LEARNING TO CHOOSE BETTER
2018/2019, Semester 2
University Administration (Office of the Senior Deputy President and Provost)
Modular Credits: 2
Have you ever regretted a choice you made? Every day, we make many decisions. However, are you aware that we all possess cognitive biases that influence our choices without us knowing? These natural biases can lead us into making seemingly prudent decisions that are illogical. In this module, we will explore some of the biases that we are naturally programmed. You will understand why we don’t even realize that we behave irrationally and make choices that are unintended. With a better knowledge of these inherent natural biases, we can think more clearly and make better decisions in our lives.
On completion of the module, learners should be able to
Critically assess their strengths and weaknesses in their prior approach to decision making
Identify their natural statistical misunderstanding when presented with a case to make judgement
Create strategies to identify the influences of these unconscious biases and mitigate making unintended decisions
Develop a portfolio of their important decisions based on metacognition
S6-04-Meeting room 2 (Inside NUS Science Library)
No make-up class, please attend all sessions.
No-shows must be accompanied by valid Medical Certificate (MC) or Approved Letters of excuse from NUS.
A brief synopsis of the 6 topics can be found herein:
Fast / slow thinking and cognitive biases
Are humans rational? We simply don’t like being told that we are not very rational and certainly not as intelligent as we think we are. Hidden in the depths of our consciousness, are some ‘characters’ that keep tempering with our ‘rationality’. And we almost consciously allow this to happen.
Based on Nobel Prize Recipient Daniel Kahneman’s book on Thinking fast and slow, we discuss how it is extremely difficult for us to overcome heuristic biases.
Although we apply statistical formulas to rationalize our decisions, we are inherently prone to fall for dazzling rhetoric and dashing figures: we believe in myths and incidents that are as improbable as they are ludicrous, because this is the way we see things.
Even so, this is not undesirable altogether as some of the intuitive abilities are an evolutionary blessing that help us understand emotions and make correct decision in split seconds.
Choosing the safe bet
People sometimes misperceive numbers irrationally and do not understand statistics very well, even if they think they do. For instance, in the law of small numbers, people may look at the results of a small sample — e.g. 100 people responding to a survey — and conclude that it’s representative of the population. This also explains why people jump to conclusions with just a few data points or limited evidence. If three people said something, then maybe it’s true? If one personally observe one incident, he/she is more likely to generalize this occurrence to the whole population.
Here, we illustrate why people falls for natural biases such as the beginner’s luck, gambler’s fallacy and house money effect. We will illustrate that sometimes we make unconscious decisions due to a skewed assessment of probability.
Choosing what to buy
One of the most powerful influences on the amount we are willing to pay for something are known as anchors or reference points. We use anchors every day to make decisions or judgements, but sometimes these anchors lead us making illogical purchases. The anchoring effect is so fundamental to how we experience the world that we usually don’t even realize its effect on us.
Here, we explore the inherent natural biases that are applied in price negotiation. We discuss in depth on the sunk cost fallacy and the endowment effect that influence our purchasing decisions.
Choosing the best medical decision
We get ill at some point in our lives. As patients, do we possess the tendency to think that medical prescriptions and thorough check-ups are indispensable to our recoveries?
Patients sometimes urge doctors for stronger medicine or further medical tests, even though none may be necessary. For example, most colds are due to viruses, and will get better by themselves, while antibiotics are only useful with bacterial illnesses and may indeed cause bad side effects.
This feeling is often reinforced when people feel better after taking antibiotics. As a result, they often believe it’s because the medicine was effective.
In this topic, we discuss the understanding of action bias, regression to the mean and the “it will get worse before it gets better” effect.
Choosing what news to believe
In this topic, we teach students how discern ‘fake news’.
According to findings released by the Government's feedback unit, REACH in 2018, more than 70 per cent of Singapore residents surveyed in a poll have come across online news that they thought were "not fully accurate".
Those surveyed were also asked whether they agreed with the statement: "
I think most Singaporeans would be able to recognise fake news
." Only one in three either agreed or strongly agreed that most Singaporeans would be able to do so, with the remaining two-thirds disagreeing or responding with "neutral" or "not sure". Moreover, only half of the respondents expressed confidence in recognising fake news.
Because of the complex events in the world today and the proliferation of fake news, it is imperative for NUS students to know how to discern truths from misinformation by understanding how the human minds succumb to subconscious biases and fallacies in our daily lives.
Choosing the right person
Here we cover some of the common natural biases that occur when choosing the right person. Whether it is picking the ideal candidate for a job or the best person as a projectmate, making decisions about people happens all the time, but can be surprisingly difficult and susceptible to inherent bias.
People usually assess candidates using a set of pre-determined selection criteria. However, we may also have other sets of subconscious criteria that leave us vulnerable to human subjectivity, biases and other influences, all without us knowing.
Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week