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The multi-agent village simulation was initially developed to examine the settlement and farming practices of prehispanic Pueblo Indians of the Central Mesa Verde region of Southwest Colorado (Kohler, 2000; Kohler et al.). The original model of Kohler was used to examine whether drought alone was responsible for the departure of the prehispanic Puebloan people from the Four Corners region after 700 years of occupation. The results suggested that other factors besides precipitation were important. We then proceeded to add economic factors into the simulation, first allowing agents to engage in reciprocal exchanges between kin. This resulted in larger populations, more complex social networks, and more resilient systems. However, the exchange was done randomly and individuals did not remember the transactions. In This work we explicitly embed the reciprocal exchange process within a cultural algorithm, where individual agents can remember individuals that they have cooperated with. Also, in the cultural space the group can learn generalizations about what kind of relative is likely to successfully respond to a request. These generalizations are used to drive changes in requestor behavior. The results of this approach produced an even larger and more complex system exhibiting greater dependence on hub nodes that are sensitive to precipitation.