A rhetorical approach to IT diffusion: reconceptualizing the ideology-framing relationship in computerization movements

In this paper we propose rhetoric as a valuable yet underdeveloped alternative paradigm for examining IT diffusion. Building on recent developments of computerization movements theory, our rhetorical approach proposes that two central elements of the theory, framing and ideology, rather than being treated as separate can be usefully integrated. We suggest that IT diffusion can be usefully explored through examining the interrelationship of the deep structures underlying ideology and the type and sequence of rhetorical claims underpinning actors’ framing strategies. Our theoretical developments also allow us to better understand competing discourses influencing the diffusion process. These discourses reflect the ideologies and shape the framing strategies of actors in the broader field context. We illuminate our theoretical approach by drawing on the history of the diffusion of free and open source software.

Paradoxical evidence integration in rapid decision processes

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions. © 2012 Rüter et al.

Dynamic Covariance Models for Multivariate Financial Time Series

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.

A systems approach to characterise the dissemination process of household water treatment systems in developing countries

Abstract: Starting in the 1980s, household-level water treatment and safe storage systems (HWTS) have been developed as simple, local, user-friendly, and low cost options to improve drinking water quality at the point of use. However, despite conclusive evidence of the health and economic benefits of HWTS, and promotion efforts in over 50 countries in the past 20 years, implementation outcomes have been slow, reaching only 5-10 million regular users. This study attempts to understand the barriers and drivers affecting HWTS implementation. Although existing literature related to HWTS and innovation diffusion theories proposed ample critical factors and recommendations, there is a lack of holistic and systemic approach to integrate these findings. It is proposed that system dynamics modelling can be a promising tool to map the inter-relationships of different critical factors and to understand the structure of HWTS dissemination process, which may lead to identifying high impact, leveraged mitigation strategies to scale-up HWTS adoption and sustained use.

Pitman-Yor Diffusion Trees

We introduce the Pitman Yor Diffusion Tree (PYDT) for hierarchical clustering, a generalization of the Dirichlet Diffusion Tree (Neal, 2001) which removes the restriction to binary branching structure. The generative process is described and shown to result in an exchangeable distribution over data points. We prove some theoretical properties of the model and then present two inference methods: a collapsed MCMC sampler which allows us to model uncertainty over tree structures, and a computationally efficient greedy Bayesian EM search algorithm. Both algorithms use message passing on the tree structure. The utility of the model and algorithms is demonstrated on synthetic and real world data, both continuous and binary.

SIMPLE REMESHING SCHEME FOR THE FINITE ELEMENT BASED SIMULATION OF DOPANT DIFFUSION IN SILICON.

Process simulation programs are valuable in generating accurate impurity profiles. Apart from accuracy the programs should also be efficient so as not to consume vast computer memory. This is especially true for devices and circuits of VLSI complexity. In this paper a remeshing scheme to make the finite element based solution of the non-linear diffusion equation more efficient is proposed. A remeshing scheme based on comparing the concentration values of adjacent node was then implemented and found to remove the problems of oscillation.