Week 5

Have you ever wondered why people are afraid of flying, in case you are not one of them? Have you ever asked yourself, is flying actually more dangerous than cars or trains? The department of transport recorded 2538 road deaths for the UK in 2008. According to the Aircraft Crashes Record Office 884 people died in a plane crash the same year. Although it looks like flying is safer than driving a car more and more people are afraid of boarding a plane. Why?

The most obvious and simplest reason is the media. Aviation disasters are more likely to be broadcasted on television than a car crash where several people died. Therefore people are more exposed to pictures and reports of accidents involving a plane than a car, which furthermore results in the development of a fear of flying. These individuals formed an association between the news, flying and a plane crash. When people asses the frequency or the likelihood of a certain event by the ease with which associations come to their mind, they use the availability heuristic (Tversky & Kahneman, 1973).

Tversky and Kahneman (1973) observed that participants estimated the number of words that has r as first letter to be higher than words that have r as third letter. The same had been proofed with the letter t in a later study (Gabrielcik & Fazio, 1984). These two observations occurred because it is easier to bring words, which start with r or t to your mind. Whereas it is harder to think of words that have these two letters as their third letter.

Schwarz and his colleagues designed three experiments to proof the availability heuristic. In the first condition participants were asked to describe either 6 or 12 examples of assertive or unassertive behaviour. Schwarz and colleagues found out that the recall affected the assertiveness or unassertiveness only when the recall process was experienced as easy. Participants gave more assertive behaviour with 6 examples and more unassertive behaviour when asked to recall 12 examples.

In the second condition participants were told that in previous studies people rated certain examples as more difficult. Although participants in Schwarz and colleagues’ study received this information, their decisions were not influenced towards the reported direction.

In the third and last condition participants heard a piece of music. They were either told that the music facilitated recall in an assertive situation or that the music facilitated recall in an unassertive situation. According to previous research recall performance showed the opposite direction.

Schwarz and colleagues’ research proofs that people assess the likelihood of occurrence of an event according to the ease of which associations come to their mind. I personally think that this theory is very accurate and accounts for a lot of situations in our social life. Before I moved to London I only saw negative news about people form Pakistan on the television. After getting in contact with some of them in university, however, I made the experience that their intellect and creative potential will never be shown on television. The availability heuristic explains this stereotypical thinking, but the best way to avoid building stereotypes is to make your own experience.

Week 4

How many clues do you need?

What did we have so far? We heard that when we have to make decisions, we either examine the most important cue that is available to us or we examine all available cues for the current situation. In case the decision maker examines all cues, he or she assigns certain values or weights to these cues and makes a decision. So far I wrote that the simpler matching heuristic is a better predictor of decision making, although, it only searches through a small subset of cues.

If you were offered £500 with a probability of .50 (500, .50) otherwise nothing or £2500 with a probability of .10 otherwise nothing (2500, .10), which gamble would you choose?

In a study with Austrian students 88% would choose the first gamble with the smaller probability of the minimum gain but the lower maximum gain (Branstätter et. al., 2006). The researchers developed the priority rule in which we consider reasons in the following order: minimum gain, probability of minimum gain, maximum gain.

However, when should one stop considering these rules, or should you always examine each of the criteria? To answer this question read the following gamble (it’s the last one, I promise!).

You were offered either £200 with a probability of .50 (200, .50) otherwise nothing or £100 for sure. If you would be offered £2000 with a probability of .50 (2000, .50) otherwise nothing or £100 for sure, you might select a gamble with a different outcome. The minimum gains differ by the same amount and the probabilities are the same. The maximum outcomes, however, are different (1. £100, 2. £1000). Therefore a stopping rule has been proposed: Decision makers stop examination when the minimum gains differ by 1/10 (or more) of the maximum probabilities.

These two rules together and the decision rule, which tell you to choose the gamble with the more attractive gain, define the priority heuristic. The priority heuristic is able to explain shortcomings in decision making, e.g. the Allais Paradox or the Certainty Effect.

Week 3

How much are YOU willing to risk?

Consider the following gamble: You are offered either a sum of money for certain or a lottery ticket that will give you a 50% chance of winning £10.000 and a 50% chance of winning nothing. What would be the certain sum that makes you indifferent whether to receive it or the lottery ticket?

Below you find two graphs that show my results with different sums and different probabilities.

These graphs show that I am a rather risk aversive person. Your graphs might look the same or you are a rather risk seeking individual, which would alter your curve from the bottom at lower sums and probabilities to the top with higher sums and probabilities.

Week 2

What do you get when you put two people in two different courts where they write down 342 decisions by 57 benches? 

Apart from a lot of paper work you might find out that the matching heuristic is a better predictor for judicial decisions than a more complex model. Dhami (2003) conducted the above-mentioned study. This study found out that the matching heuristic was a better predictor that franklins rule. 

This result might come as a surprise because franklins rule examines more cues in a decision and assigns them certain weights, as explained in my first week blog. The matching heuristic, however, searches only through a small subset of cues and bases a decision on one cue alone. 

Why, however, does this result occur? Judges are presented with a heavy caseload and are usually working under time pressure. Therefore many judges rely on the decision made by the police, previous benches, and prosecutors, and maybe unintentionally “passing the buck”. Judges also make decision as a bench and this involves shared responsibilities, which use fewer cues. 

In the end there is only one question remaining: How do they know that the right decision is the right decision?

Week 1

Have you ever wondered why judges in a court can make their decision accurately or whether they make the right decision at all? 

This week was the first lecture in Judgement and Decision Making and therefore it was a simple introduction lecture. The lecturer, Dr. David Hardman, sets as part of the assessment for the module to write a blog and contribute to a website. To do this we were supposed to set up a Google Mail account and a blog. I did these things before, and that’s why I mostly helped another student to get her mail account working and start her blog. 

The final part of the lecture, and to my mind the interesting part, was about decision making by using a multiple linear regression analysis. These models suggest that people consider certain information when making a decision. These different information are given a certain value and according to the final outcome of this analysis they make their choice. Not everyone, however, is able to use all the information that are available. Time pressure or exhaustion for example can be reasons why someone cannot use every clue. 

Can you remember one situation where you valuated every piece of information, assigned a certain value, and made the perfect decision? 

I was surprised when I read that decision making models outperformed human professionals in their areas. Libby (1976) proofed that statistical models were better with predicting the success of companies than bankers who used the same information. Maybe someone should have listened to Psychologists before borrowing money from government. One relief, however, is the fact that to get these models working one needs humans to choose the variables for the decision outcome. 

Although it only was the introduction lecture in this module, it presented some useful and interesting information about how people, especially professionals, make decisions.