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The focus of this dissertation research is to investigate the origins of cooperation in early human societies and its connection to kinship and marriage, more specifically its tie to what is known in the anthropological literature as in-law avoidance behavior. Humans are unique in that no other animal species can match the level of cooperation and altruistic behavior among humans. Examples closest to human cooperative behavior come from animals such as ants and bees; however, cooperation among these animals can be explained by kin selection, meaning the ants in an ant colony or the bees in a bee hive share the same genetic material and thus altruistic behavior serves to protect the common genetic material. However, in humans, altruistic behavior extends to those who are not genetically related and this unique ability to cooperate with strangers gave our species the evolutionary edge to survive and prosper.At the core of human cooperation lies kinship and marriage. It is through marriage that human groups can extend their kinship network ties to groups other than their own and integrate new members to their core group. Rules of kinship determine the social relations between kin and affines. Such categorization of kin relations create rules for the society to function smoothly. In-law avoidance behavior is one of these crucial rules that organizes the relationship between wives or husbands and their in-laws. Mutual avoidance stemming from mutual respect functions as a conflict aversion mechanism to facilitate the integration of affines to kinship groups. This in return increases jurisdictional hierarchies, political alliance formation and leads to increased levels of cooperation in human societies. As an early adaptation, in-law avoidance relations are observed in low-density societies across the world and they get dropped as societies get more complex and populations get denser.Previous studies on in-law avoidances did not have any means to control for the diffusive effects of geography and common origin of societies and therefore they were prone to the Galton’s problem. In this dissertation, we use a lagged network regression method to control for geographical and linguistic proximity and get unbiased and consistent results for the first time.