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Modern science is a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play? To address these questions which are highly relevant to scientists as well as to philosophers of science, 8 leading natural, engineering and social scientists reflect upon their modeling work, and 8 philosophers provide a commentary.

Intro -- GLOBAL MODEL SIMULATION: A FRONTIER OF ECONOMIC SCIENCE -- GLOBAL MODEL SIMULATION: A FRONTIER OF ECONOMIC SCIENCE -- CONTENTS -- PREFACE -- Chapter 1 INTRODUCTION -- Chapter 2 OUTLINE OF FUGI GLOBAL MODELING SYSTEM -- 2.1. REGIONAL CLASSIFICATION -- 2.2. INTEGRATED MULTIDISCIPLINARY SYSTEMS ANALYSIS -- 2.3. STRUCTURE OF GLOBAL INTERDEPENDENCE -- 2.4. GLOBAL INTERDEPENDENCE TABLE -- 2.5. POLICY IMPLICATIONS OF GLOBAL INTERDEPENDENCE TABLE -- 2.6. SOFTWARE OF FUGI GLOBAL MODELING SYSTEM -- 2.7. MAIN SUBSYSTEMS: POPULATION, ENVIRONMENT AND ECONOMIC DEVELOPMENT -- Population System -- Energy System -- Environment -- Economic Development System -- Labor and Production at Constant Prices -- Expenditures on GDP at Constant Prices -- Income Distribution - Profits and Wages -- Prices -- Expenditures on GDP at Current Prices -- Money, Interest Rates and Financial Assets -- International Balance of Payments -- Official Development Assistance -- Foreign Exchange Rates -- Chapter 3 EXAMPLES OF ESTIMATED PARAMETERS OF GLOBAL MODEL -- Chapter 4 FUTURE SIMULATIONS OF THE WORLD ECONOMY, 2009-2020 -- SCENARIO A: G20 COLLABORATION -- Chapter 5 ALTERNATIVE PATH OF GLOBAL ECONOMY AGAINST CO2 EMISSIONS -- 5.1. GLOBAL ENVIRONMENT INDICATORS: CO2 EMISSIONS -- 5.2. ALTERNATIVE PATH OF GLOBAL ECONOMY AGAINST CO2 EMISSIONS. SCENARIO B: GLOBAL CO-OPERATION FOR "GREEN REVOLUTION" -- Scenario B: Global Cooperation and Concerted Policy for Green Revolution -- Developed Economies* -- Developing Countries + Economies in Transition -- Chapter 6 POLICY EXERCISE AND SIMULATIONS OF FUTURES -- 1. Impacts of Oil Saving Technology on Oil Prices in the Futures -- 2. Impacts of New Technology Investment Supported by R&D on the World Economy -- 3. Appropriate Trade Policy against a New Global Economic Crisis.

The text explains the model-based method of intelligence analysis that represents the analyst's mental models of a subject as well as the analyst's reasoning process. It includes dynamic simulations and interactive analytic games, case studies that illustrate a wide range of intelligence problems, and a recommended curriculum for technical analysts.

In this short review article we discuss the use of Ising lattice model as a testbed for improvement of theory of both homogeneous and heterogeneous nucleation theory. First we briefly overview the classical nucleation theory (CNT), and two typical simple systems on which simulations are performed - hard spheres, and Ising lattice model. Than we overview some results obtained by this approach and point to possible new directions of research and improvement.

Abstract. Assessing the risk of a historical-level flood is essential for regional flood protection and resilience establishment. However, due to the limited spatiotemporal coverage of observations, the impact assessment relies on model simulations and is thus subject to uncertainties from cascade physical processes. This study assesses the flood hazard map with uncertainties subject to different combinations of runoff inputs, variables for flood frequency analysis and fitting distributions based on estimations by the CaMa-Flood global hydrodynamic model. Our results show that deviation in the runoff inputs is the most influential source of uncertainties in the estimated flooded water depth and inundation area, contributing more than 80 % of the total uncertainties investigated in this study. Global and regional inundation maps for floods with 1-in-100 year return periods show large uncertainty values but small uncertainty ratios for river channels and lakes, while the opposite results are found for dry zones and mountainous regions. This uncertainty is a result of increasing variation at tails among various fitting distributions. In addition, the uncertainty between selected variables is limited but increases from the regular period to the rarer floods, both for the water depth at points and for inundation area over regions. The uncertainties in inundation area also lead to uncertainties in estimating the population and economy exposure to the floods. In total, inundation accounts for 9.1 % [8.1 %–10.3 %] of the land area for a 1-in-100 year flood, leading to 13.4 % [12.1 %–15 %] of population exposure and 13.1 % [11.8 %–14.7 %] of economic exposure for the globe. The flood exposure and uncertainties vary by continent and the results in Africa have the largest uncertainty, probably due to the limited observations to constrain runoff simulations, indicating a necessity to improve the performance of different hydrological models especially for data-limited regions.