Mental models: recurring patterns that occur in life
Knowing mental models and creating mental pictures can help us understand what is going on and make better decisions
Each discipline has its own set of mental models but a subset of that can be used for general life patterns
Mental models give us access to higher level thinking but want it to be broad enough that we have the right model for the right situation
Being Wrong Less
Inverse thinking: invert the situation and think through
The inverse of being right more is to be wrong less
Unforced error: an error one makes that is solely influenced by your skill and not by others (eg. missing a badminton shot because you are bad at clears, not because the opponent shot an awesome clear)
Notice unforced errors in life and reduce them as much as you can
Antifragility: get better as you experience shocks
You can make your thinking antifragile by getting better from your mistakes
First principle thinking: use basic principles and build up to your conclusion
Think of math proofs: we use the basic building blocks to get to where we need to be
Allows you to approach unfamiliar environments in innovative ways because the basic building blocks of your thinking has not changed
Avoiding the trap of conventional wisdom
Eg: career moves should come from your own first principles of what you require in work and then apply only to positions that satisfy that.
De-risking: any first principle is simply an assumption and could be false. You need to test your assumptions
First break down your assumptions as far as you can and then test easily
In real life, this testing can simply be getting more info or talking to people
Any plan you have initially can easily be wrong (battle plans never survive first contact) so this should help you
Premature optimization: creating the perfect work too early
Minimum viable product: the minimum amount of features that a product needs before it can be tested
Build your “MVPs” and test in order to derisk
Ockham’s Razor: the simplest explanations are most likely correct
Apply the razor to your principles and ask yourself if it is actually needed to arrive at your conclusion
Conjunction fallacy: let there be events A and B. From prior knowledge, we know B is quite likely, but people that follow this fallacy also thinks the probability of A and B are also likely, when it’s not the case
Eg: Linda is a social activist. Is it more likely that she is a bank teller or that she is a bank teller and has gone to a protest? Although probability of protest is high given prior info, the conjuction of protest attendance and being a bank teller is not necessarily as high (unless every bank teller goes to protests)
The conjunction of two or more events is a low probability
Overfitting: deriving a model that is way too specific from past evidence
Often because assumptions are not simple enough
Avoid this by asking if our data can explain multiple conclusions and not only the one we are overfitting
Reference frame: your perspective on an issue may be different to another’s
Just like the physics reference frame: On a train, you see yourself as standing still but people outside train see you moving
Understand your perspective differences when making decisions
Framing: frame a situation such that you make a particular perspective more likely, all because of the words you use (eg. frame an innovative initiative as beating the competition rather than a use of expensive resources)
Be aware of framing when situations are presented to you
Nudging: similar to framing where words/design nudges you to interpret situation in a way that benefits someone else (eg. menus in restaurants often nudge you to buy certain dishes by design of menu)
Anchoring: get really influenced by your initial impression of a situation (eg. Trump anchors supporters to extreme positions so that later compromises don’t seem that bad/can be spun for political purposes)
Availiability bias: view gets biased because of info made availiable to you
Media often uses this. Immigration from Mexico is actually really low but since everyone talks about immigration, you think it is much worse than it actually is
Often caused by giving too much weight to stuff within your reference frame at the expense of the big picture
Eg: end-of-year review by manager often swayed by recent performance
Filter bubble: algorithms will filter out stories they think you won’t like, so you get even more bias, leading to echo chambers
When it comes to people, realize that everyone is coming with different experiences and perspectives. In statistics, we call this our prior
Always a third story in the conflict between two actors, which is a neutral observation of the situation
Try your level best to be impartial during conflict
Disarms opponents as it signals willingness to agree and empathy
Most respectful intrepretation: interpret the other’s party’s actions in the most respectful way possible, or give them the benefit of the doubt
Eg: Email a prof but don’t get an email back. Don’t think the prof has ill intent, but rather really busy and is trying to get back to you
Helps you build trust, which can be really useful
Hanlon’s Razor: never attribute to malic that which is adequately explained by carelessness
Someone caused a negative situation. More likely it was because they were careless and didn’t think it through rather than a deliberate malicious act
Third story, most respectful intrepretation and Hanlon’s razor attempts to overcome fundamental attribution error
Frequently make errors by atributing others’ behavior to internal/fundamental motivations rather than external factors
Self-serving bias: you tend to view yourself as having better reasons for doing something compared to someone else
Veil of ignorance: imagine ourselves ignorant of our own positions and try to think from other people’s shoes
Eg: If we were thinking about immigration policies, think about how it is to be a refugee
Just world hypothesis: people always get what they deserve because of their actions alone, not randomness
Leads to victim blaming and ignorance of birth lottery
Inaccurate at individual level
Learned helplessness: some people can’t get out of a helpless situation because they have learned to stop trying to escape it after many attempts
If we can show that their actions can make a difference, we can break this mental model
Eg: Utah gives homeless people an apartment and a social worker that can guide them through reintegration. Huge success → reduced homelessness pop. by 72%
You can be anchored to an entire way of thinking, making it very difficult to convince you a new idea if you already believe in a contradictory idea
Paradigm shift: theories don’t change gradually, but rather in bursts. Old guard will hold onto old ideas and they die, leading to a burst of change
Semmelweis reflex: data was leading to truth but explanations were incorrect, so we reject the conclusion as a whole
Confirmation bias: we are biased to interpret information that confirms our preexisting beliefs
This is why startups are often founded by outsiders, because they don’t have the same preexisting beliefs as the insiders
Backfire effect: dig into preexisting beliefs further when presented with contradictory evidence
Disconfirmation bias: subject contradictory theories to higher burden of proof than preexisting theories
Confirmation biases and related models relate to the concept of cognitive dissonance, which is the stress produced when you hold two contradictory beliefs at once
Thinking in gray: most truths are not black and white
Don’t form an opinion on something until you can get the max amount of info
Devil’s advocate: try to argue from the opposite point of view
Either you can do it or get someone who already has an opposing POV
Following your intuitions alone in uncertain situations can lead to confirmation biases, framing, anchoring and more, so slow down and think in new situations
To build up intuition in uncertain situations, either build up conclusion from first principles or perform root cause analysis
Proximate cause is the thing that immediately caused an event to happen. The root cause is the real reason (eg. Challenger exploded because hydrogen tank ignited but it was all because of organizational failure)
Perform postmortems after event (whether good or bad) and analyze via 5 Whys
Optimisitic probability bias: you want something to happen so badly that you think that the event is more likely
Anything That Can Go Wrong, Will
Unintended consequences are often predictable
Tragedy of the commons: benefits for indivdiual may often be bad for community, esp. if depleting a shared resource
Also known as the tyranny of small decisions: these small individualistic decisions lead to terrible consequences for entire community
Can be prevented if there is someone who can foresee what happens and vetoes these small decisions
Free riders: people that take advantage of resource without paying
There seems to be no harm with free riding, but if enough people do that, it can degrade the resource, leading to a tragedy of the commons
Herd immunity: don’t need the entire population vaccinated, just enough that those not vaccinated are protected
People then think that they don’t need to be vaccinated, which reduces the overall effectiveness of the vaccination program
Same concept with taxes: you don’t need everyone to pay for the country to run, but if people start to evade taxes, things get real bad
Externalities: unintended consequences that affect an entity without consent and usually comes from some external source
Eg: Pollution creates a negative externality for people living near the pollution source
Occur when there is spillover effect, where effect of activity spills over core interactions of activity
You can internalize an externality by asking entity that created the externality to pay for it. High price hopefully stops externality
Coase theorem: you can use a natural marketplace to internalize an externality
Good example is cap-and-trade systems for pollution
Requires well-defined property rights, rational actors and low transaction costs
If in charge of any system, think ahead of possible negative externalities you could create and try to avoid
Moral hazard: you take on more risk when you have info that makes you believe that you are more protected
Eg: More reckless driving in rental cars than normal cars, acting on behalf of someone
Usually because of assymetric information if the issue is with acting on behalf of others
Adverse selection: select transactions that will benefit them due partially from asymmetric info
Can lead to market breakdown
Eg: healthy people know that they don’t need to opt-in for health insurance for Affordable Care Act because P(end up in hospital) ~ 0. They pay for the fine rather than the insurance, which means the premiums rise for everyone, which leads to more healthy people leaving
Market failure is often a cause of no intervention, leading to tragedy of the commons
Interventions can also fail
Eg: research into antibiotics has dropped even though there is significant risk of bacterial infection outbreak. Since we don’t want to use antibiotics often, drugs often expire before use, so not much of a benefit from company POV. No government intervention would prevent us from having these antibiotics because business is not interested due to low profitability
Goodhart’s Law: when a measure becomes a target, it ceases to be a good measure
Eg: Facebook’s obsession over growth metrics led to breakdown of privacy and increase in filter bubbles
People will go to any length’s possible to meet target, even though the method may not be best in the long-run (perverse incentives)
Cobra effect: attempted solution makes problem worse
Streisand effect: trying to hide something leads to more attention
Hydra effect: removing one head leads to another two (eg. removing a drug lord creates two new drug lords who will cause worse problems)
Observer effect: the mere act of observing something change behaviour (causes chilling of behaviour)
When you set targets, be very careful to not create perverse incentives and pay very close attention to incentive structure. People’s self-interest should support your goals.
Boiling frog: class of unintended consequences where you do not react to gradual change, either because you don’t notice it or choose to ignore (head in sand)
Arises when people become fixated on short-term and creates technical debt
These outstanding debts create path dependence, where your future choices are now dictated by past decisions
Choose choices that perserve optionality
Downside of trying to keep options open is that it requires more resources and increases costs
Use the precautionary principle to eliminate paths that you are pursuing: be very cautious of paths that may cause harm in some way or another
First understand what are the long-term problems, work backward to figure out how it arose, then take the necessary level of precaution and pay your debt
Information overload: overwhelmed with information that you can use to make decisions
Also known as analysis paralysis because you can’t do anything because you are analyzing so much
Kind of like the model of ‘perfect is the enemy of good’: not making a choice while waiting to make a choice is a decision in itself, which actually opts for the status quo
Can deal with this by categorizing decisions into either reversible or irreversible. Irreversible decisions should be made cautiously but don’t be afraid to make reversivle decisions
Hick’s Law: more choices leads to slower decision time. Limit your choices in the first place or create multi-step decisions
Decision fatigue: more and more decision making in a limited time leads to worsening decision quality
You can frontload your decisions to a time when you are not that overwhelmed
Murphy’s Law: anything that can go wrong will go wrong
Just be prepared and have a plan when things go wrong
Spend Your Time Wisely
North star: the guiding vision of the company/yourself
You can point your actions toward your north star and prevent short-termism
The north star can evolve as you progress
Really small steps can compound into large gains
Two-front wars: dividing your attention will lead to defeat
Eg: Germany was defeated in both world wars because it had to pay attention to both fronts
Multitasking is a form of two-front warfare since your focus is divided
Context-switching is simply extra overhead and quite expensive
Focusing on one thing can even help with unconcious thinking, as the mind will start to drift to your top priority during inconsequential tasks
Deep work: spend extremely long periods of time working on single problem
Helps you focus on the most important problem that you need to solve
How do you determine what you should focus on? Use the Eisenhower Decision Matrix
Dedicate deep work time to ‘Decide’ matrix
Sayre’s Law: in any dispute, the intensity of feeling is inversely proportional to the value of the issue at stake
Parkinson’s Law of Triviality: organizations give importance to trivial issues
Bike-shedding: avoid talking about the difficult issues and instead spend all your time on the trivial issues
Timebox or schedule ahead of time to avoid spending too much time on trivial things
To choose what to work on in more detail, consider the opportunity cost of each task and choose the task with the lowest cost
Similar frameworks: opportunity cost of capital, BATNA
Always consider the leverage of your choices, which is simply the outsized multiple of outcome you can get from a set input
Highest leverage activities have the lowest opportunity cost
Much like the Pareto effect, where 20% of effort leads to 80% of outcome (also known as a power law distribution)
Each additional hour spent on a task produces diminishing returns, or sometimes even negative returns
As your progress in task, think whether there is more high-leverage task you could rather do. If the opportunity cost is high, switch
People procrastinate too much because of present bias, where near-term rewards are considered better than long-term goals
Think of procrastination as a negative interest fee, so you can see negative compounding with increased procrastination
Net Present Value (NPV) is the sum of discounted earnings
We often use hyperbolic discounting, where we value instant gratification a lot more than long-term gratification
To combat procrastination, you can commit yourself to a future in some way (eg. pay for a gym membership if you want to be fit)
Penalty for breaking it should be harsh and commitment should be specific
You can use the default effect as well, which is the effect stemming from people just accepting the default option
Organ donation increases significantly if it is opt-in vs. opt-out
You can schedule your time out in advance to make use of default
Parkinson’s Law: work expands so as to fill the time avaliable for completion
Hofstadter’s Law: it always takes longer than you expect, even when you take Hoftstadter’s Law into account
Prevent this by knowing that you don’t need perfection to finish
Sometimes you want to quit the project because of loss aversion
Due to shift in change of reference. If you have guaranteed some gain, you want to lock that in so you prevent taking risks
Always use opportunity cost to determine if you should walk or accept
Sunk-cost fallacy: costs of task have already been used so you want to complete it
Concorde fallacy: prohibitively expensive project because people kept throwing money at it due to sunk cost
Again, evaluate from opportunity cost: can I do something better with the time and money I am putting into this project
Use the third story model and mortems to help predict where you can possibly run into project traps and mitigate accordingly
When you are working on a project, know that you are likely not the first person who has encountered this problem. No need to reinvent the wheel
Pay attention to the design patterns/best practices in your field
Anti-patterns: seemingly intuitive but actually ineffective solution to common problem
Try to predict in advance if you would be using an anti-pattern
Brute-force solutions: exhaustive, not intellectual sophisticated solution
Problem is scale
You can use a heuristic solution, which uses some heuristics and trial-and-error
Facebook’s content moderation policy is mostly a heuristic solution
Algorithms are another solution. Many of them are often black boxes
Automate things that you do repeatedly to save time
Economies of scale: entity becomes more efficient with size
Amazon functions entirely on economies of scale
Another way to speed up a task is to do parallel processing
Much like divide-and-conquer strategies
Another way to get a solution in a hard situation is to reframe the problem
Disney didn’t try to reduce long wait times, but instead made those wait times enjoyable
Hackers reframed their problem to find out the best way to get your password rather than guess it, usually through social engineering
Becoming One With Nature
Natural selection: traits that give individuals a reproductive advantage helped select them over others
Good model for societal evolution and why certain ideas flourish
Evolution is happening much faster due to high interconnections
This is why we need to be open to new ideas and be highly adaptable, because our skills may not give us a long-term advantage
Best way to improve ourselves is through scientific method
Inertia: resistance to change
You have very high inertia mentally due to biases
Overcoming inertia requires payment of strategy tax, which is essentially the cost you need to pay for acting on a non-traditional strategy despite long-term changes
This is why you don’t want to have rigid long-term strategies
Shirky principle: institutions will try to preserve the problem for which they are the solution
Ex: TurboTax lobbies against automatic filing from government because that would remove the whole problem they are solving
Lindy effect: the longer something survives, the more likely it is to survive longer (eg. Shakespeare, Beatles)
Peak is the turning point when something is about to turn unpopular
It will take time for something to be unpopular due to momentum (systematic processes that entrench the object)
Inertia of culture is much greater than the inertia of company strategy. If you act on a plan that is different than the culture of the company, it won’t do well (eg. healthcare.gov release by US gov)
Because situations change quickly, having a low-inertia culture, i.e. a highly-adaptable culture, you can change your culture fast to adapt to strategy
Model of inertia: a flywheel (like a merry-go-round, where starting it requires lots of effort but maintaining is easy)
Similarly, becoming an expert is hard to start but easy to maintain
This is why multitasking is bad: hard to develop momentum on anything
Changing something is hard because of homeostasis: deviation from normal induces opposite effect (get’s too cold, body warms up. Get’s too hot, body cools)
Try to mitigate underlying reasons why homeostasis occurs
Collect data to prevent homeostasis from occuring in orgs
Identify potential energy in organization and center of gravity (eg. key influencer) and try to change that to cause change
Identify the activation energy and the catalysts to reduce activation energy
Eg: takedown of Confederate statues had high activation energy and BLM acted as a catalyst
Forcing function: prescheduled event/function that forces you to take action
Use forcing functions to act as catalysts for change
Critical mass: minimum amount of material needed to start a chain reaction
To start changing dramatically, we need a tipping/inflection point
Eg: need a certain amount of people for a party to feel like a party, and final person to arrive to meet the critical mass requirement creates tipping point
If you have expertise in an area that is about to reach the tipping point, you have massive value as your leverage increases
Technology adoption life cycle:
Innovators: takes risks and connected to merging fields
Early adopters: tries out new things once more fleshed out, don’t require social proof, often pushes idea past tipping point
Early majority: willing to adopt as soon as value prop established by early adopters. Don’t want to waste time/money
Late majority: skeptical of new things and requires social proof
Laggards: last group to adopt because of necessity
Root cause of reaching critical mass is network effect, where each person that joins service makes the service more enticing
Metcalfe’s Law: nonlinear growth in networks as you add more nodes
Cascading failures: failure feeds into more failures
Eg: airline disasters because of cascading failures, not one failure
Chaotic systems: easy to determine trend, but near impossible to determine final state
Butterfly effect: small changes can lead to big outcomes in chaotic systems
Adaptibility is key to success in chaos
Luck surface area: interact with more people to increase chance of luck
Of course, be judicious about the events you attend in order to preserve deep work times
Increasing personal entropy as there are more combinations that can lead to a good outcome (a little bit of disorder is not terrible, can actually be good)
Make better decisions using concept of polarity and creating own 2x2 matrices/graphs
Avoid black-white fallacy: things don’t always fall neatly into categories and are often continuous
Fallacy arises from in-group favoritism and out-group bias. We need to realize that most situations are not zero sum (one group loses while other wins)
There are always several factors in a negotiation and not all of them are valued equally by both parties, which means you can use give-and-take
Lies and Statistics
Human brains are conditioned for anecdotal evidence, but that isn’t really representative of the general truth
Eg: people are more likely to write a review if they are extremely impressed or disappointed with an offering, which doesn’t reflect most people’s experience with it
Correlation does not imply causation!
Usually confounding factors (third factor that influences the outcome)
Always create a hypothesis before you want to test something out
Avoid sharpshooter/moving target fallacy where you change the experiment to get the right results
Best experiments are blind randomized controlled experiments
Observers should also be blinded to avoid observer expectancy bias where the knowledge of your treatment group can impact the behaviour of the observer, influencing the experiment
Placebo effect: positive impact despite giving no treatment
Endpoint metric for experiments may be hard to collect, so we often have to use proxy metrics
University rankings use proxy metrics to determine ranks
Selection bias: the data that you have is somehow selected using some bias, making it not representative of the population
Why do more well-funded schools have better scores? Not only because of money, because well-funded schools select students that lead to better scores
Nonresponse bias: segment of population doesn’t answer survey skews results
Survivorship bias: ignore the evidence of objects that did not make it through trial (eg. classic airplane armor example)
When presented with data, always ask yourself: who is issing from sample population? Are there any methods that make this experiment non-random?
Response bias: knowing that your response matters may affect the way you answer the survey
Also caused by wording, order of questions, poor memory, etc
Try to call these biases as best as you can
Law of small numbers fallacy: drawing conclusions from small samples
Gambler’s fallacy: if independent, outcome of one experiment affects another (which is not true, independence means that outcomes do not influence each other)
Particularly true for sequence of probabilistic decisions (eg. judges giving parole may be less inclined to give approval if last 3 were given approval)
Clustering illusion: drawing patterns where there is nothing
Don’t confuse improbability with impossibility
Regression to the mean: extreme events followed by more typical events
Something rare has a low probability. Repeating that is even more rare
Note measures of centre and variance in a dataset
Normal distributions are quite common in our world and tell us that slight variance from mean is common
Central limit theorem: if you have a sufficiently large sample, take the average, and repeat a lot of times, you will get a normal distribution
Margin of error/confidence intervals tell us expected range of data that houses true parameter of data
Reducing margin of error requires a exponential rise in sample size
Conditional probability is very useful because life is full of conditions
Fallacy: P(B | A) ~ P(A | B) because discounting base rate (P(A) or P(B))
You can relate these probabilities using Bayes Theorem
Bigger sample size is always better but it takes resources
2 types of errors: false positives (falsely thought object in positive class) and false negatives (falsely thought object in negative class)
Decisions will require you to think through tradeoff of above 2 types of errors
Set false positive rate → determine sample size to detect real result with good probability (power)
Absence of evidence is not evidence of absence
Don’t focus too much on p-values because it can lead to black and white thinking
Replication crisis: we often cannot replicate the results we get from papers
p-hacking: running additional tests to look for statistically significant results
Prevent by specifying tests you want to test ahead of time
Publication bias: results that are not statistically significant are not published
Papers with non-significant results are still important
Meta-analyses: running analysis on other analysis (FiveThirtyEight)
Can get closer to truth only if sub-analysis are similar enough
Decisions, Decisions
To evaluate decisions, we often use pro-con lists
Major con: lots of grey options (can’t be easily classified as pro or con), weighting of each item seems to be equal, no interrelation, may create grass-is-greener mentality
Maslow’s Hammer: if you only have a hammer, everything looks like a nail
Pro-con lists have become hammers. Not well-suited to decision making
Can improve pro-con list by attaching numbers to each item (cost-benefit)
Talk to people who have made similar decisions in past and make sure that you didn’t miss out on any important factors
Use dollar values instead of points to value each pro/con
Intangibles should also be priced (think of how much you would pay for it)
Look at pros and cons over time to evaluate more thoroughly
Earlier benefits can be used immediately, inflation would depreciate the value of later benefits, more predicatable
Use discount rate to discount future benefits (look at NPV calculations)
To choose discount rate: use sensitivity analysis to determine benefit in future across different discount rates and think which is most likely (eg. look at inflation, change based on risk:reward)
Sensitivity analyses can be very useful: if providing dollar value of intangible is difficult, simply run an analysis and see how final decision can be affected by dollar value of intangible → uncovers key levers of decision
Discount rates aren’t great for extreme long-term consequences, like climate change mitigation
Be wary of evaluating decisions in different timelines! Discount rates cannot account for this
Use decision trees when there is a lot of uncertainity and look at expected value
You can price in different scenarios and choose relatively accurate probabilities to arrive at your expected value
Use utility values to price in the cost of intangibles
Be wary of black swan events, which are events which seem like they have small probabilities but are actually more likely than you think
Black swan events come from fat-tailed distributions, so probabilities of extreme events are higher
You can underestimate black swan events by not understanding their underlying distributions/reason or not pricing in cascading failures
Eg: floods in Houston are ‘once in 500 years” but there have been several in three years! Reason → climate change is fattening tail of extreme events
Systems thinking: draw out diagrams to understand systems and where they are vulnerable
Use simulations if necessary, like Monte Carlo
Systems behave according to Chatelier’s principle: system will react negatively to external stimuli and readjust to equilibrium (could be new equilibrium, not like homeostasis in that regard)
Hysteresis: system behaves depending on history (like path dependence)
Systems thinking can help with achieving a global optimum rather than a local optimum
Think about known knowns, known unknowns and unknown unknowns
Try to make everything known knowns
Uncover unknown unknowns through scenario analysis, where you run through different scenarios and expect what happens
Scenario analysis is challenging because you may get anchored easily, so always question assumptions and run thought experiments
Counterfactual thinking: thinking about “what if” if past was different
Beware of butterfly effect
Scenario analysis and counterfactual thinking is best done in groups, but this could lead to groupthink/bandwagon effect
Mitigate groupthink by questioning assumptions, evaluate all ideas critically, Devil’s advocate, diversity of thought, indep. sub groups
Crowdsourcing for decision making works when:
Diversity of opinion
Independent thinking
Aggregation of thinking can happen
People that can forecast well have:
Intelligence
Domain expertise
Practice in forecasting
Work in teams
Open-minded and willing to change beliefs
Understand probabilities of past events
Takes time
Revise predictions constantly
When you have come to conclusion of decision, write out thought process which enables you to find holes in your logic
Dealing with Conflict
Arms race situations (escalating conflict) is common in society
Employers want increasingly selective schools → school arms race or race for more status symbols
Avoid arms races at all costs and focus on what makes you unique
Game theory: study of strategy and decision making in adversarial situations
Nash equilibrium: set of choices of which a change of strategy by any player would worsen the outcome
Think of prisoner dilllema: equilibrium is when both betrays each other because keeping silent by either one would worsen outcome.
Best option != Nash equilbrium because the prisonner dilemma best situation is if both cooperate and not say anything, but if either one betrays, that betters the outcome for that person. Thus, cooperation is unstable
Seek out Nash equilibrium of any conflict, because that is the most likely consequence
Cooperation is usually better than betrayal in conflict.
Only reciprocate with bad behaviour if opponent iterates with bad behaviour
In games where reputation matters, cooperation is all the more important
Use a payoff matrix + decision tree to evaluate how to get to your outcome
Reciprocity: you tend to feel an oblication to return a favor regardless of whether favor was invited or not
Big way of influencing people
Commitment: being consistent with promises is important, otherwise would lead to cognitive dissonance
Liking: you are more prone to take advice from people you like, who share similar characteristics to you
You can use this via mirroring
Social proof: drawing on social cues as proof that you are making a good decision
Scarcity: you become more interested in opportunities the more scarce they are
Authority: more inclined to follow percieved authority figures
Conflicts can be framed in certain ways for a certain outcome
American Revolution started because Thomas Paine framed conflict betweeen colonists and England as Americans vs. English
Some conflicts are because of social norms and others are because of market norms. Social norms are involving no money, but market norms do. As soon as you frame it one way or another, things drastically change
Frame things such that they appear fair, but even this can be framed in different ways. Fairness = equal distribution or fairness=whoever followed procedure best gets reward?
Beware of strawmanning: reducing your argument into something much simpler, which often loses much of the nuance of the argument
Beware of ad hominem attacks
Dark patterns of influence: Potemkin villages (fake shell of actual reality), bait-and-switch tactics
Some conflicts can be so Pyhrric that the best move is not to play at all
You can use deterrants to prevent other side from playing as well
You can use stick(bad cop) and carrot(good cop) model to create better outcomes
Contain bad outcomes and stop bleeding (quick and dirty solution) if things go bad to prevent any domino effect
You can even attract bad outcomes in one place, known as honeypotting and simulatenously destroy (attempted in Iraq by US)
Domino effect often misused because people don’t understand causality
Slippery slope argument: one small thing → chain → bad outcome
Broken windows theory: broken windows → environment that encourages crime. Unclear if this is actually true
Gateway drug theory: one drug use is gateway to nmore drug use. Also unclear (correlation != causation)
Companies use this to lure people to buy products at cheap prices and hope that they inevitably buy higher-priced products
When considering if something will cause domino effect, write down list of all possible outcomes along with probabilities and then assess if you need to use containment/appeasement
You can use red line tactic to create deterrance: say that you will do something extreme to prevent others from doing some certain action
People may call your bluff, which puts your credibility on the line
War of attrition: do not get into one
If you do, you need to change the game. Eg: guerilla warfare
Generals always fight the last war: people generally approach conflict like their last conflict, which may not be a good idea
Endgame: need to consider your exit strategy
Don’t burn bridges with the exit strategy, but sometimes necessary (eg. Cortes sinks his boats, Caesar crosses Rubicon)
Unlocking People’s Potential
Joy’s law: most of the smartest work for someone else no matter who you are
Rumsfeld’s rule: you go to war with the army you have, not the army you wish you had
10x individuals: individuals who perform much above average. Can make dream team but very rare and often cannot replicate in other functions
This means that we can actually craft 10x talent with the right factors
First rule: people are not interchangeable due to personality traits
Personality traits:
Extroversion
Openness
Conscientiousness (organized vs. easygoing)
Agreeableness
Neuroticism (nervous vs. confident)
Other ways of classifying people: specialist vs. generalist, IQ & EQ
Every organization goes through three phases:
Commando: cheap, get things done, high damage
Soldier: build on commando work but need infrastructure to work bc so many
Police: hate change but build economies of scale
Foxes like details & broad, while hedgehogs and focused
Manage to the person than to the role & create new roles if necessary
Peter’s principle: managers rise to their level of incompetence
Keep this in mind for promotions
Setting up career paths is essential if you want people to stay
Higher roles tend to require more strategy than tactics
Make roles & responsibilities crystal clear using directly responsible individual
Prevents bystander effect where people don’t take responsibility because they are in a group and they assume someone else will
Use deliberate practice for mentorship
Mentor can identify edge goals and how you can achieve them while providing feedback
Spacing effect: learning effects greater when spaced out over time
Mentor-mentee should use this and rotate skills to improve
Give specific feedback but make sure you have set groundwork to show that you actually care about the person
To give tasks to mentees, think about conviction-consequence matrix
If high conviction on highly impactful idea, don’t delegate. If you have low conviction on a highly impactful idea or high convinction on low impact idea, delegate it sometimes. If low conviction on low impact idea, delegate it
Can give high conviction low impact stuff to mentees
Important to believe that team members can grow rather than fixed
Pygmalion effect: high expections → higher performance
Golem effect: low expectations → low performance
Having high expectations for people can make them perform better
If you keep putting people into challenging situations, you can breed imposter syndrome
Combat by recognizing commonality, expectation of small failures when operating out of comfort zone and connect with peers who have felt it
Dunning-Kruger effect:
Beware of Dunning-Kruger when coaching others: don’t let them get too cocky but also provide support as thye get
Maslow’s Hierarchy of Needs
Hierarchy tells us that if we want to become amazing, addressing impostor syndrome is a must
Memories of past are tainted by hindsight bias. You think certain things are obvious when looking back but they weren’t at the moment
Use counterfactual thinking to force you to consider other options that were present at the time
Record your decisions down so you can look back at it
Beware of self-serving bias
Every group has a culture: common beliefs, behavioral patterns & social norms
Low-context cultures requires very little context in communication and is very explicit and direct. High-context cultures require much more context
Other dimensions to evaluate culture: tight vs loose, hierarchical vs egalitarian, collectivist vs individualist, objective vs subjective
Shaping a good culture: establishing a solid vision, defining clear values, reinforcing via frequent communication, create processes that adhere to vision and values, lead by example, establish traditions, fostering accountability, rewarding based on cultural behaviours
Conflict is much more than firepower, but also winning hearts & minds
Companies are either there as loyalists or as mercenaries
Loyalists drawn by leadership, mission, values& location
Use Paul Graham’s comparison of maker vs. manager schedule and adjust accordingly
Culture erodes as it scales: often due to Dunbar’s number of 150 which is the maximum size of a stable, cohesive group
Too many new people at once can destroy culture
Mythical man-month: adding more people won’t speed up anything
New people have their own cost of onboarding and can’t improve the work that much faster
Boots on the ground: military terminology which means that example needs to be set in order for something to follow
To create good culture, your own boots need to be on the ground
Flex Your Market Power
Arbitrage: taking advantage of price differences for same product in two different settings
Reselling is a good example of exercising arbitrage
These opportunities don’t exist very long because others will do it
Signature of sustainable competitive advantage: market power
Power to profitably raise prices in market
This is easily done if you have a monopoly, like EpiPen companies
Markets with no power have perfect competition where everyone sells same good and prevents massive price discrepancies
Without market power, you are subjected to the whims of supply and demand
You need to pick an industry for high demand in the long run and you need to differentiate yourself to develop some market value
If you want to be extremely successful, you need to make contrarian bets
Consensus-contrarian matrix: if you are right and you are in consensus, you get regular returns. If you are right and contrarian, you get outsized returns. Otherwise you get no returns
This requires an appetite for risk
Contrarian bets require information asymmetry which you can take advantage
Known as a secret: no one else has thought of or way too risky
A secret can also be an ability to take a good idea to a great idea
Find people that are on the edge, the enthusiasts and hackers. They usually are on the cutting edge of the field
Contrarian bets require timing in order to make use of inertia
Apple Newton didn’t take off like iPad. Internet only became a huge hit after 2000
Ask yourself: why now? If new opportunity: now what?
Knowing something others don’t and having good timing still won’t guarantee success. Need great execution
First person to bring idea to market usually has first-move advantage but can also have a disadvantage if they make a lot of mistakes (others copy and avoid mistakes)
First mover hinges on being first to reach product/market fit: point at which product is a such a great fit for the market that consumers demanding more
First to P/M fit is much more successful than first to market
Much like resonant frequency
One of the best ways to reach P/M fit is to be customer centric and use scientific method to rapidly change product given feedback
Use MVPs to do the job
This can be applied everwhere: talk to community before moving, talk to current employees before taking job. Think who are customers and talk directly about your “product”
OODA loop: observe, orient, decide act
Make your OODA loop as fast as possible so that you can reach P/M fit
Helps you adapt to changing circumstances much faster than usual
Pivot if cannot reach P/M fit
Difficult because requires going against organizational inertia
Useful if current strategy is not going anywhere. Consult with advisors for more objective standpoint. Look for bright spots that you can focus in on before determining to pivot, like a beachhead
Jobs-to-be-done: think about what the role of your product is and determine whether something else can do that job better
When talking to customers, they often describe solutions rather than problems
Not just about # of customers, but also about size of customers
Your customer development must differ across scale & size
Can develop personas to help you understand customers better
Protect your position by building a moat: protected property, specialized skills/processes that take a long time to build, exclusive access, trusted brand, substantial control of distribution channel, amazing team, flywheels, faster OODA loop
Be explicit and note down what your moat is
Combine different moats to create a ‘force-field’
In other words, it creates high barriers of entry
Organizations that can create moats make customers feel locked-in because of percieved switching costs
Regulatory capture: regulatory agencies get captured by the special interest groups that they are supposed to be regulating, ultimately protecting them
Strong moats with regulatory capture can lead to winner-takes-most markets
Just because you ‘won’ the market doesn’t mean that you will win in eternity. As Andy Grove of Intel once said: ‘Only the paranoid survive’
Constantly re-evaluate strength of working moat and pivot if necessary
Innovations often mean that incumbents have tough choice of accepting and cannabilizing themselves or getting destroyed by innovation in future
Incumbents should pay attention to even the smallest of threats and use lifecycle adoption curve to model it out
Conclusion
Richard Feynman: “I learned very early the difference between knowing the name of something and knowing something”
Cargo cult: imitating and hoping that end result happens
Polynesians imitated airport but no airplanes came
Usually happens when people don’t understand what they are doing
Dangerous to use wrong model in situation. Think carefully and deeply
How to become a real superthinker:
Get a fellow partner to discuss and get feedback
Write: the act clarifies your thinking
These practices should increase your circle of competence. Be very careful when you are operating just outside of circle of competence
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