고고고고고 고고고고고 IND641 Engineering Psychology CHAPTER 8. Decision Making FEATURES AND CLASSES OF DECISION MAKING Uncertainty – involving risk Familiarity and Expertise – rapidly and little deliberation;experts 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 1. external cues filtered by selective attention (bottom-up processing) 2. 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 meta-cognition – awareness of one’s own knowledge, effort, and thought processes -- SA
Transcript
고려대학교 산업공학과
IND641 Engineering Psychology
CHAPTER 8. Decision Making FEATURES AND CLASSES OF DECISION MAKING
Uncertainty – involving risk Familiarity and Expertise – rapidly and little deliberation;experts 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
1. external cues filtered by selective attention (bottom-up processing)2. 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
meta-cognition – awareness of one’s own knowledge, effort, and thought processes -- SA
고려대학교 산업공학과
IND641 Engineering Psychology
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 -- Weber’s 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
고려대학교 산업공학과
IND641 Engineering Psychology
estimation of correlation – underestimation of high correlation, vice versa estimation in extrapolating nonlinear trends – toward more linear (fig 8.3)
1. cue diagnosticity – how much evidence a cue should offer – value and polarity2. cue reliability or credibility – the likelihood that the physical cue can be believed –
independent of diagnosticity – information value of a cue = diagnosticity x reliability3. physical features of the cue – conspicuous or salient – important bearing on attention
multiple cues1. selective attention – different weight according to their info value2. integration (bottom-up processing) of perceptual features in pattern recognition or
dimensions in an object display3. expectancies biasing -- top down processing in perceptual pattern recognition4. 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
고려대학교 산업공학과
IND641 Engineering Psychology
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
고려대학교 산업공학과
IND641 Engineering Psychology
Expectations in Diagnosis: The Role of Long-term Memory two aspects of LTM in diagnosis, reflected perception and pattern recognition
1. cue correlation -- RPD2. 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
고려대학교 산업공학과
IND641 Engineering Psychology
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 reasons1. greater cognitive difficulty dealing with negative info than with positive info2. higher cognitive effort to change the hypothesis3. 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”1. heuristics are highly adaptive under rapid and not enough mental effort and time2. shortcuts by heuristics are necessity under time pressure3. modulated or eliminated certain conditions
고려대학교 산업공학과
IND641 Engineering Psychology
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
1. Ps (probability of the state of the world) * Vxy (outcome value)2. the expected value of each option3. the greatest expected value as a choice
complicating factors to human decisions under uncertainty1. maximizing gain/minimizing the loss – minimizing the maximum loss2. difficult to assign objective values to different outcomes – safety3. subjective estimates of objective values irrelevant to objective values4. inconsistency between people’s 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 humans are trying to maximize an expected utility (subjective value of different expected
outcomes)
고려대학교 산업공학과
IND641 Engineering Psychology
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 Weber’s 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)1. subjectively overestimate of the probability of very rare events – insurance and gamble2. flat slope of its low probability end – reduced sensitivity to probability change3. perceive probability as less than actual probability framing effect
The framing effect people’s 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
고려대학교 산업공학과
IND641 Engineering Psychology
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
1. feedback is often ambiguous, in a probabilistic or uncertain world2. delayed feedback – “Monday morning quarterbacking” “hindsight bias”3. 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 offer1. attention and cue perception – pictorial presentation over numerical or verbal, proximity
compatibility principle2. diagnosis – offload working memory, making inferences (expert system )3. choice – user with different recommendations
