Mental models Flashcards
Reciprocity:
- how do you encode reciprocity?
- summarise in one sentence
- Why is reciprocity is a worthy mental model, how does it operate, what situations is it most relevant for me?
one sentence: You get what you give.
encode: (image of vampire bat)
Top ten points
1. We have evolved to reciprocate because it helps us cooperate, which offers a survival advantage. Today, reciprocity is foundation to our societies, economies and relationships.
- Reciprocity exists in the animal kingdom too. Examples of in the animal kingdom include a) host fish and cleaner fish - the cleaner fish benefits the host fish by cleaning its parasites of its body and the cleaner fish benefits by getting a meal. The deal is that the host fish doesn’t eat the cleaner fish in order for both to benefit over time. b) vampire bats die after 70 hours of not eating a blood meal. Even in high stakes like this, vampire bats will regurgitate their blood and share it with other bats, and they are more likely to be helped in a time of need.
- People feel obliged to reciprocate to reward or punish others, which maintains the social norm of reciprocity.) For this reason, you get what you give. People tend to offer to you what you have offered them. This norm begins early. Children as young as two have been found to be more likely to share with children who share with other children, even it wasn’t them they shared with.
- You have reciprocity between two parties, but also exists across multiple people such as a network (think a interest page on social media). When working with networks of people, pay it forward reciprocity is when someone does something for you and you do something for someone else. This mechanism is based on gratitude and/or reputational concern. Reputational reciprocity is when you do something for someone with an expectation that people who see you acting prosocially will be more likely to do favours for you.
- The types of reciprocity are based on the timing of the expected reciprocity and whether it is based on two people or a network of people. e.g. Regarding timing, you expect immediate reciprocity when you buy something from the shop. But gifts offered in friendships, the workplace or amongst states, reciprocity is expected ‘sometime in the future’.
- Reciprocity is most relevant for me in a) family and friends b) workplace. At the least, I want to avoid negative reciprocity to a reasonable extent, particularly with consequential people or situations by acting prosocially
- The big idea is that you can shape how people treat you by how you treat them. So think about how you would like to be treated and treat people like that. If people are treating you in a way you don’t like, reflect on if they are reciprocating what you are giving them. Through reciprocity, we can create the world we want.
Feedback loops
The value of feedback loops is to be aware of feedback you get, with the intention to iterate based on that feedback.
If you don’t listen to feedback you will likely be less aware, repeat mistakes or perpetuate ineffective systems.
Relevant to:
- Workplaces (e.g. feedback on piece of work or your performance)
- Relationships: (facial expressions, emotional tone responding to what you’re saying)
- Challenges (obstacles to habits you’re trying to develop or perpetual problems arising at work are all feedback)
It’s also valuable to be aware of the feedback you are giving, as well as receiving.
Equilibrium
The value of equilibrium as a model is recognising that systems are often dynamic and we need to take continuous action to maintain balance.
This applies to organisations, relationships, economies, democracies and more.
Entropy is a related model. Entropy is the descent towards toward. Equilibrium is when the system is in temporary balance.
Bottlenecks
Bottlenecks are ‘the limiting factor’ that prevents a system from operating more effectively.
By recognising bottlenecks, you can then develop effective solutions to improve the system.
Scale
Scale is how big or small something is. It’s value as a model is to consider the scale that you need to achieve your goals, and recognise the pros and cons of each.
Scaling things to be larger such as profits or an organisation is often seen as something positive.
But keeping things at a smaller scale can also be beneficial. Small and large scale comes at various pros and cons. The right scale for your needs depends on your goals.
For example, Japanese businesses have lasted hundreds of years by keeping their staff numbers small within a family business and developing trust and connection with locals over generations.
Margin of safety
Margin of safety relates to how much buffer you give yourself for unexpected events or outcomes.
Margin of safety is applied at work, or in engineering in building things. But learning is also building a margin of safety. The more we learn the fewer blind spots we have.
Churn
All systems have churn.
Churn is sometimes negative, such as when you lose people from your email database.
But churn is sometimes positive. You want there to be churn in political leadership, Board members to allow new ideas and reduce corruption.
Algorithms
Algorithms are a clear set of rules that provide instruction on what to do.
They can include if-then processes.
Algorithms as a model suggests a way of thinking that explores what processes can be put in place to reach our goals.
Critical mass
Critical mass refers to when a threshold is crossed which forces a change.
Examples.
Critical mass of people protesting forced the government to act. Critical mass of people on a social networking site to get it to scale.
When a system is about to hit critical mass, it only takes a small nudge to push it to another state.
Critical mass and activation energy are related models. Activation energy considers the critical mass to get something done.
Activation energy is worth considering prior to a project to see it through. Critical mass helps you be aware of when a system is almost at a tipping point.
Emergence
Emergence is when a collection of parts produces more than than the sum of their parts.
Like in a team. Together, the team can produce something greater than the sum of their individual contributions.
It’s similar to synergy.
Irreducibility
Irreducibility is making things as simple as possible, but no simpler.
It is identifying the minimum amount of structure, time, components required to achieve something.
You understand what is essential to make a system function. It’s related to first principles thinking.
Law of diminishing returns
Law of diminishing returns shows us that the relationship between input and output is not always linear.
You’ve likely hit diminishing returns when you’re eating the tenth spoonful of ice-cream compared to the first.
Hanging out with a friend for another hour after the fourth hour, may result in diminishing returns.
Adding more people to your team after a certain point, won’t produce the same output as the first few recruits to the team.
The ninth hour of sleep is not likely to be as valuable as the first.
This model also reveals that change is an essential element of moving forward. If your results are declining, you’ve likely hit diminishing returns.
Compounding
Compounding shows us that small investments made consistently over time grows exponentially.
Compounding reveals the benefits of consistency and long-term thinking when it comes to money, relationships and knowledge.
Compounding is relevant for the positive, but also for the negative. Poor habits can compound over time - like smoking.
With compounding, the biggest returns can come at the end, particularly with money and knowledge.
Sampling
Sampling is useful as a model because you understand the importance of samples which are representative, when you are trying to understand reality.
Two examples of quality data is that it is representative with sufficient diversity, and accurate.
You want to be aware of their stated view compared to their actual view. If you are getting data, you need to do it in a way that will incentivise people to state their actual view.
Recognising the limits in sampling helps you to remember that your limited map is not the territory, and prevents overconfidence.
Randomness
Randomness as a model reveals that sometimes our explanations as to why something happened is unhelpful.
If there is no way to genuinely predict what happened, then it was random.
Pareto principle
The pareto principle is based on the idea that a small amount of the inputs can create the vast majority of the outputs.
Sometimes seen in an 80:20 ratio.
20% of the effort creates 80% of the results.
80% of users only use 20% of features on an app.
20% of staff produce 80% of the work.
A small amount of the tactics produce the vast majority of the impact.
What are the inputs creating the most impact, and how can we invest into that?
Regression to the mean
Regression to the mean reminds us that moderate outcomes likely follow extreme ones.
Outlier events will happen occasionally, but we shouldn’t assume that they are the norm. Being able to repeat something multiple times will give greater confidence in the average outcome.
You don’t want to misinterpret outlier events as the norm.
Multiplying by zero
Any number multiplied by zero is zero.
This model teaches us to look for the weakest part of a system - the part that could cause the whole system to fail - and shift it from being a zero to a one.
This is about identifying what is likely to produce the worst case scenario, and improving that to avoid a disaster.
This is related to inversion, and helps to minimise risk.
Equivalence
Equivalence doesn’t mean same, but of equal value.
It reminds us that there is often multiple ways to solve a problem.
For example, reciprocity can be of equal value, rather than the same favor or gesture.
Multiple sports have an equivalence in terms of getting fit.
Surface area
Surface area is about recognising when increasing your exposure to something will help you and when it will harm you.
NB: I need to better understand what surface area is.
Global and local maxima
The maxima and minima of a mathematical function are the largest and smallest values over its domain. Although there is one maximum value - the global maximum - there can be smaller peaks of value in a given range - the local maxima.
Using global and local maxima as a model is about knowing when you have hit your peak, or if there is still potential to go higher. It reminds us that sometimes we have to go down a smaller hill first to reach a higher peak on another hill.
the model helps us see that achieving our goals is not a steady upward trajectory but a path full of peaks and valleys.
Also, it’s more powerful to make the big changes (choose the right hill or our global maxima) before we optimise the details.
Relativity
Relativity in physics shows us that two observers in relative motion experience time differently.
Relativity as a broader model reveals our perspective is limited, other people can contribute to a fuller map of a situation. It highlights that two seemingly contradictory views could both be right (or wrong).
Thermodynamics (equalibrium)
Equilibrium
- Equilibrium in physics is that everything moves towards equilibrium, such as two temperatures mixed together become the same temperature.
- As a mode, we can see that it’s difficult to keep cultures in direct contact from sharing ideas.
Thermodynamics (entropy)
Entropy exists because there are many more ways for things to be disordered than ordered. We need to invest energy to create order.
