Topical, geospatial, and temporal diffusion of the 2015 North American Menopause Society position statement on nonhormonal management of vasomotor symptoms

OBJECTIVE: We sought to depict the topical, geospatial, and temporal diffusion of the 2015 North American Menopause Society position statement on the nonhormonal management of menopause-associated vasomotor symptoms released on September 21, 2015, and its associated press release from September 23, 2015. METHODS: Three data sources were used: online news articles, National Public Radio, and Twitter. For topical diffusion, we compared keywords and their frequencies among the position statement, press release, and online news articles. We also created a network figure depicting relationships across key content categories or nodes. For geospatial diffusion within the United States, we compared locations of the 109 National Public Radio (NPR) stations covering the statement to 775 NPR stations not covering the statement. For temporal diffusion, we normalized and segmented Twitter data into periods before and after the press release (September 12, 2015 to September 22, 2015 vs September 23, 2015 to October 3, 2015) and conducted a burst analysis to identify changes in tweets from before to after. RESULTS: Topical information diffused across sources was similar with the exception of the more scientific terms "vasomotor symptoms" or "vms" versus the more colloquial term "hot flashes." Online news articles indicated media coverage of the statement was mainly concentrated in the United States. NPR station data showed similar proportions of stations airing the story across the four census regions (Northeast, Midwest, south, west; P = 0.649). Release of the statement coincided with bursts in the menopause conversation on Twitter. CONCLUSIONS: The findings of this study may be useful for directing the development and dissemination of future North American Menopause Society position statements and/or press releases

Creative destruction in science

Drawing on the concept of a gale of creative destruction in a capitalistic economy, we argue that initiatives to assess the robustness of findings in the organizational literature should aim to simultaneously test competing ideas operating in the same theoretical space. In other words, replication efforts should seek not just to support or question the original findings, but also to replace them with revised, stronger theories with greater explanatory power. Achieving this will typically require adding new measures, conditions, and subject populations to research designs, in order to carry out conceptual tests of multiple theories in addition to directly replicating the original findings. To illustrate the value of the creative destruction approach for theory pruning in organizational scholarship, we describe recent replication initiatives re-examining culture and work morality, working parents\u2019 reasoning about day care options, and gender discrimination in hiring decisions. Significance statement It is becoming increasingly clear that many, if not most, published research findings across scientific fields are not readily replicable when the same method is repeated. Although extremely valuable, failed replications risk leaving a theoretical void\u2014 reducing confidence the original theoretical prediction is true, but not replacing it with positive evidence in favor of an alternative theory. We introduce the creative destruction approach to replication, which combines theory pruning methods from the field of management with emerging best practices from the open science movement, with the aim of making replications as generative as possible. In effect, we advocate for a Replication 2.0 movement in which the goal shifts from checking on the reliability of past findings to actively engaging in competitive theory testing and theory building. Scientific transparency statement The materials, code, and data for this article are posted publicly on the Open Science Framework, with links provided in the article

Agent-Based Model of Land-Use Decision Making

An agent-based model, incorporating a small set of primarily agent-based variables, was designed to explain land-use decision making. Agents are land-owners, who allocate their labour and land for di#erent uses in regular time intervals. The goal is to understand what kind of spatial patterns emerge from different agent characteristics, and decision and learning mechanisms. Landscapes produced by the learning agent model are compared to actual land-cover data. By varying the parameter estimation schemes and the spatial metrics calculated from the simulated land-cover and the actual land-cover, the role of agent preferences for di#erent land-uses is explored. The preliminary results suggest that the model captures relatively well the quantitative patterns of land-cover changes but it is poor in predicting the location of changes. Contact: Computer Science Department Indiana University Lindley Hall 215 150 S. Woodlawn Ave

Agent-Based Model Selection Framework for Complex Adaptive Systems

Thesis (PhD) - Indiana University, Computer Sciences, 2006Human-initiated land-use and land-cover change is the most significant single factor behind global climate change. Since climate change affects human, animal and plant populations alike, and the effects are potentially disastrous and irreversible, it is equally important to understand the reasons behind land-use decisions as it is to understand their consequences. Empirical observations and controlled experimentation are not usually feasible methods for studying this change. Therefore, scientists have resorted to computer modeling, and use other complementary approaches, such as household surveys and field experiments, to add depth to their models. The computer models are not only used in the design and evaluation of environmental programs and policies, but they can be used to educate land-owners about sustainable land management practices. Therefore, it is critical which model the decision maker trusts. Computer models can generate seemingly plausible outcomes even if the generating mechanism is quite arbitrary. On the other hand, with excess complexity the model may become incomprehensible, and proper tweaking of the parameter values may make it produce any results the decision maker would like to see. The lack of adequate tools has made it difficult to compare and choose between alternative models of land-use and land-cover change on a fair basis. Especially if the candidate models do not share a single dimension, e.g., a functional form, a criterion for selecting an appropriate model, other than its face value, i.e., how well the model behavior confirms to the decision maker's ideals, may be hard to find. Due to the nature of the class of models, existing model selection methods are not applicable either. In this dissertation I propose a pragmatic method, based on algorithmic coding theory, for selecting among alternative models of land-use and land-cover change. I demonstrate the method's adequacy using both artificial and real land-cover data in multiple experimental conditions with varying error functions and initial conditions

Methodology for Comparing Agent-based Models of Land-use Decisions

The focus of the research is mainly methodological. The goal is to develop a framework for comparing computational, agent-based models for land-use decision making. The framework will allow studying spatial patterns emerging from different distributions of agent characteristics, learning and communications schemes, initial spatial configurations, and varying spatial suitabilities. The framework will also facilitate the assessment of model complexity, thus enabling a rigorous practice of model comparison and selection

Comparing agent-based learning models of land-use decision making

An agent-based model, incorporating a small set of primarily agent-based variables, was designed to explain private land-use decision making. Agents are landowners, who allocate their labor and land for different uses in regular time intervals. The goal is to understand what kind of spatial patterns emerge from different agent characteristics, and decision and learning mechanisms. Landscapes produced by two different learning models are compared to actual land-cover data. By calculating a set of spatial metrics from the simulated land-cover and comparing them to the metrics calculated from the actual land-cover data, the role of agent preferences for different land-uses is explored. The preliminary results suggest that the models captur