  We use a multi-agent system to model how agents (representing firms) may
collaborate and adapt in a business 'landscape' where some, more influential,
firms are given the power to shape the landscape of other firms. The landscapes
we study are based on the well-known NK model of Kauffman, with the addition of
'shapers', firms that can change the landscape's features for themselves and
all other players. Our work investigates how firms that are additionally
endowed with cognitive and experiential search, and the ability to form
collaborations with other firms, can use these capabilities to adapt more
quickly and adeptly. We find that, in a collaborative group, firms must still
have a mind of their own and resist direct mimicry of stronger partners to
attain better heights collectively. Larger groups and groups with more
influential members generally do better, so targeted intelligent cooperation is
beneficial. These conclusions are tentative, and our results show a sensitivity
to landscape ruggedness and "malleability" (i.e. the capacity of the landscape
to be changed by the shaper firms). Overall, our work demonstrates the
potential of computer science, evolution, and machine learning to contribute to
business strategy in these complex environments.

Multiagent Systems;Artificial Intelligence;Neural and Evolutionary Computing