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Decision-Making. November 23, 2012. Question 2: Making firecrackers.

Decision-Making

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Decision-Making

November 23, 2012

Question 2: Making

firecrackers

A firecracker factory can produce up to 70 000 firecrackers. The materials needed to produce 10 000 firecrackers cost 100 000 yuan. You need at least two workers to operate the plant, and for every 10 000 firecrackers beyond the basic production of 30 000, you need to hire another worker.

You can sell firecrackers for 72 yuan each. But customers will only buy firecrackers in the month before New Year, and the total number you can sell is somewhere between 25 000 and 50 000. You can store unsold firecrackers for sale in subsequent years, but it will cost 200 000yuan/year to store each 10 000 firecrackers. It is not possible to wait till next New Year's to find how many you can sell. If you decide to store some firecrackers for a year, you will pay the storage costs at the beginning of that year. Your MARR is 20%.

Do a scenario analysis for various levels of production and various levels of customer demand. On the basis of this analysis, you have several choices to make. First, should you buy the production apparatus at all? Secondly, if you do buy it, how many firecrackers should you produce?

Suppose a market research study would allow you to find out exactly how many firecrackers the customers would buy each year. What would it be worth paying for the results of such a study?

First, should you buy the production apparatus at all?

Secondly, if you do buy it, how many firecrackers

should you produce?

Suppose a market research study would allow you to

find out exactly how many firecrackers the customers

would buy each year. What would it be worth paying

for the results of such a study?

First, should you buy the production apparatus at all?

Secondly, if you do buy it, how many firecrackers

should you produce?

Suppose a market research study would allow you to

find out exactly how many firecrackers the customers

would buy each year. What would it be worth paying

for the results of such a study?

Given these results, what do we decide to do?

We can apply the ideas of game theory, treating our choice of

production level as our move in a game, and the level of demand

as the opponent’s response.

Theory of Games and Economic Behavior, John von Neumann

and Oskar Morgernstern, 1944

The Minimax Strategy: Assume things will turn out for the worst,

plan to minimize your losses (or maximize your gains)

under these circumstances.

The Maximax Strategy: Assume things will turn out for the best,

plan to minimize your losses (or maximize your gains)

under these circumstances.

The Principle of Minimax Regret

…also known as the Savage Principle, is based on the psychologically plausible premise that, if you make a plan based on the assumption that A will happen, and then B happens instead, you will regret losing the benefits you could have had if you'd been smart enough to guess right. So you adopt the strategy of minimizing the regret you might otherwise be obliged to feel.

To apply this, you first construct a regret matrix…

The Laplace Principle, or Principle of Insufficient Reason:

In the absence of information to the contrary, assume all outcomes are

equally likely, and choose the one with the highest expected value.

Qualitative Analysis

How to take non-monetary factors into account

in decision-making

Argument: the only way non-monetary factors should

influence decision-making is as a constraint:

``We’re not going to use baby seals as a

raw material, however profitable it is.’’

If, on the contrary, we allow trade-offs between our

non-monetary factors and cost, then there’s an implicit

rate of exchange between the factor and a dollar amount,

and we can go back to optimizing the final dollar amount.

…but in fact we don’t usually make decisions this way.

Consider the question of getting married, for example.

Accepting that we are going to base our decisions on multiple

criteria that can’t always be traded off against each other, there

are some ways of simplifying the problem:

Suppose we have N criteria and that A and B are two possible

courses of action. Then if:

Criterioni(A ) ≥ Criterioni(B) for 1 ≤ i ≤ N

we say A dominates B, and we can eliminate B from our list

of possible actions.

If A is not dominated by any other course of action, A is

efficient. So a first step is to reduce our alternatives to an

efficient set.

Example: which choices are dominated with respect

to the two criteria of hard-working and intelligent?

Bob

Moe

Andy

Jill

Sally

HW

Amy

Mary

Peter

Angus

IQ

Once we have eliminated the dominated choices, we

are left with an efficient set.

Bob

Sally

HW

Mary

IQ

Decision matrices:

Having reduced the problem to an efficient set,

we can give each option a weighted score against

a number of different criteria, thus forming a

decision matrix.

Algorithm for setting up decision matrices:

- Pick your criteria (e.g., industriousness, intelligence, charisma)
- Give each a weight to indicate its importance. Let the weights sum to 10
- Rank each alternative (Bob, Sally and Mary) against each criterion, with
- 10 being the highest.
- Find the weighted totals for each candidate and pick the highest.
- Do sensitivity analysis to see if the decision will change with minor changes
- in our preferences.

How do we get the individual scores?

Individual scores can be obtained by normalization or by

subjective evaluation.

For example, to get a 1-10 score for intelligence, we could

take the individual’s IQ score and divide by 20.

This normalises with respect to the total population, but

we might also normalise with respect to the candidate

population.

For subjective evaluation, we might want to average the

evaluations from two or three evaluators.

A variant on the decision matrix is to look at the product

of the individual scores as well as the sum. What is the effect of this, and why might we do it?

Some observations about decision-making:

It’s easier to make pairwise comparisons:

``Is Andy smarter than Bob?’’

versus

``How smart is Andy?’’

Some observations about decision-making:

It’s easier to compare specific criteria:

``Is Andy taller than Bob?’’

versus

``Is Andy a better person than Bob?’’

From these observations comes the Analytic Hierarchy Process:

Make pairwise comparisons between candidates with respect

to a criterion.

Convert the results of these comparisons into numerical scores

Make pairwise comparisons between the strength of the criteria

Convert the results of these comparisons into numerical scores

Combine the numerical scores to get an overall ranking of candidates

Apply sensitivity analysis

Example: choice of an electricity-generating technology for BC:

Criteria: Cost, Carbon Emissions, Unsightliness, Risk

Candidates: Hydro, nuclear, natural gas, oil, coal.

Pairwise comparison: How does

hydro compare to coal with

respect to price?

The preferred candidate gets a score of n, 1 ≤ n ≤ 9

The other candidate gets a score of 1/n

Comparison with respect to cost

In this case, Hydro gets a `2’ compared to coal.

We record this in a pairwise comparison matrix.

Comparison with respect to cost

Carry on and complete the matrix

Comparison with respect to cost

Next, normalize the matrix: sum each column, then divide each

entry by its column sum.

Comparison with respect to cost

Next, normalize the matrix: sum each column, then divide each

entry by its column sum.

Comparison with respect to cost

Now work out the average value of each row:

Priority Matrix

Go through the same process for each criterion to create a priority matrix.

We ask, ``How much more important is cost than risk?’’,

and similar questions, to create a pairwise comparison

matrix for the criteria themselves.

Then we normalise the columns and average the rows to create

a vector of preferences.

Now we multiply the priority matrix by the preference vector...

×

=

…and the result gives us our final preferences.

It is psychologically possible for a person to prefer coffee to

hot chocolate, hot chocolate to tea, and tea to coffee.

Such people do not make good subjects for this procedure.

Fortunately, there is a test to eliminate them.

If the decision-maker is consistent, each column in

the pairwise-comparison matrix will be a multiple

of every other.

If this is the case, the PCM will have a single non-zero

eigenvalue, λ. And λ ≈ n, where n is the rank of the matrix.

(not λ > n as in the text)

So to determine whether the decision-maker is consistent,

calculate the eigenvalues of the PCM and find the largest,

λmax.

Then calculate (λmax-n)/(n-1), the consistency index, or CI.

If the decision-maker is perfectly consistent, this should

be zero.

Compare this with the same statistic for a random matrix.

These have been helpfully tabulated by previous researchers.

Compare this with the same statistic for a random matrix.

These have been helpfully tabulated by previous researchers.

Now compare CI for the PCM with RI for a random

matrix.

If CI/RI < 0.1

then the PCM is acceptably consistent.