We saw in Chapter 4 how a particular cohort of users adapted word search sequences (part of the repair mechanism) to perform their social actions, and to further the progressivity of their talk and their L2 learning. In Chapter 5, we have shown how many changes over time in the four participants’ conduct of word search are adaptations to their interactional environment and partners. Our analyses revealed a paradox: the speakers made expert, inventive use of the universal interaction engine to convey sophisticated levels of meaning, even though they are not expert learners of the L2. The analyses in these chapters laid the ground work for the possibility of studying dyadic interactions themselves as complex systems which adapt to changes in the participants, their partners and the environment.

In this chapter, we attempt to show how the interaction engine functions as a complex adaptive system, using two different presentations. We will first demonstrate how the data from this study can be understood from a macro CDST perspective by matching its principles to the results from the CA analyses. We then apply the principles to one short excerpt from the corpus in Section 6.3, explaining how CDST principles are evident at the micro level in its functioning. This also provides a worked-through example of how the CA-CDST approach works. The following ten principles of complex adaptive systems as outlined in Section 2.4.2 will be used as the framework in Section 6.2: