Heuristics and biases in judgement and decision making

Social Judgement Theory

  • Based on the Lens Model of judgement

    • Utilizes statistical methods to infer cues used in judgements

    • Implementing a "weighing and adding" model: weigh cues and sum them

  • Approach taken:

    • Statistical approach (regression) for decision making

    • Not purely psychological theory

  • Repeated judgements can be improved by using an actuarial model instead of human judgment.

Fast and Frugal Heuristics

  • Individual heuristics discussed with examples:

    • Recognition Heuristic: Choose based on recognition

    • Take the Best: Search cues ranked by ecological validity

    • Choose the Last: Use the last successful cue

    • Minimalist Heuristic: Random selection from cues

Take the Best Algorithm

  1. Recognition Principle (Diamond 1)

  2. Searching for cue values (Diamond 2)

  3. Discrimination Rule (Diamond 3)

Probabilistic Mental Models

  • Theory specifies cognitive mechanisms for making decisions

    • Emphasizes “fast and frugal” heuristics

  • Differences from traditional models: No "weighing and adding"

  • Key process involves:

    • Sequential search for relevant cues that will enable the choice between different outcomes or options

    • Search halts upon finding a single discriminating cue

    • Concept of “One-reason decision making”

Evidence Supporting Heuristics

  • Computer simulations of City Size Judgement Study (Gigerenzer & Goldstein, 1996):

    • Accuracy:

      • Take The Best: 65.8% (3 cues)

      • Multiple Regression: 65.7% (10 cues)

    • Human Studies of population judgements (Goldstein & Gigerenzer, 2002): 90% alignment with recognition heuristic

Limitations of Heuristics

  • Despite simulation evidence:

    • Limited or partial empirical evidence for fast and frugal heuristics

    • Challenging to control for additional knowledge in judgments people make (Oppenheimer, 2003)

    • Studies indicate reliance on multiple information sources, in line with Social Judgement Theory.

Assessing Good Judgement

  • Two evaluation methods defined:

    • Correspondence Theories: does judgement correspind with the state of the world

      • Gigerenzer’s ecological rationality emphasizes the match between the structure of heuristics and environments

    • Coherence Theories: is the process of forming a judgement rational

      • Tversky and Kahneman heuristics and biases approach explore the deviation of judgement from normative laws and the errors that occur

Heuristics and Biases

  • Investigated by Tversky and Kahneman (1974):

  • Focused on judgement under uncertainty and systemic biases due to coherence failures, whether the process of froming a judgement is rational :

    • Coupled with concepts of:

      • Attribute substitution

      • Natural assessment

Attribute Substitution and Natural Assessment

  • Attribute Substitution: Judgement switches to an easier heuristic attribute, when people make a judgement about something hard

  • Natural Assessment: Certain attributes are easier to evaluate because they are judged using natural assessment of properties (size, distance, similarity, etc.).

Evidence for Substitution

  • Evidence types include:

    • Correlation of target attributes and judgments with heuristic attributes, with which they have been substituted

    • Documented biases arising from lack of coherence with normative laws, in certain situations.

    • Extensive evidence collected by Kahneman and Tversky documenting various heuristics and biases

Representativeness Heuristic

  • Definition: Assesses the correspondence between a sample and its population, etc.

  • Evaluates probability based on essential properties similarity to its source.

Compound events

  • Conjunction of two events cannot be more probable than the probability of either individual component event

  • Conjunction Fallacy: Estimating combined probabilities incorrectly higher than single events

  • Example: Linda problem by Kahneman & Tversky (1972): Context given about Linda's character. people think it is more likely that Linda is a bank teller and is active in the feminist movement than she is a bank teller

The representativeness heuristic

  • An assessment of the degree of correspondence between a sample and a population, an instance and a category, an act and an actor or, more generally, between an outcome and a model

  • Common stereotypes can skew probability assessments.

  • Judgement examples: Likelihood of being a computer games designer vs. having an investment fund.- Judgements based on resemblance to a stereotype- people make judgements based on similarity

Base Rate Neglect Problems

  • Judging likelihood of a situation often ignores relevant data:

  • Example profiles without context lead to skewed conclusions—Tom being a musician over a farmer despite demographics.

Availability Heuristic

  • A heuristic relying on immediate examples that come to mind when making a judgement

  • Evaluates frequency/probability based on ease of recollection.

Positive Aspects of Availability Heuristic

  • Serves well most of the time for frequency estimates, as frequent events are easier to recall.

  • however, at times the heuristic will produce errors, and it is these errors that have been the focus of research

Errors from the Availability Heuristic

  • People tend to asses the importance of issues by the ease with which they are retrieved from memory- and this is largely determined by the extent of coverage in the media.

Anchoring and Adjustment

  • Judgements begin with a value and then adjust relative to that point.

  • Adjustment from that anchor tends to be insufficient.

  • Can bias various types of judgement (Keren & Teigen, 2004)

  • Anchoring and adjustment does not fit within the “attribute substitution” framework. Rather, it works by increasing the plausibility of a particular value of the target attribute (Kahneman & Frederick, 2002)

Wheel of Fortune Anchor Study

  • Participants’ estimates are highly influenced by arbitrary anchor values presented beforehand.

Summary

Heuristics and biases approach has been highly influential
▶ More “psychological” than the actuarial models statistical approach of last week
▶ Reveals the biases that occur when people use heuristics when making judgements under uncertainty
▶ Gigerenzer argues that these are valuable short cuts that help us make “quick and dirty” judgements when we don’t have all the facts or can’t compute the probabilities
▶ Tversky and Kahneman focus on where these lead us to make errors