Today’s post isn’t about George W Bush, but he did say in 2006 he was the decider, my perspective is, if he had used these mental tools his decisions would have been much much better, but you be the judge.
I recently came across Charlie Munger’s 1995 speech, The Psychology of Human Misjudgment, which introduced me to an explanation of what I’d been doing all my business life – using the power of applying mental tools from a wide array of disciplines and applying these to the business questions I had.
Last week I introduced tools for experimentation mental tools, this week we review how to decide. Yes, finally what we all think Bosses do, decide… I think Bosses should ask the right questions, but I liked deciding too. So how can you decide when things are not all that simple.
Business Case — “Captures the reasoning for initiating a project or task. It is often presented in a well-structured written document, but may also sometimes come in the form of a short verbal argument or presentation.” The key question answered by a business case is : why this now? answer this and you have a case).
Here’s a great way of looking at business cases
A good business case will explain the problem, identify all the possible options to address it and highlights the best option and why you believe its best option, enabling the decision-makers to decide which course of action will be best for the organisation. It will also allow any changes to the scope or timescale of the project to be assessed against the original purpose.
Opportunity Cost — “The value of the best alternative use of funds where, given limited resources, a choice needs to be made between several alternatives. Assuming the best choice is made, it is the ‘cost’ incurred by not enjoying the possible benefits presented by the second best available choice.”
The opportunity cost of going to college is the money you would have earned if you worked instead. On the one hand, you lose four years of salary while getting your degree; on the other hand, you hope to earn more during your career, thanks to your education, to offset the lost wages.
Intuition — is an unconscious thought process that produces rapid, uninferred knowledge or solution. Though it is not analytic in the sense that it does not deliberately look for cause-and-effect relationships or data , intuition is not mere guesswork. Instead, it draws on previously acquired experiences and information and directly applies this in totality. Intuition can be visionary or delusionary, uncannily correct or horrendously wrong in its conclusions. Related: thinking fast vs thinking slow — “a dichotomy between two modes of thought: ‘System 1’ is fast, instinctive and emotional; ‘System 2’ is slower, more deliberative, and more logical.” that I set out earlier here.
Decision Trees — “A decision tree is a schematic, tree-shaped diagram used to determine a course of action or show a statistical probability. Each branch of the decision tree represents a possible decision, occurrence or reaction. The tree is structured to show how and why one choice may lead to the next, with the use of the branches indicating each option is mutually exclusive.
Sunk Cost fallacy— “A cost that has already been incurred and cannot be recovered.”For example; I have already paid a consultant $1000 (sunk cost) to look into the pros and cons of starting that new business division. He advised that I shouldn’t move forward with it because it is a declining market. However, if I don’t move forward, that $1000 would have been wasted, so I better move forward anyway. Stupid reason to do something.
Availability Bias — “People tend to heavily weigh their judgments toward more recent information, making new opinions biased toward that latest news.”
Confirmation Bias — “The tendency to search for, interpret, favour, and recall information in a way that confirms one’s preexisting beliefs or hypotheses, while giving disproportionately less consideration to alternative possibilities.”
Loss Aversion — “People’s tendency to strongly prefer avoiding losses to acquiring gains.” Lets explain this with 2 examples
In a study, participants were given $50 at the start. Then they were asked to choose between one of the 2 options:
Results? Participants acted in a risk-averse way with only 43% decided to gamble.
Then the experimenters changed the first option. Now they asked participants to chose between:
What happened when one of the options was framed as a loss? The number of participants who decided to gamble grew to 61% (this difference between the 2 scenarios is statistically significant- see below).
Here is how framing a situation as either a gain or a loss impacts our decision making:
Sample survey – is a valuable assessment tool in which a sample is selected and information from the sample can then be generalized to a larger population. Surveying has been likened to taste-testing soup – a few spoonfuls tell what the whole pot tastes like. The key to the validity of any survey is randomness. Just as the soup must be stirred in order for the few spoonfuls to represent the whole pot, when sampling a population, the group must be stirred before respondents are selected. It is critical that respondents be chosen randomly so that the survey results can be generalized to the whole population.
How well the sample represents the population is gauged by two important statistics – the survey’s margin of error and confidence level. They tell us how well the spoonfuls represent the entire pot. For example, a survey may have a margin of error of plus or minus 3 percent at a 95 percent level of confidence. These terms simply mean that if the survey were conducted 100 times, the data would be within a certain number of percentage points above or below the percentage reported in 95 of the 100 surveys.
In other words, Company X surveys customers and finds that 50 percent of the respondents say its customer service is “very good.” The confidence level is cited as 95 percent plus or minus 3 percent. This information means that if the survey were conducted 100 times, the percentage who say service is “very good” will range between 47 and 53 percent most (95 percent) of the time.
Statistical significance/ confidence interval – The likelihood that a result or relationship is caused by something other than mere random chance. Statistical hypothesis testing is traditionally employed to determine if a result is statistically significant or not. This provides a probability value that random chance could explain the result. In general, a 5% or lower value is considered to be statistically significant- this means there is only a 5% chance that the result is random and not correlated. The lower the number the better.
Margin of Error – is a statistic expressing the amount of random sampling error in a survey‘s results. It asserts a likelihood (not a certainty) that the result from a sample is close to the number one would get if the whole population had been queried. The likelihood of a result being “within the margin of error” is itself a probability, commonly 95%, though other values are sometimes used. The larger the margin of error, the less confidence one should have that the poll’s reported results are close to the true figures; that is, the figures for the whole population. Margin of error applies whenever a population is incompletely sampled.