The socioeconomic dynamics of the shifta conflict in Kenya, c. 1963-8

Using a set of oral testimonies, together with military, intelligence, and administrative reports from the 1960s, this article re-examines the shifta conflict in Kenya. The article moves away from mono-causal, nationalistic interpretations of the event, to focus instead on the underlying socioeconomic dynamics and domestic implications of the conflict. It argues that the nationalist interpretation fails to capture the diversity of participation in shifta, which was not simply made up of militant Somali nationalists, and that it fails to acknowledge the significance of an internal Kenyan conflict between a newly independent state in the process of nation building, and a group of ‘dissident’ frontier communities that were seen to defy the new order. Examination of this conflict provides insights into the operation of the early postcolonial Kenyan stateThe Arts and Humanities Research Council,The Royal Historical Society, Martin Lynn Scholarshi

Batched bandit problems

Motivated by practical applications, chiefly clinical trials, we study the regret achievable for stochastic bandits under the constraint that the employed policy must split trials into a small number of batches. We propose a simple policy, and show that a very small number of batches gives close to minimax optimal regret bounds. As a byproduct, we derive optimal policies with low switching cost for stochastic bandits.Comment: Published at http://dx.doi.org/10.1214/15-AOS1381 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Negatively Correlated Bandits

We analyze a two-player game of strategic experimentation with two-armed bandits. Each player has to decide in continuous time whether to use a safe arm with a known payoff or a risky arm whose likelihood of delivering payoffs is initially unknown. The quality of the risky arms is perfectly negatively correlated between players. In marked contrast to the case where both risky arms are of the same type, we find that learning will be complete in any Markov perfect equilibrium if the stakes exceed a certain threshold, and that all equilibria are in cutoff strategies. For low stakes, the equilibrium is unique, symmetric, and coincides with the planner's solution. For high stakes, the equilibrium is unique, symmetric, and tantamount to myopic behavior. For intermediate stakes, there is a continuum of equilibria