When “Less Is More” – A Critique of the Heuristics & Biases Approach to Judgment and Decision Making Psychology 466: Judgment & Decision Making Instructor: John Miyamoto 11/03/2015: Lecture 06-1 Note: This Powerpoint presentation may contain macros that I wrote to help me create the slides. The macros aren’t needed to view the slides. You can disable or delete the macros without any change to the presentation.

Outline Misconceptions promoted by the heuristics and biases movement. Accuracy/Effort Tradeoff – ♦ Is it a valid description of decision making practice? ♦ When is it valid? Less-is-more Bias/Variance Tradeoff – why less-is-more Psych 466, Miyamoto, Aut '15 2

Gigerenzer & Brighton (2009) Gigerenzer, G., & Brighton, H. (2009). Homo Heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, GB: Abbreviation for Gigerenzer & Brighton (2009) HB: Abbreviation for heuristics & biases movement. Psych 466, Miyamoto, Aut '15 3

Meaning of "Heuristic" "The term heuristic is of Greek origin, meaning ‘‘serving to find out or discover.’’" (Gigerenzer & Brighton, p. 108) "A heuristic technique, often called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals." (Wikipedia: Psych 466, Miyamoto, Aut '15 4

Misconceptions Promoted by HB Movement GB (p. 109) : The HB movement gave rise to three misconceptions about human reasoning: 1.Heuristics are always second-best. 2.We use heuristics only because of our cognitive limitations. 3.More information, more computation, and more time would always be better GB, p Related Hypothesis: There is an accuracy/effort tradeoff. "If you invest less effort, the cost is lower accuracy." GB, p Psych 466, Miyamoto, Aut '15 5

Accuracy/Effort Tradeoff versus "Less Is More" Effects Accuracy/Effort Tradeoff Hypothesis: "If you invest less effort, the cost is lower accuracy." GB, p "Less Is More" Effect: In some situations, less information (less effort) leads to greater accuracy. Psych 466, Miyamoto, Aut '15 6

Tallying (a.k.a. Unit Weighting or Equal Weighting) When all of the cues are dichotomous, i.e., present or absent, then tallying is the same is counting the number of positive cues and subtracting the number of negative cues. When at least some of the cues are continuous like height or income, then the cues should be converted to z-scores before computing the predicted z-scores. ♦ After computing the predicted z-scores, it is necessary to convert the predicted z-scores back to the original scale Psych 466, Miyamoto, Aut '15 7

Cross-Validation Studies For Evaluating the Predictive Accuracy of a Model Use a computer to repeat the procedure over many random splits. Psych 466, Miyamoto, Aut '15 8 Sample of Data random split 50% of data in test sample: Use fitted model to predict outcomes in the test sample. Evaluate goodness of fit. 50% of data in estimation sample: Estimate parameters of the model

Tallying versus Multiple Regression Methodology: ♦ Fit model to 50% of data; predict results for remaining 50% of data. Evaluate the accuracy of prediction. ♦ Select split of data at random. Repeat the analysis for many random splits. Compute cross-validation analyses for Tallying. Compute cross-validation analyses for Multiple Regression. Which model, Tallying (Unit Weighting) or Multiple Regression produces higher accuracy in prediction? Psych 466, Miyamoto, Aut '15 9

Tallying versus Multiple Regression (MR): Which Method has Higher Predictive Accuracy? Czerlinski, Gigerenzer & Goldstein (1999): Averaged over 20 studies, Tallying has (slightly) higher predictive accuracy than MR. Why does this happen? MR overfits the model when the sample size is small. Tallying is robust (gives stable estimates in random data). MR is less robust (its estimates are unstable when sample size is small). Psych 466, Miyamoto, Aut '15 10 GB, Figure 1 (Take-the-Best line has been omitted)

When does Tallying Outperform Multiple Regression (MR)? Tallying outperforms MR when the following criteria are met: 1.Degree of linear predictability is small (R 2 < 0.50; |  | < 0.71). 2.Sample size was less than 10  number of cues; 3.Cues are correlated with each other. Conditions 1, 2 and 3 are all associated with increased variability of regression coefficients. Main point: Everyday experience has many correlated cues but not a lot of data. Tallying should outperform MR in everyday experience. Less is more! Psych 466, Miyamoto, Aut '15 11

Background to: "Take the Best" Heuristic Prediction task: Predicting which of two options has a higher value on a criterion C. Cue validity of feature F = Probability that Object #1 has higher value on C given that Object #1 has feature F and Object #2 does not. Cue validity of F = P( C Obj.1 > C Obj.2 | Obj #1 has F and Obj #2 does not) Psych 466, Miyamoto, Aut '15 12

Background to: "Take the Best" Heuristic Suppose that F 1 and F 2 are two features. Then F 1 has greater cue validity than F 2 if P( C Obj.1 > C Obj.2 | Obj #1 has F 1 and Obj #2 does not) > P( C Obj.1 > C Obj.2 | Obj #1 has F 2 and Obj #2 does not) Intuitively, F 1 has greater cue validity than F 2 if knowing that the objects differ on F 1 gives you a better chance to guess which object has more of the criterion C than knowing that the objects differ on F 2. Psych 466, Miyamoto, Aut '15 13

"Take the Best" Strategy The Take-the-Best choice strategy has three steps: 1.Examine the cues in decreasing order of their cue validities. 2.Stop as soon as the first cue is found on which the objects have differing values. 3.Take the object that has the higher value on the first discriminating cue. Psych 466, Miyamoto, Aut '15 14

Example of "Take the Best" Strategy Task: Decide whether the Seahawks or Cardinals is more likely to win the Western Division competition. Suppose the cue are: F 1 : Has an excellent defense against the pass; F 2 : Has an excellent defense against the run. F 3 : Has an excellent passing offense; F 4 : Has an excellent running offense Suppose that cue validity of F 1 > cue validity of F 2 > cue validity of F 3 > cue validity of F 4 Psych 466, Miyamoto, Aut '15 15

Example of "Take the Best" Strategy (cont.) Decision Rule: 1.Choose Team X if Team X has F 1 and Team Y does not. If both teams have F 1 or if neither team has F 1, then proceed to Step Choose Team X if Team X has F 2 and Team Y does not. If both teams have F 2 or if neither team has F 2, then proceed to Step 3. 3.Choose Team X if Team X has F 3 and Team Y does not. If both teams have F 3 or if neither team has F 3, then proceed to Step 2. 4.Choose Team X if Team X has F 4 and Team Y does not. If both teams have F 4 or if neither team has F 4, then guess (make the decision based on a coin flip). Psych 466, Miyamoto, Aut '15 16 Main Feature of "Take the Best": You stop working on the decision as soon as a feature that distinguishes between the choices is found.

Take the Best versus Tallying versus Multiple Regression (MR) In many cases, Take the Best has greater predictive accuracy than Tallying (Unit Weighting) and MR. Less is more! Take the Best outperforms more complex decision procedures when sample size is small and there are correlations (dependencies) among the cues. Psych 466, Miyamoto, Aut '15 17

Explaining "Less-Is-More" - Why Is This True (Sometimes)? Decision strategies exhibit a bias/variance dilemma. (a.k.a. bias/variance tradeoff). Psych 466, Miyamoto, Aut '15 18

Example: Bias/Variance Dilemma In Temperature Prediction Larger bias with low variance Better Than Smaller bias with high variance Psych 466, Miyamoto, Aut '15 19

GB Figure 3, p. 118 Bias/variance tradeoff for entire year of London temperatures. Psych 466, Miyamoto, Aut '15 20

Claim: Heuristics Are Adaptively Superior to Complex MOdels Humans are better off with biased heuristics that are robust (lower variance) in small samples. Precise normative models are less accurate than heuristic models in small samples. Less-is-more. Psych 466, Miyamoto, Aut '15 21

Why Focus on Decision Errors? Make people look stupid. Make ourselves feel smart. Error patterns are clues to cognitive representations and processes. Sometimes errors are important in real life. Psych 466, Miyamoto, Aut '15 22 Amabile (1981), "Brilliant but Cruel."

How Should We Test Adaptive Value of Heuristics in the Real World? Strategy 1: Randomly sample decision. Conceptually and practically, this is hard to do. Strategy 2: Focus on how people make a specific decision. ♦ E.g., Decisions in a hospital emergency room. ♦ E.g., Specific investment decisions. Strategy 3: Study specific decisions in artificial lab settings. Psych 466, Miyamoto, Aut '15 23

Psych 466, Miyamoto, Aut '15 24 Set Up for Instructor Classroom Support Services (CSS), 35 Kane Hall, CSS: Try setting your resolution to 1024 by 768 Run Powerpoint. For most reliable start up: ♦ Start laptop & projector before connecting them together ♦ If necessary, reboot the laptop