Cooperation is the hallmark human trait which has allowed us to congregate into the vast, continent-sprawling societies we live in today. Yet, the precise social, environmental, and cognitive mechanisms which enable this cooperation are not fully understood. Toward this, lucrative insights have been borne through the use of formal computational models of socio-cognitive phenomena: In simulating our own cooperative behavior, we can better deduce the exact factors which cause it. The combined knowledge of these factors and ability to computationally simulate them allows us to further two goals: First, it empowers us with the knowledge of how to modify our social systems to better human well-being and promote more sustainable, equitable, and compassionate societies. Second, the computational aspect allows us to more directly create artificial, socially competent companions—whether robotic or entirely digital—to cooperate with us in the real world in achieving the first goal. In this thesis, I contribute to the development of artificial social cognition by examining two case studies of cooperation dilemmas: a game of social team cooperation inference known as stag-hunt, and a stylized cooperative irrigation system. Specifically, I show causal, generative models encoding hypotheses on actual mechanisms in the human mind which are able to outperform the extant state-of-the-art models in both of these cases. In the second case, I show how models like this can be automatically discovered through an algorithm known as evolutionary model discovery, greatly expediting the deduction of new models in similar domains. The results have implications not only for understanding the dynamics of human teaming and irrigation systems (the humans in algorithms), but also broader human socio-cognitive mechanisms contributing to cooperation (the algorithms in humans)—all while simultaneously allowing these mechanisms to be encoded into socially competent AI.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2053 |
| Date | 01 January 2022 |
| Creators | Miranda, Lux |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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