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The emergence of new communication technologies allows us to expand ourunderstanding of distributed control and consider collaborative decision-makingparadigms. With collaborative algorithms, certain local decision-makingentities (or agents) are enabled to communicate and collaborate on theiractions with one another to attain better system behavior. By limiting theamount of communication, these algorithms exist somewhere between centralizedand fully distributed approaches. To understand the possible benefits of thisinter-agent collaboration, we model a multi-agent system as a common-interestgame in which groups of agents can collaborate on their actions to jointlyincrease the system welfare. We specifically consider $k$-strong Nashequilibria as the emergent behavior of these systems and address how well thesestates approximate the system optimal, formalized by the $k$-strong price ofanarchy ratio. Our main contributions are in generating tight bounds on the$k$-strong price of anarchy in finite resource allocation games as the solutionto a tractable linear program. By varying $k$ --the maximum size of acollaborative coalition--we observe exactly how much performance is gained frominter-agent collaboration. To investigate further opportunities forimprovement, we generate upper bounds on the maximum attainable $k$-strongprice of anarchy when the agents' utility function can be designed.

Collaborative Coalitions in Multi-Agent Systems: Quantifying the Strong Price of Anarchy for Resource Allocation Games