Ascape: Abstracting complexity

Software tools used in science typically take a kitchen-sink approach to design. From statistics to mathematics to engineering to agent modeling, even those tools that have a strong organizing theme tend towards supporting every contingency and methodology. This impulse toward generalization and breadth is laudable and necessary. However, there is a complementary case to be made for the discipline of abstraction, parsimony, and depth, and that is the case I make for Ascape. I argue in general for the importance of abstraction in agent-based modeling. I then discuss three key abstractions enforced in Ascape, and the opportunities they create for expressibility and simplicity. While these abstractions seem especially suited to the domain of social and economic systems, they are not limited to it. By drawing concrete examples from Ascape and comparing Ascape code to other environments, I show how these apparently constraining abstractions benefit the Ascape user and developer experince. In summary, a primary goal of software design and coding is conquering complexity. The motivation behind many programming practices is to reduce a program\u27s complexity. Reducing complexity is a key to being an effective programmer. -Steve McConnell (1993

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

An Exploratory Study of Forces and Frictions affecting Large-Scale Model-Driven Development

In this paper, we investigate model-driven engineering, reporting on an exploratory case-study conducted at a large automotive company. The study consisted of interviews with 20 engineers and managers working in different roles. We found that, in the context of a large organization, contextual forces dominate the cognitive issues of using model-driven technology. The four forces we identified that are likely independent of the particular abstractions chosen as the basis of software development are the need for diffing in software product lines, the needs for problem-specific languages and types, the need for live modeling in exploratory activities, and the need for point-to-point traceability between artifacts. We also identified triggers of accidental complexity, which we refer to as points of friction introduced by languages and tools. Examples of the friction points identified are insufficient support for model diffing, point-to-point traceability, and model changes at runtime.Comment: To appear in proceedings of MODELS 2012, LNCS Springe

Of impacts, agents, and functions: An interdisciplinary meta-review of smart home energy management systems research

Smart home energy management technologies (SHEMS) have long been viewed as a promising opportunity to manage the way households use energy. Research on this topic has emerged across a variety of disciplines, focusing on different pieces of the SHEMS puzzle without offering a holistic vision of how these technologies and their users will influence home energy use moving forward. This paper presents the results of a systematic, interdisciplinary meta-review of SHEMS literature, assessing the extent to which it discusses the role of various SHEMS components in driving energy benefits. Results reveal a bias towards technical perspectives and controls approaches that seek to drive energy impacts such as load management and energy savings through SHEMS without user or third-party participation. Not only are techno-centric approaches more common, there is also a lack of integration of these approaches with user-centric, information-based solutions for driving energy impacts. These results suggest future work should investigate more holistic solutions for optimal impacts on household energy use. We hope these results will provoke a broader discussion about how to advance research on SHEMS to capitalize on their potential contributions to demand-side management initiatives moving forward

Concrete utopianism in integrated assessment models: Discovering the philosophy of the shared socioeconomic pathways

The Shared Socioeconomic Pathways (SSPs) are at the forefront of climate change science today. As an influential methodology and method, the SSPs guide the framing of numerous climate change research questions and how these are investigated. Although the SSPs were developed by an interdisciplinary group of scientists in a well-documented process, there is no apparent consensus in the literature that answers the question, "What is the philosophy of science behind the SSPs?" To investigate, the paper applies a systematic thematic qualitative content analysis to the dataset of published papers that establish the rules and expectations for using the SSPs. The research determines that there is no obvious and concise statement on the epistemological and ontological foundation of the SSPs. However, based on the evidence identified in the dataset, SSPs are implicitly, though not explicitly, consistent with a critical realist and concrete utopian philosophy as coined by Roy Bhaskar. This is the first paper to discuss the philosophical underpinning of the SSPs