Computer simulations can be defined in three categories: computational modeling simulations, human-computer simulations, and computer-mediated simulations. These categories of simulations are defined primarily by the role computers take and by the role humans take in the implementation of the simulation. The literature on the use of simulations in the international studies classroom considers under what circumstances and in what ways the use of simulations creates pedagogical benefits when compared with other teaching methods. But another issue to consider is under what circumstances and in what ways the use of computers can add (or subtract) pedagogical value when compared to other methods for implementing simulations. There are six alleged benefits of using simulation: encouraging cognitive and affective learning, enhancing student motivation, creating opportunities for longer-term learning, increasing personal efficiency, and promoting student-teacher relations. Moreover, in regard to the use of computer simulations, there are a set of good practices to consider. The first good practice emerges out of a realization of the unequal level of access to technology. The second good practice emerges from a clear understanding of the strengths and weaknesses of a computer-assisted simulation. The final and perhaps most fundamental good practice emerges from the idea that computers and technology more generally are not ends in themselves, but a means to help instructors reach a set of pedagogical goals.
Climate and simulation have become interwoven concepts during the past decades because, on the one hand, climate scientists shouldn’t experiment with real climate and, on the other hand, societies want to know how climate will change in the next decades. Both in-silico experiments for a better understanding of climatic processes as well as forecasts of possible futures can be achieved only by using climate models. The article investigates possibilities and problems of model-mediated knowledge for science and societies. It explores historically how climate became a subject of science and of simulation, what kind of infrastructure is required to apply models and simulations properly, and how model-mediated knowledge can be evaluated. In addition to an overview of the diversity and variety of models in climate science, the article focuses on quasiheuristic climate models, with an emphasis on atmospheric models.
Agent-based computational modeling (ABM, for short) is a formal and supplementary methodological approach used in international relations (IR) theory and research, based on the general ABM paradigm and computational methodology as applied to IR phenomena. ABM of such phenomena varies according to three fundamental dimensions: scale of organization—spanning foreign policy, international relations, regional systems, and global politics—as well as by geospatial and temporal scales. ABM is part of the broader complexity science paradigm, although ABMs can also be applied without complexity concepts. There have been scores of peer-reviewed publications using ABM to develop IR theory in recent years, based on earlier pioneering work in computational IR that originated in the 1960s that was pre-agent based. Main areas of theory and research using ABM in IR theory include dynamics of polity formation (politogenesis), foreign policy decision making, conflict dynamics, transnational terrorism, and environment impacts such as climate change. Enduring challenges for ABM in IR theory include learning the applicable ABM methodology itself, publishing sufficiently complete models, accumulation of knowledge, evolving new standards and methodology, and the special demands of interdisciplinary research, among others. Besides further development of main themes identified thus far, future research directions include ABM applied to IR in political interaction domains of space and cyber; new integrated models of IR dynamics across domains of land, sea, air, space, and cyber; and world order and long-range models.
Snow- and ice-related hazardous processes threaten society in tropical to high-latitude mountain areas worldwide and at highly variable time scales. On the one hand, small snow avalanches are recorded in high numbers every winter. On the other hand, glacial lake outburst floods (GLOFs) or large-scale volcano–ice interactions occur less frequently but may evolve into destructive process chains resulting in major disasters. These extreme examples document the huge field of types, magnitudes, and frequencies of snow- and ice-related hazardous processes. Mountain societies have learned to cope with natural hazards for centuries, guided by personal experiences and oral and written tradition. Historical records are today still important as a basis to mitigate snow- and ice-related hazards. They are complemented by a broad array of observation and modeling techniques. These techniques differ among themselves with regard to (1) the type of process under investigation and (2) the scale and purpose of investigation. Multi-scale monitoring and warning systems for snow avalanches are in operation in densely populated mid-latitude mountain areas. They build on meteorological and snow profile data in combination with a large pool of expert knowledge. In contrast, ice-related processes such as ice- or rock-ice avalanches, GLOFs, or associated process chains cause damage less frequently in space and time, so that societies are less well adapted. Even though the hazard sources are often far from the society—making field observation challenging—flows travelling for tens of kilometers sometimes impact populated areas. These hazards are strongly influenced by climate change–induced glacier and permafrost dynamics. On the regional or national scale, the evolution of such hazards has to be monitored at short intervals through aerial and satellite imagery and terrain data, employing geographic information systems (GIS). Known hazardous situations have to be monitored in the field. Physical models—applied either in the laboratory or at real-world sites—are employed to explore the mobility of hazardous processes. Since the 1950s, however, computer models have increasingly gained importance in exploring possible travel distances, impact areas, velocities, and impact forces of events. While simple empirical-statistical approaches are used at broad scales in combination with GIS, advanced numeric models are applied to analyze specific case studies. However, the input parameters for these models are uncertain so that (1) the model results have to be validated with observations and (2) appropriate strategies to deal with the uncertainties have to be applied before using the model results for hazard zoning or dimensioning of protective structures. Due to rapid atmospheric warming and related changes in the cryosphere, hazard situations beyond historical experiences are expected to be increasingly relevant in the future. Scenario-based modeling of complex systems and process chains therefore represents an emerging research direction.
Lin Qiu and Riyang Phang
Political systems involve citizens, voters, politicians, parties, legislatures, and governments. These political actors interact with each other and dynamically alter their strategies according to the results of their interactions. A major challenge in political science is to understand the dynamic interactions between political actors and extrapolate from the process of individual political decision making to collective outcomes. Agent-based modeling (ABM) offers a means to comprehend and theorize the nonlinear, recursive, and interactive political process. It views political systems as complex, self-organizing, self-reproducing, and adaptive systems consisting of large numbers of heterogeneous agents that follow a set of rules governing their interactions. It allows the specification of agent properties and rules governing agent interactions in a simulation to observe how micro-level processes generate macro-level phenomena. It forces researchers to make assumptions surrounding a theory explicit, facilitates the discovery of extensions and boundary conditions of the modeled theory through what-if computational experiments, and helps researchers understand dynamic processes in the real-world. ABM models have been built to address critical questions in political decision making, including why voter turnouts remain high, how party coalitions form, how voters’ knowledge and emotion affect election outcomes, and how political attitudes change through a campaign. These models illustrate the use of ABM in explicating assumptions and rules of theoretical frameworks, simulating repeated execution of these rules, and revealing emergent patterns and their boundary conditions. While ABM has limitations in external validity and robustness, it provides political scientists a bottom-up approach to study a complex system by clearly defining the behavior of various actors and generate theoretical insights on political phenomena.