Bootstrapping an Imagined We for Cooperation
- Ning Tang, Department of Statistics, UCLA, Los Angeles, California, United States
- Stephanie Stacy, Department of Statistics, UCLA, Los Angeles, California, United States
- Minglu Zhao, Department of Statistics, UCLA, Los Angeles, California, United States
- Gabriel Marquez, Department of Statistics, UCLA, Los Angeles, California, United States
- Tao Gao, Department of Statistics, UCLA, Los Angeles, California, United States
AbstractRemaining committed to a joint goal in the face of many enticing alternatives is challenging. Doing so while cooper-ating with others under uncertainty is even more so. De-spite this, agents can successfully and robustly use boot-strapping to converge on a joint intention from randomness under the Imagined We framework. We demonstrate the power of this model in a real-time cooperative hunting task. Additionally, we run a suite of model experiments to an-swer some of the potential challenges to converging that this model could face under imperfect conditions. Specifi-cally, we ask what happens when (1) there are increasingly many equivalent choices? (2) I only have an approximate model of you? and (3) my perception is noisy? We show through a set of model experiments that this framework is robust to all three of these manipulations.