Stories about the future are a powerful tool for navigating uncertainty, building agency, and detecting opportunities for transformation. For communities that have weathered colonialism, future visions grounded in local values and knowledge are especially powerful. Futures based in diverse value systems are also valuable assets for global efforts toward sustainability transformation. This thesis project began with a participatory visioning workshop in Mombera Kingdom, a community located in Malawi. We invited the kingdom’s traditional leaders to imagine positive futures for nature and people in their district. This workshop was a case study application of the Nature Futures Framework (NFF), a heuristic tool that enables explicit discussion of different ways that people value nature. Following the workshop, I applied the NFF in a novel way to translate the rich and diverse participant visions into distinct, packaged future scenarios. First, I built a Causal Loop Diagram (CLD) that represents dynamics in the present. Second, I organized possible interventions according to their expected impact on the NFF’s value perspectives. Third, I used those interventions to build three desirable scenarios of the kingdom’s future, each of which is desirable according to different values. Finally, I gathered stakeholder feedback on the scenarios at a follow-up workshop. The CLD suggests that misalignment within agricultural and energy production institutions causes failure to mediate ecosystem health and human well-being. The intervention analysis demonstrates that value-diverse visions can be translated into value-discrete scenarios. The scenarios capture images of modernity firmly grounded in Mombera Kingdom’s cultural values, rather than the culture that once colonized them. These results suggest new problem framings and strategies in the case study context. This project is a useful step toward regional- and global-scale future scenarios able to include Africa’s locally situated value systems. / AFRICAN FUTURES

The quality of mobile communication is related to signal transmissions. Early detection of the errors in transmissions may reduce the time delay of communications. The traditional error detection methods are not accurate enough. Therefore, in this report, a machine learning based approach is proposed for the link-to-system mapping problem, which can predict the outcomes (received correctly or not) of the link-level simulations without knowing the exact signals that are being transmitted. In this method, the transmission state is assumed to be a function of the features of a channel environment like the interference and the noise, the relative motion between the transmitter and the receiver and this function is obtained using a machine learning method. The training dataset is generated by simulations of the channel environment. Logistic regression, support vector machine and neural networks are the three algorithms implemented in this thesis. Experimental results show that all three algorithms work well compared to traditional methods. Neural networks provide the best results for this problem. Furthermore, the neural network model is tested with a dataset consisting of features of ten different channel environments, which verified the generalization ability of the model.