Societies are addressing increasingly complex governance challenges that necessitate collaboration between many organizations. Harnessing the emergent abilities of these collective efforts requires new administrative strategies and techniques, but if done well also provides promise for addressing important social challenges. In Maricopa County Arizona the Department of Public Health reports 632 confirmed heat-associated deaths from 2006 to 2013. In response, public health and other organizations coordinate across the County with a collection of public and private organizations and non-profit groups to provide services for heat relief as cooling centers during the summer. Here we show how participatory modeling can be used as a tool to enable this ad-hoc collaborative network to self-organize to provide more efficient service. The voluntary nature of the network imposes a structure on cooling service provision as the locations and open hours of centers are largely based on other ongoing operations. There are consequently both gaps and redundancies in spatial and temporal cooling center availability that exist when the network is examined from a system perspective. Over the last year, we engaged members of the heat relief community in central Arizona in a participatory modeling effort to help improve a simple prototype agent-based model that visualizes relevant components of the regional Heat Relief Network’s function. Through this process, the members developed systemic awareness of both the challenges and opportunities of coordination across the network. This effort helped network members begin to see cooling centers from a systems perspective, leverage their ability to see dynamic cooling center availability spatially and temporally and thus increase opportunities to align services along both dimensions. Our collaboration with the Heat Relief Network in central Arizona highlights participatory modeling as an innovative means for translating evidence to practice and facilitating knowledge dissemination, two important elements for successful applications on complexity governance.