I’ve been immersing myself in decision-making and forecasting for a quarter century or more now, with a focus on reading and researching the psychology of decision-making.
I thought now is as good a time as any to share some thoughts. So this is the first of a series of articles on this subject.
At the moment, I’m deep diving into the nature of the decisions we take – and I’ve found that every type of decision I can think of fits into one of three types:
1 – Binary decisions – yes/no, bid/don’t bid, buy/don’t buy etc.
2 – Select from options – who to hire, where to locate a company etc. (they can be single choice or multiple)
3 – Quantifiers – how much, how many, what is the value, etc.
However, I’ve also identified three other categories, which are hybrids of the above:
4 – Timing decisions – arguably another type of quantifier, but with unique qualities
5 – Threshold decision – a minimum or maximum acceptable amount (a combination of both 1 and 3)
6 – Architecture decisions – these consist of multiple ‘option decisions’, such as material, layout, colour, etc.)
If you can think of any decisions that don’t fit into this criteria, I’d love to hear from you. But for now, let’s work with the above.
Regardless of decision type, all non-trivial business decisions should be subject to a degree of analysis. To make this process easier, a variety of tools and techniques have been developed to help structure these decisions, like decision trees and lists of pros and cons. However, these techniques become inadequate when making significant decisions that impact finances.
For over two decades, computing power has enabled us to run simulations, which have become increasingly sophisticated. A financial model is one such simulation and is our specialism at Numeritas.
In a business context, significant decisions with a quantifier element are the obvious type that may benefit from numerical analysis, perhaps in the form of a financial model. ‘Binary’ and ‘select from options’ decisions require criteria to be established, which in turn may have a quantifier element. However, there are many factors outside of the financial model that impact our decision-making, which I’ll be examining in more detail over the course of the next few weeks.
To whet your appetite, let’s take a simple example of a factor, outside of a model, which can have a serious impact on our decisions; optimism bias. This is a fairly well-known phenomenon that has been highlighted by public sector procurement, which recommends explicitly adjusting for over-optimism regarding costs (HM Treasury Green Book on Appraisal and Evaluation in Central Government).
Mott MacDonald carried out a study for HM Treasury, finding that optimism bias added up to 66% to the CAPEX of non-standard civil engineering projects compared to outline business case estimates, and extended duration by up to 25%. Other project categories also had a range of uplifts, all more than 24%.
If you ask an entrepreneur to estimate their chances of success, few will admit to any risk of failure. Yet according to The Telegraph, out of 660,000 new businesses registered in the UK each year, 60% will go under within 3 years. Our optimism bias tends to outweigh our chance of success and play down potential risks. So what can we do to reduce this bias?
We can try to shift to the ‘outside view’ as a dispassionate observer who is not invested in the success of a project. Despite the inherent contradiction involved, we can seek out comparable situations from the past, and compare these to our current situation.
While we may think we can do things better than everyone else, we should start from the position that there is a 60% risk of failure in the first three years, rather than expecting this particular startup to be one of the 40% of survivors.
This is something I’ll discuss in later articles, along with the many other factors which affect decision-making. If you’d like to be notified of when the next article goes live, simply click this link and subscribe.