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Forecasting newsletter draft

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Nuno Sempere 2020-05-23 20:26:51 +02:00
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ea/ForecastingNewsletter/May2020.md
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@ -199,15 +199,13 @@ handicappers. Each judge was presented with a list of 88 variables culled from t
> We see that accuracy was as good with five variables as it was with 10, 20, or 40. The flat curve is an average over eight subjects and is somewhat misleading. Three of the eight actually showed a decrease in accuracy with more information, two improved, and three stayed about the same. All of the handicappers became more confident in their judgments as information increased.
![](https://nunosempere.github.io/ea/Forecasting/AccuracyVsConfidence.png)
The study contains other nuggets, such as:
- The study contains other nuggets, such as:
- An experiment on trying to predict the outcome of a given equation. When the feedback has a margin of error, this confuses respondents.
- "However, the results indicated that subjects often chose one gamble, yet stated a higher selling price for the other gamble"
- "We figured that a comparison between two students along the same dimension should be easier, cognitively, than a 13 comparison between different dimensions, and this ease of use should lead to greater reliance on the common dimension. The data strongly confirmed this hypothesis. Dimensions were weighted more heavily when common than when they were unique attributes. Interrogation of the subjects after the experiment indicated that most did not wish to change their policies by giving more weight to common dimensions and they were unaware that they had done so."
- "The message in these experiments is that the amalgamation of different types of information and different types of values into an overall judgment is a difficult cognitive process. In our attempts to ease the strain of processing information, we often resort to judgmental strategies that do an injustice to the underlying values and policies that we’re trying implement."
- "A major problem that a decision maker faces in his attempt to be faithful to his policy is the fact that his insight into his own behavior may be inaccurate. He may not be aware of the fact that he is employing a different policy than he thinks he’s using. This problem is illustrated by a study that Dan Fleissner, Scott Bauman, and I did, in which 13 stockbrokers and five graduate students served as subjects. Each subject evaluated the potential capital appreciation of 64 securities. [...] A mathematical model was then constructed to predict each subject's judgments. One output from the model was an index of the relative importance of each of the eight information items in determining each subject’s judgments [...] Examination of Table 4 shows that the broker’s perceived weights did not relate closely to the weights derived from their actual judgments.
As remedies they suggest to create a model by elliciting the expert, either by having the expert make a large number of judgements and distillating a model, or by asking the expert what they think the most important factors are. A third alternative suggested is computer assistance, so that the experiment participants become aware of which factors influence their judgment.
- As remedies they suggest to create a model by elliciting the expert, either by having the expert make a large number of judgements and distillating a model, or by asking the expert what they think the most important factors are. A third alternative suggested is computer assistance, so that the experiment participants become aware of which factors influence their judgment.
- [Immanuel Kant, on Betting](https://www.econlib.org/archives/2014/07/kant_on_betting.html)
Vale.