Intra-organizational Computation and Complexity

Abstract

Organizations are complex systems. They are also information processing systems comprised of a large number of agents such as human beings. Combining these perspectives and recognizing the essential non-linear dynamics that are at work leads to the standard non-linear multi-agent system conclusions such as: history matters, organizational behavior and form is path dependent, complex behavior emerges from individual interaction, and change is inevitable. Such a view while descriptive, is still far from the level of specificity and predictive richness that is necessary for organizational theory. To increase the specificity and value of our theories we will need to take into account more of the actual attributes of tasks, resources, knowledge and human cognition. In doing so, it will be possible to achieve a more adequate description of organizations as complex computational systems. More importantly, we will also achieve a greater ability to theorize about the complexity of organizational behavior. This paper describes complexity theory and computational organization theory. Then a description of organizations as complex computational systems is presented and operationalized as a computational model. Within this perspective, organizational behavior results form the actions of heterogeneous actors, the boundaries between agents, tasks, and resources are permeable, organizational roles emerge, organizational groups are networks, and information technology plays a key role as an interactive agents.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
AD1021276

Entities

People

  • Kathleen Carley

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Cognition
  • Cognitive Science
  • Complex Systems
  • Databases
  • Information Processing
  • Information Systems
  • Intelligent Agents
  • Knowledge Management
  • Machine Learning
  • Multiagent Systems
  • Network Science
  • Nonlinear Dynamics
  • Organizational Structure
  • Psychology
  • Self Organizing Systems

Readers

  • Systems Analysis and Design