NBS

Publications

NAT L INST. OF STAND 4 TECH

A111DL, 4 A20b

NBSIR 82-2596

X

Use of Decision Analysis in Arson

Program Planning

U S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Fire Research

Washington, DC 20234

November 1982

Prepared for:

I

. S . Fire Administration

QC — ideral Emergency Management Agency

1J0 ashington, DC 20472

, U56

82-2596

1982

RATIONAL BUREAU

OF STANDARDS

LIBEARV DEC G 1982

Ao\ clcc. . *

\oo

NBSIR 82-2596

USE OF DECISION ANALYSIS IN ARSON

PROGRAM PLANNING

S. Wayne Stiefel

U S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Fire Research

Washington, DC 20234

November 1982

Prepared for: U.S. Fire Administration

Federal Emergency Management Agency

Washington, DC 20472

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director
: J
TABLE OF CONTENTS

Page

LIST OF FIGURES iv LIST OF TABLES iv

Abstract 1

1. INTRODUCTION 1 1.1 Background 1 1.2 Objective 2 1.3 Approach 2

2. DECISION ANALYSIS 3 2.1 Description and General Applicability 3 2.2 Applicability of Decision Analysis for Arson Program Management . . 4

3. EXAMPLE OF METHOD 4 3.1 Data Development 4 3.2 Decision Analysis Application 7 3.2.1 Rationale 7 3.2.2 Explanation of Inputs to Decision Analysis 7 3.2.3 Explanation of Computations for Decision Analysis 9

4. BUDGET ALLOCATION 14

5. SUMMARY 17

6. ACKNOWLEDGEMENT 17

7. REFERENCES 17

iii
LIST OF FIGURES

Page

Figure 1. Flow Chart for Application of Decision Analysis to Arson

Programs 2 Figure 2. Arson Loss Model 8 Figure 3. Arson Loss Model for District 1 13

LIST OF TABLES

Page

Table 1. Arson Fire Descriptive Data for District 1 4 Table 2. Prevention and Enforcement Actions Targeted at Specific

Arson Types 5 Table 3. Strategy Effectiveness 6 Table 4. Strategy Cost Data 6 Table 5. Expected Loss Per Fire for District 1 7 Table 6. Expected Fires for Each Strategy in District 1 10

Table 7. Probability of Arson Fire by Specific Type for Each Strategy

in District 1 11

Table 8. Summary of Cost plus Loss and Net Benefit for District 1 14

Table 9. Summary of Estimated Net Benefits and Program Costs for Arson

Strategies 14

Table 10. Rank Order of Districts for Strategy B According to Net

Benefit Estimates 15

Table 11. Rank Order of Districts for Strategy C According to Net

Benefit Estimates 16

iv
USE OF DECISION ANALYSIS IN ARSON PROGRAM PLANNING

S. Wayne Stiefel

Abstract

A decision analysis approach is formulated and demonstrated

to provide a planning tool for decision makers in a city or community concerned with selection and application of arson

strategies. The Arson Information Management System (AIMS) is

used to provide information to describe the arson problem

(classify causes/motivation, incidence and magnitude for each

area in the city/community) and to establish which strategies

are appropriate to address the causes. This AIMS data plus an assessment of the cost and effectiveness of arson strategies are combined in a decision analysis framework. The framework specifies

data requirements and provides a data analysis structure. The

decision analysis has been designed to provide a measure of the net

benefits for various strategies for each area in the city/community

.

A method for using the outputs from the decision analysis to provide the most cost-effective use of an arson budget has been

developed

.

Key Words: Arson; Arson Information Management System (AIMS)

;

program management; cost benefit analysis; decision analysis.

1 . INTRODUCTION

1 . 1 Background

As more information is accumulated regarding the types of arson fires and how

they are distributed within a city or community, more guidance is possible for

application of specific strategies intended to reduce the incidence and cost of arson fires. The Federal Emergency Management Agency (FEMA) has funded seven Arson

Information Management System (AIMS) programs with community, fire and enforcement

agencies. These pilot AIMS programs were intended to indicate the extent to which

systematic collection and analysis of pertinent data can be used to improve arson prevention, control and enforcement efforts [1]^. As an initial step in gaining

understanding, AIMS data has been used to describe the arson problem by providing

information measuring arson's magnitude and characterizing arson in terms of its

location and motivation. Once such a description of the arson problem within a jurisdiction is obtained, strategies can be devised to address causes and direct

intervention at high incident locations.

1Numbers in brackets indicate literature references at the end of this report.

1
1.2 Objective

It is the objective of this report to provide a planning tool for decision

makers in a city or community for selection and application of cost-effective arson intervention strategies targeted at high arson areas.

1.3 Approach

In the approach followed in this report, AIMS data are used to describe the arson problem (classify causes ,dnotivat ion, incidence and magnitude for each area in

the city /community) and establish which strategies are appropriate to address the causes. This AIMS data plus an assessment of the cost and effectiveness of arson strategies are combined in a decision analysis framework. The decision analysis has

been designed to provide a measure of the net benefits for various strategies for

each area in the city/community . A method for using the outputs from the decision

analysis to provide the most cost-effective use of an arson budget has been developed.

Figure 1 is a flow chart illustrating the approach.

Figure 1. Flow Chart for Application of Decision Analysis to Arson Programs

In order to facilitate the explanation of this approach, a hypothetical set of

data has been developed and is used for illustration throughout the discussion.

2
2. DECISION ANALYSIS

2.1 Description and General Applicability

Decision analysis in general involves modeling of the system being impacted by

the decision. Decision analysis is applied to problems where cause and effect of

the various available actions are unclear and intuitive judgements might prove

incorrect. It also is applied when the complexity of interactions and the uncer- tainties (inherent in the evaluation of strategies) makes choice difficult. Decision

analysis allows choice among strategies to be based upon systematic consideration of

benefits, risks and costs. The process explicitly identifies factors which are considered important and shows where subjective judgements are incorporated, as well

as their likely influence.

Decision analysis is a formalized approach for assisting decision makers under

uncertainty [2]

.

It is the result of combining systems analysis with statistical

decision theory. The systems analysis structures the problem so that it can be

treated as a unified entity, while probability assessment is used to treat uncertainty. Decision analysis provides the decision maker with a cause and effect model

which suggests the likely consequences of a selected course of action.

Since uncertainty is usually the focus for most significant decisions, decision

making requires emphasis on uncertainty. If it is known for certain what outcome

will result from the alternatives available most decisions become trivial. However,

because there are many unknowns involved in most complex problems, the formal

recognition and treatment of uncertainty aids the decision maker in structuring the

bounds of the problem and determining what additional information may be worth

gathering

.

Decision analysis also requires the explicit understanding of values, trade

offs between monetary and nonmonetary values. The trade-off between property and

injuries or deaths is a typical value problem facing decision makers in fire

protection.

Decision analysis goes beyond just revealing the decision. An important

benefit of decision analysis is the understanding gained from the decision making

process. Often, through examination of the logical structure, sources of information, or values used in making decisions; new insights are gained or avenues for

communication are opened. It permits efforts to address information gaps or differences in values.

3
2.2 Applicability of Decision Analysis for Arson Program Management

Decision analysis was selected for application to provide an evaluation

framework for choosing among alternative arson reduction strategies. The decision

analysis methodology is intended to provide management with the tools necessary to

evaluate the magnitude and nature of the arson problem and to employ cost beneficial

strategies. Decision analysis has been successfully applied to other fire safety

problems involving uncertainty2

. An initial step in the formulation of a decision

analysis framework consists of understanding the many complex interactions which

lead to various outcomes. In this discussion the focus is on establishing the link

between arson reduction strategies and arson fires. Therefore, a logical structure

was developed which includes selection of a strategy and an implementation location

and measures the expected outcome. Data requirements were established based upon

the dictates of the logical structure. The following section provides, through

example, the details of the decision analysis methodology.

3. EXAMPLE OF METHOD

3.1 Data Development

Descriptive data for each area in the city or community are developed from the

AIMS. The descriptive data required would classify by arson type the number of

fires and losses (property, injury and deaths) for each area of the city or com- munity. Table 1 provides an hypothetical example of a data set for one city district.

Table 1. Arson Fire Descriptive Data for District 1

Arson Type* Number of Fires Property Loss

(1000 dollars)

Deaths Injuries

Juvenile (vandalism) 10 20 0 2 Arson- for-profit 5 150 0 4 Revenge 2 15 1 3 Pyromania 3 15 0 1 Crime Concealment 2 20 0 0 *Arson types correspond to data classifications reported by FEMA [3] .

2 Applications include upholstered furniture fire safety, marine fire

safety and electric transformer fluids fire safety. See for example, Helzer,

S.G., et al, Decision Analysis of Strategies for Reducing Upholstered Furniture

Fire Losses, Natl Bur Stand, NBS-TN 1101 (June 1979) . Current efforts at NBS

are addressing the application of alternatives to reduce residential fire losses.

4
Over time, specific arson prevention and enforcement strategies have evolved,

which have proven to be effective in reducing the arson problem. Table 2 illustrates,

by arson type, specific actions intended to reduce the arson incidence [4]

.

Table 2. Prevention and Enforcement Actions Targeted

at Specific Arson Types

Arson Type Action

Juvenile Counseling, public education

Examine juvenile record. Interviews

Arson-for-Profit Identify high risk properties

Publicize, prevent over insurance

Arson patrols, collect data

Compare arson methods. Interviews

Revenge Community-based dispute resolution

service, Counseling - Investigative

methods parallel assault or homocide

Pyromania Screening individuals - maintain files - immediate canvassing of witness

Counseling/treatment

Crime Concealment General crime prevention methods

Train fire departments and police to

recognize signs

Having developed the descriptive data for the entire city, patterns of arson

types may become evident through examination of the data. Depending upon the

patterns and mix of arson types, strategies comprised of a one or more of the

actions described in table 2 are developed. It is not necessary that the process

lead to the selection of one strategy which will be applied throughout the entire

city or community. Rather, a given strategy may apply to several districts with

other strategies developed to deal with different arson types in other districts.

The technique presented in this paper will allow for determination, within budget

constraints, of which strategy to apply in each district. For this illustration

three strategies have been developed. Strategy A employs the actions aimed at

juvenile, revenge and pyromania caused arson. Strategy B concentrates on the

arson-for-profit motive and Strategy C is a broad based approach aimed at the

juvenile, arson-for-profit, revenge and pyromania caused arson fires, but at a lower

level of effort for each motive than Strategy A or Strategy B.

Having defined a set of strategies, the next step concerns estimating their

expected effectiveness in ameliorating each type of arson at the level of resources

which will be expended. Strategy effectiveness is uncertain; however past experience provided by officials within your jurisdiction, review of additional AIMS

information, or contact with officials in jurisdictions having applied similar

strategies may provide data or judgements for assessing expected effectiveness. One

procedure which has been widely applied for obtaining expert judgements in the

absence of reliable data is the Delphi technique [5]. Table 3 provides the strategy

effectiveness matrix for our hypothetical example.

5
Table 3. Strategy Effectiveness

Arson Type

Percentage Reduction Expected

Strategy A Strategy B Strateqy C

Juvenile (vandalism) 30 2 15

Ar son-for-Prof it 0 35 15

Revenge 10 0 8 Pyromania 15 0 7 Crime Concealment 2 0 1

Table 4 provides the cost data for each strategy. Each strategy has fixed cost

consisting of a minimum staff and resources necessary to administer the program,

keep records, conduct various operations, etc. Also, for each district or area where the strategy is applied additional costs are incurred, which pertain solely to

the implementation of the strategy within that district. These are the variable

costs associated with the strategy. The total cost is the sum of the fixed cost

plus the variable cost for each district where the strategy is to be applied.

Table 4. Strategy Cost Data

Location

Variable Cost (1000 dollars)

Strategy A Strategy B Strategy C

District 1 15 20 18

District 2 20 25 23

District 3 12 15 14

District 4 15 20 17

District 5 10 15 13

Fixed Cost (1000 dollars)

Strategy A Strategy B Strategy C

City Wide 50 75 100

TOTAL PROGRAM 122 170 185

Having collected the information providing descriptive data by district;

selected candidate arson strategies, and estimated their effectiveness and cost, all

the data inputs are available to exercise the decision analysis model. The following discussion will explain how to utilize these data inputs to compute net benefits.

6
3.2 Decision Analysis Application

3.2.1 Rationale

The net benefit for implementation of a strategy can be computed simply as the

difference in the arson losses without and with the strategy minus the cost of

implementation. The outputs from the decision analysis are the expected outcomes

(cost plus loss) measured in terms of the cost for strategy implementation and the

loss or consequence of arson measured by the expected number and severity (property

loss, injuries and deaths) of the fires. Each strategy is evaluated in every

district being considered.

3.2.2 Explanation of Inputs to Decision Analysis

Figure 2 is a representation of the decision analysis framework. The symbols

in the rectangular boxes in figure 2 are computed values. The computations will be

explained later in this section. The remaining symbols are values which are provided as inputs to figure 2 from the tables previously described. The derivation of

each input value will be illustrated using our hypothetical example. The outcome

measures (property loss, injuries and deaths) in figure 2 are determined on a per

fire basis. These values will apply for each strategy, since it is assumed that

once a fire occurs its consequence is not influenced by the strategy in effect.

Table 5 shows the computation of the outcome measures from table 1 on a per fire

basis. These values can be used directly in the decision analysis and carried

through in terms of property loss (dollars) , deaths and injuries. This approach

does not, however, avoid the difficult task of balancing dollars against life

safety. Instead, this difficult and somewhat controversial task is shifted from the

analyst to the decision maker.

Table 5. Expected Loss Per Fire for District 1

Arson Type

Property Loss/

Fire

(1000 dollars)

Deaths/

Fire

Injuries/

Fire

Total Loss*/

Fire

(1000 dollars)

Juvenile (vandalism) 2.0 0 0.2 6.0

Arson-for-profit 30.0 0 0.8 46.0

Revenge 7.5 0.5 1.5 287.5

Pyromania 5.0 0 0.3 11.0

Crime Concealment 10.0 0 0 10.0

*Total loss/fire measures are computed using a value of $20 , 000/injury and

$500, 000/death

7
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The approach taken in this paper is to assign monetary value to lives saved

and injuries avoided. This approach has the advantage that it measures all the

benefits from arson strategies in the same units-dollars-which facilitates comparisons. The assignment of a monetary value, however, does raise practical and philosophical questions. A more indepth discussion can be found in the references [6,

7, 8]. For this example we have arbitrarily chosen to use a value of $500,000 for

a life saved and $20,000 for an injury avoided.

The probability that an arson fire will be caused by a specific arson type is

also an input to figure 2. It can be determined by dividing the number of fires

caused by a specific arson type by the total arson fires expected for that strategy.

For example, from table 1 the probability that an arson fire will be caused by a juvenile (PrJ ) for the no action case equals the number of juvenile fires (10)

divided by the total arson fires (22) or 0.45. Therefore, to determine similar

probabilities for each arson type under each strategy requires estimation of the

expected number of fires by arson type for each strategy (designated in figure 2 as No. Fires Str. A for strategy A) . Such estimates can be made using the information

in table 1; which applies to the no action case, and table 3, which provides the

means to adjust the no action fire incidence based on strategy effectiveness. Table

6 merges tables 1 and 3 to estimate the number of expected fires for each strategy.

Table 7 indicates the probability that an arson fire will be attributable to a specific arson type for each of the strategies. This computation is made from table

6 by summing the fires for each strategy and dividing this sum into each of the arson type fire totals.

One additional input value for the decision analysis (figure 2) requires

explanation, that is, the cost for implementating the strategy in the district.

Only the variable costs are used in the analysis and are depicted on figure 2 as $A

I to indicate in this case cost to implement Strategy A. For our example,

i these values are taken from table 4. The fixed cost for each strategy will

I be taken into account at a later stage, when it is shown how to allocate the

budget

.

3.2.3 Explanation of Computations for Decision Analysis

The symbols shown in rectangular boxes in figure 2 are computed values. The

computations are made as follows.

which equals the expected loss per fire under Strategy A is computed

by summing the products of the probability of specific arson types

(see table 7) and the expected outcome of the arson type (see table 5)

.

In our example from figure 2,

Crime Cone.

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11
where, Pr AT^ is the probability that an arson fire is caused by a specific type of arson under Strategy A and OA,^ is the expected outcome from a specific type of arson fire.

Expanding the expression in notation form

$af/f Pr ja

• °j + Pr AfpA

* °Afp + Pr RA

• °

r + Pr pyA

• 0p

y

+ Pr CA

‘ °c

= 0.38 ($6.0) + 0.27 ($46.0) + 0.10 ($287.5) + 0.14 ($11.0) + 0.11 ($ 10 . 0 )

$af/f = $46 ' 09

which equals the expected loss for all arson fires under Strategy A is

the product of $A

p/, F and No. Fires Str. A. For our example:

$A

p

= $Ajy

F

. No. Fires Str. A

= $46.09 (18.31)

$Ap = $843.91

is the sum of the variable cost for implementing Strategy A and the

expected loss under Strategy A. It is the sum of $A

I and $Ap, where

$Aj is the variable cost for implementing Strategy A (see table 4) . For our example:

$A^, = $A

X + $A

p

= $15 + $843.9

$At

= $858.9

Completing, the computations for each strategy in district 1 yields the decision

tree shown in figure 3.

It is now possible to compute the net benefits for each of the strategies. The

net benefit for a strategy is simply the difference between the losses incurred for

the no action case and the cost plus loss for that strategy. Table 8 summarizes the

3 output from figure 3 and indicates net benefit for each strategy .

3At this point we have not included fixed cost in the net benefit figures.

12
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13
Table 8. Summary of Cost plus Loss and

Net Benefit for District 1 Strategy Cost plus Loss*

($1000)

Net Benefit*

($1000)

No Action 915.09 0 Strategy A 858.90 56.19

Strategy B 856.09 79.00

Strategy C 870.81 62.28

*Does not include fixed cost for strategy implementation

4. BUDGET ALLOCATION

This section will demonstrate an approach for selection of the most costeffective arson strategy for a given budget amount. Using our hypothetical example

again, suppose we have completed a decision analysis for each of the five districts

under consideration. The results from these analyses are summarized in table 9.

Table 9 has a column for each of the Strategies A, B and C plus an additional

column for a combination of Strategies A and B. We can consider the combination of

Strategies A and B since they are directed at different types of arson and there is

4 no program overlap . The fixed cost for strategy implementation is taken into

consideration in computing total net benefits. Table 9 indicates that if our total

program budget were $170,000 we would derive our highest estimate of net benefit

($224,000) by implementation of Strategy B in all five districts.

Table 9. Summary of Estimated Net Benefits and Program

Costs for Arson Strategies

Estimated Net Benefit (1000 dollars)

Strategy A Strategy B Strategy C Strategy A&B

District 1 56.19 79.00 62.28 79.00

2 68.30 65.82 75.85 68.30

3 42.34 53.40 55.81 53.40

4 35.42 25.50 38.61 35.42

5 58.32 75.46 65.34 75.46

Fixed Cost St A -50. -50.

St B -75. -75.

St C -100.

Total Net

Benefit ($1000) 210.57 224.18 197.89 186.58

Total Program

Cost ($1000) 122. 170. 185. 292.

4 Combining Strategy C with either Strategy A or B and using the net benefit values

derived from figure 3 would be incorrect. This relates back to the estimation of

strategy effectiveness (see table 3) and the arson fire reduction expected. The

reduction expected for each arson type is dependent upon the level of effort and

is not necessarily additive. Therefore, a program strategy with a new level of

resources directed at an arson type requires a separate evaluation.

14
Suppose our budget is restricted to $125,000, what can we learn from our analyses? Let's consider each strategy and limit the implementation in districts

according to the net benefit derived for the funds expended. Rank ordering districts

according to net benefit provides an easy method for searching for the greatest net

benefit. Taking the strategies one at a time indicates the following.

Strategy A - Since the total program cost for Strategy A is $122,000,

it is an acceptable program within our $125,000 budget limitation. The estimated net benefit for Strategy A is $211,000. In

addition, there is a budget surplus of $3,000.

Strategy B - Since we are seeking the net benefit derivable from our $125,000 budget, table 10 rank orders the districts in terms of

their net benefit. Also, cumulative costs are indicated. The

initial entry of fixed cost in table 10 indicates that prior to

deriving any benefits the administrative structure must be in

place. Adding districts one at a time, we see that the addition

of district 2 causes us to exceed our budget limitation. However, by substituting district 3 we remain within the budget

constraint and achieve our greatest net benefit for Strategy B.

An estimated net benefit value of $133,000 is achieved by

implementing Strategy B in districts 1, 3 and 5, after subtracting Strategy B's fixed cost.

Table 10. Rank Order of Districts for Strategy B

According to Net Benefit Estimates

Net Benefit

($1000)

Cost

($1000)

Cumulative Cost

($1000)

Fixed Cost 75 75

District 1 79.00 20 95

District 5 75.46 15 110

c. c qo O C 1 O C L/XOL- i 1 C L- b 0 3* Ob D ±0 D District 3 53.40 15 125

Total Net Benefit: $207. 9K - $7 5K = $132. 9K

Total Program Cost: $125K

Strategy C - Table 11 indicates the results from rank ordering districts

for Strategy C. The relatively high fixed cost of $100,000

restricts us to implementing Strategy C in district 2 only for an estimated net loss of $24,000.

15
Table 11. Rank Order of Districts for Strategy C According

to Net Benefit Estimates

Net Benefit

($1000)

Cost

($1000)

Cumulative Cost

($1000)

Fixed Cost $100 $100

District 2 75.85 23 $123

Total

Total

Net Benefit: $75. 9K - Program Cost: $123K

$100K = -$24 . IK

Strategy A and Strategy B - The combined fixed cost for Strategy A and

Strategy B of $125,000 equals our budget

limitation. Therefore, this combination of

strategies is not a feasible approach.

The results from the hypothetical example indicate that Strategy A yields the

greatest estimated net benefit of $211,000 with an expenditure of $122,000. Strategy

B's implementation in districts 1, 3 and 5 achieves an estimated net benefit of

$133,000 while expending the full $125,000 budget. If the analysis indicated small

differences in net benefits between strategies, the decision for selection could be

made perhaps using other factors not considered in the analysis. The analysis

provides the knowledge that the net benefit to the community will not be significantly

altered with the implementation of either strategy.

Often, after the results of an analysis are presented, many questions are raised concerning assumptions or values used in the analysis. Care must be exercised

when one factor appears to dominate the results of an analysis in an undesired

manner. For example, if one death has been noted and it dramatically shifts the

decision to a particular strategy; a question should be raised if this event can be

expected on a regular basis or was a fluke caused by unusual circumstances. To

determine how significant a change would take place in the results the analysis can

be repeated removing the event under question from the input data. This process of

systematic variation of inputs and notation of resulting changes is known as sensitivity analysis.

The application of sensitivity analysis serves to answer many of the "what if"

questions often raised after completing an analysis. For example, what if:

(1) the monetary value used for lives saved and injuries avoided were

significantly more or less,

(2) strategy effectiveness values were different, or

(3) a larger or smaller budget allocation were available?

16
This opening up of the communication process, addressing key issues of importance to

the community, is one of the most important benefits resulting from a good analysis.

5

.

SUMMARY

This report has demonstrated an approach which uses AIMS data to characterize

the arson problem and thereby establish the basis to formulate candidate arson reduction strategies. Using example data the decision analysis framework was presented and exercised to estimate strategy net benefits , through the combination

of the AIMS data with assessments for the cost and effectiveness of the arson

strategies. The net benefits were estimated for application of the candidate

strategies in each area in the city or community. The most cost-effective arson strategy for a given budget was selected, based upon a procedure which rank orders

districts by net benefit and accounts for cummulative strategy implementation costs.

The decision analysis framework can be used to select among arson program

strategies within a single district or, as demonstrated, across a community. The

procedure specifies data requirements and provides a data analysis structure. By

specifying data requirements, needed data can be collected and where data gaps exist

considered judgements can be substituted. The data presented in this discussion

were developed only to serve our hypothetical example. To utilize the described

methods each city /community will have to develop their own data set.

The decision analysis process serves to open up communication on important

assumptions and values used in the analysis. Sensitivity analysis can be used to

answer the "what if" questions often raised following completion of a good analysis.

6

.

ACKNOWLEDGEMENT

The author would like to acknowledge gratefully the assistance of Alan Gomberg,

whose insights and feedback contributed greatly during preparation of this report.

7

.

REFERENCES

[1] For a discussion and description of the AIMS concept and pilot programs see Report on the Arson Information Management Systems Conference , Washington,

D.C., May 28-29, 1981, International Association of Arson Investigators, Inc.,

Marlboro, MA, 1981.

[2] For a general discussion of how to apply decision analysis see Raiffa, H.,

Decision Analysis : Introductory Lectures on Choice Under Uncertainty , AddisonWesley, 1968.

17
[ 3 ] Fire in the United States ; Deaths , Injuries , Dollar Loss and Incidents at

the National , State , and Local Levels in 1978 . Second Edition Federal Emergency Management Agency, (U.S.), p. 162, July 1982.

[4] A more indepth discussion of such response strategies is found in. Arson

Prevention and Control ; Program Model , Abt. Associates, Inc., Law Enforcement

and Assistance Administration, pp. 6-9, January 1980.

[5] Dailey, N. and Helmer, D. , "An Experimental Application of the Delphi Method to

the Use of Experts", Management Science 9, No. 3, p. 458, April 1963.

[6] Rawie, Carol, Estimating Economic Impacts of Building Codes , National Bureau of

Standards, NBSIR 81-2402, pp 56-62, October 1981.

[7] Cornell, M. , et al, A Survey of Methods for Estimating the Cost Value of a Human Life , U.S. Coast Guard, Washington, D.C., 1976.

[8] Jones-Lee, M. W. , The Value of Life and Economic Analysis , University of

Chicago Press, Chicago, 1976.

18
NBS-114A (rev. 2 -ec) U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

1. PUBLICATION OR

REPORT NO.

2. Performing Organ. Report No. 3. Publication Date

SHEET (See instructions) NBSIR 82-2596 November 1982

4. TITLE AND SUBTITLE

USE OF DECISION ANALYSIS IN ARSON PROGRAM PLANNING

5. AUTHOR(S)

S. Wayne Stiefel

6. PERFORMING ORGANIZATION (If joint or other than NBS, see instructions)

NATIONAL BUREAU OF STANDARDS

7. Contract/Grant No.

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

8. Type of Report & Period Covered

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City. Stote. ZIP)

U.S. Fire Administration

Federal Emergency Management Agency

Washington, D.C. 20472

10. SUPPLEMENTARY NOTES

Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11.

ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here)

A decision analysis approach is formulated and demonstrated to provide a planning

tool for decision makers in a city or community concerned with selection and

application of arson strategies. The Arson Information Management System (AIMS) is

used to provide information to describe the arson problem (classify causes/motivation,

incidence and magnitude for each area in the city/community) and to establish which

strategies are appropriate to address the causes. This AIMS data plus ap assessment

of the cost and effectiveness of arson strategies are combined in a decision analysis

framework. The framework specifies data requirements and provides a data analysis

structure. The decision analysis has been designed to provide a measure of the net

benefits for various strategies for each area in the city/community . A method for usinj

the outputs from the decision analysis to provide the most cost-effective use of an arson budget has been developed.

12.

KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolon s) Arson; Arson Information Management System (AIMS); program management; cost

benefit analysis; decision analysis.

13.

AVAILABILITY 14. NO. OF

PRINTED PAGES

[jcj Unlimited

| | For Official Distribution. Do Not Release to NTIS

| | Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C.

20402. .

Order From National Technical Information Service (NTIS), Springfield, VA. 22161

22

15. Price

$ 6.00

USCOMM-OC 6043-PC0