The UN in the lab

We consider two alternatives to inaction for governments combating terrorism, which we term Defense and Prevention. Defense consists of investing in resources that reduce the impact of an attack, and generates a negative externality to other governments, making their countries a more attractive objective for terrorists. In contrast, Prevention, which consists of investing in resources that reduce the ability of the terrorist organization to mount an attack, creates a positive externality by reducing the overall threat of terrorism for all. This interaction is captured using a simple 3×3 “Nested Prisoner’s Dilemma” game, with a single Nash equilibrium where both countries choose Defense. Due to the structure of this interaction, countries can benefit from coordination of policy choices, and international institutions (such as the UN) can be utilized to facilitate coordination by implementing agreements to share the burden of Prevention. We introduce an institution that implements a burden-sharing policy for Prevention, and investigate experimentally whether subjects coordinate on a cooperative strategy more frequently under different levels of cost sharing. In all treatments, burden sharing leaves the Prisoner’s Dilemma structure and Nash equilibrium of the game unchanged. We compare three levels of burden sharing to a baseline in a between-subjects design, and find that burden sharing generates a non-linear effect on the choice of the efficient Prevention strategy and overall performance. Only an institution supporting a high level of mandatory burden sharing generates a significant improvement in the use of the Prevention strategy

Inequality, Coalitions and Collective Action

In a model where cooperation is beneficial, but subject to cheating, and is supported by trigger strategy punishments in a repeated game, we explore the relationship between the nature of cooperation (size and composition of coalitions) and underlying inequality in the distribution of private productive assets.

Coordination and Conflict: The Persistent Relevance of Networks in International Financial Regulation

This thesis presents SiGe(C)/Si(C) multi quantum well (MQW) layers individually or in combination with Si(C) Schottky diodes as material structures to detect infrared (IR) radiation. The performance of devices was investigated in terms of SiGe/Si periodicity and quality of SiGe/Si interface. The structures were grown by chemical vapour deposition using GeH4 and SiH4 sources at 650 °C and processed into pixel arrays with sizes of 25×25, 100×100 and 200×200 μm2. The device response to thermal variations was expressed by temperature coefficient of resistance (TCR) and the signal-to-noise-ratio was evaluated by noise measurements. The strain relaxation in SiGe layers was investigated by implementing oxygen at the interface of SiGe/Si or during SiGe growth. A minor amount of 10 ppb oxygen at the interface can be detected by noise measurements while the material characterizations could reveal defects for significantly higher defect density. Oxygen and water contaminations should be accounted for in low temperature epitaxy (350-650 °C) of the layers. Furthermore, an empirical model was developed to describe the kinetics of the SiGe growth using Si2H6 and Ge2H6 as precursors at low temperature. The model takes into account the energy for dissociation of gas molecules, diffusion of the molecules from the gas boundaries toward the substrate and the incorporation of absorbed molecules. A good consistency was observed between the experimental and calculated data.QC 20150211</p

Foresighted policy gradient reinforcement learning: solving large-scale social dilemmas with rational altruistic punishment

Many important and difficult problems can be modeled as “social dilemmas”, like Hardin's Tragedy of the Commons or the classic iterated Prisoner's Dilemma. It is well known that in these problems, it can be rational for self-interested agents to promote and sustain cooperation by altruistically dispensing costly punishment to other agents, thus maximizing their own long-term reward. However, self-interested agents using most current multi-agent reinforcement learning algorithms will not sustain cooperation in social dilemmas: the algorithms do not sufficiently capture the consequences on the agent's reward of the interactions that it has with other agents. Recent more foresighted algorithms specifically account for such expected consequences, and have been shown to work well for the small-scale Prisoner's Dilemma. However, this approach quickly becomes intractable for larger social dilemmas. Here, we advance on this work and develop a “teach/learn” stateless foresighted policy gradient reinforcement learning algorithm that applies to Social Dilemma's with negative, unilateral side-payments, in the from of costly punishment. In this setting, the algorithm allows agents to learn the most rewarding actions to take with respect to both the dilemma (Cooperate/Defect) and the “teaching” of other agent's behavior through the dispensing of punishment. Unlike other algorithms, we show that this approach scales well to large settings like the Tragedy of the Commons. We show for a variety of settings that large groups of self-interested agents using this algorithm will robustly find and sustain cooperation in social dilemmas where adaptive agents can punish the behavior of other similarly adaptive agents

Exiting Treaties

This Article analyzes the under-explored phenomenon of unilateral exit from international agreements and intergovernmental organizations. Although clauses authorizing denunciation and withdrawal from treaties are pervasive, international legal scholars and international relations theorists have largely ignored them. This Article draws upon new empirical evidence to provide a comprehensive interdisciplinary framework for understanding treaty exit. It examines when and why states abandon their treaty commitments and explains how exit helps to resolve certain theoretical and doctrinal puzzles that have long troubled scholars of international affairs