Originally published on January 22, 2018
This article is the second in the Profiles in Knowledge series featuring thought leaders in knowledge management who are no longer with us — Debra Amidon was previously profiled in DEC article. Their contributions live on, and this series will provide details on their publications and their impact on others in field.
Max Henri Boisot was born on November 11, 1943 and died on September 7, 2011. He was a British architect and management consultant who was professor of Strategic Management at the ESADE business school in Barcelona. Max was known for his ideas about the information economy, the Information Space, social capital, and social learning theory.
- Remembering Max Boisot, 1943–2011 by Graham Leicester
- Max Boisot 1943–2011 by Dave Snowden
- Remembering Laobo: In Memory of Distinguished Professor Max Boisot by Lu Yuan
- Don’t forget Max Boisot by Shawn Callahan
- Review of Knowledge Assets: Securing Competitive Advantage in the Information Economy by Bill Godfrey
- Review of Information Space. A Framework for Learning in Organizations, Institutions, and Cultures by Raanan Lipshitz
- Review of Explorations in Information Space: Knowledge, Actors, and Firms by Rani Jeanne Dang
- Review of Explorations in Information Space: Knowledge, Agents, and Organization by John Barton
2. On sensemaking in enterprise-architectures by Tom Graves
The Boisot KM model is based on the key concept of an ‘information good’ that differs from a physical asset. Boisot distinguishes information from data by emphasizing that information is what an observer will extract from data as a function of his or her expectations or prior knowledge. Boisot proposes the following two key points:
- The more easily data can be structured and converted into information, the more diffusible it becomes.
- The less data that has been so structured requires a shared context for its diffusion, the more diffusible it becomes.
Boisot’s model can be visualized as three dimensional cube with the following dimensions:
- from uncodified to codified,
- from concrete to abstract,
- from undiffused to diffused.
He proposes a Social Learning Cycle (SLC) that uses the I-Space to model the dynamic flow of knowledge through a series of six phases:
- Scanning: insights are gained from generally available (diffused) data
- Problem-Solving: problems are solved giving structure and coherence to these insights (knowledge becomes ‘codified’)
- Abstraction: the newly codified insights are generalized to a wide range of situations (knowledge becomes more ‘abstract’)
- Diffusion: the new insights are shared with a target population in a codified and abstract form (knowledge becomes ‘diffused’)
- Absorption: the newly codified insights are applied to a variety of situations producing new learning experiences (knowledge is absorbed and produces learned behavior and so becomes ‘uncodified’, or ‘tacit’)
- Impacting: abstract knowledge becomes embedded in concrete practices, for example in artifacts, rules or behavior patterns (knowledge becomes ‘concrete’)
The proposed Social Learning Cycle serves to link content, information, and knowledge management in a very effective way — the codification dimension is linked to categorization and classification; the abstraction dimension is linked to knowledge creation, and the diffusion dimension is linked to information access and transfer.
- Herding Particles vs Herding Cats: Lessons for International Futures Forum from the ATLAS Experiment at CERN — London, 23 July 2010
- The City as a Complex Adaptive System: Lessons from the ATLAS Experiment at the LHC — Glasgow, 18 November 2010 — video, audio, summary, and slides
- Knowledge Assets: Securing Competitive Advantage in the Information Economy
- Information Space: A Framework for Learning in Organizations, Institutions, and Cultures
- Information and Organizations: The Manager as Anthropologist
- Explorations in Information Space: Knowledge, Agents, and Organization with Ian C MacMillan and Kyeong Seok Han
- Collisions and Collaboration: The Organization of Learning in the ATLAS Experiment at the LHC edited with Markus Nordberg, Saïd Yami, and Bertrand Nicquevert — Review by John Seely Brown: “A brilliant book that unpacks the actual doing of Big Science, including the epistemological, human, and management dimensions of running perhaps the most complex scientific experiment ever done by mankind. These different perspectives are then neatly interwoven through the Boisot I-Space framework bringing insight and coherence to this global effort. Although I have followed the development of I-Space over the years, I have never fully understood its potential until I read through this book, not once but twice. This book breaks so much new ground it is a must read for academics, policy workers, and those responsible for running complex R&D efforts in a global economy.”
- Knowledge, Organization, and Management: Building on the Work of Max Boisot by John Child and Martin Ihrig
- The Strategic Management of Intellectual Capital and Organizational Knowledge edited by Nick Bontis and Chun Wei Choo — Chapter 4: The Creation and Sharing of Knowledge
- The SAGE Handbook of Complexity and Management edited by Peter Allen, Steve Maguire, and Bill McKelvey
- Chapter 16: Complexity and Organization-Environment Relations: Revisiting Ashby’s Law of Requisite Variety (with Bill McKelvey)
- Chapter 25: Knowledge Management and Complexity