An empirically-derived control structure for the process of program understanding

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Urban modelling as storytelling: using simulation models as a narrative

This article examines the distinctions between empirical and simulation models using the metaphors of argument and narrative. It argues that all argumentation is contextualized within a narrative that is either inferred or communicated. The paper provides another semantic structure for urban models that applies elements of systems- dynamic method to construct "stories" of the past and possible futures of communities in a watershed in southern Arizona. By constructing such narratives this paper demonstrates how computer-based urban models can "tell a story"

Theoretical Validity and Empirical Utility of a Constructionist Analytics

Wing-Chung Ho offers an extensive critique of what he calls our “radical constructionist approach to family experience,” questioning the theoretical validity and empirical utility of the research program. This article responds to the charges in the broader context of the program\u27s constructionist analytics, discussing family\u27s experiential location, organizational embeddedness, and the importance of ethnographic sensibility. A brief extract of situated talk and interaction is presented to illustrate the discursive complexity and institutional bearings of family as a category of experience. The conclusion takes up the issue of whether the program is radical in conceptualization and empirical realization

A Construct-Modeling Approach to Develop a Learning Progression of how Students Understand the Structure of Matter

This paper builds on the current literature base about learning progressions in science to address the question, “What is the nature of the learning progression in the content domain of the structure of matter?” We introduce a learning progression in response to that question and illustrate a methodology, the Construct Modeling (Wilson, 2005) approach, for investigating the progression through a developmentally based iterative process. This study puts forth a progression of how students understand the structure of matter by empirically inter-relating constructs of different levels of sophistication using a sample of 1,087 middle grade students from a large diverse public school district in the western part of the United States. The study also shows that student thinking can be more complex than hypothesized as in the case of our discovery of a substructure of understanding in a single construct within the larger progression. Data were analyzed using a multidimensional Rasch model. Implications for teaching and learning are discussed—we suggest that the teacher’s choice of instructional approach needs to be fashioned in terms of a model, grounded in evidence, of the paths through which learning might best proceed, working toward the desired targets by a pedagogy which also cultivates students’ development as effective learners. This research sheds light on the need for assessment methods to be used as guides for formative work and as tools to ensure the learning goals have been achieved at the end of the learning period. The development and investigation of a learning progression of how students understand the structure of matter using the Construct Modeling approach makes an important contribution to the research on learning progressions and serves as a guide to the planning and implementation in the teaching of this topic. # 2017 Wiley Periodicals, Inc. J Res Sci Teach 54: 1024–1048, 201

Agent-based simulation of open source evolution

We present an agent-based simulation model developed to study how size, complexity and effort relate to each other in the development of open source software (OSS). In the model, many developer agents generate, extend, and re-factor code modules independently and in parallel. This accords with empirical observations of OSS development. To our knowledge, this is the first model of OSS evolution that includes the complexity of software modules as a limiting factor in productivity, the fitness of the software to its requirements, and the motivation of developers. Validation of the model was done by comparing the simulated results against four measures of software evolution (system size, proportion of highly complex modules, level of complexity control work, and distribution of changes) for four large OSS systems. The simulated results resembled the observed data, except for system size: three of the OSS systems showed alternating patterns of super-linear and sub-linear growth, while the simulations produced only super-linear growth. However, the fidelity of the model for the other measures suggests that developer motivation and the limiting effect of complexity on productivity have a significant effect on the development of OSS systems and should be considered in any model of OSS development

The Importance of Social and Government Learning in Ex Ante Policy Evaluation

We provide two methodological insights on \emph{ex ante} policy evaluation for macro models of economic development. First, we show that the problems of parameter instability and lack of behavioral constancy can be overcome by considering learning dynamics. Hence, instead of defining social constructs as fixed exogenous parameters, we represent them through stable functional relationships such as social norms. Second, we demonstrate how agent computing can be used for this purpose. By deploying a model of policy prioritization with endogenous government behavior, we estimate the performance of different policy regimes. We find that, while strictly adhering to policy recommendations increases efficiency, the nature of such recipes has a bigger effect. In other words, while it is true that lack of discipline is detrimental to prescription outcomes (a common defense of failed recommendations), it is more important that such prescriptions consider the systemic and adaptive nature of the policymaking process (something neglected by traditional technocratic advice)

Empirical Validation of Agent Based Models: A Critical Survey

This paper addresses the problem of finding the appropriate method for conducting empirical validation in agent-based (AB) models, which is often regarded as the Achilles’ heel of the AB approach to economic modelling. The paper has two objectives. First, to identify key issues facing AB economists engaged in empirical validation. Second, to critically appraise the extent to which alternative approaches deal with these issues. We identify a first set of issues that are common to both AB and neoclassical modellers and a second set of issues which are specific to AB modellers. This second set of issues is captured in a novel taxonomy, which takes into consideration the nature of the object under study, the goal of the analysis, the nature of the modelling assumptions, and the methodology of the analysis. Having identified the nature and causes of heterogeneity in empirical validation, we examine three important approaches to validation that have been developed in AB economics: indirect calibration, the Werker-Brenner approach, and the history-friendly approach. We also discuss a set of open questions within empirical validation. These include the trade-off between empirical support and tractability of findings, the issue of over-parameterisation, unconditional objects, counterfactuals, and the non-neutrality of data.Empirical validation, agent-based models, calibration, history-friendly modelling

How to teach and think about spontaneous wave function collapse theories: not like before

A simple and natural introduction to the concept and formalism of spontaneous wave function collapse can and should be based on textbook knowledge of standard quantum state collapse and monitoring. This approach explains the origin of noise driving the paradigmatic stochastic Schr\"odinger equations of spontaneous localization of the wave function $\Psi$. It reveals, on the other hand, that these equations are empirically redundant and the master equations of the noise-averaged state $\hat\rho$ are the only empirically testable dynamics in current spontaneous collapse theories.Comment: Almost the published version p3-11 in "Collapse of the Wave Function", ed.: S Gao, Cambridge University Press, 201

From Expectations to Experiences: Using a Structural Typology to Understand First-Year Student Outcomes in Academically Based Living-Learning Communities

This longitudinal study investigated to what extent noncognitive variables (e.g., expectations for college) and the college environment (i.e., academically based living-learning communities) influence students\u27 college experience. This research goes beyond grouping all living-learning students into one category, which has dominated much of the literature, by using an empirically derived structural typology for living-learning communities (Inkelas, Longerbeam, Leonard, & Soldner, 2005). Results suggest that being a student in a collaborative living-learning community is more likely to predict greater peer academic interactions and an enriching educational environment. Implications for practice and future research are discussed

Models and metrics for software management and engineering

This paper attempts to characterize and present a state of the art view of several quantitative models and metrics of the software life cycle. These models and metrics can be used to aid in managing and engineering software projects. They deal with various aspects of the software process and product, including resources allocation and estimation, changes and errors, size, complexity and reliability. Some indication is given of the extent to which the various models have been used and the success they have achieved