Uslu, SuleymanKaur, DavinderRivera, Samuel J.Durresi, ArjanBabbar-Sebens, MeghnaTilt, Jenna H.2023-04-072023-04-072021-02Uslu, S., Kaur, D., Rivera, S. J., Durresi, A., Babbar-Sebens, M., & Tilt, J. H. (2021). A Trustworthy Human–Machine framework for collective decision making in Food–Energy–Water management: The role of trust sensitivity. Knowledge-Based Systems, 213, 106683. https://doi.org/10.1016/j.knosys.2020.1066830950-7051https://hdl.handle.net/1805/32295We propose a hybrid Trustworthy Human–Machine collective decision-making framework to manage Food–Energy–Water (FEW) resources. Decisions for managing such resources impact not only the environment but also influence the economic productivity of FEW sectors and the well-being of society. Therefore, while algorithms can be used to develop optimal solutions under various criteria, it is essential to explain such solutions to the community. More importantly, the community should accept such solutions to be able realistically to apply them. In our collaborative computational framework for decision support, machines and humans interact to converge on the best solutions accepted by the community. In this framework, trust among human actors during decision making is measured and managed using a novel trust management framework. Furthermore, such trust is used to encourage human actors, depending on their trust sensitivity, to choose among the solutions generated by algorithms that satisfy the community’s preferred trade-offs among various objectives. In this paper, we show different scenarios of decision making with continuous and discrete solutions. Then, we propose a game-theory approach where actors maximize their payoff regarding their share and trust weighted by their trust sensitivity. We run simulations for decision-making scenarios with actors having different distributions of trust sensitivities. Results showed that when actors have high trust sensitivity, a consensus is reached 52% faster than scenarios with low trust sensitivity. The utilization of ratings of ratings increased the solution trustworthiness by 50%. Also, the same level of solution trustworthiness is reached 2.7 times faster when ratings of ratings included.en-USPublisher PolicyDecision support systemsGame theoryTrustworthy Human–Machine systemsFood–Energy–Water managementArticle