The 7 Cs of effective decisionmaking General model

The 7 C’s of effective decision-making

General model of decision-making 1. Identify the problem – this is the most critical step in organizational decision-making. 2. define objectives – establishing the parameters of success. 3. make a predecision – most critical in mitigating decision biases and errors. 4. generate alternatives 5. evaluate alternatives – note: balanced view 6. make a choice 7. implement the chosen alternative 8. follow-up

This lecture will focus on Cognition Linearity Likelihood

Ben Franklin to Joseph Priestly, 1772 I cannot, for want of sufficient premises, advise you what to determine, but if you please I will tell you how… my way is to divide half a sheet of paper by a line into two columns; writing over the one pro and over the other con. Then during three or four days’ consideration, I put down under the different heads short hints of the different motives, that occur to me for or against the measure. When I have thus got them altogether in one view, I endeavor to estimate the respective weights…to find at length where the balance lies…and, though quantities, yet, when each is thus considered, separately and comparatively, and the whole matter lies before me, I think I can judge better, and am less liable to make a rash step; and in fact I have found great advantage for this kind of equation, in what may be called moral, or prudential algebra Dawes, R. M. (1986) Proper and improper linear models. International Journal of Forecasting, 2, 5 -14.

Linearity Tscore = 1 1 + 2 2 + … n n lets suppose that Tscore = a hypothetical absolute correct answer, that 1 is equal to the influence, or correlation between 1 and the true score, and 1 through n indicate the universe of the appropriate cues related to the true score. Each indicator can either be positively (+) or negatively (-) related to the true score. There has been consistent evidence over the past 50 years that a linear model will outperform predictions made by even experts (Meehl, 1954). Brunswik, E. Perception and the Representative design of psychological experiments, 2 nd edition, Berkeley: University of California Press, 1956. Meehl, P. E. (1954). Clinical versus Statistical Predictions: A Theoretical Analysis and Revision of the literature. Minneapolis: University of Minnesota Press.

Linearity decision errors: weights 1. Ease of recall (bias based on vividness and recency). Example: Which of the following causes more deaths in the United States each year? A. Stomach cancer B. Motor vehicle accidents Russo and Shoemaker (1989)

Linearity decision errors: weights 2. Contrast (Cues influence each other through differentiation and association – halo error). Example: Real estate agents sometimes exploit the contrast effect by showing buyers a run-down overpriced property before the home that is under serious consideration (Plous, 1993). Lottery winners reported less pleasure from daily activities including: watching television, talking with friends or eating breakfast (Brickman, Coates, & Janoff-Bulman, 1978). Plous, S. (1993). The psychology of judgment and decision making. Mc. Graw-Hill: New York. Brickman, P. , Coates, D. , & Janoff-Bulman, R. (1978). Lottery winners and accident victims: Is happiness relative? Journal of Personality and Social Psychology, 36, 17 -927.

Linearity decision errors 1 errors: 3. bounded rationality: individuals can only handle about 5 -7 different pieces of information at any one time. 4. satisficing: the tendency to make decisions in a sequential fashion such that the first acceptable choice is taken. 5. time constraints: within organizations, many important decisions are made under temporal duress.

Linearity decision errors 1 errors: 6. disjunctive decision making: (only evaluating the cues one at a time using a step-ladder technique) – J. C. Penney used to take prospective top management team members out to lunch and he would observe whether they salted their soup before they tasted it. If they did they were thought to be lacking in inquisitiveness and would not be offered the position.

Linearity is based on a Brunswik lens model (Ben Franklin) while likelihood is based on a Bayesian model (Carl Jung) of decision making. Both models describe why human judgment is often suboptimal. The main difference between the Brunswik and the Bayesian model is that the Brunswik is focused on accuracy based on cues and betas while the Bayesian is based on probability.

Likelihood Randomness or theory of synchronicity (Carl Jung) – coincidences occur more frequently than one would expect by chance and are the work of an unknown force (a causal). "a causal phenomena must exist. . . since statistics are only possible anyway if there also exceptions" (1973, Letters, 2: 426). He asserts that ". . . improbable facts exist--otherwise there would be no statistical mean. . . " (ibid. : 2: 374). Finally, he claims that "the premise of probability simultaneously postulates the existence of the improbable" (ibid. : 2: 540).

Likelihood Probability and risk are everywhere: in the brakes of your car, the food we eat, the questions we ask. In general, we are good at understanding simple odds (I. e what is the chance that we can roll a 1 on a 6 sided die). However, we given additional information, or any level of uncertainty our ability to determine likelihood degrades. There are two main organizational errors we make based on probability assessments. 1. We anchor to the positive 2. We panic when in the negative

Reported in Life magazine: 1950. On March 1 of that year 15 members of the West side Baptist church choir in Beatrice, Nebraska, were due to practice at 7: 15 PM, but for one reason or another all 15 members were late that particular evening, The minister’s family was late because his wife ironed the daughter’s dress at the last minute, a couple of choir members couldn’t start their car, the pianist who usually arrived early fell asleep after dinner…and so on. In all there were at least 10 ordinary reasons for the various members to be late. At 7: 25 the West Side Baptist Church absolutely blew up. Fireman thought that the explosion, based on its ferocity was caused by Natural Gas…

A German mother…photographed her infant son in 1914 and left the film at a store in Strasbourg to be developed. In those days, film plates were sold individually. World War I broke out and, unable to return, the woman gave up the picture as lost. Two years later she bought a film plate in Frankfort, nearly 200 miles away, to take a picture of her newborn daughter. When developed, the film turned out to be a double exposure, with the picture of her daughter superimposed over the earlier picture of her son. Through some incredible twist of fate, her original film had apparently never been developed, had been mislabeled as unused, and had eventually been resold to her.

On October 5, 1990, the San Francisco Examiner reported that Intel Corporation, a leading computer chip manufacturer was suing another chip maker for infringing on Intel’s 386 microprocessor trademark (Sulivan, 1990, October 5). Intel had learned that the rival company was planning to release a chip called the “AM 386”. What is remarkable is how Intel discovered this. As fate would have it, both companies employed someone named Mike Webb, and both Mike Webbs had checked into the same hotel in Sunnyvale, California, at the same time. Then, after both men had checked out the hotel received a package addressed to one of the Mike Webbs. The package – which contained documents referring to an AM 386 was misdelivered to the Mike Webb at Intel who then turned it over to Intel’s lawyers.

Likelihood: Anchoring to the positive Confusion of the inverse 85% of cabs are blue and 15% green. A cab was in a hit and run accident last night and a witness testifies that the cab was green. Based on the lighting conditions it is determined that there is an 80% likelihood that the individual was able to correctly determine the color. What is the probability that the cab was green?

Likelihood: Anchoring to the positive Bayesian Model of probability P = (base rate) (certainty) -------------(base rate) (certainty) + (1 - certainty)(1 -base rate) P = (. 8) (. 15) -------------(. 8) (. 15) + (. 2)(. 85) =. 41% chance it was green. Edwards, W. (1954). The theory of decision making. Psychological Bulletin, 51, 380 -418. Edwards, W. (1962). Dynamic decision theory and probabilistic information processing. Human Factors, 4, 59 -73.

Likelihood: Anchoring to the positive Anchoring Question 1: Estimate how thick a piece of paper will be if folded over itself 100 times. Question 2: Assuming a world population of around 5 billion people, how wide would a cubeshaped tank have to be to hold all the human blood in the world.

Likelihood: Panic in the negative Psychophysical errors This error is thought to be so robust that it nears the realm of physical law. – Prospect theory (Kahneman & Tversky, 1979) a curvilinear effect such that individuals tend to be risk adverse when they are in a positive frame and risk seeking when they are in a negative frame. – Betting – If two individuals bet a lunch on a football game, who tends to be the first person to say “double or nothing”

Likelihood: Panic in the negative Framing The government is preparing to combat a rare disease expected to take 600 lives. Two alternatives: Program A will save 200 lives. Program B has a 1/3 chance of saving all 600 but a 2/3 chance of saving no-one. Which program do you prefer This is positively framed (72% chose program A) Program C: 400 people will die. Program D has a 1/3 probability that no one will die but a 2/3 probability that all 600 would die. This is negatively framed (78% chose program D).

Escalation of commitment 3 requirements – A. a substantial amount of previous investment of time and money. – B. clearly negative information regarding the previous choice – C. dilemma of either abandoning the previous course of action or continuing commitment. 25 years of research has demonstrated that when responsible for the initial choice, decision makers tend to throw good money after bad and escalate their commitment to losing courses of action.

When is the escalation of commitment error most powerful? Consider a project: Boston’s “big dig” project was undertaken to build an underground highway through the city. The project soon became wildly over budget. No politician dared end it. It was finally completed but the workmanship is now under question and the city may have to redo many parts of the highway.

When is the escalation of commitment error most powerful? Boston’s big dig. The two powerful drivers toward continuing a failed course of action are: – A. Sunk-costs = previous investments in time and money. – B. Completion = the powerful need to complete what was started.

Escalation of commitment 3 ways to reduce the escalation of commitment. – A. Reduce the level of felt responsibility by changing the decision maker. – B. Reduce the need to “save face” by lowering the threat of punishment. – C. Task the decision to groups.

Groups versus individual decisions Positives of group decisions – pooling of resources – specialization of labor – usually results in greater acceptance Negatives of group decisions – potential to waste time – group conflict – intimidation by leaders or assertive members

When are groups superior to individuals in decision making? – Complex problems groups are composed of heterogeneous members ideas are communicated freely the culture of the group is accepting of ideas – Groups are superior to even the best individuals.