1. Lecture 7 Fast & frugal decision making

2. Simon: A Behavioral Model of Rational Choice
Summary in three slides

3. The aim of the paper The organism has psychological limits
These limits mean that exhaustive search is not always optimal
Classical models would imply you know all possible outcomes
But this is computationally too demanding
Aims to replace ”economic man” with a theory that is more realistic

4. Simplified payoff functions

5. Satisficing Assume you need to reach payoff of X
Search for alternatives until v(A)>X
Choose A and terminate search
Same principle can be extended to multiple criteria:
Only accept job that has sufficient wage, is not too far, …

6. Gigerenzer & Goldstein: Reasoning the Fast and Frugal Way: Models of Bounded Rationality

7. Here’s a 5-minute summary of the first two pages, maybe we can watch it together:

8. Classical rationality
Bayesian models
Rationality
Probability
Computation
Information
Probability axioms
Full information

9. Critique of classical approach
Here, Bayes’ theorem and other “rational” algorithms quickly become mathematically complex and computationally intractable, at least for ordinary human minds. [--] If one would apply the classical view to such complex real-world environments, this would suggest that the mind is a supercalculator like a Laplacean Demon (Wimsatt, 1976)— carrying around the collected works of Kolmogoroff, Fisher, or Neyman - Gigerenzer, p. 2

10. Laplace’s demon - Pierre Simon Laplace
"We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes."
- Pierre Simon Laplace

11. Critique of behavioral approach
On the other hand, the heuristics-and-biases view of human irrationality would lead us to believe that humans are hopelessly lost in the face of real world complexity, given their supposed inability to reason according to the canon of classical rationality, even in simple laboratory experiments. - Gigerenzer, p. 2

12. The overarching dilemma
Classical approach
What models can we compute?
Heuristics and biases
How can we succeed in decisions?
If classical models are intractable: What kinds of models are not? If heuristics and biases is right: How can we succeed in our lives?

13. The Road to New Bounded Rationality
Fast and frugal heuristics
Simon’s bounded rationality
Laplacean classicism
Intractable
“fit a lot of things into it by foresight and hindsight” (Simon)

14. Fast and frugal
Classical
Fast

15. Fast and frugal
Classical
Frugal

16. Introducing Take The Best
Heuristic decision rule
Order cues by validities, start from best
Go to next cue
Look for cue values
If cue discriminates (+/-, +/?, -/?), choose better valued option
If no choice, loop back to 2.

17. Holiday example Rome Berlin London Paris Is it in Europe? +
Can I get by with English? -
Is the weather warm and sunny?
Is it cheap to travel there?

18. All in all
Replacing classical reasoning with psychologically plausible rules
Is it true simple rules cannot do well?
Questioning HB
Overcoming ”If you’re simple, you must be stupid”
Criticizing the norm of optimal rules (regression, bayesian updating etc.)
Well, big claims… Evidence would be nice!

19. The Experiment Binary choice: which city is bigger?
Option set: 83 German cities with >100,000 inhabitants
Competing rules:
Take The Best
Tallying
Weighted Tallying
Unit-weighted Linear
Weighted Linear
Regression

20. Cues
Ecological validity = when it discriminates, how often is it correct?
Discrimination rate = how often does it discriminate?

21. Recognition principle
In the simulation, some cities were unrecognized – no known cue values If one is recognized and the other isn’t, TTB chooses recognized Recognition validity 0.80 was empirically measured

22. TTB result: recognition is powerful

23. Competing rules get extra info
Competing rules are all compensatory
Good values can compensate for bad ones
Price – quality
Competing rules were given all cues (incl. recognition)
Regression weights unique for each individual
Imputed missing values = average of known ones

24. Speed

25. Accuracy

26. Summary of results
TTB is a plausible fast and frugal heuristic Performs as well as complex linear regression in this setting

27. Concerns / critique
This was just a simulation This was just one problem Who says people actually use such a heuristic? But: the point was to show that a heuristic can do as well as a complex algorith Remember ecological validity – sensitivity to environment

28. Extra: More HB critique from Gigerenzer

29. Extra: Differences in philosophy
[--] a discrepancy between the dictates of classical rationality and actual reasoning is what defines a reasoning error in this program. Both views accept the laws of probability and statistics as normative, but they disagree about whether humans can stand up to these norms. - Gigerenzer, p. 2 Gigerenzer has some fundamental differences in view Not clearly in this article, but in other books/articles

30. Extra: what is probability?
Kahneman is from the subjective probability school or bayesianism Gigerenzer represents frequentists This fight is actually an ancient struggle – and still going strong!

31. Extra: bayesianism vs. frequentism
Bayesians ”It will rain tomorrow with p=0,65” is a sensible statement These p can be elicited
Frequentists Probabilities = frequencies No subjective probabilities exist -> asking about them is silly Cf. How big is green?