Artificial Intelligence in Organization and Management Theory

Models of Distributed Activity

Edited by

Michael MASUCH and

Massimo WARGLIEN

Center for Computer Science in Organization and Management (CCSOM) University of Amsterdam Amsterdam, The Netherlands

m 1992

NORTH-HOLLAND AMSTERDAM • LONDON • NEW YORK • TOKYO

CONTENTS

INTRODUCTION Michael Masuch ARTIFICIAL INTELLIGENCE IN ORGANIZATION AND MANAGEMENT THEORY 1

1. Method-driven scientific progress 1 2. Some important AI concepts 2 4. AI for organization theory 15

CHAPTER 1 Thomas W. Malone ANALOGIES BETWEEN HUMAN OR- GANIZATIONS AND ARTIFICIAL INTELLIGENCE SYSTEMS: TWO EXAMPLES AND SOME REFLECTIONS 21

1. Introduction 21 2. A first example: intelligent information routing 22

2.1 Theories about lateral information flows in organizations 23 2.2 The blackboard architecture in AI systems 23 2.3 An organizational blackboard for adhocracies 25 2.4 The Information Lens — An intelligent information

sharing system 26 2.5 Lessons from this example 29

3. A second example: tradeoffs in task assignment structures 30 3.1 Distributed computer systems 30 3.2 Analysis of tradeoffs in alternative coordination

structures 33 3.3 Application to predicting effects of information

technology on organizational structures 34 3.4 Lessons from this example 36

4. Reflections: what is coordination? 36 4.1 Defining coordination 37 4.2 Processes underlying coordination 38 4.3 Where do our examples fit? 39 4.4 Conclusions 40

CHAPTER 2 Henk W. M. Gazendam ORGANIZATION THEORIES AS GRAMMAR 41

1. Grammars, theories, and symbol systems 41 1.1 Theory component 41 1.2 Metalevel reasoning 43 1.3 Characteristics of symbol systems 44 1.4 Modeling organizations as symbol systems 45

2. The use of formal grammars 46 3. An organization grammar 47

3.1 An example grammar 49

3.2 Organization grammar specification 52 3.3 Basic category choice 52 3.4 An example: organization grammar 53

4. Grammar-based problem solvers 56 4.1 Connections between grammars 56 4.2 The necessity of multi-grammar problem solving 57 4.3 Multi-agent, multi-heuristic problem solving, and

pluralism 58 4.4 The implementation of a pluralistic problem solver . . . . . . 60

Appendix: Notation schema 62

CHAPTER 3 Robert W. Blanning KNOWLEDGE, METAKNOWLEDGE, AND EXPLANATION IN INTELLIGENT ORGANIZATIONAL MODELS 65

1. Introduction 65 2. The organizational intelligence paradigm 67 3. Human organizations as physical symbol systems 68

3.1 Organizational knowledge 69 3.2 Organizational metaknowledge and organizational

heuristics 74 3.3 Metaknowledge and environment 77

4. Explanation of organizational decision processes 80 5. Conclusion: can organizations think? 82 Appendix 83

CHAPTER 4 Kathleen Carley, Johan Kjaer-Hansen, Allen Newell, Michael Prietula

PLURAL-SOAR: A PROLEGOMENON TO ARTIFICIAL AGENTS AND ORGANIZATIONAL BEHAVIOR 87

1. Introduction 87 1.1 Plural-Soar: a prolegomenon to artificial agents and

organizational behavior 88 2. Organizational theory and computer simulations 91 2. The Soar architecture 94 3. Plural-Soar 97

3.1 Warehouse Task 97 3.2 Plural-Soar agents 100 3.3 Coordination scheme 105 3.4 Measuring performance and simulations 106

4. Results 107 5. Discussion 113 6. Conclusion 116

CHAPTER 5 Jonathan Bendor, Terry M. Мое BUREAUCRACY AND SUBGOVERNMENTS - A SIMULATION MODEL 119

1. Introduction 119 2. The model 126

3. Simulation results: bureaucratic goals 129 3.1 Bureaucratic goals and institutional structure 130 3.2 Simulation results: imperfect learning 132 3.3 Learning and institutional structure 134

4. Concluding remarks 135 Appendix: The simulation runs 138

CHAPTER 6 Serge Taylor A CLASSIFIER MODEL OF THE EVOLUTION OF ORGANIZATIONAL STRUCTURE: THE EVOLVING LEGISLATURE 143

1. Introduction 143 1.1 Organizational dynamics from adaptive learners 143

2. Modeling the evolution of beliefs 146 2.1 From "good notions" to "better ideas" 148

3. Modeling the evolution of legislative structure 150 4. What experiments with the evolving legislature might tell us . . 156 5. Conclusions 167 Appendix 170

CHAPTER 7 Michael D. Cohen WHEN CAN TWO HEADS LEARN BETTER THAN ONE? RESULTS FROM A COMPUTER MODEL OF ORGANIZATIONAL LEARNING . 175

1. Introduction 175 1.1 Learning through features 176

2. The task setting 177 2.1 The learning model to be studied 178 2.2 Simulation results 180

3. Analysis of performance patterns 182 3.1 Assessing the generality of the two forces 186

Appendix: Summary of model cycle 188

CHAPTER 8 Massimo Warglien EXIT, VOICE, AND LEARNING: ADAPTIVE BEHAVIOR AND COMPETITION IN A HOTELLING WORLD 189

1. Introduction 189 2. Life in a Hotelling world 190 3. An adaptive model of exit, voice, and competition in a Hotelling

world 194 4. Reasons of the heart and reasons of interest 201 5. Adaptation within a multidimensional feature space 205 6. Intraorganizational competition and the emergence of policy

cycles 210 7. Concluding remarks 212

CHAPTER 9 Kevin Crowston MODELING COORDINATION IN ORGANIZATIONS 215

1. Introduction 215 1.1 An example: computer software company 216

2. Data-flow models 219 2.1 Data collection 220 2.2 Example 222

3. Intentional models 225 3.1 Representing knowledge about actions 226 3.2 An example 227 3.3 Uses of models 229 3.4 Designing computer-support systems 229 3.5 New organizational designs 230

4. Coordination and organization theory 231 4.1 Alternative coordination strategies 232

5. Conclusion 234

CHAPTER 10 Bernardo A. Huberman THE VALUE OF COOPERATION 235

1. Distributed intelligence 235 2. Cooperative problem solving 237 3. Combinatorial implosions 241

CHAPTER 11 Helmy H. Baligh, Richard M. Burton, Borge Obel ORGANIZATIONAL CONSULTANT: LEARNING BY DOING 245

1. Introduction 245 1.1 An expert system for the contingency theory of

organization 246 2. Microlink International 250

2.1 History and product 250 2.2 Microlink International: structure and markets 251

3. Input to Organizational Consultant 254 4. Analysis by the Organizational Consultant 271

4.1 The Organizational Consultant's recommendations and sensitivity analysis 272

4.2 Implications for Microlink International 272 4.3 What can we learn by doing? 275 4.4 Further development 276 4.5 Conclusion 277

CHAPTER 12 D.S. Bree, A. Brand, J.F. Schreinemakers, J.H.M. Verheijden ANIMAL FARM: AN INTELLIGENT KNOWLEDGE-BASED COMPUTER SYSTEM 279

1. Introduction 279 1.1 Modern information processing: the possibilities 280

2. The intelligent knowledge-based system: ANIMAL FARM 280 2.1 The health manager 282 2. 2 The production manager 284 2.3 The financial manager 286 2.4 A day in the life of ANIMAL FARM 288 2.5 The required information 289

3. Economic and social aspects 291

CHAPTER 13 Dennis A. Gioia COMMON GROUND? THE INTERSECTION OF ARTIFICIAL INTELLIGENCE AND ORGANIZATION AND MANAGEMENTTHEORY 295

1. Introduction 295 2. An ethnographic approach to the intersection of AI and OMT . . . .297 3. Questions as food for thought 301 4. Questions from a wider perspective 307 5. Conclusion 309

ABOUT THE AUTHORS 311

REFERENCES 319

SYSTEMATIC INDEX 343