This dissertation focuses on robust coalition formation between selfish agents in a dynamic environment where contracts are unenforceable. Previous research on this topic has covered each different aspect of this problem, but no research successfully addresses these factors in combination. Therefore, a novel approach is required. This dissertation accordingly has three major goals: to develop a theoretical framework that describes how selfish agents should select jobs and partners in a dynamic, contractless environment, to test a strategy based on that framework against existing heuristics in a simulated environment, and to create a learning agent capable of optimally adjusting its coalition formation strategy based on the level of dynamic change found in its environment. Experimental results demonstrate that the Expected Utility (EU) strategy based on the developed theoretical framework performs better than strategies using heuristics to select jobs and partners, and strategies which simulate a centralized “manager”. Future work in this area includes altering the EU strategy from an anytime strategy to a hill-climbing one, as well as further game theoretic explorations of the interactions between different strategies. / text