How, where and why GIS is taught has been debated heavily in the geography literature. This article is a contribution to that debate, because it offers a new perspective from which to teach GIS: problem-based learning. In a problem-based learning classroom, theoretical foundations and the real world of problems are understood as constitutive of one another, rather than theory being prioritised over the real world of experience. In this paper, the author describes an introductory-level GIS class in which GIS was taught with a problem-based learning pedagogy. The problem around which the class focused was a proposal to add a new school district in the San Antonio, Texas metropolitan region. This article describes the class, including the nature of the problem and the way GIS skills were sequentially taught and integrated into the analysis of that problem.KEY WORDS: Problem-based learning, GIS, urban fragmentation IntroductionThere seems to be a dichotomy between how we teach geography in higher education and the nature of geographic problems and questions in the world. We teach separate classes in systematic geography, regional geography, cartography, and geographic information systems (GIS), yet expect students to address regionally specific, systematic geographic problems (sometimes with GIS skills) and present their findings cartographically. Reframing our presentation in terms of problems, i.e. using problem-based learning (PBL), may begin to allow our students to integrate concerns, conceptualisations and methods when faced with real-world situations. In this paper, I advocate the use of PBL in teaching GIS. In the class I describe, a problem framed the context in which geographic information systems skills were introduced to an interdisciplinary, undergraduate class of non-geographers. By the end of the semester, the students were presenting their findings to area school administrators, conversing with city government officials and compiling their findings for future publication.The acquisition of knowledge-in this case, the acquisition of knowledge concerning GIS technology-may occur in one of two ways: it may occur in a reductionist sense, such that theory and application are separated, and theory is later applied to various situations; or it may occur in conjunction with learning how to solve real-world problems, such that the problems are understood as interrelated with the practices. The latter approach will be detailed in this paper. The problem in this case came in the form of a proposal from the office of the Mayor of San Antonio, Texas, to carve a new school attendance district out of the extant districts in order to encourage economic development in one of the less economically developed parts of the city. Adjacent school districts received the proposal with great disdain, since they would lose revenue in the form of both property taxes and state aid when the new school district was created from the area covered by the existing districts. GIS capabilities were required to properly assess the impact ...
Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at least in part, by the perceived importance of fostering human-like adjustable autonomy and behaviors in social systems. But, efficient scalable and robust social systems are difficult to engineer. One difficulty exists in the design of how society and agents evolve and the other diffi- culties exist in how to capture the highly cognitive decision-making process that sometimes follows intuition and bounded rationality. We present a multi-agent architecture called CASE (Cognitive Agents for Social Environments). CASE provides a way to embed agent interactions in a three-dimensional social structure. It also presents a computational model for an individual agent’s intuitive and deliberative decision-making process. This chapter also presents our work on creating a multi-agent simulation which can help social and economic scientists use CASE agents to perform their tests. Finally, we test the system in an urban dynamic problem. Our experiment results suggest that intuitive decision-making allows the quick convergence of social strategies, and embedding agent interactions in a three-dimensional social structure speeds up this convergence as well as maintains the system’s stability.
Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at least in part, by the perceived importance of fostering human-like adjustable autonomy and behaviors in social systems. But, efficient scalable and robust social systems are difficult to engineer. One difficulty exists in the design of how society and agents evolve and the other difficulties exist in how to capture the highly cognitive decision-making process that sometimes follows intuition and bounded rationality. We present a multi-agent architecture called CASE (Cognitive Agents for Social Environments). CASE provides a way to embed agent interactions in a three-dimensional social structure. It also presents a computational model for an individual agent’s intuitive and deliberative decision-making process. This chapter also presents our work on creating a multi-agent simulation which can help social and economic scientists use CASE agents to perform their tests. Finally, we test the system in an urban dynamic problem. Our experiment results suggest that intuitive decision-making allows the quick convergence of social strategies, and embedding agent interactions in a three-dimensional social structure speeds up this convergence as well as maintains the system’s stability.
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