References

References

1. Houghstow, A. Learning from Syria: Applying Environmental Modeling Toward Strategic Peacebuilding Interventions. (Harvard University, 2017).

This text is a master’s thesis. Please contact amber@peace-rising.org for a copy.

2. Sheldon Himelfarb. Can Big Data Stop Wars Before They Happen? Foreign Policy(2014). Available at: http://foreignpolicy.com/2014/04/25/can-big-data-stop-wars-before-they-happen/. (Accessed: 24th February 2018)

3. UNEP. Early Warning Systems: A State of the Art Analysis and Future Directions. (2012). Available at: https://na.unep.net/siouxfalls/publications/Early_Warning.pdf. (Accessed: 24th February 2018)

4. Verisk Maplecroft. Climate Change Vulnerability Index 2017. (2017). Available at: https://reliefweb.int/sites/reliefweb.int/files/resources/verisk index.pdf. (Accessed: 24th February 2018)

5. Prigg, M. The world at war: Stunning interactive map reveals every conflict currently active around the world. Daily Mail Online (2017). Available at: http://www.dailymail.co.uk/sciencetech/article-4453666/The-world-war-Interactive-map-reveals-conflicts.html. (Accessed: 24th February 2018)

6. Grasso, V. F. & Singh, A. Draft Report: Early Warning Systems: State-of-Art Analysis and Future Directions. Available at: https://na.unep.net/geas/docs/early_warning_system_report.pdf. (Accessed: 24th February 2018)

7. Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. (Penguin Books, 2017).

This book is accompanied by an amazing website summarizing each of the solutions, www.drawdown.org. Readers can search for solutions by topic or rank.

8. UK Department for International Development. UK aid stops ‘untold horror’ of child pneumonia deaths in Syria this winter. UK Government Press Release (2017). Available at: https://www.gov.uk/government/news/uk-aid-stops-untold-horror-of-child-pneumonia-deaths-in-syria-this-winter. (Accessed: 24th February 2018)

9. USAID. Mission, Vision and Values | U.S. Agency for International Development. (2018).

Available at: https://www.usaid.gov/who-we-are/mission-vision-values. (Accessed: 24th February 2018)

10. Oxfam. Our work | Oxfam International. (2018). Available at: 

https://www.oxfam.org/en/explore/issues-we-work-on. (Accessed: 24th February 2018)

11. UN Security Council. About the United Nations Security Council. Available at: http://www.un.org/en/sc/about/. (Accessed: 24th February 2018)

12. Bill and Melinda Gates Foundation. Bill and Melinda Gates Foundation Commits $300M (€255M) to Help Farmers in Africa and Asia Cope with Climate Change. Bill and Melinda Gates Foundation Press Room (2017). Available at: https://www.gatesfoundation.org/Media-Center/Press-Releases/2017/12/Gates-Foundation-Commits-300M-USD-to-Help-Farmers-in-Africa-and-Asia-Cope-with-Climate-Change. (Accessed: 24th February 2018)

13. Hsiang, S. M. & Burke, M. Climate, conflict, and social stability: What does the evidence say? Clim. Change 123, 39–55 (2014). Available at: https://www.researchgate.net/publication/258162306_Climate_Conflict_and_Social_Stability_What_Does_the_Evidence_Say. (Accessed: 24th February 2018)

14. Lockheed Martin. Worldwide Integrated Crisis Early Warning System (W-ICEWS). Lockheed Martin Corporation (2018). Available at: https://www.lockheedmartin.com/us/products/W-ICEWS/W-ICEWS_overview.html. (Accessed: 24th February 2018)

15. Batchelor, A., Clark, A., Houghstow, A., Iler, S. & Miller, S. How to Address Climate Change with Effective Giving. (2017).

This report was prepared for the Ray and Tye Noorda Foundation as part of the Harvard Effective Altruism Philanthropy advisory fellowship. Please contact amber@peace-rising.org for a copy.

16. Perry, C. Machine Learning Can Help Predict Violent Conflicts In Africa. (2015). Available at: https://www.iafrikan.com/2015/07/14/machine-learning-predict-violent-conflict-africa-army-rebels-drc-congo-somalia/. (Accessed: 24th February 2018)

17. GDELT Project. The Datasets Of GDELT As Of February 2016 – GDELT Blog. The Official GDELT Project Blog (2016). Available at: https://blog.gdeltproject.org/the-datasets-of-gdelt-as-of-february-2016/. (Accessed: 24th February 2018)

18. Beck, N., King, G. & Zeng, L. Improving Quantitative Studies of International Conflict: A Conjecture. Am. Polit. Sci. Rev. Vo1 94, 21–34 (2000). Available at: https://gking.harvard.edu/files/gking/files/improv.pdf. (Accessed: 24th February 2018)

19. David Fumo. Types of Machine Learning Algorithms You Should Know. Towards Data Science (2017). Available at: https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861. (Accessed: 24th February 2018)

20. Benyamin, D. A Gentle Introduction to Random Forests, Ensembles, and Performance Metrics in a Commercial System. CitizenNet Blog (2012). Available at: http://blog.citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics. (Accessed: 24th February 2018)

21. Weidmann, N. B. Conflict Prediction via Machine Learning: Addressing the Rare Events Problem with Bagging. Available at: http://nils.weidmann.ws/sites/default/files/weidmann08prediction.pdf. (Accessed: 24th February 2018)

22. Experfy Editor. Domain Expertise vs Machine Learning Debate – Experfy Insights. Experfy Blog (2014). Available at: https://www.experfy.com/blog/domain-expertise-vs-machine-learning-debate. (Accessed: 24th February 2018)