Complex Adaptive Network Systems (CANS) draft 2
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Complex Adaptive Network Systems (CANS) draft 2

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A Complex Adaptive Network System (CANS) is a social network system that is decentralised and can evolve to achieve its goals (or purposes), based on its own narratives; a set of evolved rules; and......

A Complex Adaptive Network System (CANS) is a social network system that is decentralised and can evolve to achieve its goals (or purposes), based on its own narratives; a set of evolved rules; and these are related to a history of past circumstances. CANS respond to their environment and themselves be “nested” within other network systems such as group; groups within an organisation; a group that strategically plans projects related to other network systems such as markets, or communities, or environmental ecosystems. Each are forms of interrelated and interacting system networks.

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  • Thank you very much Richard for your prompt response, and its thoughts and advice. My apologies about the CANS draft, which was posted in an unintentionally rough and unpolished manner (due to cross commitments). The draft was sent to several contacts and have also received quick feedback, as I have from you. The speedy responses surprised and delighted me. Will get back to you. The next draft may take a week or two to revise (and polish). Best wishes.
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  • Hi David - Thanks for sharing your thoughts. Your work on CANS looks interesting and certainly incorporates CAS thinking. Rather than critique your presentation, I'll just mention some elements of CAS which I still carry in my brain and therefore attach the label 'significant' to them. One is 'constraints' - possibly the same as your boundaries. These should be optimal - too many and the system loses dynamism, too few and it can lapse into chaos. Constraints in my mind are linked to purpose and design (not a very CAS word I agree) - what is the system supposed be producing, what constraints will allow it to maximise production in a self-sustaining way (ie without damaging agents or the environment). Dave Snowden's riff on YouTube https://www.youtube.com/watch?v=Miwb92eZaJg demonstrates the characteristic of different systems through the medium of organising a children's party. There are many other items by Dave on YouTube. So agents come together around resources (which could be physical or mental or virtual) which will provide the means of production towards achieving the network's purpose. I believe that the unintended consequences and by-products of a CAS actually create the tools which can make systems highly functional - a bit like language development in humans. Another key part of Snowden's thinking is around narrative - the stories people tell themselves and others - and how as humans we continually seek meaning in ourselves, in others and in our environment. This is important because we act on our understanding of the world, which is never perfect. Another of Snowden's ideas is that of the safe-fail experiment - for example a project that expands our understanding of our system and helps us towards our purpose. If it fails then nothing is harmed and we retain the knowledge gained. I guess my bias is towards organisational utilisation of CAS so I tend to grapple with the practicalities of implementation rather than description. I firmly believe that complexity should be simple to apply and reducible to a few basic principles. Hope this helps.
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  • 1. Complex Adaptive Network Systems (CANS) A variation based on Complex Adaptive Systems (CAS) David Alman April 2014 Draft 2
  • 2. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 2 Contents 1 A CANS are Network Systems.................................................................................................3 1.1 Definition .........................................................................................................................3 2. CANS Characteristics..........................................................................................................3 3. Network Model Examples......................................................................................................5 3.1 Multileveled/Multilayered System Networks..................................................................5 3.2 Distributive Networks ......................................................................................................6 3.3 Area Grouping Network...................................................................................................7 7 Conflict Analysis ......................................................................................................................8 7.1 Conflict Network Analysis ................................................................................................8 7.2 Reframing Conflict Networks...........................................................................................9 8 Agent Based Modelling (ABM)..............................................................................................10 Conclusion................................................................................................................................11 References ...............................................................................................................................12 About the author .....................................................................................................................13
  • 3. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 3 Complex Adaptive Network Systems (CANS) 1 CANS are Network Systems. 1.1 Definition A Complex Adaptive Network System (CANS) is a network of social interacting agents that, as a whole, represent a system. In this respect CANS include:  Humans referred to as Agents;  A social network, of which there are different forms; A social network demonstrating human characteristics such as interrelationships (e.g. conflict, cooperative, and competitive relationships); ideological narratives; rules; and purposes;  A boundary that characterises what is inside and outside of the network system. 2. CANS Characteristics CANS are dynamic network systems able to adapt to and evolve (i.e. co-evolve) in their changing environment: There is no separation between a CANS and its environment as CANS respond and adapt to their changing environment. CANS has a number of characteristics such as:  A “Distributed” Network where there is no single centralised control mechanism that governs social system network behaviour. Rather control of a CANS tends to be highly dispersed. In this respect there is no hierarchy of command and control in a CANS. There is no planning or managing, but there is a constant re- organising to find the best fit with the environment, where the CANS is continually self organising through the process of emergence and feedback.  Any coherent behaviour in a system arises from competition and cooperation among the agents themselves. Some system networks tend toward order not disorder through a process of spontaneous self organisation (based on evolved simple rules).
  • 4. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 4  Network “Connectivity” where a decision or action by one part within a CANS will influence all other related parts but not in any uniform manner.  CANS interact in networks and form patterns of behaviour that could not have been predicted from understanding each particular agent, and continuously improves its efficiency to achieve its aims and objectives.  Network “Co-evolution” where network behaviour can change based on their interactions with one another and with the environment. Additionally, patterns of network behaviour can change over time.  Most CANS are “nested network systems”: Systems within other systems and many are systems of smaller systems. CANS is part of many different network systems most of which are themselves part of other network systems.  Chaos does have a place in CANS in that systems exist on a spectrum ranging from equilibrium to chaos. A system in equilibrium does not have the internal dynamics to respond to its environment (and slowly or quickly die). On the other hand a system network in chaos ceases to function. The most productive state to be in is at the edge of chaos where there is maximum variety and creativity, leading to new possibilities.  CANS history grows from their own evolutionary environment. CANS evolve and form a narrative about what they are about. While these system networks evolve they constantly assess their past and present in order to inform their future. History in terms of how they are rooted and evolved gives CANS a self- generated “learning loop” from which it can increase its rate of emergence.  The future is unpredictable. As a CANS organises, it creates a multitude of competing, complimentary and counter intuitive “alternatives” from which it will derive its ‘future’. This allows the network system to employ the maximum amount of variety and creativity in securing its future, whatever that may be. An optimal network system exists on the edge of “chaos” where a CANS has the ability to choose alternative futures is optimised, informed by network knowledge that is generating a wide range of possibilities and alternatives.
  • 5. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 5 3. Network Model Examples 3.1 Multileveled/Multilayered System Networks CANS are decentralised networks of interacting humans (Agents) who frame and reframe their purpose and react and respond to feedback from their external environment. CANS networks can be multileveled/multilayered where agents at one level can competitively or cooperatively interact with agents at another network level. A CANS can also be part of another CANS, as exampled in Diagram 1. Diagram 1 A Multileveled CANS Example A work group CANS An organisation CANS A market CANS A community CANS An eco system CANS
  • 6. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 6 3.2 Distributive Networks Where each “node” is connected to neighbouring nodes within a decentralised network. A node can represent agents, groups, communities, and so on as exampled in Diagram 1. Diagram 2. Distributive Network A Distributive network can also reflect layers or levels as shown in Diagram 3. Diagram 3. Distributive Lattice Network
  • 7. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 7 3.3 Area Grouping Network A group of nodes, such as teams, who are in the same layer or level of a network. This is analogous to peer groups relying on equal rather than on hierarchical arrangements, as exampled in Diagram 4. Diagram 4. Area Grouping Network 3.4 Ramification Network Where control in the network is highly dispersed and decentralised, where the network is split into related agent networks, as exampled in Diagram 5. Diagram 5 Ramification Network
  • 8. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 8 7 Conflict Analysis 7.1 Conflict Network Analysis Conflict, and competition, between CANS agents, irrespective of the form of network they are in, can be resolved and collaboration assisted by carrying out a Conflict Network Analysis and applying a process that improves relationships, cooperation, and meaningful understanding. A Conflict Network Analysis uses types of conflict issues as a means of assessment as shown in Table 1. Types of Agent Conflict issues CANS Conflict Issue Examples Structural issues e.g.what are the rule sets affecting agent behaviour Value based issues e.g. What is the history of the conflict, and reinforcing Narratives? Relationship based issues e.g. competition, cooperation Information based issues Interest based issues e.g. what are the interests underlying conflicts? Needs based issues e.g. What are the agent Network’s needs? Table 1
  • 9. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 9 7.2 Reframing Conflict Networks CANS agent conflicts can be resolved by the network agents working through and addressing their issues. Diagram 6 is a shortened and amended version of Tuckman’s theory of group development applied to groups as CANS, and extended to broader CANS applications. Diagram 6. Stages in Network Development A Conflict Network Map is exampled is Diagram 7 that are based on conflict issues referred to in Table 1. In terms of addressing System Network conflict a conflict resolution process can explore:  Alternatives: What actions are possible”  Expectations: What future consequences might follow from each alternative? How likely are possible consequences, assuming that alternatives are chosen?  Preferences: How valuable (to agents) are the consequences associated with each of the alternatives?  Decision rule: How is a choice made among alternatives in terms of the values of their consequences? Network Forming Network Storming Network Norming Network break up
  • 10. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 10 Diagram 7. A Conflict Network Map 8 Agent Based Modelling (ABM) Agent-based models consist of dynamically interacting rule-based agents that interact to create real-world-like scenarios. ABMs are software systems that can simulate the evolution of a CANS by, for example, explaining the emergence of social network patterns such as in community behaviour, market performance; impact on ecosystem sustainability.
  • 11. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 11 Conclusion A Complex Adaptive Network System (CANS) is a social network system that is decentralised and can evolve to achieve its goals (or purposes), based on its own narratives; a set of evolved rules; and these are related to a history of past circumstances. CANS respond to their environment and themselves be “nested” within other network systems such as group; group within an organisation; a group that strategically plans projects related to other network systems such as markets, or communities, or environmental ecosystems. Each are forms of interrelated and interacting system networks. In developing CANS material three related areas are considered to explain what is, and what is involved in, Complex Adaptive Network Systems:  Conflict Adaptive Systems (CAS);  Network models relevant to CAS  Conflict Networks
  • 12. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 12 References Complex Adaptive Systems. Serena Chan. Downloaded on 13/4/14 from http://web.mit.edu/esd.83/www/notebook/Complex%20Adaptive%20Systems.pdf What are Fractal Systems? Peter Fryer and Jules Ruis. Downloaded on 13/4/14 from http://www.fractal.org/Bewustzijns-Besturings-Model/Fractal-systems.htm Understanding and defeating a complex adaptive system. Lieutenant Colonel Ian Langford. Downloaded on 13/4/14 from http://www.army.gov.au/Our-future/LWSC/Our- publications/~/media/Files/Our%20future/DARA%20Publications/AAJ/2012Summer/Compl ex-Adaptive-System-AAJ-Vol9-No3-Summer-2012.pdf Navigating Complexity by Arthur Battram. First published by The Industrial Society, London in 1998, and by Stylus Publishing inc, Sterling USA. The Conflict Mapping Chart. L. Shay Bright Downloaded on 13/4/14 http://www.cmsupport.org/ConflictMapping/ConflictMappingChart_ShayBright.pdf Field guide to conflict analysis. Downloaded on 14/4/14 http://www.fao.org/docrep/008/a0032e/a0032e0d.htm Analysing actor networks while assuming "frame rationality". Pieter Bots Downloaded on 14/4/14 http://www.hks.harvard.edu/netgov/files/NIPS/PWG_BOTS_Analyzing_actor_networks_14_ June_2008.pdf Systemic Conflict Transformation: Reflections on the Conflict and Peace Process in Sri Lanka Downloaded on 13/4/14 http://www.berghof- handbook.net/documents/publications/dialogue6_ropers_lead.pdf Group Dynamics Donelson Forsyth Downloaded on 13/4/14 http://www.cengagebrain.com.mx/content/forsyth68220_0534368220_02.01_chapter01.p df
  • 13. Complex Adaptive Network System (CANS) David Alman Draft 1 Page 13 About the author David Alman lives in Brisbane, Queensland, Australia, and is the business owner of Proventive Solutions, which offers services in Organisational Health. Organisational Health is a broad overview term that refers to assessing and improving performance and well being of both an organisation and its employees, recognising there is a nexus between the two. David writes blogs, articles and PowerPoints on subjects related to organisational health, productivity, conflict management, and systems thinking. These can be accessed through a Google website and Word Press website. Please refer to: https://sites.google.com/site/proventivesolutions/ and http://davidalman.wordpress.com/home-page-welcome/