202303071018 Status: #📚 #book #antilibrary Tags: #epistemology #mental-models # Mental Models “Multidisciplinary Thinking” Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful. — George Box 1. The map is not the territory 2. Circle of competence 1. Falsifiability 3. First principles thinking 1. Necessity v Sufficiency 4. Second order thinking 5. Probabilistic thinking (Bayesian Reasoning) 1. Correlation v Causation 6. Inversion 7. [[Occam’s Razor]] 8. [[Hanlon’s Razor]] **The Map is Not the Territory** Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful. — George Box ** ** - Maps are useful abstractions. They must be abstractions, for if not, they’d cease to be the map (or, cease to be useful). Therefore, they’re always limited. They’re also time-bound. The represent a snapshot in time. - They mustn’t be mistaken for the territory itself. - Newtonian physics was a map. As was Einstein’s theories on special and general relativity. While the latter supplanted the former (and quantum physics supplants both) all still serve as useful maps for different areas of physics. - Consider the concept of emergence here, per Sean Carrol - Tragedy of the Commons: game theoretic concept of a shared resource being available to all. If all act in their own interest, the resource depletes, and thus is unsustainable. If all act in the interest of the commons, the resource can be sustainably mined. The imbalance comes from recognising the gains as an individual, but amortising the losses as a group, as the costs are divided among all. - Reality is the ultimate update: a good Bayesian knows that priors need to be updated based on experimental data (posterior credences). - Consider the cartographer: maps are not objective artefacts, they come loaded with the biases and perspectives of those creating them. Consider the motivations and POV of those who construct the maps. - Maps can influence territories: people following plans can transform the territories they’re occupying, and maps can become prophetic rather than diagnostic. **Circle of Competence** ** ** We shall be unable to turn natural advantage to account unless we make use of local guides. — Sun Tzu - The lifer vs the stranger: the Lifer has built up a deep picture of a local place over a long time. The stranger walks in thinking he understands a local place in a short time, because the place is small. - Circle of competency is all about knowing what your unknowns are. - The world is dynamic, and constantly updating. - Therefore competency is a muscle and needs to be constantly flexed, less it atrophy. - This requires curiosity, monitoring and feedback. - First, you have to be willing to learn. Learning comes when experience meets reflection. - Second you need to map what you want your circle of competence to be, so you can track it. Keeping a journal of your performance helps here. Raw data is brutal on the ego, but good for growing your competency. - Finally, you must occasionally solicit external feedback. This helps build a circle, but is also critical for maintaining one. Atul Gawande hired a coach so that he could solicit honest feedback about what he was doing that was suboptimal. He also figured out how to better give feedback to other doctors. - Be wary of incentives when consulting Lifers. - Queen Elizabeth set up a Privy Council — a royal advisory board. Not a bad shout. - This is why Dalio suggests getting a small group of people who you respect that disagree with you. - Falsifiability: Karl Popper’s approach to science was effectively saying that “If X is the outcome of an experiment, then Y will be demonstrably not true.” A way of twisting an experiment around to not verify or validate but falsify or disprove. **First Principles** I don’t know what’s the matter with people: they don’t learn by understanding; they learn by some other way—by rote or something. Their knowledge is so fragile! — Richard Feynman - Not looking for absolute truths, only the the foundations on which we’re building. - Fridge designer needs to know the second law of thermodynamics, a physicist might want to challenge the SLoT, and so would need a different set of first principles. - This works with principles of Emergence. - If we never learn to take something apart and reconstruct it, we’ll always be bound by what others tell us, and therefore the ways things have always been done. - Socratic questioning generally follows this process: - Clarifying your thinking and explaining the origins of your ideas. (Why do I think this? What exactly do I think?) - Challenging assumptions. (How do I know this is true? What if I thought the opposite?) - Looking for evidence. (How can I back this up? What are the sources?) - Considering alternative perspectives. (What might others think? How do I know I am correct?) - Examining consequences and implications. (What if I am wrong? What are the consequences if I am?) - Questioning the original questions. (Why did I think that? Was I correct? What conclusions can I draw from the reasoning process?) - To improve something, we need to understand why it is successful or not. Otherwise, we are just copying thoughts or behaviors without understanding why they worked. - First Principles is effective because it allows us to understand what’s really working within a system, method, or outcome, and then change what we need to to make something that best achieves a goal. - Mental Models: - Good at helping us to figure out what we do and don’t know, what the limits of our understanding is. - This can help point our our biases and assumptions. - Necessity and Sufficiency: - Being clear about what is table stakes, and what actually makes the difference between turning up and winning. (It’s usually luck.) - “Without them you definitely won’t be successful, but on their own they are not sufficient for success.” - In mathematics these are called “sets”(Set Theory) in which different objects are classed into different groups, and often nested. **Second-order Thinking** ** ** Technology is fine, but the scientists and engineers only partially think through their problems. They solve certain aspects, but not the total, and as a consequence it is slapping us back in the face very hard. — Barbara McClintock - Second order thinking is considering the consequences of your actions. - The effects of the effects. - This is hard because: 1) you’re typically invested in solving the problem ahead of you and who wants to rain on that parade, and; 2) the world is complex and it’s hard to game it all out (thought experiments) especially if there are emotional incentives not to (1). - Developing trust by thinking through second-order effects. Relates back to Grandad’s maxim “a good deal is one where both parties feel like they could have got more”. Gains are maximised over time. Think of the TLV. - Arguments are more persuasive when we can show we’ve through through the second-order effects. - Be careful of Slippery Slope and Analysis Paralysis **Probabilistic Thinking** ** ** - Trying to estimate the probability of something happening. - Bayesian vs Frequentist: considering the likelihood of an outcome before an experiment, and then updating the likelihood based on the data. From prior to posterior. - Everyday intuitive probabilities are heuristics. - The core of Bayesian thinking is this: given that we have limited but useful information about the world, and are constantly encountering new information, we should probably take into account what we already know when we learn something new.** ** - Also called “Priors” or “Base Rate” - Conditional probability: is the observed outcome dependent on contextual factors, or is it independent? Be mindful of the conditions that surround the event. - Fat-tailed curves: where the distribution of possible events isn’t clustered into predictable bell curves, and you can get power law-type distributions tipping the scales. These are unpredictable because they allow for exponential outliers. - Consider anti fragility — availing yourself with the capacity to benefit from life’s unpredictability. Favour preparation over prediction. (Strength through Agility) - Insurance is a whole industry built on Bayesian probabilities. ** ** **Inversion** ** ** The test of a first-rate intelligence is the ability to hold two opposing ideas in mind at the same time and still retain the ability to function. One should, for example, be able to see that things are hopeless yet be determined to make them otherwise. — F. Scott Fitzgerald** ** - Starting at the end of the problem—the outcome you want to achieve. - Start by assuming that what you’re trying to prove is true (or false), and then work out the consequences of that assumption. - Start by thinking about what you’re looking to avoid, and then look at what other options are left. - When thinking about what we want, look to mitigate the downsides, not just augment the upsides. - This is why a diversified portfolio with a backbone of ETFs is a strong call. - Also, John Bogle created the first index fund through Vanguard. - Invert, always invert when you are stuck. Think about what you’re trying to avoid, and then look to solve from there. **Occam’s Razor** ** ** Anybody can make the simple complicated. Creativity is making the complicated simple. — Charles Mingus “Extraordinary claims require extraordinary proof.” — Carl Sagan - “It is vain to do with more what can be done with fewer” - Simple explanations are more likely to be true than complicated ones. - The more complex an explanation it is, the more likely it is to be wrong, because if you break down each component part into its likelihood you have to then multiply those uncertainties. - As simple as possible. And absolutely no simpler. **Hanlon’s Razor** ** ** - “Don’t attribute to malice what can be explained by stupidity.” - The explanation most likely to be right is the one that contains the least amount of intent. - Why? Because the world is complex, and because you’re not the centre of it. Just because you feel personally aggrieved by something doesn’t mean someone intended that outcome. In fact, it’s far more likely that they weren’t considering you at all. - The Linda problem: conditional base rates/probabilities. (Fallacy of conjunction) - Always assuming malice puts you at the centre of everyone’s world. That’s incredibly self- --- # References -