Liv Boeree on Poker, Aliens, and Thinking in Probabilities

Want to listen along? Here's the episode – https://www.preposterousuniverse.com/podcast/2018/07/23/episode-6-liv-boeree-on-poker-aliens-and-thinking-in-probabilities/​

Full disclosure: I'm not an expert on anything. Maybe I'm an entertainer (at least my wife says that) or just an ignoramus with opinions. The closest I've come to professional gambling was losing $20 at a casino once and deciding that was enough financial education for one lifetime. Well, there was that one time I won at roulette and left with my wife in a hurry, as if the casino might change its mind about letting us keep the money. I've always been too shy to play at a real poker table, so I lost all my poker money online instead. I've never actually been to Vegas, but I appreciate it as a "thinking in bets" exercise. There's a certain beauty in realizing that real poker is played without cards – it's the all-in moments of life where we must decide with incomplete information. But when I heard this conversation between Sean Carroll and professional player Liv Boeree, I was fascinated by how much poker thinking applies to everyday decision-making.

One message from the episode that stuck with me is the chance that a poker player will lose everything on red. It's essentially a funnel for the house. Casinos know that even the most disciplined players might surrender their skill-based winnings to pure chance games on their way out the door. It's a metaphor for how easily hard-earned advantages can evaporate in a moment of impulse.

So grab your favorite beverage (I recommend something with caffeine – this gets into probability theory) and let's explore how poker can make us all better thinkers:

From Physics to Poker: A Surprising Career Path

Insight: Liv Boeree started with a physics degree and intended to return to science after "a few years" of professional poker. Years later, she's still a pro, having found unexpected success in the game.

My Take: This resonates with my own meandering career path. I always wanted to work with computers, but I thought my job itself would be temporary. I imagined I'd end up like a hacker in some cheap movie, hanging out in a basement doing nothing particularly career-oriented. And here I am, 25 years later, still grinding in corporate environments. It's as if I'm stuck in a pattern that repeats with almost clockwork precision: startup-scaleup-corporate... and then back to the beginning. Roll that boulder up again! It's funny how our "temporary" detours often become our main roads. I wonder how many of us are living lives we once considered Plan B? There's something deeply familiar about following unexpected opportunities rather than sticking to a rigid plan. Perhaps that's why, despite the repetitive cycle, I find a strange comfort in this corporate Groundhog Day.

Skill vs. Luck: The Poker Paradox

Insight: Unlike golf, where results closely correlate with skill level, poker results (especially short-term) can be wildly disconnected from skill due to luck. Even over 100 tournaments, worse players might outperform better ones.

My Take: This explains so much about why I struggle with poker! My brain craves the immediate feedback loop of "do thing → see result → know if it was good." Poker breaks this by inserting a massive randomness variable. It's like trying to learn any new skill where the feedback is inconsistent – sometimes you're rewarded for an action, sometimes punished for the exact same move. No wonder I stick to games where my terrible decisions lead to predictably poor outcomes.

The business world operates similarly. How many mediocre companies thrive while brilliant ones fail due to timing, market conditions, or just plain luck? Having cycled through startups, scaleups, and corporations repeatedly over 25 years, I've witnessed this firsthand. Some truly terrible ideas get funded into the stratosphere while brilliant innovations languish. It's enough to make you question whether skill matters at all. This is probably why we're so obsessed with success stories and "how they did it" articles. We want to believe there's a formula, when often it's just someone who got lucky and then reverse-engineered a narrative.

Poker vs. Chess: Games with Incomplete Information

Insight: Chess is a complete information game where both players see the board. Poker involves hidden information, requiring probabilistic thinking to handle uncertainties about opponents' hands.

My Take: This distinction explains why I'm terrible at poker but decent at chess. In chess, my overthinking is an asset – I can analyze visible pieces and calculate outcomes. In poker, my brain melts trying to calculate probabilities while also reading people (another skill I lack – I once mistook someone's allergic reaction for emotional distress).

Life is more like poker than chess. We make decisions with incomplete information. Should I take this job? Move to this city? Date this person? We never have all the information, yet we must act anyway. Maybe I should start viewing these decisions through a poker lens rather than trying to "solve" them like chess problems. Actually scratch that, the only reason I can play a decent game or two of chess is that I use no blunder strategy and chose weaker players than myself.

Math vs. Psychology in Poker

Insight: Liv emphasizes that mastering the game relies 90% on understanding game theory and math, while psychology plays a lesser role in poker. Intuition and reading people are secondary skills developed through experience.

My Take: This completely upends the popular conception of poker as primarily about "reading people" and bluffing. It's similar to discovering that being a successful chef is 90% about understanding food chemistry and 10% about creative plating.

This is actually encouraging, as someone who's terrible at reading social cues (I once continued a conversation with someone for 15 minutes before realizing they were trying to end it). It suggests that even socially awkward people like me could theoretically excel at poker if we master the mathematical foundations. Though I suspect my face would still broadcast my hand like a billboard, if we forget the fact that I am bad at math as well.

Phil Locke's Bathroom Break: Probability in Practice

Insight: Poker player Phil Locke once announced at a movie premiere that there was an "83% chance" he'd need to use the bathroom before the film ended. Liv wasn't sure if his precise probability was a joke or a demonstration of his estimation skills.

My Take: I love this anecdote because it captures how probability thinking seeps into everyday life for poker players. Meanwhile, I'm over here saying things like "I might need to use the bathroom" with zero quantification.

Imagine applying this level of probabilistic precision to daily conversations:

"There's a 72% chance I'll arrive late to our meeting."

"I'm 91% certain I left my keys in the kitchen."

"Based on our conversation so far, there's a 64% probability you're getting annoyed with my excessive precision."

The scary part is I'm actually tempted to start doing this. But at least my friends would have precise advance warning even though they'd hate it.

Game Theory Optimal Poker: Solvers and Strategy

Insight: Modern poker players use "solvers" – software that runs Monte Carlo simulations to determine game theory optimal (GTO) strategies for various scenarios. These tools provide guidance on percentages for checking, raising, calling, and bluffing.

My Take: As a tech nerd, I'm fascinated by how poker has evolved into this computational arms race. It reminds me of how chess transformed after Deep Blue beat Kasparov. The human element remains, but it's now enhanced by algorithmic thinking.

What strikes me is how this mirrors other fields. Investment firms use algorithms to make trading decisions. Dating apps use matching ones. Even content creators use analytics to optimize engagement. We're increasingly outsourcing our decision-making to algorithms, then trying to learn from them. I wonder if future generations will naturally think more algorithmically because they grew up in this environment?

Nash Equilibrium: Game Theory in Practice

Insight: A Nash Equilibrium is a state where no player can improve their outcome by changing strategy alone if others maintain theirs. In poker, theoretically, players could start with a GTO strategy and break even long-term. However, since humans can't play perfectly, there are opportunities to take advantage of opponents' deviations.

My Take: I first encountered Nash Equilibrium in "A Beautiful Mind" and thought, "Neat concept, will never use this." Yet here it is, practically applied in poker! This is why I should have paid more attention to the movie instead of scrolling the phone.

The fascinating part is how this applies beyond games. In negotiations, relationships, even traffic patterns, there are equilibrium states where unilateral changes hurt the person making them. Yet we constantly see people making these suboptimal moves (myself included). I once tried to find an alternative route everyone else seemed to ignore, only to discover there was a very good reason everyone avoided it (imagine somebody suggest improvement for your merge request and after month or trying to find a workaround you agree to merge the original one). Nash would not have been impressed.

Rock Paper Scissors: Not So Random

Insight: When playing rock-paper-scissors, humans tend to follow predictable patterns, such as switching after playing the same move multiple times. Sean Carroll tried to exploit a New York Times app trained on millions of human games and lost badly because it anticipated these tendencies.

My Take: This is humbling. Even in a game as simple as rock-paper-scissors, we can't achieve true randomness. Our brains are pattern-making machines, even when we're trying not to make patterns!

I've noticed this in my own attempts to generate "random" passwords or sequences – they're never truly random. My password heuristic is simple: they must be easy to remember. Any security expert worth their salt actually understands this is built into normal human behavior. We can pretend we'll remember 30-character strings of gibberish, but our brains aren't wired that way. There's always some underlying pattern I'm unconsciously following. This makes me wonder how many other areas of life I think I'm being unpredictable in, but am actually following obvious patterns that others can exploit. My shopping habits? My conversation topics? My excuses for missing deadlines? (Sorry, boss, if you're reading this and recognizing a pattern...)

Humans and Heuristics: Our Mental Shortcuts

Insight: Humans excel at using heuristics (rules of thumb) to simplify complex situations like multi-player poker, where even AI doesn't know the game-theoretically optimal strategy. We're skilled at creating approximate models that work effectively.

My Take: This is where humans still have an edge over AI – our ability to create useful approximations with limited data. We don't need to process millions of examples to develop workable heuristics. We can play a few rounds of a new game and develop a "feel" for it.

I've always thought that the ability to quickly develop useful mental shortcuts isa crucial survival skill. When systems change overnight, those who can rapidly update their models thrive, while those who cling to old frameworks struggle. I've seen how older generations sometimes struggle with concepts like cryptocurrency precisely because it violates their hard-earned heuristics about value being tied to something tangible. Our mental models warn against anything that seems too disconnected from physical reality, and who can blame us? Our life experience taught us different rules.

Stereotyping in Poker: Bayesian Updating

Insight: Liv creates initial stereotypes of players based on appearance, age, gender, and demeanor. However, she actively updates these models as she observes their play. She uses Bayesian updating, adjusting beliefs as she gathers stronger evidence, building more nuanced ones over hundreds of hands.

My Take: This is fascinating because it shows how stereotyping can be both useful and dangerous. Initial stereotypes provide a starting point when information is limited, but the key is updating them quickly as new evidence arrives.

I'm guilty of clinging to initial impressions too long. I once thought a colleague was aloof and unfriendly based on our first meeting, and it took me months to update that model despite mounting evidence that he was just an busy. My brain kept filtering new information through my initial stereotype. Poker players can't afford this luxury – they must update rapidly or lose money. Maybe we should all pretend there's cash on the line when forming opinions about people?

Thinking in Probabilities: A New Mental Framework

Insight: Playing poker professionally changed Liv's thinking from black-and-white to probability-focused. Now, she estimates probabilities in everyday situations, like assessing the likelihood of making it to appointments on time based on traffic conditions.

My Take: This might be the most valuable takeaway for everyday life. Most of us operate in a binary world of "will happen" or "won't happen," when reality exists in probabilities.

I've been trying to adopt this mindset, especially for planning. I'm learning to think "There's a 70% chance I'll finish by Friday, 95% by Monday," instead of saying "I'll finish this project by Tuesday." This feels more honest and helps set better expectations. Though I admit, telling my manager "There's a 43% probability I'll complete this on time" doesn't always go over well. Some people prefer comforting certainty over uncomfortable probabilities.

The Drake Equation: Cosmic Uncertainty

Insight: The Drake equation, used to estimate the number of intelligent civilizations, faces enormous uncertainty, particularly regarding the fraction of planets where life develops. This factor has a range spanning over 200 orders of magnitude, making specific estimates highly speculative.

My Take:This discussion fascinated me as someone who spent countless childhood hours wondering if aliens were real (and if they'd be impressed by my LEGO collection). The uncertainty in the Drake equation (not the canadian) is mind-boggling – 200 orders of magnitude! That's like saying "this number could be 1 or it could be a 1 followed by 200 zeros."

This puts our cosmic loneliness in perspective. We don't know if we're the only intelligent life in the universe or if it's teeming with civilizations. Both possibilities are equally supported by our current knowledge. It's like being in a dark room and not knowing if you're alone or at a party where everyone is very quiet. The uncertainty itself is profound.

Human Extinction: A "Terribly Big Shame"

Insight: Liv agrees with Sean that human extinction would be bad, calling it a "terribly big shame" that would represent lost potential on a universal scale. While she doesn't subscribe to moral realism, she acknowledges that even constructed morals highlight the negative implications of humanity's disappearance.

My Take: I find the understated British assessment of human extinction as a "terribly big shame" both hilarious and profound. It's like describing the sun exploding as "rather inconvenient" or the heat death of the universe as "a bit of a downer."

But there's something beautiful about framing extinction in terms of lost potential rather than moral absolutes. We don't need objective cosmic morality to value the continuation of consciousness and complexity in the universe. Even if we're just temporary patterns in an entropy-increasing universe (as Sean explains), there's something worth preserving in our collective experiment of existence.

Final Thoughts: Poker as a Life Philosophy

After diving into this conversation, I'm convinced that poker thinking offers valuable tools for navigating our uncertain world. The probabilistic mindset, Bayesian updating, and game theory concepts apply far beyond card games.

What strikes me most is how poker forces intellectual honesty. You can't succeed by fooling yourself about probabilities or clinging to outdated models of other players. The cards and chips provide immediate feedback. Life rarely gives us such clear loops, making it easier to maintain comforting delusions.

Perhaps we should all approach life decisions with more poker-like thinking. This means assessing probabilities rather than seeking certainties, updating our beliefs with new evidence, and recognizing when we're playing a game of skill, a game of chance, or (most commonly) some messy combination of both.

And as Liv's experience reminds us, even if you master the skill game of poker, casinos know there's always a chance you'll lose everything on red. That's their funnel – luring you in with a game of skill, then tempting you with games of pure chance. The house always wins in the end, which might be the most important probability lesson of all.

As I reflect on this, I'm reminded of my own philosophy that the real poker is played without cards. It's in those all-in moments of life where we must make high-stakes decisions with incomplete information. Whether it's changing careers, moving to a city, or committing to a relationship, these are the true tables of our lives.

If you enjoyed these reflections from an admitted ignoramus who occasionally entertains his wife with half-baked ideas and lucky roulette spins, you might enjoy my other newsletter issues where I apply similar overthinking to other topics I'm barely qualified to discuss.

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