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Recent work has suggested the foreign-led reconstruction effort carried out in Afghanistan and Iraq can mitigate violence because it helps win the “hearts and minds” of local people. For the case of Afghanistan, we show there is no evidence behind such an assertion. Analyzing unique data on Commander’s Emergency Response Program (CERP) spending across the country from 2005 to 2009, we find no discernible effect of the reconstruction effort on violence. In light of the absence of empirical evidence for the success of the CERP, we suggest the hearts and minds credo currently guiding U.S. policy be reconsidered. [D74, H56, O1]

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We study the long-run effects of conflict on social attitudes, with World War II in Central and Eastern Europe as our setting. Much of earlier work has relied on self-reported measures of victimization, which are prone to endogenous misreporting. With our own survey-based measure, we replicate established findings linking victimization to political participation, civic engagement, optimism, and trust. Those findings are reversed, however, when tested instead with an objective measure of victimization based on historical reference material. Thus, we urge caution when interpreting survey-based results from this literature as causal.

Visual Commonsense Reasoning (VCR) predicts an answer with corresponding rationale, given a question-image input. VCR is a recently introduced visual scene understanding task with a wide range of applications, including visual question answering, automated vehicle systems, and clinical decision support. Previous approaches to solving the VCR task generally rely on pre-training or exploiting memory with long dependency relationship encoded models. However, these approaches suffer from a lack of generalizability and prior knowledge. In this paper we propose a dynamic working memory based cognitive VCR network, which stores accumulated commonsense between sentences to provide prior knowledge for inference. Extensive experiments show that the proposed model yields significant improvements over existing methods on the benchmark VCR dataset. Moreover, the proposed model provides intuitive interpretation into visual commonsense reasoning. A Python implementation of our mechanism is publicly available at https://github.com/tanjatang/DMVCR