Complex Systems

Immigrant Integration as a
Complex Adaptive Social
Agnes Meinhard, PhD
System Complexity
“Science has explored the microcosms and the
macrocosms: we have a good sense of the lay
of the land. The great unexplored frontier is
(Heinz R. Pagels, The Dreams of Reason, 1988)
Brief Overview
of Current Study
Relationship among System
• Many studies have
separately examined
individual aspects of
the model; however,
examining complex
social systems requires
studying not only its
components, but also
how they are related
(Ostrom, 2009).
Types of Systems
Spectrum of Complexity
Far from
Close to
Close to
Far from
Characteristics of
Complex Adaptive Systems
1. Non-linearity
This construct means that small actions can stimulate large reactions
(otherwise known as the butterfly effect) in which highly improbable,
unpredictable and unexpected events have huge impacts.
2. Emergence
The appearance of patterns occurs due to the collective behavior
What emerges cannot be planned or intended. The whole of the
interactions becomes greater than the sum of the separate parts.
3. Dynamical systems change
Interactions within, between and among subsystems and
parts are volatile, turbulent, and cascade rapidly and
Characteristics of
Complex Adaptive Systems
4. Adaptation
Interacting elements respond and adapt to each other so that what emerges
and evolves is a function of ongoing adaptation among both interacting
elements and the elements and their environment.
5. Uncertainty
Processes and outcomes are unpredictable, uncontrollable and
unknowable in advance. There is no clear idea what might happen
or how likely possible outcomes are.
6. Co-evolutionary
As interacting and adaptive agents self organize,
ongoing connections emerge that become coevolutionary as the agents evolve together (co-evolve)
within and as part of the whole system over time.
Key Findings
• By conducting a historical scan we observe that a
rather simple system of immigration based on
economic considerations and controlled by the
Federal Government has evolved into a complex
social system, one that involves many different
partnerships on many different levels.
• The system did not evolve in a systematic or linear
fashion as evidenced by the series of asymmetrical
agreements between the Federal Government and
the various provinces.
Key Findings
• A new level of self-organizing collaborative partnerships
has evolved in the form of wider intersectoral
partnerships at the local/municipal level.
– These are excellent examples of innovations evolving through
the interactions and intersecting needs of several groups of
– Smaller communities wanting to attract newcomers or retain
those already there, realized that they needed to create a
welcoming community.
– These local initiatives are also very important in eliminating
barriers to economic, social and cultural integration.
Key Findings
• A good example of co-evolution is how the concept of
integration has evolved from an expectation of unilateral
movement by the immigrant towards the host culture, to
recognizing bilateral responsibility for integration.
– But even more so, it is clear that the very concept of what it
means to be Canadian has co-evolved with the influx of
– Canadian culture and values today are not what they were 50
years ago, or even 20 years ago.
– This perhaps is the best example of co-evolution of a system
through self-organizing.
Implications for Policy
• When evaluating the effectiveness of a system, or a
system intervention or innovation, it is well to keep
in mind that most social systems are self-organizing
adaptive systems; therefore simple linear measures
will not suffice.
• Better methods of evaluation would rely on
determining how the system self-organizes or adapts
in response to an intervention or environmental
– What roles do the various parts of the system play in
responding to the challenge?
Implications for Policy
– How do the various parts interact or relate to each other?
– What new initiatives are undertaken in response?
– This kind of analysis will provide an indication of how
important the system deems the challenge to be and how
quickly or slowly it is moving to resolution.
• Since outcomes cannot really be predicted in
complex systems from the initial stimulus,
noticing patterns of behaviour and comparing
them with other patterns can give an inkling
of where the system is moving.
Implications for Policy
• Chaos during times of change is not to be feared.
Order will ensue.
• Some parts of the system may be conducive to
planned and controlled organizing.
– This would occur in situations where there is high
agreement among the players about a certain issue, and
there is high certainty and predictability of actions because
of the structure of the system (Stacey, 1996).
– Such a structure would be akin to a bureaucracy, for
example. Therefore it is important to understand that
different strategies may be needed for different parts of
the system.