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Quantitative Mocleling
of Human Performance
in Complex, Dynamic
Systems
Sheldon Baron, Dana S. Kruser,
and Beverly Messick Huey, editors
Panel on Human Performance Modeling
Committee on Human Factors
Commission on Behavioral and Social Sciences and Education
National Research Council
NATIONAL ACADEMY PRESS
Washington, D.C. 1990
OCR for page R2
NationalAcademy Press · 2101 Constitution Avenue, N.W. . Washington, D.C. 20418
NOTICE: The project that is the subject of this report was approved by the Governing Board of the
National Research Council, whose members are drawn from the councils of the National Academy of
Sciences, the National Academy of Engineering, and the Institute of Medicine. The members of the
committee responsible for the report were chosen for their special competences and with regard to
appropriate balance.
This report has been reviewed by a group other than the authors according to procedures
approved by a Report Review Committee consisting of members of the National Academy of
Sciences, the National Academy of Engineering, and the Institute of Medicine.
The National Academy of Sciences is a private, nonprofit, self-perpetuating society of dis-
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PANEL ON HUMAN PERFORMANCE MODELING
l
SHELDON BARON (Chair), Computer and Information Sciences
Division, BBM Laboratories, Inc., Cambridge, Massachusetts
RENWICK E. CURRY, Tycho Inc., Palo Alto, California
CHARLES P. GREENING, Human Factors Branch, Naval Weapons
Center, China Lake, California (retired)
EARL HUNT, Department of Psychology, University of Washington,
Seattle
CHARLES C. JORGENSEN, Engineering Physics Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee
NEVILLE P. MORAY, Department of Mechanical and Industnal
Engineering, University of Illinois
RICHARD W. PEW, Computer and Information Sciences Division, BEN
Laboratories, Inc., Cambridge, Massachusetts
WILLIAM B. ROUSE, Search Technology, Inc., Norcross, Georgia
THOMAS B. SHERIDAN, Engineering and Applied Psychology,
Massachusetts Institute of Technology
ROBERT J. WHERRY, JR., Robert J. Wherry, Jr. Company, Chalfont,
Pennsylvania
HAROLD P. VAN COW, Study Director
STANLEY DEUTSCH, Study Director, 198~1987
BEVERLY M. HUEY, Staff Officer
DANA S. KRUSER, Consultant
. . .
1H
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COMMITTEE ON HUMAN FACTORS
DOUGLAS H. HARRIS (Chair), Anacapa Sciences, Inc., Santa Barbara,
California
PAUL ~ ATIEWELL, Graduate School of Business Administration, New
York University
MOHAMED M. AYOUB, Institute of Biotechnology, Texas Tech
University
JEROME I. ELKIND, Systems Integration, Xerox Corporation,
Sunnyvale, California (retired)
MIRIAN M. GRADOICK, Human Resources, AT&T Corporation,
Basking Ridge, New Jersey
OSCAR GRUSKY, Department of Sociology, University of California,
Los Angeles
THOMAS K LANDAUER, Information Sciences Division, Bell
Communications Research, Morristown, New Jersey
NEVILLE P. MORAY, Department of Mechanical and Industrial
Engineering, University of Illinois
RAYMOND S. NICKERSON, BEN Laboratories, Inc., Cambridge,
Massachusetts
CHRISTOPHER I. WICKENS, Aviation Research Laboratory, University
of Illinois, Savoy
ROBERT C. WILLIGES, Department of Industrial Engineering and
Operations Research, Virginia Polytechnic Institute and State
University
J. FRANK YATES, Department of Psychology, University of Michigan
1V
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Contents
FOREWORD
PREFACE . . .
1 INTRODUCTION..........................
Scope, 1
What Is Human Performance Modeling?, 2
Output Versus Process, 3
Predictive Versus Descriptive, 3
Prescriptive (Normative) Versus Descriptive, 4
p-Down Versus Bottom-Up, 4
Single-~sk (Limited Scope) Versus Multitask (Com-
prehensive), 5
Modeling Methodology, 5
Why Use Human Performance Models?, 6
. .
V11
Processes That May Benefit From Their Use, 6
Alternative (or Complementary) Methodologies to Modeling, 7
Benefits of Human Performance Modeling, 9
Genealogy of Human Performance Models, 10
2 APPROACHES TO HUMAN PERFORMANCE MODELING...16
Models of Limited Scope, 16
Larger, or Integrative, Approaches, 18
Information Processing, 20
Control Theory, 27
Ask Network, 34
Knowledge-Based, 42
Summary of Modeling Approaches, 50
v
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3 APPLICATIONS .............................................. 50
Human Performance Models In Aircraft Operations, 53
Flight Control, 53
Aircrew Workload, 55
Air-to-Surface Search and Targeting, 58
Human Performance Models in Nuclear Power Operations, 59
Background, 59
Current Issues, 63
Summary, 64
Human Performance Models in Maintenance Operations, 64
Background, 64
Summary, 65
Human Performance Models in Supe~o~y Control, 67
Background, 67
Summary, 71
4 ISSUES AND RESEARCH RECOMMENDATIONS
Overview, 72
Specifics, 74
Complex/Comprehensive Human Performance Models, 74
Model Parameterization, 76
Problems With Validation, 78
Underutilization/Inaccessibility of Human Performance
Models, 80
Potential for Misuse or Misunderstanding, 82
Mental Models to Account for Mental Aspects of Frisks, 83
Developing and Using Knowledge-Based Models, 84
Accounting for Individual Differences, 85
Conclusion, 86
REFERENCES
INDEX ........
V1
..... 72
. . 87
..95
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Foreword
The Committee on Human Factors was established in October 1980
by the Commission on Behavioral and Social Sciences and Education of
the National Research Council. The committee is sponsored by the Office
of Naval Research, the Air Force Office of Scientific Research, the Army
Research Institute for the Behavioral and Social Sciences, the National
Aeronautics and Space Administration, the National Science Foundation,
the Air Force Armstrong Aerospace Medical Research Laboratory, the
Army Advanced Systems Research Office, the Artay Human Engineering
Laboratory, the Federal Aviation Administration, and the Nuclear Regula-
tory Commission The principal objectives of the committee are to provide
new perspectives on theoretical and methodological issues, to identity basic
research needed to expand and strengthen the scientific basis of human fac-
tors, and to attract scientists both inside and outside the field for interactive
communication and performance of needed research.
Human factors issues ame in every domain in which humans interact
with the products of a technological society. To perform its role effec-
t~vely, the committee draws on experts from a wide range of scientific and
engineering disciplines. Members of the committee include specialists in
such fields as psychology, engineering, biomechanics, physiology, medicine,
cognitive sciences, machine intelligence, computer sciences, sociology, edu-
cation, and human factors engineering. Other disciplines are represented in
the working groups, workshops, and symposia organized by the committee.
Each of these disciplines contributes to the basic data, theory, and methods
required to improve the scientific basis of human factors.
. .
V11
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A
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Preface
Human factors work in the systems development process involves both
analytic and empirical studies of design alternatives. The use of human
performance models (HPMs) to evaluate candidate designs has become
increasingly important as the cost, personnel, and time required to perform
full-scale simulation studies have increased. People are an essential part
of human-machine systems, and it is substantially easier and less expensive
to consider the impact of human capabilities and limitations on system
operation and modify the system before it is built, than to modify it to
conform to human limitations after it has been constructed.
The development and use of human performance models have grown
steadily since He successful application of servo-theory in the l950s to
tracking and other manual control skills. However, a number of problems
and unresolved issues have restricted the utility and application of HPMs
in the design and development of systems. Many different approaches to
modeling have been taken, and a wide varieW of limited models that focus
on some particular aspect of human performance has been developed. The
potential utilibr of these models would increase if an integrated representa-
tion of human performance was developed that users and managers could
easily understand and support.
Most models that exist today are generally poorly understood except by
those who have coninbuted to their development. Even seemingly simple
models are fairly complex when examined in detail. A particular problem is
understanding the rationale behind the assumptions, choice of parameters,
and actual values that are incorporated into a model. A great deal of
experience with a model is required to fully comprehend its sensitivity to
parameter changes and its robustness in different applications. Because
of the general lack of knowledge of the limitations and utility of various
1X
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x
PREFACE
models, an incautious user may apply a model inappropriately, whereas a
conservative user may avoid employing a model well suited to a particular
need. Of equal concern is the problem of vending and validating models in
the context of complex systems. This is compounded because new systems
require that operators learn to use them, so a new system is essentially
never available to experts to study.
Inhere is evident need for guidance on ways to improve the utopia
of existing models, to create more comprehensive models, and to validate
them. Th meet this need, the Committee on Human Factors established the
Panel on Human Performance Modeling. The focus of the worldng group
was on HPMs that are specifically useful in the design and development of
complex systems.
The general purpose of the working group was to assess the capabilities
and limitations of existing models, the conditions under which these models
are useful, and the poss~bili~ of creating more comprehensive models of
human behavior. It was beyond the scope of the panel to compare the
solution of a problem with and without a model or to compare models
with one another; however, alternative and complementary methodologies
to modeling are discussed briefly. The primary goals of the panel were
.
approaches;
to evaluate the strengths and weaknesses of alternative modeling
to assess the conditions under which current models are of practical
use;
· to assess the potential for developing comprehensive models of
human performance by integrating existing models c)r other alternative
means; and
· to recommend research or other courses of action necessary to
improve HPMs.
Chapter 1 of this report is introductory in nature and includes defi-
nitions and characterizations of human performance modeling and a dis-
cussion of its purposes. The chapter closes with a historical perspective on
modeling approaches in the form of a genealogy of human performance
models.
In Chapter 2, the approaches deemed most relevant to the development
of comprehensive human performance models are discussed. These include
models of limited scope and their relation to comprehensive model devel-
opment and use; an information processing approach; a control-theoretic
approach; a task network approach; and a knowledge-based systems ap-
proach. Brief descriptions of the approaches are provided along with their
specific strengths and caveats on their use. A summarizing overview con-
cludes the chapter by observing that a number of promising models for
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PREFACE
X1
complex human-machine interaction exist, but none are mature enough for
general use.
In Chapter 3, several applications of HPMs are discussed with a view to
providing the reader with an indication of this scope as well as some of the
problems In HEM use. Applications in the operation (control) of aircraft
and nuclear power plants, in maintenance tasks, and in the monitoring and
control of highly automated systems (supervisory control) are discussed.
Chapter 4 concludes the report with a discussion of the views of
the working group concerning the most important issues in developing
comprehensive HPMs and recommendations for addressing them in terms
of both use and needed research
The panel, which met over a four-year period starting in December
1983, included 10 experts in the areas of mathematical psychology, cogni-
tive psychology, experimental psychology, human factors, modern control
theory, and artificial intelligence. Special thanks are extended to the mem-
bers of this group for their unflagging cooperation and involvement in this
project.
I would also like to acknowledge the people who managed the me-
chanics of transforming the products of the working group into this report.
Thanks are due to Stanley Deutsch, former study director, who guided the
project ~ the beginning; to Harold Van Cott, the current study director,
for keeping the project on track; to Dana Kruser, project coordinator, for
putting the materials submitted by the working group into first draft form
and editing first drafts; and to Beverly Huey for editing the fina} drafts. I
express my sincere gratitude to each of these individuals for their significant
contn~utions.
Sheldon Baron, Chair
Panel on Human Performance Modeling
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