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

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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

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Gigerenzer & Brighton (2009)

• Gigerenzer, G., & Brighton, H. (2009). Homo Heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, 107-143.

• GB: Abbreviation for Gigerenzer & Brighton (2009)

• HB: Abbreviation for heuristics & biases movement.

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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: https://en.wikipedia.org/wiki/Heuristic)

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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. 109.

Related Hypothesis: There is an accuracy/effort tradeoff.

• "If you invest less effort, the cost is lower accuracy." GB, p. 109.

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Accuracy/Effort Tradeoff versus "Less Is More" Effects

• Accuracy/Effort Tradeoff Hypothesis:"If you invest less effort, the cost is lower accuracy." GB, p. 109.

• "Less Is More" Effect: In some situations, less information (less effort) leads to greater accuracy.

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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

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Cross-Validation Studies For Evaluating the Predictive Accuracy of a Model

• Use a computer to repeat the procedure over many random splits.

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Sample of Data

random split

50% of data intest sample:

Use fitted model to predict outcomes in the test sample.

Evaluate goodness of fit.

50% of data inestimation 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 Regressionproduces higher accuracy in prediction?

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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 whenthe 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).

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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 (R2 < 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!

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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( CObj.1 > CObj.2 | Obj #1 has F and Obj #2 does not)

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Background to: "Take the Best" Heuristic

• Suppose that F1 and F2 are two features.

Then F1 has greater cue validity than F2 if

P( CObj.1 > CObj.2 | Obj #1 has F1 and Obj #2 does not)

> P( CObj.1 > CObj.2 | Obj #1 has F2 and Obj #2 does not)

Intuitively, F1 has greater cue validity than F2 if knowing that

the objects differ on F1 gives you a better chance to guess which

object has more of the criterion C than knowing that the objects

differ on F2 .

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"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.

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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: F1: Has an excellent defense against the pass;

F2: Has an excellent defense against the run.

F3: Has an excellent passing offense;

F4: Has an excellent running offense

• Suppose that

cue validity of F1 > cue validity of F2 >

cue validity of F3 > cue validity of F4

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Example of "Take the Best" Strategy (cont.)

Decision Rule:

1. Choose Team X if Team X has F1 and Team Y does not.

If both teams have F1 or if neither team has F1,

then proceed to Step 2.

2. Choose Team X if Team X has F2 and Team Y does not.

If both teams have F2 or if neither team has F2,

then proceed to Step 3.

3. Choose Team X if Team X has F3 and Team Y does not.

If both teams have F3 or if neither team has F3,

then proceed to Step 2.

4. Choose Team X if Team X has F4 and Team Y does not.

If both teams have F4 or if neither team has F4,

then guess (make the decision based on a coin flip).

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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 decisionprocedures when samplesize is small and there are correlations (dependencies) among the cues.

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Explaining "Less-Is-More" - Why Is This True (Sometimes)?

• Decision strategies exhibit a bias/variance dilemma.(a.k.a. bias/variance tradeoff).

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Example: Bias/Variance Dilemma In Temperature Prediction

Larger bias with low variance

Better Than

Smaller biaswith high variance

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GB Figure 3, p. 118

• Bias/variance tradeoff for entire year of London temperatures.

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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.

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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 importantin real life.

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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.

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