You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)

  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)

  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required

  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Leading large: emergent learning and adaptation in complex social networks

Fulltext:


Authors:

James K. Hazy

Research group:

Value-Driven Innovation and Foresight

Publication Type:

Journal article

Venue:

International Journal of Complexity in Leadership and Management

Abstract

The mechanism by which adaptation in large organisations emerges from localised bottom-up processes remains largely unexplored. This paper describes the emergence of a learning algorithm in organisations which crosses levels of analysis. It posits that what is essentially a neural network arises naturally in organisations with individuals as nodes, interactions as edges, and influence relationships among them performing a function that is analogous to synaptic weights. This network structure enables organisations to adapt through a process of influence process structural learning that is analogous to back propagation learning in traditional neural networks. The model describes leadership within top management as expressing the organisation’s response to environmental stimuli about which top managers have little direct knowledge. Leadership acts to change influence relationships among managers by altering their relative status and reputations. The theory implies that influence relationships exhibit a

Bibtex

@article{Hazy2711,
author = {James K. Hazy},
title = {Leading large: emergent learning and adaptation in complex social networks},
volume = {2},
number = {1/2},
pages = {52--73},
month = {November},
year = {2012},
journal = {Int. J. Complexity in Leadership and Management},
url = {http://www.ipr.mdu.se/publications/2711-}
}