Several myths underpin our understanding of decision making. We expose five of these myths - and identify ways to improve decisions in conditions of uncertainty

1. How much information is
needed to make better
decisions?
WHAT’S THE TRUTH HERE?
There are so many views on the amount and type of information required to make a good
decision. Conventional wisdom tells us we need as much information as we can get, and
that we should keep our options open for as long as possible. But, what about ‘analysis
paralysis’? What part does intuition play? And how long should we wait before finally
making that important decision?
With the surge in research into behavioural economics, we now know that there are
several myths that prevail around decision making. These are worth exploring in order to
understand the answer to some of these questions.
SOME COMMON MYTHS OF DECISION MAKING
I have highlighted five relatively common misconceptions about information and decision
making. They address the areas of:
- how much information is needed
- who makes the decision
- how the decision is made.
In each case, we discuss conventional wisdom, and then explore some of the behavioural
implications.
© Norman Chorn 2020 • norman.chorn@brainlinkgroup.com • (0416) 239 824 • Page 1
Dr Norman Chorn

2. Myth 1: It is best to collect as much informations as possible and keep
options open: Research shows that large amounts of information and multiple options
cause confusion, loss of focus and inefficiency in cognitive functioning. Furthermore, we
know that the resultant confusion creates stress and an inability to consider all the
information in an optimal way.
Even if we score a list of weighted criteria against each option, we are left with a sense of
unease that the decision is sub-optimal.
Myth 2: People make irrational decisions by not considering all the
information: When looking back at previous decisions made by others, we are often
surprised by the ‘irrationality’ of these decisions, and the fact that key information was
overlooked. However, we know that most people’s rationality is ‘bounded’ by the
information to which they have access.
These ‘boundaries’ are defined by their role and situation, since we all have to be selective
in the information we are able to consider. In this sense, most decisions ARE rational in
terms of what information has been considered.
Myth 3: Experts make the best decisions: This seemingly logical conclusion was
overturned during the early stages of behavioural research into decision making.
Startlingly, evidence suggests that while experts may be good at establishing the criteria
against which decisions can be made in complex situations, algorithms and artificial
intelligence (AI) are far more accurate and efficient in making decisions when using these
criteria.
Experts often tend to ignore their own criteria, or apply them inconsistently, when making
complex decisions
Myth 4: It is often best to rely on intuition when situations are complex and
uncertain: Malcolm Gladwell’s ‘Blink’ made popular the notion that ‘thin slices’ of
information can be non-consciously processed at lightning speed by so-called intuition,
particularly where situations are complex and uncertain. This process uses pattern
recognition and association to draw conclusions far more quickly than conscious cognitive
processes.
Despite the popularity of this belief, we need to remember that the use of intuition is
ineffective in situations in which the decision maker has little experience, or when the
‘system’ is not subject to regular patterns of behaviour. Furthermore, stress, heightened
emotions and arousal will render intuition less reliable or accurate.
Myth 5: People use past evidence to judge the probability of a future event
occurring: Many people rely on the rather naive notion of the ‘law of averages’ - ie that if
heads repeatedly comes up in a coin-toss, tails must follow, since the two sides of the coin
© Norman Chorn 2020 • norman.chorn@brainlinkgroup.com • (0416) 239 824 • Page 2

3. should be equally represented in the long run. This ‘law’ is not really helpful when judging
the probability of random events, since future events have no ‘memory’ of past events.
Notwithstanding this, people regularly rely on what is know as ‘frequentist’ (classical)
statistics when judging the probability of a future event (“lightening never strikes twice in
the same place”). Instead, we know that the probability of a future event changes as we
gather new data or evidence about that event. This is the basis of ‘Bayesian’ statistics, an
approach in statistics used to modify probability in the light of recent events and evidence.
And behavioural research demonstrates that we usually ignore this important process - ie
we fail to update our beliefs about future probability on the basis of recent events. (We are
NOT Bayesians in the way we make decisions about the probability of future events).
SO, HOW CAN WE IMPROVE OUR DECISION MAKING?
Four approaches are outlined to address these myths - and make a difference to the
decision-making process in times of uncertainty.
They relate to:
1. How to know when you have collected sufficient information to make a decision
2. How to orientate yourself in a decision-making situation
3. How to update the probability of an event occurring, based on experience or recent
information
4. How and when should you trust your intuition in complex and uncertain situations.
1. Know the ‘optimal stopping point’ (in data collection)
Scylla and Charybdis were mythical sea monsters in ancient Greek mythology. They
reputedly guarded opposite sides of the Strait of Messina between Sicily and Calabria.
Navigating the strait was likened to being caught in the horns of a dilemma - sailors had to
choose between two equally dangerous extremes in order to survive.
This is the challenge often faced when choosing between collecting more information
(leaving no stone unturned) or making a decision too late (the one that got away). How do
you find the appropriate balance between ‘look’ vs ‘leap’?
Research in mathematics and statistics has established that there is an ‘optimal stopping
point’ - the stage during information collection when you have reached the point at which it
is optimal to make a decision . The answer is 37%. This is where you have optimised the1
amount of information collected and the time taken to make a key decision .2
So, if there are a number of cases to study or people to interview before making a decision
- you can make your decision based on 37% of the cases studied or people interviewed.
Chow, Y.S.; Robbins, H.; Siegmund, D. (1971). Great Expectations: The Theory of Optimal Stopping. Boston: Houghton Mifflin.1
Ferguson, Thomas S. (2007). Optimal Stopping and Applications. UCLA.2
© Norman Chorn 2020 • norman.chorn@brainlinkgroup.com • (0416) 239 824 • Page 3

4. You can select the best candidate based on what you have seen at that point. This
represents the best balance between leaving no stone unturned (collecting the necessary
information) and making the decision too late (the one that got away) .3
2. Orientate yourself in the situation
Colonel John Boyd , an American airforce officer, developed the ‘OODA’ acronym to4
describe the four-stage process for making good decisions in difficult and uncertain
situations. Observe, orientate, decide and act is the somewhat obvious process referred
to here.
However, it is the ‘orientate’ stage in which we are interested, since this is the stage
where the decision-maker seeks to check on their confirmation bias - ie: are you simply
seeing what you want to see? The goal during the orientation phase is to find potential
mismatches between what you have observed, and the judgement you may have made.
How do you overcome confirmation bias during the ‘orientate’ phase?
- Recognise that simply being aware of your bias is not sufficient to deal with it - but it
is a good starting point
- Try not to jump to conclusions too readily - take in all the information
- Embrace and welcome the surprise caused by the mismatch between what you’ve
observed and your ‘usual’ diagnosis - use it to rethink your initial hypothesis
- Seek to prove yourself wrong - why was my initial hypothesis incorrect?
Recognising any mismatch between your observation and the judgement you have made
is the critical focus in the ‘orientate’ phase. When this occurs, it is essential to schedule
time for reflection and to consider the points above.
3. Rethink the probability of a future event
Thomas Bayes - an English statistician and theologian - pioneered the approach that
allows us to update our views on the probability of an event based on recent experience
and new evidence. This is in contrast to classical statistics , which assumes that the5
probability of a future event is based on the overall average frequency of that event in the
long run - eg: “this type of flood occurs once in a hundred years”. So classical statistics
would suggest a low probability for the same flood occurring two years in a row.
Classical statistics is strongly underpinned by conservatism bias - the tendency we have to
insufficiently revise our beliefs in the light of new evidence. We generally overweight our
Hill, Theodore P. (2009). "Knowing When to Stop". American Scientist. 97: 126–1333
Boyd, John R. (3 September 1976). Destruction and Creation. U.S. Army Command and General Staff College.4
Everitt, B.S. (2002) The Cambridge Dictionary of Statistics, CUP5
© Norman Chorn 2020 • norman.chorn@brainlinkgroup.com • (0416) 239 824 • Page 4

5. understanding of the ‘average frequency’ of the event, and underweight any new
information or evidence .6
Bayesian statistics, on the other hand, recognises that the probability of a future event
should be updated by new data or evidence about that event. It would appear, therefore,
that the Bayesian approach is likely give us a better understanding about the probability of
random events, particularly in uncertain situations.
Sadly, as mentioned above, human decision making does not adopt a complete Bayesian
approach due to the conservatism bias and the so-called anchoring effect. Humans are
slow to adjust their decision making about future events in the light of new data or
evidence .7
How can we overcome this natural tendency to place a low value on new data and
evidence?
- Recognise that your previously held theories may be similar to ‘sunk costs’. It’s not
important what you have spent or invested in developing them - it’s more important to
think of what it may cost to hold onto them
- ‘Rent’ rather than ‘buy’. The point here is that you could benefit by holding your
current ideas lightly (renting)), rather than ‘owning’ them (buying). The cost of
ownership is high, as you have much invested in proving them correct
- Be wary of overvaluing ideas or information that is easy to understand. Sometimes
the difficult and complex information may be more valuable
- Use a variety of ‘trip wires’ to remind you that you may be overvaluing certain
information. This includes the use of checklists (to consider all the information) and
algorithms that apply sound principles consistently
- Make time to re-think and re-test your base assumptions and theories at regular
intervals - particularly if the recent data and evidence does not support these.
4. Trust you intuition in specific situations
Complex problems are very different to complicated problems. In a complicated problem,
there is a recognised ‘best practice’ solution, and this is achieved by way of a logical
reductionist process (ie: we work through the problem methodically by analysing the
various elements).
Complex problems, on the other hand, are characterised by multiple viewpoints - often in
conflict with one another. Furthermore, there is no recognised ‘best practice’ solution, and
the problem has to be solved by studying the whole system and allowing the solution to
Edwards, Ward. "Conservatism in Human Information Processing (excerpted)". In Daniel Kahneman, Paul Slovic and Amos Tversky.6
(1982). Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press.
D. Kahneman, P. Slovic and A. Tversky. (1982). Judgment under uncertainty: Heuristics and biases. New York: Cambridge University7
Press.
© Norman Chorn 2020 • norman.chorn@brainlinkgroup.com • (0416) 239 824 • Page 5

6. ‘emerge’. It is clear why an intuitive approach, which relies only on ‘thin slices’ of
information, might be attractive in these instances.
However, as we know, intuition can often fool us, given that its reliability can be affected by
anchoring, priming and stress. So when can we use this powerful tool that is made
available through the immense capacity and speed of the brain’s non-conscious
functioning? Research reveals three situations where this can be used:8
- Where you are an expert in a given situation - and have been recognised as such by
others (eg: an international chess master)
- Where the situation in which you are operating is subject to regular patterns (eg: the
development of software)
- Where you are able to get immediate feedback as to the correctness of your decision
(eg: bomb disposal).
Admittedly, this is a limited set of conditions under which you can trust your intuition. But it
seems sensible if we consider that intuition is based on a fast recollection of previous
patterns you have experienced, and that these can only be trusted if you have
experienced regular success under these conditions, and the conditions are relevant to
you current situation.
________________________________________________________________________
Dr Norman Chorn is a strategist and organisation development practitioner with the
BrainLink Group. He uses principles of neuroscience to address the challenges of
developing strategy in a complex and uncertain environment. His particular areas of
focus are strategy in conditions of uncertainty; organisational and cultural alignment;
and strategic leadership.
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D. Kahnemann (2011). Thinking, Fast and Slow. New York: Farrer, Straus and Giroux8
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