The machine learning consultancy: https://truetheta.io/ Want to work together? See here: https://truetheta.io/about/#want-to-work-together The Kelly Criterion provides the optimal strategy when betting on random outcomes with known probabilities. SOCIAL MEDIA LinkedIn : https://www.linkedin.com/in/dj-rich-90b91753/ Twitter : https://twitter.com/DuaneJRich Github: https://github.com/Duane321 Enjoy learning this way? Want me to make more videos? Consider supporting me on Patreon: https://www.patreon.com/MutualInformation TIMESTAMPS 0:00 Intro/The Game 1:16 Growth Rates 3:43 People Play Terribly 4:46 The Best Strategy Possible 7:10 The Kelly Formula Is Dangerous CODE https://github.com/Duane321/mutual_information/tree/main/videos/kelly_criterion SOURCES I first came across the Kelly Criterion in [1], which features it as a powerful example of the relevance and usefulness of information theory. In chapter 6, you'll find an exact and more general discussion of this idea. Wikipedia, [2], was also useful, as it is frequently. That's where I learned about [3], which was an interesting case study about how bad we are at this game. As an aside, I actually ran the experiment on a friend and didn't find similar results - I guess she was pretty smart! She bet about 3-5 dollars on each flip, with the wagers moving to the larger side of this range as the wealth increased. [4] is the original paper. It's a nice explanation, but relies on some background knowledge of information theory. If you're curious about the connection to information theory, this is the place to look. -------------------------------- [1] T. M. Cover and J. A. Thomas. Elements of Information Theory. 2nd edition. John Wiley, 2006. [2] Kelly Criterion, Wikipedia, https://en.wikipedia.org/wiki/Kelly_criterion [3] V. Haghani, R Dewey, "Rational Decision-Making Under Uncertainty: Observed Betting Patterns on a Biased Coin", Journal of Portfolio Management, 2016 [4] J. L. Kelly Jr, "A New Interpretation of Information Rate", Bell System Technical Journal, 1956