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One useful tactic is to talk to people who have made similar decisions and ask them to point out costs or benefits that you may have missed.
When writing down costs and benefits, you will find that some are intangible. Continuing the house example, when you buy a house, you might have some anxiety around keeping it up to date, and that anxiety can be an additional “cost.”
when faced with intangibles like these, you still want to assign dollar values to them, even if they are just rough estimates of how much they are worth to you.
is that benefits you get today are worth more than those same benefits later.
Second, most economies have some level of inflation, which describes how, over time, prices tend to increase, or inflate. As a result, your money will have less purchasing power in the future than it does today.
Third, the future is uncertain, and so there is risk that your predicted benefits and costs will change.
sensitivity analysis, which is a useful method to analyze how sensitive a model is to its input parameters.
Given that the discount rate is always a key driver in cost-benefit analyses, figuring out a reasonable range for the discount rate is paramount.
One decent approach is to use the rate at which you can borrow money.
If you were able to invest that money in a new investment at a high enough rate, this second bond is potentially more attractive in the end. When making a comparison, you therefore must consider what could happen over the same time frame.
In computer science, there is a model describing this phenomenon: garbage in, garbage out. If your estimates of costs and benefits are highly inaccurate, your timelines don’t line up, or your discount rate is poorly reasoned (garbage in), then your net result will be similarly flawed (garbage out).
the decision tree. It’s a diagram that looks like a tree (drawn on its side), and helps you analyze decisions with uncertain outcomes.
Because these new values include more than the exact cost you’d have to pay out, they are called utility values, which reflect your total relative preferences across the various scenarios.
You would get more utility out of that concert because of your preference.
In fact, more broadly, there is a philosophy called utilitarianism that expresses the view that the most ethical decision is the one that creates the most utility for all involved.
Nevertheless, utilitarianism is a useful philosophical model to be aware of, if only to consider what decision would increase overall utility the most.
Decision trees are especially useful to help you think about unlikely but severely impactful events.
One thing to watch out for in this type of analysis is the possibility of black swan events, which are extreme, consequential events (that end in things like financial ruin), but which have significantly higher probabilities than you might initially expect.
As applied to decision tree analysis, a conservative approach would be to increase your probability estimates of low-probability but highly impactful scenarios like the bankruptcy one.
Black swan events, though, often come from fat-tailed distributions, which literally have fatter tails, meaning that events way out from the middle have a much higher probability when compared with a normal distribution.
Another reason why you might miscalculate the probability of a black swan event is that you misunderstand the reasons for its occurrence.
A third reason is that you may underestimate the possibility and impact of cascading failures
falters,
you may first have to take a step back and try to make sense of the whole system before you can even try to create a decision tree or cost-benefit analysis for a particular subset or situation.
Systems thinking describes this act, when you attempt to think about the entire system at once.
For example, when thinking about making an investment, you might start to appreciate how seemingly unrelated parts of the economy might affect its outcome.
One solution is literally to diagram the system visually. Drawing diagrams can help you get a better sense of complex systems and how the parts of the system interact with one another.
causal loop diagrams (which showcase feedback loops in a system) and stock and flow diagrams (which showcase how things accumulate and flow in a system).
As a further step, you can use software to imitate the system, called a simulation.
(Two such programs that do this online are Insight Maker and True-World.)
Simulations help you more deeply understand a complex
Chatelier’s principle, named after French chemist Henri-Louis Le Chatelier, states that when any chemical system at equilibrium is subject to a change in conditions, such as a shift in temperature, volume, or pressure, it readjusts itself into a new equilibrium state and usually partially counteracts the change.
Or in economics, if a new tax is introduced, tax revenues from that tax end up being lower in the long run than one would expect under current conditions because people adjust their behavior to avoid the tax.
hysteresis, which describes how a system’s current state can be dependent on its history.
In physics, when you magnetize a material in one direction, such as by holding a magnet to another piece of metal, the metal does not fully demagnetize after you remove the magnet. In biology, the T cells that help power your immune system, once activated, thereafter require a lower threshold to reactivate.
partially remember their states, such that what happened previously can impact what will happen next.
path dependence (see Chapter 2), which more generally describes how choices have consequences in terms of limiting what you can do in the future.
In engineering systems, for example, it is useful to build some hysteresis into the system to avoid rapid changes.
Similarly, on websites, designers and developers often build in a lag for when you move your mouse off page elements like menus. They build their programs to remember that you were on the menu so that when you move off, it doesn’t abruptly go away, which can appear jarring to the eye.
Then you can feed these results into a more straightforward decision model like a decision tree or cost-benefit analysis.
A Monte Carlo simulation is actually many simulations run independently, with random initial conditions or other uses of random numbers within the simulation itself.
Think of it as a dynamic sensitivity analysis.
For example, venture capitalists often use Monte Carlo simulations to determine how much capital to reserve for future financings.
Many funds use Monte Carlo simulations to understand how much they ought to reserve, given their current fund history and the estimates of company success and size of potential financings.
Without such knowledge, you can get stuck chasing a local optimum solution, which is an admittedly good solution, but not the best one.
global optimum.
You want to be on that bigger hill. But first you have to have a full view of the system to know the bigger hill exists.
unknown unknowns,
You can turn some of these into known knowns by doing de-risking exercises (see Chapter 1), getting rid of the uncertainty.
they don’t do much business in August. An adviser with more experience can help identify these risks from the start and turn these into known knowns.
