k.m. corker, ph.d.industrial & systems engineering human factors experiments ise 212 fall 2006...

K.M. Corker, Ph.D. Industrial & Systems Engineering

Human Factors Experiments ISE 212 Fall 2006

Lecture 2: Evidential Reasoning www.engr.sjsu.edu/kcorker

[email protected]

Kevin Corker

San Jose State University

9/5/06

Get your facts first, and then you can distort them as much as you please. --- Mark Twain (1835 - 1910)


Summary of Basic Inference The subject is given some evidence and must decide what, if any, is implied by

the evidence• If P then Q• Evidence: P• Therefore Q (valid inference)

– Affirm the antecedent (modus ponens) (positivist)• Evidence: not-Q• Therefore not-P (valid inference) (only if P& Q are bivalent, i.e., P can

only be true if Q is true – Deny the Consequent (modus tollens) (Not intuitive)

• But is the basis of critical rationalism

• E.G.: If the lecture is fascinating, then the students will stay awake: The students are not awake; therefore the lecture is not fascinating


Critical Rationalism

• Karl Popper’s method is based in Modus Tollens – Critical Rationalism.

• Two problems with induction: – psychological problem of finding what you are expecting to find (biases

abound) and – logical problem of extending experience to what we have not experienced.

• He thus found 'common sense' as a scientific justification inadequate method of prediction and statements about what we have not experienced cannot be deemed as 100% 'true'.

• He also noted that a verificationist approach is less likely to result in new discoveries, as it simply seeks to confirm the beliefs of the scientist.


Human Decision & Evidential Reasoning Models Normative Models

1) Econometric Models : Expected Value of Decision Outcome People calculate the potential value of each option– Pick the option with the highest expected valueRaffle with 10% chance to win $5.00EV = .10 * $5.00 = $0.50• Simple exampleWhich gamble would you rather play?A: 20% chance of winning $5.00B: 30% chance of winning $4.50EV(A) = .20 * $5.00 = $1.00EV(B) = .30 * $4.50 = $1.35Expected value suggests you should choose BBut Prospect theory –People value a certain gain more than a probable gain with an equal or greater expected

value; the opposite is true for losses.–Would you rather win (or lose) $1 and 0% risk or $2 with 50/50 risk?–Take the sure thing?


Expected Utility

The Expected Utility model:

EU = Σ ( weighti * utilityi)• Expected Utility is a rational model

– All choices are transitive

– Everything is evaluated relative to a global scale.

– But

Human Decision & Evidential Reasoning Models


• People treat gains and losses differently– Losses loom larger than gains

• The same situation feels worse when framed in terms of losses than when framed in terms of gains.– Sunk Cost Bias (Policy Driver)– Culturally Sensitive

Kahneman and Tversky: Framing Theory


Framing & Prospect


Cue Integration and Estimation

• Humans are reasonable estimators of mean likelihood. But:– Bad at Non-linear systems extrapolation

• Rice Grain or Lilly pad pond examples

– Bad at estimating tails (representativeness issues) • Prior probability (or base-rate frequency) of outcomes often

ignored

• Insensitivity to sample size (Bayesian Inference)

• Subject to Availability Biases (

• Familiarity (salience, recency–driven by experience)


Example

• 1x2x3x4x5x6x7x8 = ?

• 8x7x6x5x4x3x2x1 = ?


Heuristics & Biases

• Anchoring • Evaluation of simple, conjunctive (and) &

disjunctive (or) events:• Overestimate conjunctive, underestimate disjunctive

• Frequency Gambling• Overestimate of certainty and reliance on prior (P)

• Confirmation Bias • Seeking confirmatory evidence

• Automation Bias • Overestimate of the quality of machine accuracy and reasoning

ability


Human Uncertainty and Subjective Likelihood

• Bayesian Decision Making – Combination Method is Bayesian Calculus and uncertainty is captured in the transition

probabilities of the world model• Fuzzy Logic

– Assessment of vagueness or belongingness • On a scale from 0 to 1 what degree of belongingness does the incoming information have:

– E.g., 4 students are sleeping– on a class-interestingness scale (measured in boredom units) I assign that snooze level a 0.2 of boring.

• Dempster Shaeffer Theory – Uncertainty is measured by an interval (like a confidence interval in stats) and the span of the

interval is the uncertainty.• Subjective probability vs. Subjective likelihood

– Probability: Assessment of the probability of the observed event (1 in 10 chance of having 1 or more students sleeping) based on an internal probability model informed by experience.

– Likelihood: Assessment only based on the present observation without reference to past experience.

• Naturalistic Decision Making (Recognition Primed Decision making) – Pattern matching using heuristics to select most likely decision based on stored experience

with those patterns


Case Study


Essence of Case

• Draft standard, ISO defines Adaptive Cruise Control (ACC) as “an enhancement to conventional cruise control systems which allows the subject to follow a forward vehicle at an appropriate distance by controlling the engine and/or power train and potentially the brake.

• ACC systems have forward-looking sensors and algorithms that calculate the range and closing rate to a lead vehicle and adjust the cruising speed appropriately.

• ACC systems are distinguished from Forward Collision Avoidance System (FCAS) in that they do not take evasive action.

• Warnings may be provided but are not required since ACC’s are described and marketed as convenience systems, not safety systems.


• The plaintiff is a 46 year-old female who sustained serious and permanent injuries as a result of rear-end collision.

• Her vehicle ACC was set to 70 mph, and 1 second time gap (the minimum setting)

• Prior to the start of the accident both cars were traveling at 30 m/s, the plaintiff was following at a constant distance of 30 m

• At time t = 0, the lead vehicle suddenly decelerated hard to avoid hitting a deer reaching a deceleration rate of 9 m/s,s.

• The ACC system of the plaintiff’s vehicle responded within 200ms to the lead vehicle deceleration with a maximum available deceleration of 3 m/s,s (0.3g).


• The plaintiff initiated emergency braking a t = 2.0 sec, achieving a deceleration of 9 m/s,s

• The collision occurred at t = 3.2s

• The relative velocity at impact was 31 mph

• Safe time to initiate braking (no collision would be ~1.1sec)


Heuristics • Bounded Rationality concept of satisficing,

which occur when decision makers stop the search for a solution when the first alternative is found that meets all constraints. Or meets most constraints – Probably not the optimal solution– Fast & frugal heuristics


Bibliography • Fischhoff, B., Slovic, P., Lichtenstein, S., Read, S. & Combs, B. (1978). How safe is safe enough? A psychometric

study of attitudes towards technological risks and benefits. Policy Sciences, 8, 127-152. Reprinted in P.Slovic (Ed.), The perception of risk. London: Earthscan, 2001.

• Kahneman, Daniel, and Amos Tversky (1979) "Prospect Theory: An Analysis of Decision under Risk",Econometrica, XVLII (1979), 263-291.

• Payne, J.W., Bettman, J.R., & Johnson, E.J. (1988). Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 534-552.

• Klein, G. (1996). Nature of uncertainty in naturalistic decision making. Proceedings of the Human Factors and Ergonomics Society 40th Annual Meeting, 1, 178.

k.m. corker, ph.d.industrial & systems engineering human factors experiments ise 212 fall 2006...

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