PRECONDITIONS FOR DECISION MAKING

1
PRECONDITIONS FOR DECISION MAKING

• IS THERE A PERFORMANCE GAP?
• Assumes there are standards to judge
• Assumes there is monitoring and feedback
• IS THE DECISION MAKER AWARE OF THIS GAP?
• Consciousness of the gap
• Significance to the organization
• DOES THE DECISION MAKER HAVE THE RESOURCES TO
  ACT?
• Knowledge and ability to fix the problem
• Budgets, personnel, power
• IS THE DECISION MAKER MOTIVATED TO ACT ON THE
  GAP?
• Rewards and risks weighed
• Risk-averse or risk-seeker?

2
TYPES OF DECISIONS

• ROUTINE
• COMMON PROBLEMS WITH WELL-DEFINED SOLUTIONS
• Rules, procedures, computer software packages
• There is an obvious best solution or
  alternative
• ADAPTIVE
• A COMBINATION OF MODERATELY UNUSUAL AND ONLY
  PARTIALLY-KNOWN PROBLEMS AND ALTERNATIVES
• Incremental changes or modifications of past
  decisions and practices
• Selecting from a set of known alternatives, but
  unsure of the outcomes
• INNOVATIVE
• UNUSUAL OR AMBIGUOUS PROBLEMS WHICH REQUIRE
  UNIQUE OR CREATIVE ALTERNATIVE SOLUTIONS
• Emphasizes radical change, innovation,
  brainstorming
• Not sure what the alternatives are, not sure
  whether any will work

3
RATIONAL DECISION MAKING MODEL

• DEFINE AND DIAGNOSE THE PROBLEMS
• SEPARATE SYMPTOMS FROM CAUSES
• CLARIFY THE OBJECTIVES TO BE ACHIEVED
• IDENTIFY CRITERIA TO BE USED
• SEARCH FOR ALTERNATIVE SOLUTIONS
• WE MUST HAVE OPTIONS FOR EACH PROBLEM
• COMPARE AND EVALUATE ALTERNATIVES
• EXPECTED RESULTS, COSTS, POTENTIAL SIDE EFFECTS?
• RECOMMENDED SOLUTIONS
• JUSTIFY WHY THIS CHOICEWHY NOT THE OTHERS?
• IMPLEMENTATION
• STEPS AND PROCEDURES TO FOLLOW TO ENSURE SUCCESS
• MONITORING AND CONTROL

4
RATIONAL MODEL ASSUMPTIONS

• PROBLEM CLARITY
• The problem is clear and unambiguous. The
  decision maker is assumed to have complete
  information regarding the decision situation.
• KNOWN OPTIONS
• The decision maker can identify all the relevant
  criteria and can list all the viable
  alternatives. Furthermore, the decision maker is
  aware of all the possible consequences of each
  alternative.
• CLEAR PREFERENCES
• The criteria and alternatives can be ranked and
  weighted to reflect their importance.
• CONSTANT PREFERENCES
• The decision criteria are constant and the
  weights assigned are stable.
• NO TIME OR COST CONSTRAINTS
• The decision maker can obtain full information
  about criteria and alternatives because it is
  assumed there are no time or cost constraints.
• MAXIMUM PAYOFF
• The rational decision maker will choose the
  alternative that yields the highest perceived
  value.

5
BOUNDED RATIONALITYSIMON (57)

• BASED ON A LIMITED PERSPECTIVE
• SOME IMPORTANT CRITERIA ARE NOT IDENTIFIED
• NOT ALL ALTERNATIVES ARE CONSIDERED
• SEQUENTIAL EVALUATION OF ALTERNATIVES
• CONSIDER OPTIONS ONE AT A TIME
• MAY ONLY COMPARE AND CONTRAST TWO OPTIONS
• SATISFICING
• IS THE OPTION UNDER CONSIDERATION OK or GOOD
  ENOUGH?
• SELECTION OF THE FIRST TOLERABLE OPTION
• JUDGMENTAL HEURISTICS AND BIASES
• OVERCONFIDENCE Im 70 suremore like 50/50
• ANCHORS -- Get stuck on some initial
  info15-50 millPrimacy Effect
• CONFIRMATION Bias that remembers only data
  that supports your predisposition
• AVAILABILITY Information that is readily
  available and vivid, we dont dig deeper
• ESCALATION OF COMMITMENT More investment in a
  bad decision

6
DECISION MAKING

• UNDER CONDITIONS OF
• CERTAINTY
• RISK
• UNCERTAINTY
• AMBIGUITY
• LEADS TO SOLUTIONS THAT ARE
• OPTIMIZED
• SATISFICED

7
FOUR DECISION MAKING CONDITIONS

• CERTAINTY
• DECISION MAKERS KNOW WHICH OBJECTIVES THEY WANT
  TO ACHIEVE
• ALTERNATIVES ARE CLEARLY DEFINED
• KNOWLEDGE OF OUTCOMES IS COMPLETE
• ALL INFORMATION NEEDED IS FULLY AVAILABLE
• RISK
• DECISION MAKERS KNOW WHICH OBJECTIVES THEY WANT
  TO ACHIEVE
• ALTERNATIVES ARE CLEAR
• LIKELIHOOD OF OUTCOMES IS SUBJECT TO CHANCE
• GOOD INFORMATION IS AVAILABLE
• UNCERTAINTY
• DECISION MAKERS KNOW WHICH OBJECTIVES THEY WANT
  TO ACHIEVE
• ALTERNATIVES ARE INCOMPLETE
• LIKELIHOOD OF OUTCOMES IS NOT UNDERSTOOD
• INFORMATION IS INCOMPLETE
• AMBIGUITY
• OBJECTIVES TO BE ACHIEVED ARE NOT CLEAR
• ALTERNATIVES ARE DIFFICULT TO DEFINE
• INFORMATION ABOUT OUTCOMES IS UNAVAILABLE

8
DECISION STYLESTHOMPSON TUDEN (59)

• CLEAR PREFERENCES
• REGARDING OUTCOMES
• YES, Known NO Agreement
• with Certainty Not Known
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - - - - - - - - - - - - - - - - - -
  - - - -
• YES, We Know
• What To Do
• CLEAR UNDERSTANDING COMPUTATIONAL COMPROMISE
• OF CAUSE / EFFECT - - - - - - - - - - - - - - - -
  - - - - - - - - - - - - - - - - - - - - - - -
• RELATIONSHIPS JUDGMENTAL BLUE-SKY
• (Inspirational)
• NO, We Dont
• Know What to Do - - - - - - - - -
  - - - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - - - - - - - - - - -
• DO YOU KNOW WHAT OUTCOMES YOU WANT AND WHAT TO
  DO TO GET THEM?

9
THREE RATIONALDECISION PROCESSESCOMPENSATORY,
CONJUNCTIVE, DISJUNCTIVE

• COMPENSATORY PROCESS (CLASSICAL)
• ALL IMPORTANT CRITERIA ARE CLEARLY IDENTIFIED
• CRITERIA ARE WEIGHTED ACCORDING TO IMPORTANCE
• ALL ALTERNATIVES CAN BE MATHEMATICALLY MODELED
• EXPECTED VALUE IS CALCULATED FOR EACH OPTION
• THE BEST EXPECTED VALUE IS THE OPTIMAL CHOICE
• USE OF EXPECTED VALUES ALWAYS LEADS TO THE
  OPTIMAL SOLUTION
• VERY HIGH PERFORMANCE ON ONE CRITERION CAN OFFSET
  WEAKNESSES IN ANOTHER
• ARE YOU CONFIDENT THE WEIGHTS AND VALUES ARE
  CORRECT?

10
COMPENSATORY DECISION PROCESSCLASSICAL /
ECONOMIC APPROACH

• Price MPG Room Power Style EXPECTED
• ORIGINAL GRID .40 .20 .10 .10 .20 VALUE
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - -
• CAR A 9,000 50 .4 .4 .6
• CAR B 12,000 35 .6 .7 .9
• CAR C 14,000 28 .7 .9 .7
• CAR D 10,500 32 .7 .7 .4
• CONVERTED GRID (so all s are standardized)
• CAR A .889 1.00 .4 .4 .6 .7556
• CAR B .667 .70 .6 .7 .9 .7168
• CAR C .571 .56 .7 .9 .7 .6404
• CAR D .762 .64 .7 .7 .4 .6528
• Ideal 8,000 50mpg 1.0 1.0 1.0

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CONJUNCTIVE PROCESSOR MULTIPLE HURDLES APPROACH

• ALL IMPORTANT CRITERIA ARE CLEARLY IDENTIFIED
• CRITERIA CAN BE RANKED OR ORDERED IN IMPORTANCE
• CUTOFF LIMITS ARE SET FOR EACH CRITERION
• ALTERNATIVES ARE COMPARED TO THE CUTOFF LIMITS
• ONLY ALTERNATIVES WITHIN ALL CUTOFF LIMITS
  SURVIVE
• NOT AN OPTIMIZING PROCESS---NO SOLUTION
  GUARANTEED
• THE PROCESS MAY NARROW DOWN THE OPTIONS, BUT
  DOESNT
• GUARANTEE A BEST SOLUTION WILL BE FOUND.
• IN FACT, IT IS POSSIBLE THAT ALL ALTERNATIVES MAY
  BE ELIMINATED
• BY THE CUTOFF LIMITS, LEAVING US WITH NO
  RECOMMENDATION.
• EXCEPTIONAL STRENGTH ON ONE CRITERION CANNOT MAKE
  UP FOR A
• WEAKNESS OR LACK ON ANOTHER CRITERION.
• ALL THE MINIMUMS ON ALL THE CRITERIA MUST BE MET.

12
CONJUNCTIVE DECISION PROCESSMULTIPLE HURDLES
APPROACH

• Price MPG Style Power Room PASSED ALL
• Rank 1 2 3 4 5 CUTOFFS
• Original Grid - - - - - - - - - - - - - - - - - -
  - - - - - - - - - - -
• CAR A 9,000 50 .6 .4 .4
• CAR B 12,000 35 .9 .7 .6
• CAR C 14,000 28 .7 .9 .7
• CAR D 10,500 32 .4 .7 .7
• CUTOFFS Max Min Min Min Min
• 13,000 30 .6 .6 .6
• RUNNING THE HURDLES
• CAR A OK OK OK FAIL ---
• CAR B OK OK OK OK OK YES
• CAR C FAIL ----
• CAR D OK OK FAIL ----

13
DISJUNCTIVE PROCESSOR BEHAVIORAL

• WE HAVE SOME CRITERIA, BUT THE LIST SEEMS
  INCOMPLETE
• UNABLE TO EITHER WEIGHT OR RANK CRITERIA BY
  IMPORTANCE
• WE CAN PERCEIVE OUTSTANDING ATTRIBUTES FOR EACH
  OPTION
• WE LIST THE STRENGTHS WEAKNESSES OF EACH
  OPTION
• WE PICK OUR CHOICE BASED ON A REVIEW OF THESE S/W
  LISTS
• THE DECISION PROCESS IS UNSYSTEMATIC,
  INCONSISTENT
• AN ALTERNATIVE WITH A BAD ATTRIBUTE IS
  FREQUENTLY REJECTED
• FINAL CHOICES ARE USUALLY MADE USING A SINGLE
  CRITERION WHICH
• THE DECISION MAKER HAS DECIDED TO FOCUS UPON.
• THESE DECISIONS ARE MUCH MORE SUBJECTIVE THAN
  THEY APPEAR,
• AND ARE DIFFICULT TO DEFEND IF CHALLENGED.

14
DISJUNCTIVE DECISION PROCESSBEHAVIORAL,
STRENGTHS/WEAKNESSES APPROACH

• Price Style Power MPG ????
• Original Grid - - - - - - - - - - - - - - - - - -
  - - - - - - - - - - -
• CAR A Good Bad Good
• CAR B Good
• CAR C Bad Good
• CAR D Bad
• Eliminate the alternatives with Bad
  evaluations, and see what is left.
• Thus, we buy Car B because I liked the color and
  my wife liked the
• interior!
• NOT RATIONAL OR SYSTEMATIC, BUT WE THINK WE WERE
  LOGICAL IN
• WHAT WE DID BEFORE SELECTING THE CAR.

15
ENVIRONMENTAL SCENARIOS

• WHAT ARE YOUR ASSUMPTIONS ABOUT THE ENVIRONMENT?
• POSSIBLE STATES OF NATURE (Scenarios)
• MUTUALLY EXCLUSIVE
• THINGS WE DONT CONTROL
• POSSIBLE ACTIONS WE CAN TAKE (Alternatives)
• ALTERNATIVES OR OPTIONS
• THESE ARE THE CHOICES WE CAN MAKE (WE CONTROL
  THESE)
• EXPECTED OUTCOMES FOR EACH POSSIBLE ACTION
• REVENUES, COSTS, PROFITS
• UNITS PRODUCED, HOURS WORKED, ETC.
• LIKELIHOOD OF EACH OUTCOME
• CERTAINTY
• RISK or PROBABILITY
• TOTAL UNCERTAINTY

16

• PAYOFF TABLES
• SINGLE-PHASE PROBLEMS
• RECURRING, REPETITIVE DECISIONS
• CAN ILLUSTRATE THE CLASSICAL DECISION PROCESS
• DECISION TREES
• MULTI-PHASE PROBLEMS
• NEAR-UNIQUE, ONE-TIME-ONLY DECISIONS

17
A PAYOFF TABLE ILLUSTRATIONUNDER TOTAL
UNCERTAINTY

• EXPECTED PROFITS / ACRE
• STATES OF NATURE / ENVIRONMENTAL SCENARIOS
• ALTERNATIVE CROPS NORMAL
  WET DRY VIOLENT
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - -
• CORN 900 450 -800 250
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - -
• POTATOES 800 -300 400 500
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - -
• HAY/GRASS 300 500 0 100
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - -
• 1. MAXI-MAX (Optimist) Corn 900,
  Potato 800, Hay 500
• 2. MAXI-MIN (Pessimist) Corn -800,
  Potato -300, Hay 0
• 3. MINI-MAX (Regret) Regrets..Corn 1200,
  Potato 800, Hay 600
• 4. AVERAGE (Rational) Ex ValueCorn 200,
  Potato 350, Hay 225

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BUILDING A REGRET MATRIX

• EXPECTED PROFITS / ACRE
• STATES OF NATURE / ENVIRONMENTAL SCENARIOS
• ORIGINAL MATRIX
• ALTERNATIVE CROPS NORMAL
  WET DRY VIOLENT
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - -
• CORN 900 450 -800 250
• POTATOES 800 -300 400 500
• HAY/GRASS 300 500 0 100
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - -
• REGRET MATRIX
• CORN 0 50 1200 250
• POTATOES 100 800 0 0
• HAY/GRASS 600 0 400 400
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - - - - - -
• MINIMIZE THE MAXIMUM REGRETS
• Corn 1200, Potatoes 800, Hay 600

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A PAYOFF TABLE ILLUSTRATIONUNDER RISK (ASSIGNED
PROBABILITY)

• EXPECTED PROFITS / ACRE
• PROBABILITIES COME FROM GOOD GUESSES.
  CALCULATE THE EXPECTED VALUES
• INDIAN JOES ESTIMATES Normal 30, Wet 25,
  Dry 20, Violent 25
• STATES OF NATURE / ENVIRONMENTAL SCENARIOS
• ALTERNATIVE CROPS NORMAL WET DRY
  VIOLENT EXPECTED
• WEIGHTS .30 .25 .20 .25
  VALUES
• CORN 900 450 -800 250 285
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - -
• POTATOES 800 -300 400 500
  370
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - -
• HAY/GRASS 300 500 0 100
  240
• 
  
• You should PLANT POTATOES EVERY YEAR. In any
  one year youll either make 800, lose 300, make
  400, or make 500 but over the years, youll
  average 370 of profits.

20
A PAYOFF TABLE ILLUSTRATIONUNDER RISK (FACTUAL
PROBABILITY)

• EXPECTED PROFITS / ACRE
• PROBABILITY INFORMATION COMES FROM RELIABLE
  (FACTUAL) SOURCES
• WEATHER BUREAU HISTORY Normal 35, Wet 30,
  Dry 15, Violent 20
• STATES OF NATURE / ENVIRONMENTAL SCENARIOS
• ALTERNATIVE CROPS NORMAL WET DRY
  VIOLENT EXPECTED
• WEIGHTS .35 .30 .15 .20
  VALUES
• CORN 900 450 -800 250
  380
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - -
• POTATOES 800 -300 400 500
  350
• - - - - - - - - - - - - - - - - - - - - - - -
  - - - - - - - - -
• HAY/GRASS 300 500 0 100
  275
• You should PLANT CORN EVERY YEAR. In any one
  year youll either make 900, make 450, lose 800
  or make 250 but over the years, youll average
  380 of profits.

21
A PAYOFF TABLE ILLUSTRATIONUNDER RISK (WHAT IS
THE VALUE OF PERFECT INFORMATION?)

• WE KNOW ONLY GOD CONTROLS THE WEATHER, BUT IF WE
  HAD PERFECT PREDICTION EACH YEAR, WED KNOW
  EXACTLY WHAT THE WEATHER WOULD BE AND WHAT WE
  SHOULD PLANT. WED HAVE NORMAL CONDITIONS 35
  OF THE TIME, AND DURING THOSE TIMES, WED PLANT
  CORN. 30 OF THE TIME IT WOULD BE WET AND WED
  PLANT HAY, ETC.
• STATES OF NATURE / ENVIRONMENTAL SCENARIOS
• ALTERNATIVE CROPS NORMAL WET DRY
  VIOLENT EXPECTED
• WEIGHTS .35 .30 .15 .20
  VALUES
• CORN 900 450 -800 250
  380
• POTATOES 800 -300 400 500
  350
• HAY/GRASS 300 500 0 100
  275
• Expected Profits given Perfect Information
  (EPPI) 625.
• Expected Value of Perfect Information (EVPI)
  EPPI EV 625 380 245
• Therefore, you can increase your profits by up to
  245 if you have a perfect weather forecast each
  spring.