B: I can’t even calculate logarithms, you know, M.
M: You don’t have to. You have a calculator.
B: It’s not about the calculator. It’s about how the mind works.
M: Fine. Think of logarithms as an inverse function. If exponentiation is growth, logarithms are the path back to the root. Like asking, “How many times do I multiply a number by itself to get here?”
B: That’s abstract.
M: Everything is. Even probability. A function mapping uncertainty to a number between 0 and 1.
B: So, if certainty is 1 and impossibility is 0, everything else is a weighted guess?
M: Not a guess. A likelihood. Bayesian updating. Prior knowledge, new evidence, revised belief. P(A|B) = [P(B|A) * P(A)] / P(B).
B: Bayesian inference. Like adjusting expectations after every observation?
M: Exactly. Every experience, a new data point. Updating the function that predicts the world.
B: Sounds like cognitive bias.
M: Cognitive bias is just a miscalibrated probability function. A system trained on bad data.
B: So, what’s the equation for life?
M: Maximum likelihood estimation. You optimize for the best possible outcome given incomplete information.
B: And the loss function?
M: Regret.
B: Then what’s the derivative of regret?
M: Growth. If you’re differentiable at all.
__________
B: If regret is the loss function, then life is an optimization problem. What are we minimizing?
M: Suffering, maybe. But Yalom (Staring at the Sun) would say we can’t eliminate existential anxiety—only integrate it. The function doesn’t go to zero; we just smooth the curve.
B: Nietzsche (Thus Spoke Zarathustra) would say suffering is necessary. That without struggle, there’s no transformation. No Übermensch.
M: He’d call it amor fati—loving fate. If suffering is inevitable, fold it into the function. Jordan Peterson (Beyond Order) would call that responsibility. Choosing order over chaos, meaning over nihilism.
B: But Kahneman (Thinking, Fast and Slow) would remind us our cognition is flawed. That our System 1—fast, instinctive—leads, and our System 2—slow, rational—justifies. That what we think is reason is often post-hoc rationalization.
M: And Sapolsky (Behave) would add that biology predetermines a lot. Genes, hormones, neurochemistry—each a prior probability in our Bayesian function. Free will? Maybe just noise in the dataset.
B: So we’re stochastic systems, optimizing within constraints.
M: Homo sapiens are just self-referential models. Harari (Homo Deus) would say our myths—religion, money, even self—are the algorithms we run to keep civilization functional.
B: And if the function fails? If suffering outweighs meaning?
M: That’s Camus (The Myth of Sisyphus). The question of whether to push the rock or let it roll back. To live despite the absurd.
B: And therapy? Yalom, again?
M: The gift of therapy is holding space for uncertainty. A safe sandbox for error correction. It’s Newton’s Method applied to the self—iterating, refining, converging.
B: But never reaching a final answer.
M: Because the equation isn’t solvable. Only approximated.
__________
B: Do you ever think about how everything—people, choices, history—is just dust in a system too large to even be measured?
M: Dust in a giant, screaming, multi-multi-universe that doesn’t care we exist? Yeah. All the time.
B: And yet people act like their problems are absolute. Like their heartbreak, their ambitions, their regrets—any of it really matters.
M: Statistically, it doesn’t. We are noise. Fluctuations in entropy. A temporary deviation before the function smooths itself out.
B: But that’s the paradox, isn’t it? We know we are nothing, yet we feel everything.
M: Observers trapped inside the system, unable to see beyond it. A feedback loop of meaning-making because our brains evolved to care—even when there’s nothing to care about.
B: Harari would say it’s all a shared delusion. Nietzsche would say the only way forward is to create our own values. Kahneman would say we don’t even make rational choices, just feel like we do.
M: And Yalom would say, in the end, death is the only certainty. The final data point. The end of the function.
B: So what do we do with that? If nothing matters?
M: Nothing matters on a universal scale. But on a human scale? Everything does.
B: That’s such a contradiction.
M: No. That’s the only way to survive the contradiction.
B: So we just keep living. Keep making equations out of noise. Keep pretending dust isn’t dust.
M: It’s either that—or let the void win.
B: …Fine. But if the void starts winning, you better remind me why I’m still here.
M: Always.
__________
B: M, have you ever thought about how our decisions are influenced by algorithms?
M: You mean like in “Algorithms to Live By” by Brian Christian and Tom Griffiths?
B: Exactly. They discuss how computer algorithms can help us make better choices in everyday life.
M: Right, like the optimal stopping problem—looking at the first 37% of options, then choosing the next best one.
B: Yes, and Judea Pearl’s “The Book of Why” delves into causality, explaining how understanding cause and effect can improve our decision-making.
M: So, combining these ideas, we can use algorithms to guide our choices and understand the causal relationships behind them.
B: Precisely. It’s about applying computational thinking to navigate the complexities of life.
M: And recognizing that while algorithms can assist us, understanding the “why” behind our decisions is equally crucial.
B: It’s a blend of optimizing our choices and comprehending the underlying causes—a holistic approach to decision-making.
M: By integrating these perspectives, we can make more informed and effective decisions in our daily lives.
B: Agreed. It’s fascinating how these concepts intersect to provide a deeper understanding of our actions.
M: And ultimately, help us lead more deliberate and meaningful lives.
B: Absolutely. It’s a powerful synergy between computation and causation.
M: A testament to the value of interdisciplinary thinking.
B: Indeed. It broadens our horizons and enhances our approach to problem-solving.
M: A reminder that diverse perspectives can lead to profound insights.
B: And that continuous learning is key to personal growth.
M: Well said, B.
B: Thank you, M.
M: Shall we explore more on this topic?
B: I’d love to.
M: Great. Let’s dive deeper into the applications of these ideas.
B: Looking forward to it.
M: Me too.
B: Let’s begin.
M: After you.
B: Alright.
M: Lead the way.
B: Here we go.
M: I’m ready.
B: Let’s do this.
M: Together.
B: Always.
M: To new discoveries.
B: And deeper understanding.
M: Cheers to that.
B: Cheers.
M: Let’s continue.
B: Yes.
M: Onward.
B: Onward.
________
References
• Christian, B., & Griffiths, T. (2016). Algorithms to live by: The computer science of human decisions. Henry Holt and Co.
• Galfard, C. (2015). The universe in your hand: A journey through space, time, and beyond. Flatiron Books.
• Harari, Y. N. (2017). Homo Deus: A brief history of tomorrow. Harper.
• Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
• Nietzsche, F. (2006). Thus spoke Zarathustra: A book for everyone and no one (T. Common, Trans.). Oxford University Press. (Original work published 1883)
• Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books.
• Peterson, J. B. (2018). 12 rules for life: An antidote to chaos. Random House Canada.
• Peterson, J. B. (2021). Beyond order: 12 more rules for life. Penguin Books.
• Sapolsky, R. M. (2017). Behave: The biology of humans at our best and worst. Penguin Press.
• Sapolsky, R. M. (2023). Determined: A science of life without free will. Penguin Press.
• Yalom, I. D. (1992). When Nietzsche wept: A novel of obsession. Basic Books.
• Yalom, I. D. (2002). The gift of therapy: An open letter to a new generation of therapists and their patients. Harper Perennial.
• Yalom, I. D. (2008). Staring at the sun: Overcoming the terror of death. Jossey-Bass.
• Yalom, I. D. (2015). Creatures of a day: And other tales of psychotherapy. Basic Books.
Cheers All the Writers.
