TYPES OF HEURISTICS: Fast &
Frugal Heuristics vs. Other Methods
selected from Gigerenzer & Todd Simple Heuristics That Make Us
Smart Oxford 1999
(compiled by Edward G. Rozycki, Ed.D. )
RETURN
edited 10/18/18
STEPS HEURISTICS/ |
Preconditions |
Step 0 |
Step 1: Search Rule |
Step 2: Stopping Rule |
Step 3: Decision Rule |
Notes w/ pg #'s |
Recognition RH Chap 2 |
Pairwise random presentation of objects (PRPO) |
See if item of selected pair is recognized. |
If neither or both recognized, back to 0. |
If only one and not more than one item recognized, stop |
Select recognized object. |
Heuristic def's: ecological validity: discrimination rate: Conflicts avoided 81 Rules non-compensatory 81 |
Minimalist MH 79 |
(PRPO) |
Cue randomly selected. Use RH |
Random selection of cue; if none found, guess. |
If cue values are 1,0 go to 3; otherwise step 1 |
Object with 1 has higher value. |
|
Take the Last TLH 80 |
History of Cue used to solve previous problem: Einstellung**;) |
(PRPO) Cue randomly selected. Use RH. |
Einstellung** From second problem, start with last problem's successful cue. |
If not stopped, use cue antecedent to failed one from previous problem. |
Object with 1 has higher value. Keep history of successful cues. |
|
Take the Best TBH 80 Order by perceived validities |
(PRPO) Cue NOT randomly selected. Use RH. |
Ordered Search: Use cue of highest validity not yet tried for present task.. |
If choice fails, used next highest validity cue. |
Compare cue values Object with 1 has higher value. |
||
Franklin 76 (adapted) weightings not subjective as were Franklin's |
Weighted linear combination of cues (WLCC) |
CS Weightings may vary. |
When all cues and weights determined |
CMI by sum of products of weights by cues. Select highest product. |
"Commandments" of unbounded rational judgment: a. complete search (CS): find all information available b. compensation (CAI): combine all pieces of information 83 |
|
Dawes |
Linear strategy, unit weights |
CS |
Weights all = 1. Determine all cues. |
See Franklin Step3. |
||
Multiple Regression |
Attempt to minimize least squares in regression line thru all data points |
CS |
Expected Value of CMI |
Accept regression with least sum of squares. |
||
Expected Value |
CS, CMI |
All values with associated probabilities |
Choose highest of all E(V)'s. |
See also Gigerenzer & Todd, p. 143
**Einstellung: attitude, setting, stand