CHAPTER 8. Decision Making

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CHAPTER 8. Decision Making

• FEATURES AND CLASSES OF DECISION MAKING
• Uncertainty involving risk
• Familiarity and Expertise rapidly and little
  deliberationexperts not always more accurate
• Time one shot vs. evolving decisions time
  pressure
• Classes of Decision-Making Research
• rational or normative decision making decisions
  according to optimal framework optimal beta
• cognitive or information processing approach
  biases, limitations, heuristics
• naturalistic decision making decision making
  under real environment -- expertise, complexity
• AN INFORMATION PROCESSING MODEL OF DECISION
  MAKING (fig 8.1)
• cue (ambiguous incorrect) seeking -- selective
  attention experiences and attentional resource
• diagnosis situation assessment or situation
  awareness
• external cues filtered by selective attention
  (bottom-up processing)
• LTM hypotheses and the estimation of the
  likelihood or expectancy (top-down processing)
• often incorrect -- uncertain nature of cues or
  vulnerabilities for selective attention and WM
• iterative search for further info -- feedback
  loop to cue filtering , confirmation
• choice of an action risk and the estimation of
  values
• feedback loop
• assist in diagnosis -- troubleshooting
• learning to improve the quality of future
  decision

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• WHAT IS GOOD DECISION MAKING?
• the expected value of a decision
• produce good outcomes
• expertise
• DIAGNOSIS AND SITUATION AWARENESS IN DECISION
  MAKING
• Quality of diagnosis
• the role of perception in estimating a cue
• the role of attention in selecting and
  integrating the info by the cues
• the role of LTM to establish possible hypotheses
  or beliefs
• the role of WM to update and revise beliefs or
  hypotheses
• Estimating Cues Perception
• human as intuitive statistician
• perception of mean relatively well
• perception of proportions
• dichotomous observation reasonably accurate
  between 0.05 and 0.95 (midrange)
• more extreme proportion conservative, biased
  away from the extremes of 0 and 1.0
  conservative tendency (never say never), salience
  or infrequent event -- overestimate
• estimation of the variance (fig 8.2)
• estimate the variability as less if the mean of
  the values is greater -- Webers Law of
  psychophysics -- the concept that a
  just-noticeable difference in a stimulus is
  proportional to the magnitude of the original
  stimulus
• disproportionately influenced by the extreme
  values

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• estimation of correlation underestimation of
  high correlation, vice versa
• estimation in extrapolating nonlinear trends
  toward more linear (fig 8.3)
• Evidence Accumulation Cue seeking and Hypothesis
  Formation (fig 8.4)
• cue properties
• cue diagnosticity how much evidence a cue
  should offer value and polarity
• cue reliability or credibility the likelihood
  that the physical cue can be believed
  independent of diagnosticity information value
  of a cue diagnosticity x reliability
• physical features of the cue conspicuous or
  salient important bearing on attention
• multiple cues
• selective attention different weight according
  to their info value
• integration (bottom-up processing) of perceptual
  features in pattern recognition or dimensions in
  an object display
• expectancies biasing -- top down processing in
  perceptual pattern recognition
• not parallel to perceptual pattern recognition --
  iterative testing and retesting of a belief
• Attention and Cue Integration
• Information Cues are Missing
• what they do not know (missing cues) seek these
  cues before a firm diagnosis
• Cues are Numerous Information Overload
• the likelihood of a correct diagnosis can
  increase as more cues are considered
• people do not use the greater info to make better
  and more accurate decisions because the
  limitation of human attention and working memory
  seem to be so great

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• under time stress more info deteriorated
  decision making performance
• selective filtering strategy compete for the
  time available for the integration of info more
  info leads to time consuming filtering process at
  the expense of decision quality
• Cues are Differentially Salient
• the salience should be directly related to the
  info value of the cue in making a decision, not
  just detecting a fault
• the info that is difficult to interpret or
  integrate underweighted
• the absence of a cue what is not seen, symptoms
  not observed
• Processed Cues Are Not Differentially Weighted
• do not effectively modulate the weight of a cue
  based on its value ? as if they were of equal
  value ? reducing the cognitive effort required to
  consider different weights
• weighting varies in more of an all or none
  fashion (fig 8.5)
• why use as if heuristic? ? cognitive
  simplification
• Expertise and Cue Correlation
• multiple cues with highly correlated each other
  and equally weighted (fig 8.6) ? intuitive form
  of info integration, similar to perceptual
  pattern recognition ? closely associated with
  expertise
• RPD (recognition-primed decision making)
• recognizes the pattern of cues as a typical cues
  in prior experience? rapid and relatively
  automatic categorization ? expert decision makers
  under high time stress
• no correlation, no time pressure, a single cue
  salience abandon RPD, rather a slower, more
  analytical diagnosis

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• Expectations in Diagnosis The Role of Long-term
  Memory
• two aspects of LTM in diagnosis, reflected
  perception and pattern recognition
• cue correlation -- RPD
• hypothesis frequency most expected, most
  frequent diagnostic category
• Representativeness
• diagnosis by comparing cues, symptoms, or
  perceptual evidence with the set that is
  representative of the hypothesis on the basis of
  experience in LTM ? typical of RPD or visual
  pattern recognition
• nothing wrong but used when the cues are somewhat
  ambiguous without adequately considering the base
  rate, probability or likelihood
• physical similarity to a prototype hypothesis
  dominates probability consideration
• if the physical evidence is ambiguous (missing)
  use probability ? availability heuristic
• Availability Heuristic
• approximating prior probability people
  typically entertain more available hypotheses
• factors influencing the availability of a
  hypothesis (absolute frequency or prior
  probability)
• recency
• hypothesis simplicity
• elaboration in memory of the past experience
• Belief Changes Over Time Anchoring,
  Overconfidence, and the Confirmation Bias
• Overconfidence Bias
• overconfident in their state of knowledge or
  beliefs will not be likely to seek additional
  info (which may refute the hypothesis), even when
  it is appropriate to do so

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• Anchoring Heuristic
• not all hypotheses are treated equally ? mental
  anchor to the initially chosen hypothesis ?
  first impressions are lasting ? primacy in
  memory
• recency effect in cue integration
• primacy is dominant when info sources are fairy
  simple and integration procedure is one that
  calls for a single judgment of belief at the end
  of all evidence (sequentially)
• if the sources are more complex and often require
  an explicit updating of belief after each source
  is considered, then recency tends to be more
  likely (simultaneously)
• The Confirmation Bias
• a tendency for people to seek info and cues that
  confirm the tentatively held hypothesis or
  belief, and not seek those that support an
  opposite conclusion of belief ?cognitive tunnel
  vision
• three possible reasons
• greater cognitive difficulty dealing with
  negative info than with positive info
• higher cognitive effort to change the hypothesis
• influence the outcome of actions taken on the
  basis of the diagnosis, which will increase their
  belief that the diagnosis was correct ?
  self-fulfilling prophecy
• Implications of Biases and Heuristics in
  Diagnoses
• humans as a bundle of biases
• heuristics are highly adaptive under rapid and
  not enough mental effort and time
• shortcuts by heuristics are necessity under time
  pressure
• modulated or eliminated certain conditions

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• CHOICE OF ACTION
• Certain Choice (fig 8.7)
• compensatory method ? satisficing rule good
  enough
• EBA (elimination by aspects) reduce the
  cognitive effort, satisfactory
• Choice Under Uncertainty The Expected Value
  Model (fig 8.8)
• costs and values (benefits) maximizing the
  expected value of a choice
• Ps (probability of the state of the world) Vxy
  (outcome value)
• the expected value of each option
• the greatest expected value as a choice
• complicating factors to human decisions under
  uncertainty
• maximizing gain/minimizing the loss minimizing
  the maximum loss
• difficult to assign objective values to different
  outcomes safety
• subjective estimates of objective values
  irrelevant to objective values
• inconsistency between peoples estimates of
  probability and the objective probabilities
• Biases and Heuristics in Uncertain Choice
• Direct Retrieval
• past experience, familiar domain, clear state of
  the world
• RPD (recognition-primed decision making) under
  high time pressure
• Distortions of Values and Costs

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• prospect theory (Kahneman and Tversky, 1984)
• potential loss exerts a greater influence over
  decision-making behavior than does a gain of the
  same amount loss aversion
• both positive and negative limbs are curved
  toward the horizontal -- perceived value of
  Webers Law of Psychophysics
• Perception of Probability
• kahneman and Tversky a function relating true
  prob. to subjective prob.
• three critical aspects for understanding risky
  choice (fig 8.10)
• subjectively overestimate of the probability of
  very rare events insurance and gamble
• flat slope of its low probability end reduced
  sensitivity to probability change
• perceive probability as less than actual
  probability ? framing effect
• The framing effect
• peoples preference with a choice between a risk
  and a sure thing
• risk-seeking bias between negatives
  (avoidance-avoidance conflict)
• risk-aversion bias between positives (risk and
  sure thing)
• change in loss or gain, depending on the neutral
  point or frame of reference for the decision
  making framing effect (frames of reference)
• sunk cost bias
• Rationally, the previous history of investment
  should not enter into the decision for the
  future. Yet it does.
• investors for poor previous decision sure loss
  and risky loss
• newcomer sure thing option is neither loss
  nor gain -- 0 utility and expected loss bias
  to terminate the investment

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• IMPROVING HUMAN DECISION MAKING
• Training Decision Making Practice and Debiasing
• Domains of decision making whether expertise
  develop from practice or not (table 8.1)
• problems of learning in decision making ? the
  role of feedback in decision making problems
• feedback is often ambiguous, in a probabilistic
  or uncertain world
• delayed feedback Monday morning
  quarterbacking hindsight bias
• feedback is processed selectively fig 8.11
• debiasing tailoring more specific training to
  target certain aspects of decision making flaws
• reduced overconfidence, diagnostic information
  from the absence of cues, away from nonoptimal
  anchoring bias
• provide more comprehensive and immediate feedback
  in predictive and diagnostic tasks (probability
  rather than frequency)
• Proceduralization
• a technique for outlining prescriptions of
  techniques that should be followed to improve the
  quality of decision making fault tree and
  failure modes analysis
• successful in certain real-world decisions that
  are easily decomposable into attributes and
  values
• Automation Displays and Decision Aids
• three major categories of assistance that
  automation can offer
• attention and cue perception pictorial
  presentation over numerical or verbal, proximity
  compatibility principle
• diagnosis offload working memory, making
  inferences (expert system )
• choice user with different recommendations

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