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.

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

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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).

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 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.

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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

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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

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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

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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

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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

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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.

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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

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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

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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/