Suchergebnisse

1. Introduction -- 2. The 'Truth' about Pakistan: Knowledge Production and Circulation in International Relations -- 3. The 'Truth' about Pakistan: Knowledge Production and Circulation in Area Studies -- 4. The 'Truth about Pakistan: Knowledge Production and Circulation in Think Tanks -- 5. Knowledge Production and Circulation in Pakistani International Relations -- 6. Conclusion

We extend Milgrom and Weber's affiliated valuations model to the multi-unit case. We show that the discriminatory auction has a unique equilibrium, that corresponds to Milgrom and Weber's first-price equilibrium in the 2-bidder, constant marginal valuations case. This unique equilibrium therefore leads to lower expected prices than the equilibrium of the English auction where the units are bundled together. Hence we show that in an auction of a single object where the object can be divided into k parts and a bidder's valuation for each part is the same, it is not possible to increase revenue by using a multi-unit discriminatory auction. With more than two bidders and constant marginal valuations we show that the first-price equilibrium is an equilibrium of the multi-unit discriminatory auction. Back and Zender show this in the common values case which is a special case of affiliated valuations. We also show that the first-price equilibrium does not hold with decreasing marginal valuations.

We characterize the steady state of a market with random matching and bargaining, where the sellers' goods can perish overnight. Generically, the quantity traded is suboptimal, prices are dispersed and there is a dead-weight loss caused by excess supply or demand. In the limit as the cost of staying in the market tends to zero, only the amount of trade tends to the efficient level, the other two non-competitive characteristics remain. We discuss the implications of these findings on the foundations of competitive equilibrium and on the robustness of the results in the literature on durable-good markets.

Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country's economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology's practice and usage in flood prediction.

Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country's economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology's practice and usage in flood prediction.