1. Immigrant Integration as a Complex Adaptive Social Systems Agnes Meinhard, PhD

2. 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 complexity (Heinz R. Pagels, The Dreams of Reason, 1988)

3. Brief Overview of Current Study

4. Relationship among System Components 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).

5. Types of Systems Spectrum of Complexity Close to Agreement Far from Agreement Close to Certainty Far from Certainty Chaotic Complex Simple Complicated Predictable Unpredictable

6. 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 unpredictably

7. Characteristics of Complex Adaptive Systems 6. Co-evolutionary As interacting and adaptive agents self organize, ongoing connections emerge that become co- evolutionary as the agents evolve together (co-evolve) within and as part of the whole system over time. 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. 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.

8. 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.

9. 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 stakeholders. – 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.

10. 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 immigrants. – 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.

11. 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 stimulus – What roles do the various parts of the system play in responding to the challenge?

12. 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.

13. 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.

14. Fractals