Kahneman (psykolog med nobelprisen i økonomi – tag den polit’er! 😉 )er spændende læsning.
Her er en liste over de bias-fælder vi falder i når vores hjerne hurtigt prøver at skabe mening og retning i det den ser (uanset hvor mangelfuldt billedet er – det kalder Kahneman system 1).
- Law of small numbers: People don’t understand statistics very well. As a result, they 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 you personally observe one incident, you are more likely to generalize this occurrence to the whole population.
- Assigning cause to random chance: As Kahneman wrote, “statistics produce many observations that appear to beg for causal explanations but do not lend themselves to such explanations. Many facts of the world are due to chance, including accidents of sampling. Causal explanations of chance events are inevitably wrong.”
- Illusion of understanding: People often create flawed explanations for past events, a phenomenon known as narrative fallacy. These “explanatory stories that people find compelling are simple; are concrete rather than abstract; assign a larger role to talent, stupidity, and intentions than to luck; and focus on a few striking events that happened rather than on the countless events that failed to happen… Good stories provide a simple and coherent account of people’s actions and intentions. You are always ready to interpret behavior as a manifestation of general propensities and personality traits — causes that you can readily match to effects.”
- Hindsight bias: People will reconstruct a story around past events to underestimate the extent to which they were surprised by those events. This is a “I-knew-it-all-along” bias. If an event comes to pass, people exaggerate the probability that they knew it was going to occur. If an event does not occur, people erroneously recall that they thought it was unlikely.
“Hindsight bias has pernicious effects on the evaluations of decision makers. It leads observers to assess the quality of a decision not by whether the process was sound, but by whether its outcome was good or bad… We are prone to blame decision makers for good decisions that worked out badly and to give them too little credit for successful moves that appear obvious only after the fact… When the outcomes are bad, [people] often blame [decision makers] for not seeing the handwriting on the wall… Actions that seemed prudent in foresight can look irresponsibly negligent in hindsight.”
- Confirmation bias: Within WYSIATI, people will be quick to seize on limited evidence that confirms their existing perspective. And they will ignore or fail to seek evidence that runs contrary to the coherent story they have already created in their mind.
- Overconfidence: Due to the illusion of understanding and WYSIATI, people may become overconfident in their predictions, judgments, and intuitions. “We are confident when the story we tell ourselves comes easily to mind, with no contradiction and no competing scenario… A mind that follows WYSIATI will achieve high confidence much too easily by ignoring what it does not know. If is therefore not surprising that many of us are prone to have high confidence in unfounded intuitions.”
- Over-optimism: People have a tendency to create plans and forecasts that are “unrealistically close to best-case scenarios.” When forecasting the outcomes of risky projects, people tend to make decisions “based on delusional optimism rather than on a rational weighting of gains, losses, and probabilities. They overestimate benefits and underestimate costs. They spin scenarios of success while overlooking the potential for mistakes and miscalculations… In this view, people often (but not always) take on risky projects because they are overly optimistic about the odds.”
Er du mere interesseret så læs Think fast and slow, eller Michael Lewis: The Undoing Project.