Lecture 3 concepts Flashcards
Name the 4 different problems that cognitive biases/heuristics are the result of and may help us address.
- Information overload: our brain uses tricks to only select the information we are most likely to use.
- Lack of meaning: we only see a tiny part of the very confusing world. To construct meaning out of the bits and pieces that come to our attention, we fill in the gaps and map the information to our existing mental models.
- Constrained by time and information: in many situations, we need to act fast and do not have sufficient information.
- How do we know what needs to be remembered for later? We can only afford to keep around the bits of information that are most likely to prove useful in the future.
Bounded rationality (Herbert Simon)
Describes the way that humans make decisions that depart from perfect economic rationality since we are limited by our mental capacity, the information available to us, and time. Instead of striving to make the “best” choices, we often settle on making merely satisfactory choices.
Information Processing Model
Input => Sensory memory (holds unlimited information ups to 3 seconds). When giving attention, this information will be processed in the working memory (± 7 items for 5-20 seconds). With rehearsal, this information will be encoded and processed in the long-term memory.
Cognitive load theory
Explains the limits and errors of our psychological capacities to effectively process the world into mental schemas for long-term memory storage and future recall.
Gigerenzer on decision-making and uncertainty
Most of the time, people have insufficient information. For most situations/events we can only rely on a limited degree of certainty. People make use of heuristics, mental shortcuts, or simple ‘rules-of-thumb’ that are the result of evolved capacities of the brain. According to Gigerenzer, heuristics lead to good, fast and efficient decisions, often outperforming ‘rational’, ‘complex’ and ‘optimising’ strategies.
The gaze heuristic (Gigerenzer)
Entails the fixation of one’s gaze to the object and adjustment of the running speed so that the angle of the gaze remains constant while approaching an object.
Example: catching a ball, the miracle of the Hudson. The captain had to make a quick decision where to land the plane => he chose to land the plane in the river Hudson, instead of flying a bit further to a nearby airport. It demonstrated a reliance on a key, salient piece of information to make a live-saving decision.
Recognition heuristic
The wisdom of the ignorant can outperform experts. In many situations, just recognising a little bit can already be enough: for example when Americans and Germans were asked whether Detroit or Milwaukee was the bigger city. However, ignorance does not always outperform experts.
Take-the-best heuristic
A mental shortcut we use when making decisions between alternatives, so that we can quickly make decisions without having to know all the information about each alternative. For example, when buying milk you decide that will only look at the price, and not at other aspects.
The 1/N rule
Data falls into 2 categories:
1. Information that is useful to predict the future
2. Arbitrary information, or ‘error’, ‘noise’ that does not predict
Since the future is unknown, it is impossible to distinguish these. The 1/N rule ignores all the information, making it immune to errors in the data. It bets on the wisdom of equal allocation and diversification of funds. Complex strategies only become better if they have lots of data over a long time period.
Fast and frugal decision tree
Can be constructed if the number of decision factors can be simplified (reduced) and used to construct one-reason yes/no decision rules.
When do heuristics lead to better and more accurate decisions?
In situations of uncertainty, under time pressure, when one has to predict the future, or when personal choices are being made
Gigerenzer’s Adaptive Toolbox Theory
Our minds are like an adaptive toolbox of cognitive and behavioral strategies (heuristics), that lead to good, fast and efficient decisions, often outperforming ‘rational’ and ‘optimal’ strategies!
Which meanings can probability have? What are the consequences of these multiple meanings?
With the example, 30% probability that it is going to rain, it could either mean that:
1) 30% of the time of any given day it is going to rain
2) In 30% of the area it is going to rain
3) 30% of experts agree that it is going to rain
Ecological rationality
Main question: Under what circumstances and situations (in what environments) do different decision-making strategies work, and when do they not?
States that cognitive processes, including simple heuristics, are not per se rational or irrational, but their success rests on their degree of fit to relevant contexts and environments. The key is therefore to understand how cognitive and environmental structures slot together.
