When stuck consider this to nudge your decision in the right direction

Recently I have made a number of decisions that turned out not satisfactory for my use case. Worst of all I was trying to justify why my decisions still made sense when I knew that they did not. We can never make perfect (optimal) decisions as we simply do not know all possibilities. Even if we did we cannot go through all of them as mere mortal souls we do not have the time.

This does not mean we should give up. As a minimum, decisions should be satisfactory for your use case. They ideally should have upside in becoming very good decisions and have low downside in becoming very bad decisions in time. Finally, decisions should avoid convergence towards good and bad oscillation as this is the place where you get stuck not knowing if decision should be changed.

To bring this to life I am going to use an example from my personal life, my electric car purchase. Before I show how this decision was made and what has actually happened I want to introduce some basic concepts.

Concepts

Criteria, Weight and Score

We can measure the satisfiability of our decision by checking if a decision meets criteria, criteria item needs to be answered either TRUE or FALSE. For the electric car decision, satisfiability criteria can be “on average will it cost cheaper to drive in an electric car” the answer in my case at the time was TRUE.

Each satisfiability criteria can also have a weight. This gives each criteria an importance factor. If you have 10 criteria items you can assign a number that is lower than 1 and that makes up 1 for all of the items in aggregate.

Criteria and weight combined provide you with the score. Examples will be provided shortly.

Decision

Decision is a path or solution that you have chosen against your criteria. Once you have a criteria and weight you will search available paths or solutions to satisfy your criteria as much as possible.

Opportunity and Risk (Upside and Downside)

When you make a decision you want it to have a lot of opportunity to go right and benefit you, but also you want to pick a decision that is low risk. In other words you want your decision to have high upside and low downside.

Case study

I have experienced some change in my life, so I have decided to save some money. One of the obvious places where I could make a change was my car, so I had to make a decision of which car I should get next. I told myself that my next purchase after my diesel car will be an electric car. I felt that I had to choose this path to help the environment and of course save some cash.

Going Electric, Nissan Leaf

So here is my criteria and decision score prior to getting the car:

Electric Decision, Initial Score

Opportunities and risks that I have considered before purchasing the car:

Opportunity

• As charging networks expand range anxiety will be reduced and reliability will improve this can also slightly increase the value of my car
• I can help the environment by driving electric
• Reduced cost of maintenance and cheaper charging

Risk

• Battery will degrade meaning car will be harder to sell
• As more cars enter the market it will depreciate my car much more than average combustion engine car
• Charging time will impact my time and quality of life

My criteria and decision score after 7 months of using the electric car (posterior):

Electric Decision, Revised Score

Case study results

Notice driving range and good charging network became key factors. It should not be surprising as you purchase a car to get you places, and ideally on time. While the electric car was extremely reliable, the charging network was not at all. Charging stations were constantly broken making me constantly late. It was my fault for getting a car with low range, however if the charging network was more ubiquitous and reliable it would have been less of an issue.

As a side note, it is a shame that only Tesla so far have managed to provide a reliable changing network.

I have made a bad decision by getting an electric car with low range, going electric while charging network support is poor and not living in the house, which meant I could not get the convenience of charging overnight which ended up impacting my quality of life surprisingly a lot.

In hindsight there were a lot of unknowns, and most importantly my original decision score did not reflect reality of electric car ownership in my circumstances. The only thing I could do now was revise my decision in the direction of a higher score and less risks.

With all of this in mind I have decided to give it 5 more years until I go electric again. I have purchased an old small combustion engine car with good fuel efficiency so that I can wait out the electric revolution and do as minimal environmental damage as possible. Given cars are mostly liabilities (this is different in the luxury car market) there is no opportunity here, just risk. To minimise my risk, I have purchased a car that is known to be very reliable and cheap to maintain.

Combustion Engine Decision, New Score

Bias and nudging out of it

From a logical perspective, most people reading this are probably thinking, what is your point? This is obvious.

Problem is that we are human and there are a number of biases that disrupt our thinking. When the decision outcome is very bad you know you have to revise your decision. When the decision outcome is very good, you know it is good, you just need to keep going with it.

But what if something is good and bad at the same time? What if something is just good but there is a high risk of it going very bad? This is when we get stuck and we could do with some bias reminders to nudge us in the right direction.

Here are some biases that I have borrowed from Wiki for you to consider when you are stuck:

• Confirmation bias, tendency to search for, interpret, favor, and recall information in a way that confirms or supports one’s prior beliefs or values.
• Salience bias, the tendency to focus on items that are more prominent or emotionally striking and ignore those that are unremarkable, even though this difference is often irrelevant by objective standards.
• Effort justification is a person’s tendency to attribute greater value to an outcome if they had to put effort into achieving it. This can result in more value being applied to an outcome than it actually has.
• Neglect of probability, the tendency to completely disregard probability when making a decision under uncertainty.
• Zero-risk bias, the preference for reducing a small risk to zero over a greater reduction in a larger risk.
• Escalation of commitment, irrational escalation, or sunk cost fallacy, where people justify increased investment in a decision.
• Plan continuation bias, failure to recognize that the original plan of action is no longer appropriate for a changing situation or for a situation that is different from anticipated.
• Disposition effect, the tendency to sell an asset that has accumulated in value and resist selling an asset that has declined in value.
• Endowment effect, the tendency for people to demand much more to give up an object than they would be willing to pay to acquire it.
• Pseudocertainty effect, the tendency to make risk-averse choices if the expected outcome is positive, but make risk-seeking choices to avoid negative outcomes.
• Hot-cold empathy gap, the tendency to underestimate the influence of visceral drives on one’s attitudes, preferences, and behaviours.

Bias in action

To use a few examples from the above. I was really stuck with the car decision. Main reason why I was stuck is because I have paid for the car probably more than I should have due to “going green” excitement at the time. I was trying to find ways to make it work, escalation of commitment bias was at work here mostly due to the effort justification bias, as I had to research the car, find the charging spots, join the electric car community, change the way I drive, etc.

To snap out of this I have asked myself if I was to buy a car now would I buy this car again? Answer was a resounding no. Would I pay for it what I have paid only 8 months ago? Answer was again a resounding no. By doing this I was able to break from the endowment and disposition effect and start revising my decision.

Conclusion

This article is not about electric cars, but about decision making and convergence towards better decisions. At the root of a bad decision there is unsatisfied criteria. Once a suboptimal decision is made all we can do is just move away from that decision in the direction of a good decision, obvious right?

What is a very good decision? Very good decision is where opportunities around the good decision have materialised and ideally it is where opportunities have presented themselves that you did not expect at all. In the electric car example if the government had decided to reduce all electric charging costs to near zero, like they did in the early days. Or car manufacturers were supported by the government to upgrade older electric car batteries to extend range and to reduce manufacturing CO2 further and promote the used car market.

Decisions at times feel like playing Minesweeper. Keep exploring decisons that converge towards very good.

While these opportunities did not materialise with my electric car nor was I naive enough to expect these kind of opportunities. There are “very good” golden goose decisions out there. You need to converge towards them by looking for good decisions with the high upside and low downside.

This write up can be considered a follow up to Antifragile meets fragile reality.