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 7/24/16
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 noncompensatory 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