A case for making climate aid help small-scale farmers in Sub-Saharan Africa prevent land grabbing – and the would-be priorities of members of relevant NGOs

This thesis looks at the issues of land grabbing and climate change in Sub-Saharan Africa. Taking the situations of small-scale farmers – the dominant group of people population-wise and producers of 90 percent of the subcontinent’s food – as its focus, it also looks at how climate change might make land grabbing worse. It is argued that climate change will do so because of: decreased agricultural productivity both in land-grabbing countries and in Sub-Saharan Africa; the rise of the agrofuel industry; because of improving terms-of-trade for primary commodities; because of carbon compensation schemes; and because of risk diversification in the face of extreme weather events, among other factors. Agrofuels will likely play the largest part. Therefore, the thesis also proposes a new type of climate aid to help small-scale farmers prevent land-grabbing. It argues that the chances for the proposed climate aid to become a reality are reasonable, foremost because of geopolitical struggles in general and for agricultural land in particular, but hopefully also because the international community realizes that helping small-scale farmers manage these double threats will gain the whole world. However, the aid will neither be called ‘climate aid to prevent land grabbing’ or the like, nor will it be disbursed through the UNFCCC platform and the Green Climate Fund. This is due both to the proposed climate aid’s controversial elements in the eyes of prospective land grabbers and to the current lack of climate aid. Unfortunately, one of the two parts of the thesis suffered from a methodology come undone. This had major consequences for the main research effort, namely to learn from small-scale farmers, represented to some extent by members of relevant NGOs, about which priorities the proposed climate aid should have according to them, if it was to be implemented. Since the methodology broke down (a development discussed in the thesis) the thesis in large part is a call for further research

RETHINKING RURAL LIVELIHOODS IN AFGHANISTAN

Community/Rural/Urban Development,

The Cowl - v.26 - n.13 - Feb 26, 1964

The Cowl - student newspaper of Providence College. Volume 26, Number 13 - February 26, 1964. 8 pages

Explaining and Refining Decision-Theoretic Choices

As the need to make complex choices among competing alternative actions is ubiquitous, the reasoning machinery of many intelligent systems will include an explicit model for making choices. Decision analysis is particularly useful for modelling such choices, and its potential use in intelligent systems motivates the construction of facilities for automatically explaining decision-theoretic choices and for helping users to incrementally refine the knowledge underlying them. The proposed thesis addresses the problem of providing such facilities. Specifically, we propose the construction of a domain-independent facility called UTIL, for explaining and refining a restricted but widely applicable decision-theoretic model called the additive multi-attribute value model. In this proposal we motivate the task, address the related issues, and present preliminary solutions in the context of examples from the domain of intelligent process control

Spartan Daily, September 24, 1971

Volume 59, Issue 3https://scholarworks.sjsu.edu/spartandaily/5560/thumbnail.jp

PeerNomination : relaxing exactness for increased accuracy in peer selection

In peer selection agents must choose a subset of themselves for an award or a prize. As agents are self-interested, we want to design algorithms that are impartial, so that an individual agent cannot affect their own chance of being selected. This problem has broad application in resource allocation and mechanism design and has received substantial attention in the artificial intelligence literature. Here, we present a novel algorithm for impartial peer selection, PeerNomination, and provide a theoretical analysis of its accuracy. Our algorithm possesses various desirable features. In particular, it does not require an explicit partitioning of the agents, as previous algorithms in the literature. We show empirically that it achieves higher accuracy than the exiting algorithms over several metrics