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Summary on heuristics
Topic: Heuristics
by Annie, 2018 Cohort
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
The concept of heuristics in human-decision making was developed during the 1970-80s by Amos Tversky and Daniel Kahneman, and their paper was assigned as the base reading for this primer. This paper highlights, that when faced with uncertainty we simplify the decision-making process and use heuristics, otherwise known as cognitive shortcuts. Heuristics are used when it is not possible to assess the entire situation or calculate an optimal decision. There are many different types of heuristics, but the most well-known heuristics are: adjustment and anchoring, availability, and representatives, which we will explore below.
Adjustment and Anchoring#
Adjustment and anchoring occurs when we rely on a piece of information that is offered first, then it becomes a mental reference point or anchor. When provided with a second piece of information we adjust our perspective relative to our prior knowledge. For example, this happens during sales in stores. If we go to buy a new pair of pants and see two prices on a price tag, the first one quite high but crossed out and then followed by a new sale price. In determining how much we would be willing to pay, the crossed-out price would be the anchor and the second price the adjustment. This may lead us to buying the pants even if we otherwise wouldn’t of, if it had of just been listed at the second price. This presentation of a reduction in price is an anchoring effect.
Availability#
The availability heuristic is when we make a choice based on immediate and easy examples that come to mind when evaluating a decision. As in we rely on information that is available to us rather than taking a step back and looking at the bigger picture. As per the Tversky and Kahneman paper, investors may judge the quality of an investment based on what was recently in the news and ignore other important information such as its potential to generate profit.
Representatives#
The representativeness heuristic is when we make judgements based on the idea of what something should be or what they should represent. For example, in the image below, we would normally assume that more leaves would mean a larger carrot, however that is not the case.
In addition, people make assumptions about the carrots despite a base rate or understanding of prior probabilities. For example, if 70% of the carrots are big regardless of their leaf size, this will not be taken into account when one makes a judgement about the size of the carrots in relation to the leaf size under uncertainty.
Connection to Complexity#
Heuristics are mostly effective and efficient, however they can also to lead to predictable and systematic errors. Learning more about these heuristics could allow us to improve our decisions made under uncertainty. Thus, these heuristics can act as a tool to measure uncertainty which aides us in understanding the nature of complexity. Further, in understanding the process of everyday decision making we can learn how it affects complex problems. For example, if we take Climate Change, and acknowledged that ethical consumerism could play a huge role is resolving this issue. Then we could take this toolkit of heuristics and use it to encourage people to buy more green products by making them look more affordable or by placing them within mainstream media etc. This demonstrates how heuristics can be used within the context of complexity.
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Disclaimer#
This content has been contributed by a student as part of a learning activity.
If there are inaccuracies, or opportunities for significant improvement on this topic, feedback is welcome on how to improve the resource.
You can improve articles on this topic as a student in "Unravelling Complexity", or by including the amendments in an email to: Chris.Browne@anu.edu.au
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
The concept of heuristics in human-decision making was developed during the 1970-80s by Amos Tversky and Daniel Kahneman, and their paper was assigned as the base reading for this primer. This paper highlights, that when faced with uncertainty we simplify the decision-making process and use heuristics, otherwise known as cognitive shortcuts. Heuristics are used when it is not possible to assess the entire situation or calculate an optimal decision. There are many different types of heuristics, but the most well-known heuristics are: adjustment and anchoring, availability, and representatives, which we will explore below.
Adjustment and Anchoring#
Adjustment and anchoring occurs when we rely on a piece of information that is offered first, then it becomes a mental reference point or anchor. When provided with a second piece of information we adjust our perspective relative to our prior knowledge. For example, this happens during sales in stores. If we go to buy a new pair of pants and see two prices on a price tag, the first one quite high but crossed out and then followed by a new sale price. In determining how much we would be willing to pay, the crossed-out price would be the anchor and the second price the adjustment. This may lead us to buying the pants even if we otherwise wouldn’t of, if it had of just been listed at the second price. This presentation of a reduction in price is an anchoring effect.
Availability#
The availability heuristic is when we make a choice based on immediate and easy examples that come to mind when evaluating a decision. As in we rely on information that is available to us rather than taking a step back and looking at the bigger picture. As per the Tversky and Kahneman paper, investors may judge the quality of an investment based on what was recently in the news and ignore other important information such as its potential to generate profit.
Representatives#
The representativeness heuristic is when we make judgements based on the idea of what something should be or what they should represent. For example, in the image below, we would normally assume that more leaves would mean a larger carrot, however that is not the case.
In addition, people make assumptions about the carrots despite a base rate or understanding of prior probabilities. For example, if 70% of the carrots are big regardless of their leaf size, this will not be taken into account when one makes a judgement about the size of the carrots in relation to the leaf size under uncertainty.
Connection to Complexity#
Heuristics are mostly effective and efficient, however they can also to lead to predictable and systematic errors. Learning more about these heuristics could allow us to improve our decisions made under uncertainty. Thus, these heuristics can act as a tool to measure uncertainty which aides us in understanding the nature of complexity. Further, in understanding the process of everyday decision making we can learn how it affects complex problems. For example, if we take Climate Change, and acknowledged that ethical consumerism could play a huge role is resolving this issue. Then we could take this toolkit of heuristics and use it to encourage people to buy more green products by making them look more affordable or by placing them within mainstream media etc. This demonstrates how heuristics can be used within the context of complexity.