics, a book that is an accessible and entertaining introduction to economics for the layperson.

Charles shares with us his journey into becoming a prolific author, and why he decided to write Naked Economics, Naked Statistics, and so on. He also warns the use of big data and the implications it can have if used improperly. This episode is packed with insights into money, statistics, and some of the important problems the world is currently facing.

WHAT YOU'LL LEARN

[4:25] Charles’s tips on learning a subject effectively

[12:41] What is money, and why does it matter?

[21:40] How statistics can be used to make solve problems

[26:55] Why humans are so bad at appreciating and conceptualizing probabilities

[33:02] Important soft skills that technically oriented people need

QUOTES

[11:56] “Big Data is...a powerful weapon...it really can be put to great effect. Used improperly, you can do some enormous damage.”

[33:07] “...even if you are very technically oriented, you have got to have an awareness of sociology, psychology, great literature and the like.”

[36:23] “...if your data reflects some underlying problem, then any model you build from that data will just embed it more firmly in cement.”

[48:15] ...”stop thinking about what you're doing and look around the world and see what's missing”

FIND CHARLES ONLINE

LinkedIn: https://www.linkedin.com/in/charles-wheelan-a6220911/

Website: http://www.nakedeconomics.com/

Twitter: https://twitter.com/CharlesWheelan

SHOW NOTES
[00:01:19] Introduction for our guest

[00:02:45] How did you become so interested in statistics?

[00:04:16] Was there a lot of self study involved in learning statistics?

[00:05:06] How he wrote Naked Statistics

[00:06:51] What is economics?

[00:09:19] Does big data impact how economics works?

[00:11:21] Does big data change how the invisible hand works?

[00:12:35] What is money and why does it matter?

[00:16:43] Money in a world of contactless payments

[00:18:18] The impact of digital currencies on society

[00:20:15] Money and intersubjective reality

[00:21:22] How to use statistics to make business work better

[00:23:12] Which form of bias should we be most wary of?

[00:24:40] How will COVID affect the election

[00:26:49] Why are humans so bad at appreciating conceptualizing probabilities?

[00:29:26] Why is it important that we cultivate an intuition for what probabilities represent?

[00:30:39] Why we shouldn't buy the extended warranty

[00:32:38] What's going to separate them from the rest of the world, the rest the competition.

[00:32:54] What soft skills do you need to be successful?

[00:37:19] Charles Wheelan predicted COVID in his book The Rationing

[00:37:37] Draw parallels between the fiction you wrote and the reality that we're experiencing today

[00:39:03] How he came up with the story for The Rationing

[00:41:07] Which aspect of human nature do you think from your fiction has shown itself to become a reality with our current situation?

[00:43:18] What's the one thing you want people to learn from this story?

[00:44:35] The lightning round

Special Guest: Charles Wheelan, PhD.

Next Episode

Explaining Humans | Camilla Pang

On this episode of The Artists of Data Science, we get a chance to hear from Camilla Pang, a scientist specializing in translational bioinformatics.

At the age of eight, she was diagnosed with autism spectrum disorder and struggled to understand the world around her and the way people were.

Her book, “Explaining Humans:What science can teach us about life, love, and relationships” is an original and incisive exploration of human nature and the strangeness of our social norms.

Camilla shares with us her journey into science, and her mission to understand human behavior at a young age. She also discusses the potential impacts of machine learning and A.I within the next few years, and the importance of understanding the nuances in data scientists that create individuality.

WHAT YOU'LL LEARN

[7:18] Potential negative impacts of A.I

[17:00] Learning to embrace errors

[38:11] Getting over the perfectionist mindset

[39:30] Important soft skills you need to cultivate

[44:17] Advice for women in STEM

QUOTES

[6:59] “...before we get on to making the most of A.I we first need to make the most out of human minds.”

[17:20] “an error in one context is a solution in the next”

[47:10] Don’t judge yourself for thinking outside the box. Stay true to yourself and your vision.

[55:23] “...just because you don't fit in a system, doesn't mean you weren't born to make a new one.”

FIND CAMILLA ONLINE

LinkedIn: https://www.linkedin.com/in/camilla-pang-8b177b69/

Instagram: https://www.instagram.com/millie_moonface/

Twitter: https://twitter.com/millzymai

SHOW NOTES

[00:01:32] Introduction for our guest

[00:02:59] A large, open-ended question.

[00:04:32] What you think the next big thing in machine learning is going to be in the next two to five years,

[00:06:08] What do you think would be the biggest positive impact on society?

[00:07:04] What do you think would be scariest applications of machine learning in the next two to five years?

[00:07:51] What do you think separates the great Data scientists from the merely good ones?

[00:09:47] Talk to us about the terms neurotypical and neurodiverse. Would you mind defining these terms for our audience?

[00:11:29] What does it mean to think in boxes and what does it mean to think in trees?

[00:14:59] Why are most people stuck in box thinking?

[00:15:49] How to be a tree thinker

[00:16:50] What can we do to start embracing errors in our own lives?

[00:19:27] What do proteins have to do with personality and interpersonal relationships?

[00:20:50] How could we use this understanding of proteins to be better colleagues and better teammates at work?

[00:23:09] Never let your fear define your fate

[00:25:16] Gradient descent in layman’s terms

[00:26:47] How to use gradient descent to find our path to prioritize and identify our goals?

[00:28:37] How can we use Bayes Theorem for empathy and managing the relationships that we have with ourselves?

[00:31:02] What neural nets can teach us about ourselves

[00:32:17] Is data science an art? Or is it a science?

[00:33:30] How does the creative process manifest itself in Data science?

[00:35:11] How to take better notes

[00:37:26] How to stop being a perfectionist

[00:39:10] Why soft skills are hard work

[00:42:54] We’re both INFJ’s!

[00:44:26] Advice for women in STEM

[00:46:21] What can the Data community do to foster the inclusion of women in Data science and AI and STEM?

[00:47:00] What's the one thing you want people to learn from this story?

[00:48:37] The lightning round

Special Guest: Camilla Pang, PhD.

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