lecture 18/19 assets 2 Flashcards

1
Q

who made the og equation for good decisions

A

Daniel Bernoulli, expected value = odds of gain * value of gain

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2
Q

why does that equation not go well erry day

A

errors in odds, errors in value

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3
Q

example errors in odds

A

lottery, death by shark etc

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4
Q

example errors in value

A

supermarket ice cream, losing ticke tor losing money

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5
Q

MCA stand for

A

multi Criteria analysis

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6
Q

disads and advs of MCA

A

ADVANTAGE
fast and dirty approach
combine with others to becoem powerful

Disadvantages
weigthings hard to agree on
inconsistencies within weighting criteria

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7
Q

Risk based Decision making

A

risk=odds*consequence

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8
Q

AHP stand for

A

Analytical Hierarchy process

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9
Q

what is AHP and adv and disadv

A

pairwise comparison method

Advantages
creates ratio data
industrially accpeted

disadvantages
if dont understand its just a black box
labour intensive

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10
Q

AHP steps

A

1 define decision options
2structure elements into criteria, sub criteria
3 make pair wise comparison of each criteria
4 calculate weighting and consistency ratios
5 evaluate alternatives according to weighting
6 evaluate BcA benefit cost analysis
7 make decision

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11
Q

AHP disadvantages

A

get lazy when doing pairwise analysis

need more iterations for consistency

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12
Q

what is the Delphi method

A

experts answer quesitons in two or more rounds
after each round annonymous summary of eperts decisions as well as reasons for their judgement
next round experts revise their judgement
keep going until stop criteria

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13
Q

whats the idea of cognitive fluency

A

easy doe not mean true

ie clear text is not more true than unclear text.

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14
Q

sunk cost fallacy

A

-staying in bad movie because you paid for it, end up losing money and time instead of just money

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15
Q

what is self serving bias again

A

if i succeed it is because of me, if i fail its becaus eof someone else. operating in groups is more rational

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16
Q

sudo diagnostic information

A

info you think is important but isnt, correlation not mean causation

17
Q

subtlety diagnostic info

A

you think isnt important but is

18
Q

conformation bias

A

once you have decieded then new info is either blown off or is interpreted to serve your bias

19
Q

inside outside view?

A

basically inside=positive view

oiutside is more negative view

20
Q

top down vs bottum up approach

A

top down starts with big picture, goasl etc, goes down

bottum up starts with piecing together element sto give rise to complex system