Making Your Decisions Easier: An Approach To Uncertainty

Bill Seota
January 31, 2023

Making Your Decisions Easier: An Approach To Uncertainty

Decision-making is one of the most common tasks we perform daily. Questions become more involved as they increase in difficulty. Should I quit my job and start my own business? How should I invest my money (assuming I™m earning more than I need)? Many of these questions can be answered numerically by probability models of some kind. Unfortunately, most people don™t have the time or resources to engineer a proper model. So we often end up forming opinions and preferences through approaches that ignore the possible underlying mechanisms behind the choice we™re trying to make. Like making an educated guess. So how can we tell the difference between good guesses and bad ones? Is there a way to quantify these guesses for decision makers to compare different alternatives more effectively and make better-informed decisions as a result? As it turns out, the answer is yes!

Four years ago, I faced a difficult choice about what to do with my life. I came up with three opportunity choices: go to school, get a job in a public organisation, or get a job in a private organisation.

I used a few frameworks, from sheer imagination to decision matrices. But my risk-averse former self would have probably preferred to use a more probabilistic approach, such as the Monte-Carlo Simulation, to measure my projected quality of life.

Decisions, Decisions¦

Let™s pretend we™re in 2018 and I want to see how a Monte-Carlo simulation can help me make this major decision.
I set up an experiment:

1. For each opportunity, I consider the five factors that are most important to my decision:

  • Time Freedom
  • Financial Gain
  • Learning Happiness
    (Career) Progression

2. I then assign each factor™s range of values (my sentimental utility), with a maximum of 10. For example, the time freedom I will obtain from school would be somewhere between 8 and 10. I add up the Random Selection values and obtain a Quality of Life Score. The tables below show the results from 1 simulation for each opportunity presented to me.

Table 1: Quality of life score in school
SchoolMinimumMaximum Rand Selection
Time Freedom81010
Financial gain010
Quality of Life Score 26
Table 2: Quality of life score in Public Service
Public ServiceMinimumMaximum Rand Selection
Time Freedom353
Financial gain3105
Quality of Life Score 34
Table 3: Quality of life score in Private Sector
Private Sector MinimumMaximum Rand Selection
Time Freedom253
Financial gain9109
Quality of Life Score 34

Every time the simulation runs, each Random Selection value changes to a random value between the minimum and the maximum.

After running 1,000 Simulations, I obtained the average or expected quality of life for each opportunity.

Expected Quality of Life 29.03
Minimum Score 23
Maximum Score 35

Public Service
Expected Quality of Life 34.49
Minimum Score 31
Maximum Score 40

Private Sector
Expected Quality of Life 33.52
Minimum Score 25
Maximum Score 41

The Practicality of the Monte-Carlo Simulation

The appropriateness of the Monte-Carlo simulation depends on the use case. Probability-based models have applications in decisions that affect various industries from finance and technology to retail and logistics.

However, Monte-Carlo simulations are not always appropriate to use. In property valuation, for instance, inputs are often a range of values determined by industry standards. These values are adjusted by the valuer’s reasonable judgement. Although it may be an interesting exercise, it is unacceptable to provide a mean or median estimate from, say, 1,000 simulations. A similar stance is taken in many corporate finance applications, where financiers generally prefer intuitive and user-friendly models. Since Excel is pretty much the world’s choice of database, simulation model use cases in finance are few and far between.

Nonetheless, I have found Python and R to work well for simulations, especially when using large datasets. To the overachievers, you can even then feed the results of the simulations into a spreadsheet if you really must Excel!

Life is about making decisions, so it’s important to know how to deal with uncertainty. One doesn™t need to use an involved model to make easy and insignificant decisions, like when to eat. However, it may be a good idea to use a model or even a simple framework, like decision tables, to make more involved decisions. Such decisions as those that could mean the difference between a good year and tough times that last.