When a transaction has asymmetric information, the party with less information is subject to adverse selection.
How it applies
Adverse selection doesn’t have to be limited to scenarios where the decisionmaker has less information. The concept can also be applied to someone who is disadvantaged in another way. Consider asymmetric opportunity, as an example; Decisionmaking frameworks apply differently when the decisionmaker faces adversity, because there is generally a looser coupling between the decision and the outcome.
See also: Asymmetry of Information
Asymmetric information occurs when one agent in a transaction has more information than another.
Generally, information can create advantageous positions for the person with the additional information.
However, there are some rare instances where less information may be better, for different reasons. For example, dealing with the overwhelm of having too much information could create a “beginner’s luck” situation.
Also known as the availability heuristic. Whatever is most recently remembered, within reach of our cognition or spatial awareness, is likely to influence our cognition. This also has the effect of causing us to weigh our judgments most heavily based on that availability.
A term that describes something that gets better with stress.
Generally, living beings are considered antifragile.
In direct contrast, something fragile breaks easily under stress.
Of note, this designation is not the same as being resilient; resilient means remaining close to an equilibrium under stress.
Anti-fragility is marked specifically by improvement under stress.
We tend to entrench ourselves in our existing belief (see confirmation bias), and when presented with contradictary evidence, we tend to reject it and entrench even further into our preexisting belief.
In chemistry, the energy required to change from one chemical state to another. You might also encounter a similar phenomenon in physical change called “latent heat” - the energy needed to complete a phase change from one physical state to another.
How it applies
It may seem at first glance that everything we experience is linear. For example, when we add heat to water, we see the temperature increase.
Activation energy, however, is a non-linear curve required to create some type of change. This energy isn’t lost or wasted, but is required for the change to take place.
This applies to habit forming, as an example; once you take the first step towards exercising, you are more likely to continue. This is also where the phrase “the first step is always the hardest” comes from.
This could apply to career planning as well; getting your first job in an industry is likely harder than getting your second job.
See also: Conservation of Resources
Benefits of scale is illustrated simply by the phrase “you have to have money to make money.” Multiple mechanics are at play for benefits of scale, and they are often multiplicative.
For example, with large enough scale, you may no longer have to spend as much of your resources on recruiting talent, advertising your brand, or establishing a production pipeline.
Scale is also important particularly for litigation; because a single lawyer or small team of lawyers can represent a huge corporation in a given legal case, the resources needed to fight individual suits are often miniscule in comparison to the resources brought by the prosecution.
The mental picture of the benfits of scale is an exponential graph, where opportunity is exponentially correlated with capital scale.
An effect where the initial frame of reference has a major effect on later perspectives.
This is surprisingly often inescapable, even if the anchor is well-known to be a facade.
For example, a car lot using artificially high prices is not as silly of a tactic as it may immediately seem.
Big and small number biases are fundamentally difficult to grasp. But to gain an intuition, try to visualize a stack of 1,000,000,000,000 quarters. Now visualize a stack of 1,000,000,000,000,000 quarters. If you are like most people, the height of each of these stacks is initially quite similar. In reality, the second stack is 1000x higher than the first stack. (Of course it is! But our brains don’t see those numbers accurately.)
Similarly, imagine a cup of water. Now try to imagine pouring all but 0.00003% of the water out. How much exactly would be left? Trying to visualize this, most people would say a few drops, or just enough to keep the bottom layer of the glass wet. In a 16oz cup, .0000048oz, or about 136 micrograms. That’s less than the weight of 3 grains of salt.
These biases extend further into our perception. For example, when we see “99.999% chance”, we perceive it to be further from 100% than 99%. We’re less likely to spend resources to improve life saving treatments from 93% to 96% effectiveness than we are from 99% to 100%. This “cure” effect is similar to the “possibility” effect: we buy lottery tickets because our chances of winning the lottery are non-zero (“possible”).
A base rate is how often something happens within some sample. For example, the base rate of hours of sleep for a given population may be 8 hours. You can have base rates for your own behaviors, too.
When evaluating frequencies of events and other statistical representations of information, it’s important to do so in light of the base rate. For example, if I say that “someone will die of a lightning strike in the US in the next 30 minutes”, am I predicting a likely future? The only way we can predict (given no specific weather information) is by using base rates.
In the lightning strike example, we can look at a base rate of yearly lightning strikes (in the US, 51 fatalities occur per year - that’s a little less than 1/week - so 30 minutes has about a 1/340 chance of being correct. So how about likelihood of dying if you are hit by a lightning strike? How can we figure out this base rate? The annual injury rate of lightning strikes is around 240k globally, and around 6k of those are fatalities.
6k / 240k is less than 2.5% - in other words, being struck by lightning is safer than some surgeries.
If we didn’t have base rates, we wouldn’t know that 98 of 100 people survive lightning strikes.
Also known as the “agency dilemma.” An agency is motivated to act in their own interests, which is often competing with or in direct contradiction to the interests of the principal.
How it applies
This is one of the most important models to understand, because it has broad-sweeping effects. We’ll list a handful of examples here.
- Realtor and home-seller - The realtor’s upside is much smaller than the home seller’s. Leaving a house on the market for an extra few weeks is less attractive than taking a sub-optimal deal, as the guarantee of the majority of the upside is often a stronger incentive than the small delta for the optimal deal.
- Contractor and Consumer - Especially if the contractor’s work is based on hourly rates, the contractor is incentivized to spend a longer amount of time on a project than is necessary to complete the work. The customer is incentivized to shorten the project as much as possible. This often leads the contractor to provide insurance by a guaranteeing a ceiling. (Note: Assuming the ceiling isn’t based on an artifically high estimate, this extra risk for the agent can be compensated for by charging a higher hourly rate.)
- Pharmacies and Patients - At a macro-level, the pharmacy industry is incentivized to promote the effectiveness of a given drug, and simultaneously not eliminate the need for their drug (by entirely curing the given disease). (Note: with this said, pharmaceuticals have drastically improved life expectancy and quality of life for the majority of people who have access to them.)