lnformatlon Clrcular 91 78 

A Catastrophe-Theory Model for 

Simulating Behavioral Accidents 

By William E. Souder 

UNITED STATES DEPARTMENT OF THE INTERIOR 

Donald Paul Hodel, Secretary 

BUREAU OF MINES 

David S. Brown, Acting Director 
lnformatlon Clrcular 91 78 

A Catastrophe-Theory Model for 

Simulating- ~ehavioral Accidents 

By William E. Souder 

UNITED STATES DEPARTMENT OF THE INTERIOR 

Donald Paul Hodel, Secretary 

BUREAU OF MINES 

David S. Brown, Acting Director 
Library of Congress Cataloging-in-Publication Data 

Souder, William E. 

A catastrophe-theory model for simulating behavioral accidents. 

(Information circular / United States Department of the Interior, Bureau of Mines ; 9178 

Bibliography: p. 18- 19 

Supt. of Docs. na: I 28.27: 

1. Mine accidents-Psychological aspects. I. Title. 11. Series: Information circular (United 

States. Bureau of Mines) ; 9178 . 

i 
CONTENTS 

page 

Abstract .................................... 1 

Introduction ................................ 2 

Background ................................. 2 

Focus and scope of this study ................... 3 

Methodology ................................ 3 

Accident sample ............................ 3 

Variables and rating scales ................... 3 

Content analyses measurements .............. 3 

Content analyses results ...................... 4 

Data reduction ............................. 4 

Univariate analyses ......................... 5 

Contributing factors ........................ 5 

Statistical analyses of significance .............. 6 

Multivariate analyses ....................... 6 

Path analyses .............................. 7 

Systems model of behavioral accidents ........... 7 

Behavioral accidents ........................ 7 

Relative importance of the variables ........... 7 

Cascading network effects: an example ......... 8 

Some implications .......................... 9 

Simulating accident causes .................... 9 

Network preprocessors ........................ 10 

Psychological conditions (PC) ................. Behavioral conditions (BC) ................... Supervisor ability (FA) and management concern 

for safety (MC) ............................ Environmental conditions (EC) ................ Adjustive behaviors (JB) ..................... Cusp catastrophe model ....................... Theory and example ........................ Application to behavioral mine accidents ....... Behavioral dynamics ......................... Behavioral accident simulator (BAS) ............. Illustrative application of the BAS .............. Frank: a hypothetical case ................... Coding this case into the BAS ................ Results from the BAS ....................... Philosophy of using the BAS ................... Laboratory tests of the BAS .................... Field tests of the BAS ......................... Summary and conclusions ..................... Recommendat ions for further research ........... References .................................. 

1 . Behavioralaccidentmodel ....................................................................... 2 

2 . Pathanalysisnetwork .......................................................................... 7 

3 . Results of network calculations ................................................................... 10 

4 . Networkwithpreprocessors ..................................................................... 11 

5 . Cuspcatastrophemodel ........................................................................ 12 

6 . Effects of various BAS scenarios .................................................................. 15 

7 . Effectsofstress(SLE) .......................................................................... 16 

8 . Effects of supervisor abilities (FA) ................................................................. 16 

9 . Effects of various behavioral profiles (BC) .......................................................... 16 

TABLES 

1 . Summary list of variables ....................................................................... 2 . Example of a rating scale for the norm variable .................................................... 3 . Statistically significant variables ................................................................ 4 . Nonsignificantvariables ....................................................................... 5 . Contributingfactors ........................................................................... 6 . Pairwisedatamatrix .......................................................................... 7 . Numbers of entering and exiting paths for each variable in figure 1 .................................... 8 . VP, data .................................................................................... 9 . Networkcalculations .......................................................................... 10 . Preprocessor variable definitions ................................................................ 11 . Selected results from BAS experience questionnaires ................................................ 12 . Selected results from pre- and post-BAS questionnaires for BAS users who did not believe in training ........ 
A CATASTROPHE-THEORY MODEL FOR 

SIMULATING BEHAVIORAL ACCIDENTS 

By William E. Souder 

ABSTRACT 

Behavioral accidents are a particular type of accident. They are caused by inappropriate individual behaviors and faulty reactions. Catastrophe theory is a means for 

mathematically modeling the dynamic processes that underlie behavioral accidents. 

Based on a comprehensive data base of mining accidents, a computerized catastrophe 

model has been developed by the Bureau of Mines. This model systematically links 

individual psychological, group behavioral, and mine environmental variables with 

other accident causing factors. It answers several longstanding questions about why 

some normally safe behaving persons may spontaneously engage in unsafe acts that 

have high risks of serious injury. Field tests with the model indicate that it has three 

important uses: It can be used as an effective training aid for increasing employee 

safety consciousness; it can be used as a management laboratory for testing decision 

alternatives and policies; and it can be used to help design the most effective work 

teams. 

Operations research analyst. Pittsburgh Research Center. Bureau of Mines. Pittsburgh, PA (now professor of industrial engineering and director of the 

technology management studies institute, The University of Pittsburgh). 
INTRODUCTION 

The following are four examples of normally safebehaving persons who suddenly stepped out of character 

and knowingly committed unsafe acts. Their acts had disastrous consequences for them. 

1. Three experienced divers and life-saving instructors 

ignored normal safety procedures to go on a night dive, 

incompletely equipped, in an unexplored underwater cave. 

All three drowned. 

2. An experienced 45-yr-old supervisor assisting a 

work crew suddenly turned and walked into the path of the 

crew's bulldozer and was crushed before anyone could stop 

him. 

3. A 40-yr-old senior electrician who was completing 

his work shift suddenly swung an iron wrecking bar over 

his shoulder and abruptly turned to return it to the 

toolcrib. The end of the bar struck the 400,000-V overhead 

cable he had just installed, killing him instantly. 

4. An experienced mine employee knowingly walked 

under a bad roof, pointing out various roof flaws to his 

companion. He was crushed by a sudden fall of that roof. 

What caused these reckless behaviors? These were not 

rational acts: there were few rewards and enormous personal risks in these acts. These were not inexperienced 

and untrained personnel. They were mature, intelligent, 

and responsible individuals. They behaved safely all their 

lives, espoused safe behaviors, and served as role models 

for their peers. The puzzling, unanswered question remains: why did they do these things' 

These are examples of a particular type of accident: the 

behavioral accident. In a behavioral accident, the primary 

cause is an inappropriate reaction or maladjustive behavior of the individual to external stimuli. Behavioral accidents involve complex interactions among individual 

perceptions, attitudes, personalities, values, tolerances, 

prior experiences, and work environments. As the four 

cases suggest, individual and group phenomena may contribute to behavioral accidents. Elements of carelessness, 

inattention, thoughtlessness, poor habits, machoism, 

bandwagon effects, and thrill seeking are suggested 

within these cases. Because of their many causal factors, 

the diagnosis and prevention of behavioral accidents is 

often elusive and difficult. 

This Bureau of Mines report describes research to define, empirically measure, and model behavioral accidents. Perhaps a greater degree of understanding of the 

phenomenon can lead to its prevention. 

BACKGROUND 

Human error often results from a mismatch between 

individual capacities and workloads. Overloading employees beyond their capacities can set up stresses that cause 

them to make errors. Conversely, underloading employees 

may not arouse them sufficiently, causing them to make 

errors as a result of ,boredom and inattention. 

However, real-life situations are much more complex 

than these simple statements might imply. Everyone's capacity for work differs. Moreover, people's capacities may 

change as a result of their most recent experiences, daily 

variations, and other factors. One of these factors is the 

individual's perception of an overload. For example, it may 

not matter what the ergonomic standards say: if a person 

perceives he or she is overloaded then these perceptions 

will guide his or her behavior. Since individual perceptions can be highly variable, the capacity-workload equilibrium is likely to be correspondingly variable. To further 

complicate matters, some persons can adjust to work overloads while others cannot. For example, some experienced 

automobile drivers automatically adjust to fatigue and adverse road conditions by increasing their intensity of concentration and alertness. But not everyone can so easily 

adjust their behaviors in this fashion. Thus, the capacityworkload equilibrium is a complex, dynamic, individual 

phenomenon (14, 1 t?L2 

Figure 1 depicts the system of factors that have been 

found to relate to human errors (14, 18). If the capacityworkload equilibrium is disturbed by some combination of 

the factors shown in figure 1, a human error may occur. 

Whether or not an error does in fact occur is a function of 

the individual's adjustive behaviors. Individuals who accordingly adjust their behaviors to changes in supervision, 

work groups, external stimuli, and the other factors shown 

in figure 1 may moderate the effects of these changes, 

thereby avoiding a human error. 

Note that even if a human error does occur it will not 

necessarily lead to an accident, as illustrated in figure 1. 

The external conditions may not be right for an accident to 

happen. For example, adjustive behaviors may enable a 

driver to compensate for a slick spot on the highway, thus 

avoiding skidding into an oncoming automobile. But if 

there is no oncoming automobile, then conditions are not 

right for a collision and adjustive behaviors are relatively 

less important. 

Factors related Factors related Factors related 

to the to the to the organlzatlon 

work envlronrnent \"dlviduaI/ and work group 

/ capa&ty-'workload eqjlllbrium \ 

Factors related to the 

?ercept~ons manaaernent and superv~s~on 

External stimuli 

t Human + error J 

External condltlons ' I 

Cant luence No acc~dent 

I 

Acc~dent 

a Italic numbers in parentheses refer to ~tems in the list of references at 

the end of this report. Figure 1.-Behavioral accident model. 
FOCUS AND SCOPE OF THIS STUDY 

The focus of this study was on individual adjustive Based on these analyses, and the use of catastrophe theory 

behaviors and their relationships with the variables sys- concepts (20, 221, a computer model was constructed that 

tem shown in figure 1. Why, how, and when individuals simulates behavioral accident systems. The model was 

successfully adjust and the consequences of failures to ad- tested and evaluated by a sample of mine employees. Recjust were the topics of this research. ommendations were made for routinely using the model 

The adjustive behaviors of underground miners were within mine operating firms. 

studied by analyzing a sample of fatal-accident reports. 

METHODOLOGY 

ACCIDENT SAMPLE 

Mine Safety and Health Administration (MSHA) reports of fatal underground bituminous coal mine accidents 

from April 1979 to March 1985 were selected as the target 

population for the study of behavioral accidents. The 

choice of this target population was a compromise. Accident reports for earlier periods frequently lacked the necessary detailed information. On the other hand, a long 

enough time span was needed to cover a range of economic 

conditions. And it was desirable to include the most recent 

time periods in order to capture the latest conditions 

within the industry. 

This target population was refined by selectively removing various reports. Reports with inadequate writeups 

or incomplete information were removed. Since the focus 

was on individual psycl~ological and behavioral phenomena, reports involving multiple fatalities, equipment failures, and inadequate training or inexperience of the 

victim were removed. Reports from mines with injury 

rates over 50 pct above the industry average were also 

removed. The objective was to obtain a population where 

many of the traditional accident causes were absent, yet 

fatalities still occurred because of inappropriate individual adjustive behaviors. 

These procedures resulted in a population of 358 fatal 

accident cases. Since this population was too large to be 

thoroughly studied within the available staffing and time 

constraints, sampling was used. The population was stratified by accident type, victim skill class, geographic location of the mine, and mine size. Accident reports were then 

randomly sampled from each stratum in numerical proportion to their occurrence in the 358 accident case population. 

These procedures resulted in 60 fatal-accident cases 

for study. The stratification and random sampling insured 

that a range of important .phenomena were present within 

a representative sample of 60 cases. As noted later in this - report, these careful sampling procedures permitted the 

behavioral accident phenomena to be distinguished from 

the multitude of other confounding causes and factors. Behavioral accident phenomena are likely to be an important component of most accidents. However, their presence 

may remain undected because they are obscured by a multitude of other causes. 

VARIABLES AND RATING SCALES 

Based on an in-depth analysis of selected literature (5, 

12, 14, IS), the 20 variables listed in table 1 were chosen 

for this study. Each of these variables relates to accident 

phenomena with individuals, and each is reported to be 

empirically measurable (5, 12, 14, 18, 21 1. 

Some of the variables in table 1 are objectively measurable; examples are age, experience, and size. Others 

are highly subjective, such as carefulness, alertness, and 

confidence. Many of these variables can be measured by 

observation, supervisor's ratings, peer ratings, or personnel records. Others require carefully standardized rating 

scales, for example, field dependency. The so-called fielddependent individual is unable to extract salient information from a complex background (12, 21 ). For example, an 

individual who is unable to distinguish a zebra that is 

standing in front of a striped background evidences field 

dependency. Standard tests have been developed for measuring degrees of field dependency (21 1. 

Rating scales were devised for measuring each variable, as illustrated in table 2. In pilot tests of interrater 

reliability, a panel of 10 qualified judges applied these 

scales in nine different exercises. After a brief training 

and learning period, the judges ratings showed no statistically significant differences (using Cochran Q, binomial, 

and kappa statistical tests of agreement (11, 17)). Moreover, in various trials of the scales, the author correctly 

repeated the results with 94 pct accuracy. Thus, it appears 

that the rating scales are highly reliable and repeatable 

(11. 17-18). 

CONTENT ANALYSES MEASUREMENTS 

Each of the 60 MSHA reports was carefully read and 

summarized to highlight its contents. In following established procedures (1. 81, each report was then reread and 

rated by the author on each of the variables listed in table 

1, using scales like the one illustrated in table 2. While 

this approach of reading and rating text based on the reader's impressions may be open to some arbitrariness, it is a 

seriously accepted methodology. Two other readers who 

were trained in content analyses methods reproduced the 

author's ratings with 90- to 96-pct accuracy, using random 
Table 1.-Summary list of variables 

Name I Definition ] Number ' 

INDIVIDUAL FACTORS 

-- 

WORK ENVIRONMENT 

Self-control .................. Impulsivity .................. Experience .................. 

3 

5 

6 

7 

8 

9 

12 

14 

Age ........................ Safety ...................... Carefulness ................. Initiative .................... Alertness ................... 

Evasiveness ................. Training ..................... Field dependency ............ information processing. 

Extent to which victim maintained restraint over emotions ............... 15 

Extent to which victim demonstrated impulsive behaviors ............... Vlctim's level of experience ....................................... 

. . Policy ...................... 1 Number of safety policies promulgated by firm ........................ 1 17 

ORGANIZATION AND WORK GROUP 

Victim'sage .................................................... Percentage of safe behaviors demonstrated by victim .................. Extent to which victim showed carefulness in task behaviors ............ Extent to which victim demonstrated safety initiatives .................. Extent to which victim correctly observed danger signals that preceded 

accident. 

Extent to which victim acted to avoid or evade a potential accident situation 

Recentness of training received by victim ............................ 

Extent to which victim was psychologically field dependent for perceptual 

Commitment ................. Size ....................... Rate ....................... 

Integration .................. I Degree to which victim was integrated with work group ................. 14 

Extent to which firm demonstrated commitment to safety ............... Sizeoffirm .................................................... lniurv rate at firm ................................................ 20 

Confidence .................. I Degree of confidence in crew members shown by supervisor ............ 1 18 

MANAGEMENT AND SUPERVISION 

'Identification number assigned for subsequent discussion and analysis. 

Attitude ..................... Norm ....................... Enforcement ................. 

Table 2.-Example of a rating scale for the norm variable 

Top management attitude toward safety ............................. Safety norm of immediate supervisor ............................... Extent to wh~ch safetv oolicies were enforced ......................... 

Indicatots 

Supervisor cautioned crew members to be aware of poor roof .................. Supervisor admonished crew to constantly check to see that cables were neatly 

stowed. 

Supervisor frequently held informal safety meetings ......................... Supervisor stopped the work to remove a posslble hazard ..................... Supervisor did not hold any regular safety meetings ......................... Supervisor seldom stressed carefulness and safety .......................... Supervisor otlen took chances and behaved carelessly ....................... Supervisor permitted crew to take shortcuts ................................ lnadequate information provided about supervisor's safety norm. 

'See table 1 tor definition. 

Rating 

High safety norm or + 

Do. 

Do. 

Do. 

Low safety norm or - . 

Do. 

Do. 

Do. 

Inadequate data or 0. 

samplings of text from the reports. This is a relatively In addition to the content rating data, various contribhigh interrater statistic that lends more confidence to the uting factors, such as failure to comply with safe operating 

results presented here. Additional standard precautions procedures and failure of management, were often cited in 

were also taken to increase the validity of the results (1, 8, the MSHA reports by the investigating teams. These 

19). items were carefully noted and recorded for further analyses. 

CONTENT ANALYSES RESULTS 

DATA REDUCTION 

The content analyses-produced a string of 60 + , - , or 

0 scores (one for each accident case) for each variable listed 

in table 1. Five of the twenty variables were then eliminated from further consideration because their degree of 

causal involvement (DCI) was too low. The ith variable was 

eliminated when DCI,, defined as 

was less than or equal to 0.60. Here, N,(O) is the number of 

times the ith variable was rated 0 for inadequate data in 

the content analyses (see table 2). Equation I effectively 

eliminates any variables that could not clearly be scored 

either + or - in at least 60 pct of the accident cases in the 

content analyses. Although this was a rather intuitive approach to data reduction, it was effective. The five variables thus eliminated were variables 1,4,5,17, and 18 from 

table 1. 
UNlVARlATE ANALYSES 

Of the 15 variables that survived the degree of causal 

involvement test, 9 occurred often enough among the 60 

accident cases to be statistically significant. These results 

are summarized in table 3.'Thus, the accident cases examined were characterized by some problem or some deficiency in these nine aspects. That is, low supervisor safety 

norms, carelessness, low safety initiatives, lack of alertness, poor evasiveness, poor enforcement, high field dependency, poor self-control, and impulsivity characterized the 

cases. These results are consistent with the conventional 

wisdoms about accident causation (14, 18). 

On the other hand, table 4 presents results that are 

not consistent with the conventional wisdoms. The conventional wisdoms hold that youthful employees, weak 

safety commitments by the firm, lack of employee training, lack of employee experience, large firm size, and an 

environment of high injury rates are primary causes of 

fatalities (14, 18). As the results in table 4 show, the sample of fatal accidents examined was not characterized by 

these attributes. The sample was purposely selected in 

such a way that these attributes were removed from it (see 

"Accident Sample" section). The victims were not young, 

inexperienced, and deficient in training. Over half of the 

firms were large, accident rates at the firms were not 

above average, and management commitments to safety 

were strong. Nevertheless, fatal accidents occurred. 

Clearly some other factors must have caused the accidents 

studied. 

These results thus support the central thesis of this 

study: the victim's own in;ldeclu;ltc adjustivc bch;~viorx 

can be the primary cause of an accident. Such behavioral 

accidents can occur in spite of the fact that other variables 

and factors all point to a generally safe, potentially 

accident-free environment. 

CONTRIBUTING FACTORS 

Table 5 shows the incidence. I,, and statistical significance of the jth contributing fhctors cited in the reports by 

the investigating teams. The incidence is given by 

where NC, is the number of times the jth contributing 

factor was cited. As with equation 1, this is a rather intuitive approach to reducing the data. 

As table 5 shows, the incidence of failures of management (failure to enforce safety commitments made by the 

firm, failure to eliminate known hazards, etc.) and failures 

to comply with approved safe operating procedures were 

statistically significant. That is, the investigators cited 

these factors a significant number of times. Similarly, as 

table 5 shows, the investigating teams cited faulty employee judgments and lax supervisors, who permitted unsafe practices, as significant contributing factors. These 

results are consistent with the conventional wisdoms. 

These are precisely the factors that research has repeatTable 3.-Statistically significant variables ' 

Table 4.-Nonsignificant variables 

Name 

Norm ....................... 

Carefulness ................. 

Initiative .................... Alertness ................... 

Evasiveness ................. 

Enforcement ................. 

Field dependency ............ 

Selfcontrol .................. Impulsivity .................. 

Number ' 

3 

'Binomial statistical test, with level of significance for rejection set at 0.10. See reference 17 (pp. 3642) for binomlal test used. 

Note that in this test. N = N, ( + ) + N,( - ), where N, ( + ) and N,( - ) are the number of times the ith variable was scored + and - , 

respectively. 

'Identification number assigned for subsequent discussion and analysis. 

Comments 

In 76 pct of fatalities, immediate supervisor evidenced a low safety 

norm. 

In 80 pct of fatalities, victims did not show careful behaviors in performing 

various tasks. 

Safety inittatives were absent in 80 pct of fatalities ................. In 86 pct of fatalities, victims apparently failed to correctly observe 

pertinent danger signals that preceded the accident. 

In 98 pct of fatalities, victims failed to act to avoid or evade pending 

danger. 

In 81 pct of fatalities, supervisor did not enforce established safety rules 

and practices. 

In 95 pct of fatalities. victims appeared unable to extract salient information from a complex background (high field dependency) 

In 89 pct of fatalities, victims evidenced low selfcontrol ............. In 79 pct of fatalities, victims demonstrated impulsive behaviors, with 

little foresight into consequences and little regard for personal safety. 

Name 

Age ........................ 

Commitment ................. 

Training ..................... 

Size ....................... 

Experience .................. Rate ....................... I Over half (53 pet) of sttes had injury rates below industry mean ....... I 20 

'Binomial statistical test. with level of significance for rejection set at 0.10. See reference 17 (pp. 3642) for binomial test used. 

Note that in this test. N = N, ( + ) + N,( - ), where N, ( + ) and N,( - ) are the number of times the ith variable was scored + and - . 

Number 

2 

6 

7 

8 

9 

11 

14 

15 

16 

Comments 

About half (45 pct) of victims were over 35 yr old, thus youthfulness was 

not a significant factor in the fatalities studied. 

Over half (52 pct) of cases were characterized by a strong safety 

commitment by firm. 

In over half (51 pct) of the fatalities, victims had received formal training 

for job within prior 3 months. Thus, recentness of training did 

not deter the fatalities studied. 

Over half (52 pct) of firms studied were large size (annual outputs above 

industry mean). 

Over half 159 oct) of victims had more than 10 vr exoerlence ......... 

respectively. 

21dent~ficat~on number ass~gned for subsequent dis~:usslon and analysis. 
Table 5.-Contributing facton 

edly shown to be accident contributors. When these items 

are present, they foster other events and behaviors that 

cause accidents (18). 

The last four factors listed in table 5 were not cited a 

statistically significant number of times. One of these factors was chance events. Chance events or acts, of nature 

were cited in only about one-sixth of the cases. Two other 

factors, equipment failures and faulty equipment designs, 

were cited in fewer than one-third of the cases examined. 

The fourth factor, emphasis on output at the expense of 

safety, was cited in less than one-fourth of the cases. Thus, 

Factor ' 

Failure of management ........................................... Failure to comply w~th approved safe operating procedures 

Employees used unsafe judgment .................................. Supervisors permitted unsafe practices ............................. Chanceevents .......................................... ....... Equipment failures .............................................. Faulty equipment designs ........................................ Desire for output at the expense of safety ............................ 

neither chance events, equipment failures, faulty equipment design, nor output pressures were significant contributors to the fatal accidents studied. This result runs 

counter to some prevalent beliefs that these factors are 

common causes of accidents (13, 18). However, they did not 

cause the accidents studied. Rather, the accidents studied 

appear to have been caused by something else. In this 

sense, these results provide further support for the thesis 

that at least some serious accidents are the result of poor 

adjustive behaviors of individuals. 

STATISTICAL ANALYSES OF SIGNIFICANCE 

NS Not statistically significant. 

'Each accident may have more than 1 contributing factor. 

'Identification number assigned for subsequent discussion and analysis. 

'Binomial statistical test. with level of significance for rejection set at 0.10; x = NC, N = 60 in the binomial test. 

Number 

21 

22 

27 

28 

23 

24 

25 

26 

MULTIVARIATE ANALYSES 

The preceding procedures resulted in a total of 13 variables and factors that characterized the accident cases. 

How do these variables and factors interrelate as a system 

of accident causes? 

In answer to this question, table 6 presents the results 

from a binomial test of the pairwise interrelatedness of the 

statistically significant items from tables 3 and 5. The 

statistical significance of a relationship between any pair 

of variables was determined from the binomial equation 

for p(x): 

I, 

0.500 

.783 

,800 

.600 

,133 

,250 

,317 

,233 

Here P = Q = 112, x is the frequency of content score 

mismatches between that pair of variables, N is the total 

number of pairs of nonzero scores for that variable pair 

from the content analyses, and p(x) is the probability of 

occurrence of x. A content score mismatch is the case of a 

+ score in one variable and a - score in the other variable 

from the content analyses. Only values of p(x) less than or 

equal to 0.10 are acceptable. For more details on the binomial equation and the binomial statistical test see Siege1 

(1 7). 

As may be seen from table 6, some of the variables 

were found to be unrelated (blank cell), some were not 

statistically significantly related, and others were statistically significantly related (values of 0.10 or less). Many of 

Significance a 

0.01 

< .01 

< .01 

.O1 

NS 

NS 

NS 

NS 

Table 6.-Paimise data matrix' 

NS Not statistically significant (p(x) > 0.10). 

'Data above the diagonal are x/N, where x = frequency of score mismatches and N = total number of pairs of nonzero scores 

from the content analyses for that variable pair. A score mismatch is the case of + in 1 variable and - in the other. A blank ce)l 

indicates either insufficient data to suggest a relationship or no logical reason for computing x/N statistic. Insufficient data were 

considered to exist where either x z 24 or N s 18. Data below the diagonal are p(x) computed from equation 3. See Siegel(17) 

for additional information. 

'See tables 1 and 5 for definitions of variables and factors. 

Variable 

or 

factor2 

2 ..... 6 ..... 7 ..... 

8 ..... 9 ..... 11 .... 14 .... 15 .... 16 .... 21 .... 22 .... 27 .... 28 .... 

11 

10144 

7/44 

0.036 

.001 

2 

0.001 

.001 

.W1 

NS 

.OOl 

NS 

,001 

,001 

6 

9/47 

0.001 

.001 

,001 

.001 

.OO 1 

,058 

.002 

,096 

.001 

.002 

14 

8/34 

8/31 

4/34 

6/37 

0.001 

.001 

.W1 

21 

13/50 

10125 

4/31 

5/31 

6/19 

3/20 

NS 

NS 

NS 

7 

10139 

7/39 

0.001 

.001 

.001 

,001 

.001 

.001 

.001 

.001 

15 

13/35 

11/35 

7/34 

4/29 

8/36 

NS 

0.001 

22 

19/44 

7/30 

10139 

5/32 

2129 

21/40 

NS 

16 

14/36 

10136 

3/22 

7/32 

7/34 

13/34 

5/35 

1 3/35 

0.001 

8 

6/40 

8/36 

0.001 

.017 

.001 

.001 

.001 

.001 

NS 

27 

15/66 

5/28 

6/39 

4/35 

3/38 

5/34 

8/38 

19/44 

0.002 

9 

8/40 

6/36 

0.001 

,001 

.001 

NS 

,001 

.001 

.001 

28 

5/28 

8/28 

8121 

3/21 

13/29 

17/50 

18152 
the variables were related at the 0.001 level of statistical 

significance. 

It was decided to eliminate from further consideration 

any relationships that were not significant at the 0.002 

level or less. This rule eliminated 4 of the 44 relationships 

shown in table 6, while maintaining a highly desirable 

level of confidence that the remaining relationships were 

in fact the important ones. 

PATH ANALYSES 

By using a path analysis procedure and the 0.002 

level of significance rule, causal chains were deduced from 

the data in table 6 (2-3, 17, 19). Figure 2 was then constructed from these results. 

The numbered nodes in figure 2 represent the variables and factors from tables 3 and 5 that survived the 

content ratings and statistical analyses. Three types of 

variables survived, as noted in figure 2; psychological variables, behavioral variables, and management variables. 

The arrows in figure 2 depict the causal pathways that 

were deduced from the path analysis procedures. For example, the arrow running from node 6 (carefulness) to 

node 7 (initiative), near the center of figure 2, indicates 

that carefulness influences the extent to which individuals demonstrate safety initiatives in their behaviors. 

Moreover, this relationship holds true on the average in 

998 or more cases out of 1,000, because of the 0.002 level of 

significance rule. The path analysis procedures deduced 

that the causality runs from node 6 to node 7 and not from 

node 7 to node 6 (1-3, 17). This happens to be the most 

intuitively logical causality for these two variables. In 

other cases, the causality could have gone either way and 

the path analysis procedure was used to make the final 

determination of the direction (2-3, 8, 18). 

Psychological 

variables 

Behavioral 

/ Management 

variables 

/ 

KEY 

Variable L leads to variable j 

Figure 2.-Path analysis network (see tables 1 and 5 for 

definitions of nodes). 

The path analyses resulted in node 27 as the terminal 

node in the network, as shown in figure 2. That is, node 27 

does not lead to any other subsequent nodes. Therefore, 

the descriptor potential fatality, variable PE was attached 

directly to this node in figure 2. Thus, figure 2 represents 

the system of variables that were found to lead to the 

potential for a fatal accident. 

SYSTEMS MODEL OF BEHAVIORAL ACCIDENTS 

BEHAVIORAL ACCIDENTS 

As the preceding results have shown, serious accidents can and do happen to well-trained, highly experienced and mature personnel working in mines that have 

good safety records and strong safety commitments. 

Chance events, equipment failures, and pressures to produce were not the causes. Rather, these accidents appeared 

to be caused by the inappropriate decisions and adjustive 

behaviors of the victims themselves in response to hazardous conditions. In short, these were behavioral accidents.= 

These results suggest that behavioral accidents may 

represent a hardcore type of accident that is highly resistant to conventional treatments. Enormous amounts of 

time and money are being spent on conventional safety 

and training programs. Yet serious accidents continue to 

occur and accident rates stubbornly resist falling below 

some threshold levels. It may be that the bulk of accidents 

As previously noted. 60 behavioral accident cases were found from a total 

population of 358 fatal accident cases. Though this is not a relatively high 

rate of occurrence. it represents a qeat cost in terms of human life. 

Moreover, since all accidents would seem to have some behavioral content. 

the importance of behavioral accident phenomena may be much greater 

than this statistic might indicate. 

that could be eliminated through conventional treatments 

has now been eliminated. What remains may be the hard 

core of behavioral accidents. More powerful, innovative 

means may be needed to eliminate them. The systems 

model shown in figure 2 is a basis for these more innovative approaches. 

RELATIVE IMPORTANCE OF THE VARIABLES 

Figure 2 can be used to determine the relative importance of various variables with respect to the potential for 

a fatality. Knowledge of the relative importance of the 

variables is the first step in taking actions to control them. 

Table 7 presents an analysis of the numbers of arrows 

(paths) entering and exiting each variable (node) in figure 

2. The total number of exiting paths from the psychological variables set is 12. The management set has the same 

number of exiting paths. This is a relatively large number 

of exiting paths for a network of this size, thus indicating 

that these variable sets are relatively important. That is, 

both the psychological and management variables affect 

several others. They are important affecting variables. 
Similarly, table 7 shows that the behavioral variables 

carefulness, initiative, and employee judgment are the 

most important affected variables. The most important 

affecting variables are the psychological variables field 

dependency and evasiveness, the behavioral variable failure to comply, and the management variables failure of 

management and supervisor permitted. 

These aspects determine the way in which the psychological, behavioral, and management variables dynamically interact to culminate in the potential for a fatality. 

Starting from any node (variable) in figure 2, the effects of 

a change in that variable can be tracked as it cascades its 

way through the network like a ball in a pinball machine, 

impacting other variables, compounding their effects, and 

building a potential for an accident. 

CASCADING NETWORK EFFECTS: 

AN EXAMPLE 

Let us examine the effects of two management factors: 

the failure of management to implement strong safety policies (21) and the failure of supervisors to eliminate unsafe 

practices (28). As figure 2 shows, these two factors directly 

affect two important psychological variables: alertness 

and evasiveness (variables 8 and 9). This result says that 

lax safety management and supervision can directly cause 

employees to fail to recognize, interpret, and act upon subtle cues and danger signs of an imminent accident. This is 

a very reasonable finding. Employees who are not sensitized to accident cues by their management are likely to 

either not see those cues or fail to act on them, or both. 

Note that this finding has some very significant impli

reaching consequences. For example, once an individuals 

alertness and evasive mechanisms have been dulled by lax 

management practices, this may spill over into other aspects of his or her behavior. The individual may tend to 

behave unsafely in all aspects of his or her life. Retraining 

may thus not be effective under these circumstances. In 

fact, conventional training, which is directed at behavior 

modification, may have any little effect on a person whose 

alertness and evasiveness have been dulled (14, 18-19). 

These effects may be seen from the model in figure 2. 

The impacts of low alertness (variable 8) and impaired 

evasive abilities (variable 9) are shown to continue to cascade throughout the network, building and amplifying 

themselves as they go. Impaired evasive abilities lead the 

individual to impulsive behaviors and poor judgments 

(variable 16 and factor 27). If the individual involved happens to be highly field dependent (variable 14-unable to 

extract salient information from a complex environment), 

then the effects of the low alertness and impaired evasive 

abilities are further magnified. The individual's selfcontrol (variable 15) becomes diminished and the individual's judgment (factor 27) is further impaired. Under these 

conditions, the individual becomes less likely to comply 

with approved safe operating procedures (factor 22). Thus, 

his or her awareness of dangers and his or her ability to 

avoid them (variables 8 and 9) are further dulled in a kind 

of feedback-reinforcement cycle. Careful behaviors (variable 61 thus rise and fall in proportion to the strengths of 

these influences. As a result, the individual is further 

pushed toward a fatal accident. Recall that all of this began by seemingly trivial failures by management and by 

the supervisor. 
SOME IMPLICATIONS 

What do these results tell about how to decrease underground mine fatalities? Since management actions and 

styles were found to directly influence employee carefulness, safety initiatives, and judgments, it is clear that 

management quality is a key leverage point for reducing 

accidents. This is not new: the effects of management quality on employees are well known. What is new here is the 

finding that management quality directly influences two 

important individual psychological factors: employee 

awareness of dangerous conditions and employee actions 

to avoid them. 

Thus, there are three major implications. First of all, 

the preeminence of management as a factor in preventing 

accidents has been reaffirmed. Fatalities can be substantially reduced by improving the general quality of mine 

management and first-line supervision. Second, miner 

training should be more concentrated on improving miner 

pattern recognition skills and perceptual information 

processing abilities. The ability of miners to perceive ambiguous cues of pending accidents, e.g., poor roof conditions, is a key to accident prevention. Third, training mine 

supervisors to emphasize and reinforce alertness skills 

among their subordinates is an essential ingredient. This 

is important to cementing the linkage between management quality and individual behaviors. 

Figure 2 suggests yet another way to decrease fatalities: change the psychological variables that lead an individual into accidents. At first, this may sound like some 

sort of recommendation aimed at psychologically altering 

the individual. But it is nothing quite so radical. Rather, 

adjustive behavior modification is the suggested approach. 

Employees can be trained to have greater self-control, to 

become less impulsive in crises, to be more alert, and to 

react more appropriately to accident situations. Though 

this is not a new idea, it is somewhat nove! for the mining 

industry. Most training courses emphasize unitary behaviors and the one best way to do the job, rather than alternative ways and multiple roles in the work environment. 

The ability to flex to meet the situation and to select the 

most suitable response from a repertoire of many responses is essential in a complex environment like an underground mine. 

These ideas all come directly from figure 2. They are 

innovative ideas. They are only a sampling of the kinds of 

recommendations and innovative approaches that an analysis of figure 2 can yield. 

SIMULATING ACCIDENT CAUSES 

Figure 2 may be viewed as a system flow model in 

which activities at any node (variable) create influences 

that flow along the paths (arrows) into other nodes. The 

volume or amount of any flow, VP,, along any path from 

node i to node j is given by 

VP, = (N - x)/N. (4 

The cumulative flow, VN,, at any node j is given by 

Here N and x are the corresponding items from table 6 for 

the path from node i to node j. The potential for a fatality, 

PF, is given by 

where (VN,),,, is the maximum value of the mth node for 

the system being considered. 

The VP, data for the network in figure 2 are shown in 

table 8. Note that the VP,, data are computed only for 

those paths that resulted from the path analyses, as shown 

in figure 2. For example, table 6 shows fi data for the 

relationship between node 2 and node 6. But the path 

analyses indicated that these data resulted from the indirect relationship of node 2 to node 6 that runs through 

nodes 28 and 11. ~herk is no direct path between node 2 

and node 6. Hence, there is no corresponding VP,, datum 

in table 8. 

Table 9 presents the results of repetitively applying 

equations 4 and 5, starting with initial unitary activities 

in variable 14 and factors 22 and 21. For example, case 1 

assumes that there is initially one act of field dependency 

on the part of some one individual. That is, case 1 assumes 

that the individual is unable to extract salient information from a complex or ambiguous environment in one 

instance. To illustrate, suppose the individual is standing 

in the pathway of an oncoming shuttle car, but he or she is 

unable to determine whether the shuttle car is approaching rapidly or is stopped. Case 1 shows what happens as a 

result of this one failure. This one act grows and compounds to an ultimate value at node 27 that is 8.23 times 

its initial unitary value. Using equation 6, this translates 

to a 54.9 pct potential for a fatality, PF, as noted in the last 

line of table 9. 

The effects of other single acts are shown as cases 2.3, 

and 4 in table 9. For example, in case 2, the effects of a onetime failure of the employee to comply with established 

safety policies is shown to compound to a 12.2-pct potential for a fatality. Case 3 shows that a one-time failure of 

management compounds to a 42.5-pct potential. The maximum potential for an accident when starting from variable 14 and factors 22 and 21 is 100 pct, as shown in case 4. 

Thus, for the variables set examined here, problems in the 

psychological variable (field dependency) are the most 

Table 8.-VP,, data ' 

Variable i 

2 .. 6 .. 7 .. 8..0 

9..0 

11.0 

14 . 15.0 

16 . 21 . 22 . 

28 . 

See tables 1 and 5 for definitions of variables and factors. 

Variable j 

2 

0 

0 

0 

0 

.74 

0 

0 

6 

0 

0 

,850 

0 

.840 

.76 

0 

.73 

0 

.77 

.71 

7 

0 

.820 

0 

0 

0 

.69 

.87 

.88 

27.000000 

.83 

8 

0 

0 

0 

.89 

0 

0 

.84 

.85 

0 

9 

0 

0 

000 

0 

0 

.84 

0 

0 

0 

0 

.86 

11 

0 

0 

0 

0 

0 

0 

0 

.85 

0 

1.00 

14 

00 

00 

0 

00 

0 

0 

0 

0 

0 

15 

000 

000 

.76 

.88 

0 

0 

0 

000 

0 

21 

00 

00 

00 

00 

0 

00 

0 

0 

0 

0 

00 

0 

16 

0 

0 

.81 

0 

.86 

0 

0 

0 

0 

22 

.93 

0 

0 

0 

0 

27 

0.730.82 

0 

.750 

.850 

.89 

0 

0 

.85 

.790 

000 

0 

.65 

28 

0 

0 

0 

0 

0 

0 

0 
Table 9.-Network calculations likely to result in a fatality. Management failure is the 

1 Case 1 1 Case 2 I Case 3 1 Case 4 

INITIAL VALUES 

Fatality potential (PF) ..... pct ..... 1 '54.9 . 1 12.2 1 42.5 1 100 

'Sample calculation: 

VN, = (VNd (VPp,J + (VNI') (VP"J + (VN,) (VP,,.J 

COMPUTED VALUE 

next most likely cause of a fatality, and an individual failure to comply with an established safety procedure is the 

least likely to result in a fatality. Note that these results 

could be different for a different set of initial values and 

nodes. 

The data from table 9 are plotted in figure 3 in such a 

way as to display the tracking of the contributions that 

each key variable makes to the potential for a fatality. 

Figure 3 clearly demonstrates the impact of the behavioral 

variables. In each of the four cases, the node flows, VN,, 

are all relatively low until the behavioral variables are 

reached within the system. At that point, the node flows 

increase dramatically. This increase is greatest in case 4, 

where multiple interactions of the management, psychological, and behavioral variables occur. 

Thus, it becomes apparent from these few sample calculations that the network model depicted in figure 2 and 

table 7 has many potentials for simulating accident situations. That model is further developed, in the following 

section, in order to make it more realistic and relevant for 

mining applications. 

VN ,, .......................... VN , .......................... VN2, .......................... 

VN , ........................... VN, .......................... VN,, .......................... VN, ........................... VN, .......................... VN, ........................... VN,, .......................... VN,, .......................... VN, ........................... VN , ........................... VN,, .......................... 

PF, case 1 = (VN,, for case 1)1 (VN,, for case 4) 

= (8.2314.99) x 100 = 54.9 pet. 

0.0 

.O 

1.0 

0.0 

.O 

.O 

.84 

.78 

1.55 

1.52 

1.54 

'3.80 

4.87 

8.23 

I Management I Psychological I Behavioral /' 

1.0 

1 .O 

1.0 

1.0 

.O 

.O 

I Variables I Variable8 Variables 

- I 

I 

I 

I 

I 

- I 

I 

I 

0.0 

1.0 

.O 

Node j= 2 11 9 8 15 16 6 7 2 7 

Norm I Evasiveness I sell-control ( carefulness I Employee 

Enforcement Alertness Impulsivity Initiatlve judgment 

KEY VARIABLES 

Figure 3.-Results of network calculations (table 9). 

NETWORK PREPROCESSORS 

In the simulation exercises, initial values of some key 

variables were simply assumed in order to determine the 

ultimate potential for a fatality under several conditions. 

In reality, several psychological, behavioral, managerial, 

and environmental conditions that naturally surround the 

network in figure 2 often determine the initial values of 

these key variables (4, 6, 9, 15). These conditions are natural preprocessors for the network. These preprocessors can 

be used to flexibly model particular real situations. These 

situations will then drive the fixed data base of relationships shown in table 8, which are the flow rates for the 

paths depicted in figure 2. 

Seven preprocessors are used: psychological conditions (PC), behavioral conditions (BC), supervisor ability 

(FA), management concern for safety (MC), environmental 

conditions (EC), physiological state (PSI, and adjustive be- 
haviors (JB). These preprocessor variables are defined in 

table 10 and are presented as part of the network shown in 

figure 4. Note that the discussion here focuses on the development of these preprocessors, a subsequent section of 

this report discusses their use. 

PSYCHOLOGICAL CONDITIONS (PC) 

Research (4, 6, 9, 19) indicates that the three psychological conditions listed in table 10-pattern recognition 

skills, alertness, and discriminatory abilities-strongly 

impact a person's likelihood of acting to avoid an accident. 

For example, pattern recognition training teaches visual 

differences between faulty and safe roofs in a mine. Alert 

miners will observe these differences. Discriminating miners will know the appropriate actions to take based on 

these observations. 

Research (1-2) suggests that pattern recognition 

skills (X,), alertness (X,), and discriminatory abilities (X,) 

combine according to 

PC = [(X 41.67) + (XJ2.5)] - (X,/2.O), (7) 

where PC gives their combined effects. The parameter PC 

is an input to node 14 of the network in figure 4. The value 

of PC is the initial value of VN,, in calculations like those 

in table 9. Thus, a large value for PC will have a large 

impact on node 14; a small value will have a small impact. 

Recall that node 14 is the variable field dependency (psychological ability to distinguish salient cues from an am-' 

biguous environment). When the value of PC is high, the 

flow into node 14 is high and field dependency is high. 

Similarly, when the value of PC is low, field dependency is 

necessarily low. 

BEHAVIORAL CONDITIONS (BC) 

The four behavioral conditions shown in table 10- 

volatility, machoism, consistency, and influence-can bias 

individual reactions to emergencies (4, 6, 9). For example, 

persons who characteristically act in a macho fashion may 

become involved with more accidents than those who do 

not. This may be especially true where others emulate the 

macho individual. 

KEY Ion1 

u Var~able I leads to var~able 

a User inputs that define 

pi eprocessors 

- - - - Feedback loops 

@ Prwrocessors, calculated from 

user inputs 

Figure 4.-Network with preprocessors. 

Preprocessor 

Table 10.-Preprocessor variable definitions 

Definition 

Psychological conditions (PC): 

Pattern recognition skills (X,) ..................... Alertness (XJ ................................. Discriminatory abilities (X3 ...................... Behavioral conditions (BC): 

Volatility (Y,) .................................. Machoism(Y3 ................................ Consistency (YJ ............................... Influence(Y,) ................................. Supervisor ability (FA): 

Leadership skills (LS) .......................... lnterpetsonal abilities (IA) ....................... Technical proficiency (lP) ....................... Planning skills (PP) ............................ Communication skills (CS) ...................... Directing abilities (DA) .......................... Management concern for safety (MC) ............... Environmental conditions (EC): 

Perceived ambiguity of job-role (PJA) .............. Production pressure and fatigue (PPF) ............. Job physical annoyances (PA) .................... Percetved economic climate (PEC) ................ Stressful personal life events (SLE) ............... Physiological state (PS) ........................... Adjustive behaviors (JB): - Aggression ................................... 

Projection .................................... Withdrawal ................................... Sublimation .................................. 

Adaptation ................................... 

Consultation .................................. 

Extent to which individualHas received formal training in pattern recognition. 

Is an accurate observer. 

Knows appropriate actions to take in an emergency. 

Extent to which individualReacts irrationally to crises. 

Has compelling need to demonstrate his "manliness." 

Behaviors are consistent day to day. 

Is emulated by others. 

Ability to lead others. 

Ability to get along with others. 

Proficiency in technical aspects ot work directed. 

Ability to plan work of others. 

Ability to communicate with others. 

Ability to direct work ot others. 

Extent to which top management evidences concern for safety. 

Degree of understanding or lack ot clarity about nature ot the job. 

Amount of perceived pressures to get out the product or produce at a high level. 

Extent to which employee feels job is dirty, noisy, or otherwise physically annoying. 

Extent to which employee feels trapped in an underpaid andlor lowly esteemed job. 

Extent to which employee appears to be affected on job by traumatic personal or family svents. 

Extent to which individual is physiologically qualified for particular job. 

Misplaced direction of pent-up emotions, stubborn and persistent nonadjustive reactlon in face of 

evidence that this is inappropriate or ineffectual behavior. 

Attributing one's own failures to another person or thing, blaming others for own shortcomings. 

Use of fantasies, emotional flight, repression, or regression to infantile behaviors. 

Replacing urge to vent one's anger and frustrations with higher level substitutes without ever fully 

resolving basic conflicts or issues. 

Adjustments that accommodate the stimuli but fail to make permanent and complete internal 

reconciliation. so that recidivism is likely. 

Complete and thorough resolution of issues through consultative sessions between employee 

and supervisor or other affected parties. 
ENVIRONMENTAL CONDITIONS (EC) 

EC* = F, (JB, EC), (13) 

The five environmental conditions listed in table 10 where F, and F, are functions. Then FA* and EC* will 

have repeatedly been found to seriously affect employee respectively affect nodes 28 and 9 as shown in figure 4. 

work behaviors (13-15). An employee who perceives that The exact impacts of EC* and FA* are governed by the 

his or her job is ambiguous, feels under pressure to get out cusp catastrophe model described in the following section. 

CUSP CATASTROPHE MODEL 

THEORY AND EXAMPLE 

Thom's catastrophe theory (13, 20) enables the combined influence of the environmental conditions, the supervisor's ability, the intervention factor, and the 

adjustive behaviors to be depicted as shown in figure 5. 

The behavioral accident potential; BAP, is given by 

BAP = F, (FA*, EC*). (14) 

where F, is &me function. 

Zeeman (22)cites a vivid illustration of the cusp catas- I 

trophe theory model shown in figure 5 with a friendly dog 

that is teased to the point of biting. Starting at point B on 

the behavioral surface in figure 5, where the dog is 

friendly and affectionate. the dog is purposely teased by 

its master. Thus, the dog's environment is depreciated and 

a corresponding movement occurs from left to right along 

the 1-EC* axis in figure 5, where 0 5 EC* 5 1. The 

teasing also depreciates the relationship between the dog 

/ mh\ ]~99re=.n Behavor~ol acc~dent 

surfoce-, n zone 

KEY 

FA*= F, (IF; FA) 

EC'=F2(JB;EC) 

Figure 5.-Cusp catastrophe model. 

\ 

FA* 
and its master, so that a corresponding movement occurs 

toward the origin along the FA* axis in figure 5. The combined effects of these actions is to move the dog along the 

pathway from point B towards point C. As the teasing 

continues, the dog becomes increasingly more agitated. 

The dog reaches its threshold of tolerance at point C, 

where it suddenly jump shifts its behavior to point D on 

the upper surface of the cusp. Or, to put it bluntly, the dog 

bites its master. 

Several subsequent events are now possible. When the 

dog's master ceases the teasing and goes off to tend his or 

her wounds, the dog's aggressive behavior may gradually 

decay back to point B along the decay path DB. Or the 

dog's behavior may recede along path DA and jump shift 

back to point B. Or the dog may remain at point D for some 

time, as long as its master continues to maintain the corresponding EC* and FA* stimuli. Still other more complex 

outcomes are possible if this static model is permitted to 

undergo dynamic changes. For example, suppose the teasing permanently changes the dog's personality. It is not 

uncommon for a dog to be turned into a mean and vicious 

animal through repeated teasing. This could be modeled 

as a shift in the shape and location of the cusp. Or the cusp 

could collapse once point D has been reached, i.e., the dog 

only attacks once and then regresses into a totally submissive state (19). 

It is clear from these discussions that this is a static 

model with some limitations. But it is also clear that this 

static model may be quite relevant for modeling accident 

behaviors. 

APPLICATION TO BEHAVIORAL MINE 

ACCIDENTS 

It is easy to see how the story of the teased dog can be 

analogous to the underground miner who becomes agitated. At point B in figure 5, either the individual is in 

harmony with his or her environment or the supervisor's 

intervention factor (IF) is high enough that the individual's adjustive behavior is adequate for him or her to cope. 

Change any of the variables that contribute to this equilibrium, i.e., give the individual a less skilled supervisor, a 

worse environment, a supervisor who is less able to intervene, etc., and dramatic things may happen. The individual's adjustive behaviors may jump shift to aggression at 

point D in figure 5. Once at point D, the individual could 

remain there for relatively long periods of time, since the 

behavioral surface is relatively flat in the vicinity of point 

D. Large changes in the environment or the supervisor's 

abilities may be required to move the individual away 

from point D. All the subsequent events reviewed above 

may now occur, i.e., the individual's behavior may decay 

back to point B, the individual may remain aggressive. the 

individual's behavior may jump shift to other positions, 

etc. It may be noted that in terms of the JB scale in table 

10, point D is aggression, point C is projection, and point B 

is consultation. The other JB scale positions, i.e., adaptation, etc., are points along the path BC. 

The output from figure 5 is a value for BAP, calculated 

according to equation 14. As shown in figure 4, the value 

of the parameter BAP goes directly into node 9 of the network. Recall that node 9 was the variable evasiveness (see 

table 1). Thus, BAP controls the ability of the individual to 

avoid an imminent accident. 

Even with the addition of the cusp catastrophe mechanism, the network in figure 4 is primarily a static model. 

However, one important dynamic aspect can easily be 

added: behavioral reinforcement. 

Real, vicarious, or social experiences can reinforce unsafe behaviors. To illustrate, suppose a normally safebehaving individual knowingly performs a familiar job in 

a careless manner and does not have an accident. This 

experience may reinforce the individual's deviant behavior and increase the likelihood that he or she will repeat 

the careless act. Or, to put it more bluntly, because he or 

she got away with it one time he or she will probably try it 

again. This same result can occur if the individual has 

never actually experienced the careless act, but thinks 

about it longingly (vicarious experience) or observes others doing it (social learning). 

To account for such phenomena, three feedback loops 

are included in figure 4. One loop runs from node 15 to 

node 9, a second feedback bop runs from node 16 to node 9, 

and a third runs from node 27 to node 22. The first loop 

permits changes in self-control to feed into evasiveness. 

That is, as one's self-control decreases (or increases), one's 

evasive ability also decreases (or increases). The second 

feedback loop permits impulsivity to feed into evasiveness. 

The third loop permits individual judgments to feed into 

compliance behaviors, i.e., once poor judgments occur they 

can further cloud the individual's compliance behaviors. 

Many other feedback loops are possible. However, these 

three appeared to be the most prominent ones for the applications here (16, 19). 

With feedbacks, the cumulative flows at any node j 

are given by 

VN*, = VN, + C,(VN,) (VP,,), (15) ' 

where VN, is the value at the kth node where the feedback 

originates and VP, is the value of the feedback path. Here 

VP,, = A (VP,,), (16) 

for the case where 

The coefficient, A, is parametrically set to reflect particular situations. For example, if social learning is believed to 

influence an individual's behaviors by 20 pct, i.e., he or 

she is affected 20 pct of the time, then A would be set at 

0.20 for that case. Different values of A may be used in 

each of the three feedback loops (19). 
BEHAVIORAL ACCIDENT SIMULATOR (BAS) 

A review of the literature and a consideration of the CD and AB in figure 5), and the behavioral buildup path 

application here (12, 16, 22) suggested that the following (path BC in figure 5). 

functional forms of equations 13 and 14 should be used. In The probability of a fatality (the output from node 27 

the high accident zone of figure 5, of figure 4) is given by 

BAP = [a,(l - EC*)l"~'-EC"', 

where 

EC* 2 a,. 

In the low accident zone, 

BAP = [a,( 1 - EC')lcR, 

where 

EC* c a,. 

(18) where CK is the chance that an individual experiencing 

(19) the conditions within the network will be killed and 

In equations 18 through 20, 

EC* = a,EC - a, (x,JB + x,FA*), (22) 

for x, = 0 or 1, when x, = 1 or 0, and when e = 2.718 +. 

Note that equation 22 is simply a convenient functional 

way to combine equations 12, 13, and 14. By using equation 22, FA* becomes an input to EC* instead of an input 

to BAP as shown in figure 4. The coefficients a,, a,, etc., are 

parameters set by the model builder that control the positioning and length of the behavioral decay path (path DS 

in figure 5), the jump shift and return shift paths (paths 

where a, b, c, d and k are suitably chosen constants, and 

where e = 2.718 +. 

The model, as defined by equations 4 through 24, and 

the VP,, data base shown in table 8, were programmed in 

conversational mode using the Fortran 77 language. This 

computer program, called the behavioral accident simulator (BAS), can be run on VAX, DEC-1099, and IBM personal computer systems (19). The BAS provides many 

different parametric options to the user, so that a variety 

of scenarios can be run and a variety of conditions can be 

simulated. Graphic outputs, tabular outputs, and several 

user-selected reports are available. The conversational. 

nature of the BAS permits the user to flexibly interact 

with the program at the computer keyboard. Users can 

describe various situations and receive immediate printouts of the fatality probabilities and other statistics for 

those situations. 

ILLUSTRATIVE APPLICATION OF THE BAS 

FRANK: A HYPOTHETICAL CASE 

To illustrate the use of the BAS, the following is a 

purely hypothetical case of Frank, an underground miner 

at the hypothetical XYZ mine. Frank is currently assigned to one of the dirtiest and least attractive jobs at the 

XYZ mine. He often works overtime, he believes he is generally underpaid, and he feels he is under a great deal of 

pressure to get out the product. Frank's life philosophy is: 

"you gotta fight everybody just to stay even in this world." 

He is currently contesting his third wife's divorce suit. 

Yesterday, his new Corvette was vandalized and over 

$1,000 of uninsured damage was done to the interior of the 

car. Frank is a generally aggressive person with many 

pent-up emotions. He is a poor observer, often flies off the 

handle, and is prone to react inappropriately to emergencies. He has a consistent need to demonstrate his macho 

self-image and he thinks training is for sissies. Unfortunately, Frank often influences his peers to act just like 

him. Frank's supervisor is technically competent, but he is 

an inadequate planner, director, and leader. The supervisor has poor interpersonal skills and does not communicate well with the crew. However, top management at the 

XYZ mine is very safety conscious. 

CODING THIS CASE INTO THE BAS 

It is apparent from this description that the hypothetical Frank has behavioral and psychological characteristics 

that incline him toward accidents. Moreover, he is currently experiencing some personal life stresses, a poor 

working environment, and an ineffective supervisor. By 

using scales that were designed to be used with the BAS. 

(7, 18), Frank's situation was coded into the BAS on a 

personal computer as scenario A. For example, using the 

scales for the variables listed in tables 10, Frank's environmental conditions (EC) and stressful life events (SLE) 

were coded as follows in scenario A: PJA = 100, PPF = 

100, PA = 100, PEC = 100, and SLE = 100. A score of 100 

indicates the worst possible conditions. From table 10, 

Frank's behavioral conditions (BC) were coded as follows: 

volatility (V,) = 1, machoism (V,) = 1, consistency (V,) = 

0, and influence (V,) = 1. These are the worst scores possible for these variables. 

Four other scenarios were also similarly coded irito 

the BAS. In scenario B, the impacts of changing Frank's 

supervisor were simulated by recoding the BAS with a 

superior-performing supervisor while leaving all the other 

data the same. In scenario C, the codes were altered to 
simulate the effects of transferring Frank and his coworkers to a cleaner and more attractive job environment. Scenario D simulates the effects of transferring Frank out of 

the work group, thus breaking up the work clique. Scenario E simulates the effects of retraining Frank in a way 

that would totally modify his behavior. 

RESULTS FROM THE BAS 

It should be noted here that the purpose of these exercises is not to get absolute answers about how to deal with 

Frank. Rather, the purpose is to gain insights about the 

impacts of various management alternatives and what-if 

conditions, as one way of learning more about the entire 

accident system. For example, there is an implication in 

scenario E that Frank can be totally retrained. In reality, 

this is highly unlikely. Yet, experimenting with this alternative enables one to learn a great deal about accident 

systems and how they operate. 

Figure 6 plots the results of the above five scenarios 

from the BAS. Scenario A, the current situation, gives 

intolerable results: Frank's chances of being killed are extraordinarily high. Thus, it is clear that this current situation cannot be permitted to exist. Some changes must be 

made. Would things be better under a new supervisor? 

Scenario B responds to this question. As figure 6 shows, 

though Frank's chances of being killed are lessened under 

the new supervisor, his chances still remain intolerably 

high. Scenario C, transferring Frank and the crew to a 

better working environment, substantially lowers the 

> 

0 

A B C D E 

SCENARIO 

current sltuatlon I 

New environment - 

- 

Figure 6.-Effects of various BAS scenarios. 

chances of being killed. This result is shown in figure 6. 

But major decreases in the chances of being killed do not 

occur until scenario D, where Frank is transferred out of 

the work group and the work clique is broken up. Still, 

Frank's chances of being killed remain unacceptably high. 

It is apparent from the scenario E results that in this 

extreme example Frank himself must be changed if his 

chances of being killed are to be lowered to acceptable 

levels. 

PHILOSOPHY OF USING THE BAS 

Though the example with Frank was purely hypothetical, it was nevertheless an informative and valuable exercise. The BAS demonstrated the relative effectiveness of 

several alternative management policies. The BAS showed 

that, under the given conditions, the most potent management action is to break up the work clique and form a new 

work group. Some other common alternatives, e.g., bringing in a new supervisor and altering the work environment, were found to be much less effective. These are 

valuable results. They show that time and effort should 

not be wasted on the other alternatives: they will not be 

effective. This was not intuitively apparent from the description given for Frank's case. 

Thus, the BAS can make four valuable contributions. 

First, it encourages managers to explore a wide variety of 

creative new alternatives that they might not otherwise 

consider. The very nature of the BAS encourages managers to ask what-if questions. Its sole purpose is to simulate real world conditions as a basis for generating and 

experimenting with creative solutions. The BAS catalyzes 

a kind of brainstorming that encourages one to generate 

and try out a variety of conditions and scenarios. For example, in the hypothetical case with Frank, a natural 

question to ask is: "What would happen if we could make 

the mine perfectly clean and quiet?" Of course, this is not 

feasible. But by asking an exaggerated question of the 

BAS, the extreme cases can be tested. The result in scenario C, was highly revealing: even making the mine conditions perfect would not solve the problem. The BAS has 

indicated the need to look somewhere else for the solution. 

A more creative solution, breaking up the work group, was 

then tried in scenario D. This was found to be highly effective. Given these results, management can now try other 

proposed solut ions-somew here between the impossibly 

perfect mine conditions and the possible but impractical 

breakup of the work group. For example, what happens if 

we clean up the mine a little, provide some very directed 

training, and modify the work team somewhat? Such combinations can now be tested with the BAS, in order to move 

toward a solution that is both feasible and practical to 

implement. 

Second, the BAS is a unique management laboratory 

where alternative actions can be tried before they are implemented. By using the BAS, the relative effectiveness of 

various alternatives can be studied as a basis for selecting 

the most cost-effective choices. For example, in the hypothetical case with Frank, bringing in a new supervisor is 

an intuitively appealing solution. But the BAS demonstrated that this would have been an ineffective and costly 

solution in this case. The BAS gives managers the opportunity to try new policies and actions before implementing 

them. This capability to test the impacts of new safety 

policies, organization designs, and changes in mine environments before developing or implementing them is 

clearly highly valuable. 

Third, the BAS emphasizes the systems approach to 

accidents. It permits one to look at the total system of 

accident factors and to observe the ways in which they 
I I I I 

0.2 0.4 0.8 0.8 1.0 

Good Bad 

WORK ENVIRONMENT (EC) 

Figure 7.-Effects of stress (SLE). 

Good Bad 

WORK ENVIRONMENT (EC) 

Figure 9.-Effects of various behavioral profiles (BC). 

g

real test of the BAS is a matter of whether or not it actually improves mine safety. 

0.2 0.4 0.8 0.8 1.0 

Good Bld 

WORK ENVIRONMENT (EC) 

Figure 8.-Effects of supervisor abilities (FA). 
FIELD TESTS OF THE BAS 

Twenty-eight persons from the mining industry experimented with the BAS on an individual basis. They included miners, mining engineers, and mine managers. 

Each subject used the BAS to generate scenarios of interest and observed the results. These scenarios included 

tests of various work environments, stresses, psychological conditions, behavioral conditions, management safety 

policies, supervisor abilities, crew composition, organizational arrangements, and employee adjustive behaviors. 

Before experimenting with the BAS, each subject completed an attitude questionnaire. A postexperience questionnaire was then administered to each subject at the 

completion of the BAS session. Each session lasted from 2 

to 3'h. 'hbles 11 and 12 present the results from the preand post-BAS experience questionnaires. The following 

are comments from post-BAS experience questionnaires 

(each comment is from a different subject). 

1. 'b many times management does not seem to exhaust all their alternatives before they try something. 

What I mean here is that they simply do not look at all the 

alternative things they could do. They just do whatever 

first comes to mind. If firing the employee comes to mind 

first, then that is what they do. The BAS says "wait a 

minute. Let's look at some other alternatives." And then it 

shows you what these alternatives can do. You get to see 

the effectiveness of each alternative before you do it. Now 

that is worthwhile. Even if some of the relations are made 

up in the model and not exact, they at least seem to go in 

the rieht direction. That's all vou need. 

Working with the BAS can be a useful experience 

for mine managers. It will help them do things that reduce 

mine accidents. 

3. The BAS is a good tool for new employees. But it 

might end up as a tool for management to get rid of certain 

individuals, instead of trying to change things within the 

organization itself. 

4. Most accidents seem to be caused by mental lapses, 

or at least a "hurry-up to get the job done" attitude. Safety 

meetings and films, as boring as they are, are helpful if you 

will only remember one small item from them and practice : L 

1L. 

5. Management and employees should look into this 

computer program and see how they can make changes 

based on what it might suggest to them. 

6. The BAS is clearly useful to anyone dealing with 

hazards. 

7. The BAS is a good device for mine managers and 

for foremen to learn skills that will improve their judgments in coping with problem employees and dificult situations. 

8. Because there may be vast differences in individual receptiveness to safety messages, something like the 

BAS is a good idea as a training device. No matter how you 

feel about training, this thing is sort of interesting and it 

holds your attention. 

9. The BAS gives management better insights into 

reducing accidents, improving work relations, and understanding employees. 

10. I can see this as a very effective working tool in the 

mining industry. But management must be willing to take 

the time to care about their employees, to be willing to 

better understand human nature and to be willing to invest dollars to improve safety. This is a good tool for learning a lot about employees and how they act. But, if 

managers don't care about that, then your model is no good. 

11. This is the first time I have ever seen all the accident factors put together like this. It really shows the relationships and gives you a feel for the whole thing. 

Table 11.-Selected results from BAS experience 

questionnaires, percent 

Agreement 

Pre-BAS questionnaire: High accident rates are inherent in the lob ............. 40 

The miner's own behavior is seldom the cause of acc~dents 46 

Conventional safety training is ineffective .............. 38 

Post-BAS questionnaire: The BAS clarifies behaviors that lead to accidents ...... 92 

The BAS is a useful training device for mine supervisors . 92 

The BAS is a useful training device for miners .......... 88 

The BAS is more effective than conventional tralning methods ...................................... 88 

The BAS sharpens a person's ability to recognize and avoid 

hazards ....................................... 73 

Use of the BAS could help reduce mine accident rates ... 76 

12. This is a good thing to help analyze the human 

factors and how they influence safety in the mine. I hope 

the possibilities exist to make this available to all mining 

companies for training of their supervisors and employees. 

13. The BAS gives good appreciation for the hazards 

and the miner's own reactions. It shows how one's own gut 

reactions in a time of crisis can be the wrong thing to do. 

14. A very good program that should be made available 

to the industry for training supervisors as well as workers 

alike. 

15. The whole problem is management. Until they will 

Table 12.-Selected results from pre- and post-BAS 

questionnaires for BAS users who did not believe in training 

pay something for safety, nothing is going to help. 

16. This should be required of all the employees who 

are known to work carelessly. You should pick out the ones 

who don't work safe and make them take this. 

17. I had fun, but I would like to see how this correlates 

with real data and situations. 

18. Like any training, just doing this does not make 

you a more safe person. 

19. I doubt that this is useful in real situations. I don't 

see how this will be very useful. Some people just work 

safer than others, just like some people are more messy 

than others. You can't change people. 

As table 11 shows, before working with the BAS, many 

of the subjects felt that high accident rates were inherent 

in mining. Thus, they seldom perceived that the miner's 

own behavior was a cause of accidents. Consistent with 

this, many of the subjects felt that conventional training 

was ineffective. However, as a result of using the BAS, 

these attitudes dramatically changed. As table 11 shows, 

over 80 pct of the subjects felt the BAS was a useful and 

effective training aid. Most of the subjects agreed that the 

BAS clarifies behaviors that lead to accidents and 

sharpens a person's ability to recognize and avoid hazards. 

Three-fourths of the subjects said that using the BAS could 

help reduce mine accident rates. 

For some subjects, these attitude shifts were rather 

dramatic. 'hble 12 summarizes the attitude shifts-& 

Level of signilicance of 

changes2 pc_t_- 

> 99 

> 99 

> 95 

Individual miners can do very little to 

reduce accident rates ........... Mine accidents are caused by a system of interacting factors ......... 

Miners otten take chances and do 

things that are not very safe ...... 

'1.0, strongly agree; 5.0, strongly disagree. 

ZWilcoxian 1-tailed test of statistical significance; numbers represent confidence levels; 0.9.. 99 pct means there is 99-pct confidence that a real change 

in attitudes and beliefs has occurred. 

Averages1 

PreBAS 

1.67 

3.89 

3.72 

PostBAS 

4.28 

1.33 

2.22 
those subjects who stated in their pre-BAS experience 

questionnaires they did not believe conventional training 

was effective. Before their BAS experiences, these nonbelievers felt that individual miners could do little to prevent 

accidents. They had minimal appreciation for the ways 

that accidents could be caused by a system of interacting 

factors, and they generally denied that miners often take 

chances. Their BAS experiences caused statistically significant changes in these attitudes. After their BAS exercises, these nonbelievers had a much greater appreciation 

for mine accident systems. They became much more aware 

of the ways that miners unwittingly take risks and the 

ways in which individual behaviors are vitally important 

within the entire accident system. They still did not believe in conventional training. However, they thought the 

BAS was a potentially effective new approach to training. 

The post-BAS experience questionnaire comments 

summarize both the favorable and unfavorable opinions. 

Three messages are clearly conveyed in these comments. 

One, the BAS was generally viewed as a potentially useful 

and effective device. It may be much more effective than 

many existing conventional training aids. Two, the BAS is 

a potentially useful device for managers to use in exploring alternative safety actions, work environment designs, 

and organization structures. Three, the actual effectiveness of the BAS in any application appears to depend 

greatly on the creativity of the users and the philosophy of 

use behind the BAS. The BAS is at best an information 

collection and decisionmaking aid. It can help the user 

gain numerous valuable insights, but it cannot be expected to make decisions or provide the ultimate answers. 

SUMMARY AND CONCLUSIONS 

A simulation model of human accident systems was 

developed based on an analysis of MSHA accident reports, 

some theories about human behavior, and a mathematical 

approach called catastrophe theory. A Fortran computer 

program, the behavioral accident simulator (BAS), was 

written for this model. 

BAS users sit at a computer terminal or personal computer and experiment with various scenarios of their 

choosing. They observe the impacts of their decisions and 

their choices on accident rates and fatalities. The BAS 

enables the user to see the impacts of each decision and 

each behavior on every variable in the system. Users are 

able to vividly observe how these various factors cascade 

together to cause fatal accidents. 

The BAS has been extensively tested in over 200 laboratory simulations and in a pilot field test with 28 coal 

mine industry personnel. These experiences demonstrated 

that the BAS is a highly valuable decision aid and training device. Mine managers can use the BAS as a kind of 

management laboratory to ask what-if questions about alternative organizational arrangements, working conditions, and other actions as a basis for choosing the best 

ones. Trainers can use the BAS as an innovative and effective way to teach mine management skills. Moreover, the 

BAS can be used by mine employees to improve their understanding of how their individual behaviors can cascade 

into serious accidents. 

The BAS is a unique and innovative approach to the 

study and alleviation of human error accidents. It emphasizes experimentation with the entire system of individual, group, organizational, management, and mine 

variables to reduce accident rates. The pilot field test with 

mine employees demonstrated that this approach can significantly improve employee safety attitudes and mine 

management decisionmaking. 

RECOMMENDATIONS FOR FURTHER RESEARCH 

The success of the BAS encourages the development 

and implementation of advanced BAS-type models 

throughout the mining community. More sophisticated, 

precise, and powerful models should be developed, perhaps 

using stochastic variables and nonlinear dynamics techniques. Stochastic variables would permit more accurate 

modeling of actual human accident behaviors. Nonlinear 

dynamics techniques could be used to comprehensively 

treat the complex and varying interactions between these 

variables that are common in the real world. Because of its 

deterministic nature, the BAS is limited in its capabilities 

to handle these realities. However, now that its utility has 

been demonstrated, a step up to more elaborate models 

seems warranted. Because of their greater precision and 

power, more elaborate models would be both easier to validate and more useful to the mining community. 

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