Telepresence in cross-site business process simulation - lessons learnt in technology,social interaction and organizational learning

Re-engineering global business processes requires innovative methods for inter-organizational learning. This article describes and analyses an experimental telepresence process simulation that was applied to develop and train the cross-site New Product Introduction process of a global telecommunication company. Interactive, real-time audio and video connections over the Internet supported the process-oriented discussion between two groups in distant locations. Identical visual process charts and other information, projected on the walls of the two auditoriums, helped the two teams to discuss and understand the process. Audibility of the discussion was crucial for learning, but the visual 'talking heads' of the participants distorted concentration. Facilitation at both ends and local simulation periods smoothed the interaction and the imbalances between the locations. The simulation achieved positive effects in immediate learning and in motivation for change, although the telepresence caused some limitations. The ultimate goal, the feeling of real presence, was not reached. But when time gets more expensive, and travelling less preferable, telepresence simulation can be a feasible group-mode e-learning method for inter-organizational learning.     

Likelihood-free inference via classification

Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data. While widely applicable, a major difficulty in this framework is how to measure the discrepancy between the simulated and observed data. Transforming the original problem into a problem of classifying the data into simulated versus observed, we find that classification accuracy can be used to assess the discrepancy. The complete arsenal of classification methods becomes thereby available for inference of intractable generative models. We validate our approach using theory and simulations for both point estimation and Bayesian inference, and demonstrate its use on real data by inferring an individual-based epidemiological model for bacterial infections in child care centers.     

Structure Learning of Contextual Markov Networks using Marginal Pseudo‐likelihood

Markov networks are popular models for discrete multivariate systems where the dependence structure of the variables is specified by an undirected graph. To allow for more expressive dependence structures, several generalizations of Markov networks have been proposed. Here, we consider the class of contextual Markov networks which takes into account possible context‐specific independences among pairs of variables. Structure learning of contextual Markov networks is very challenging due to the extremely large number of possible structures. One of the main challenges has been to design a score, by which a structure can be assessed in terms of model fit related to complexity, without assuming chordality. Here, we introduce the marginal pseudo‐likelihood as an analytically tractable criterion for general contextual Markov networks. Our criterion is shown to yield a consistent structure estimator. Experiments demonstrate the favourable properties of our method in terms of predictive accuracy of the inferred models.     

Local dimension reduction of summary statistics for likelihood-free inference

Statistics and Computing - Approximate Bayesian computation (ABC) and other likelihood-free inference methods have gained popularity in the last decade, as they allow rigorous statistical inference...     

From where do legislators draw scientific knowledge? Organizations as scientific authorities in four countries' parliamentary debates

Organizations far beyond traditional academic institutions have become prolific science producers, with many now providing evidence-based advice for national governments and policy-makers. Neo-institutional sociology explains organizations' growing investment in research activities and research-based policy advice by the all-embracing scientization and the expansion of the educated population, phenomena observable throughout the world. There is, however, considerably less knowledge about how the organizations' increased knowledge production and the supply of science-based policy advice are reflected in national policy-making, including the legislative work of parliaments, and to what extent distinct organizations are deemed authoritative in different countries. In this paper, we examine how different organizations are used as scientific authorities in parliamentary debates over new legislation. Drawing on analyses of 576 parliamentary debates from Australia, Finland, Kenya, and the United Kingdom, we study what organizations are acknowledged as scientific authorities and the relative weight of different organization types in the context of political debates over new legislation. The results reveal that while organizations in general are frequently evoked as scientific authorities in all four countries, there is remarkable variation in the types of organizations considered authoritative in different national contexts. We elaborate these findings by analysing ways in which politicians evaluate organizations as sources of scientific authority. While the same set of evaluative schemas are used in all four countries, each is typically applied to certain types of organization. The results suggest that both the supply of scientific policy advice and political culture shape legislators' rhetorical practices when drawing on organizations' scientific authority.     

Robustness and Precision: How Data Quality May Influence Key Dependent Variables in Infant Eye‐Tracker Analyses

In recent years, eye‐tracking has become a popular method for drawing conclusions about infant cognition. Relatively little attention has been paid, however, to methodological issues associated with infant eye‐tracking. Here, we consider the possibility that systematic differences in the quality of raw eye‐tracking data obtained from different populations and individuals might create the impression of differences in gaze behavior, without this actually being the case. First, we show that lower quality eye‐tracking data are obtained from populations who are younger and populations who are more fidgety and that data quality declines during the testing session. Second, we assess how these differences in data quality might influence key dependent variables in eye‐tracking analyses. We show that lower precision data can appear to suggest a reduced likelihood to look at the eyes in a face relative to the mouth. We also show that less robust tracking may manifest as slower reaction time latencies (e.g., time to first fixation). Finally, we show that less robust data can manifest as shorter first look/visit duration. We argue that data quality should be reported in all analyses of infant eye‐tracking data and/or that steps should be taken to control for data quality before performing final analyses.     

Experimental designs for binary data in switching measurements on superconducting Josephson junctions

Summary.  We study the optimal design of switching measurements of small Josephsonjunction circuits which operate in the macroscopic quantum tunnelling regime. In the experiment, sequences of current pulses are applied to the Josephson junction sample, while the voltage over the structure is monitored. The appearance of a voltage pulse to a single applied current pulse, being governed by the laws of quantum mechanics, is purely random. Starting from the D -optimality criterion we derive the optimal design for the estimation of the unknown parameters of the underlying Gumbel-type distribution. As a practical method for the measurements, we propose a sequential design that combines heuristic search for initial estimates and maximum likelihood estimation. The design presented has immediate applications in the area of superconducting electronics, implying faster data acquisition. The experimental results presented confirm the usefulness of the method.     

Estimation of True Salmonella Prevalence Jointly in Cattle Herd and Animal Populations Using Bayesian Hierarchical Modeling

The Finnish salmonella control program (FSCP) for beef production is based on both randomized and selective testing of herds and animals. Sampling of individual animals in abattoirs is randomized. Herds are selectively tested for salmonella on the basis of clinical symptoms and/or other factors. Risk assessment of FSCP is inherently difficult due to the complexity of the complete data set, especially if the detailed labeling of the testing types is lost. However, for a risk assessment of the whole production chain, methods for exploiting all available data should be considered. For this purpose, a hierarchical Bayesian model of true salmonella prevalence was constructed to combine information at different levels of aggregation: herds in geographical regions and individual animals arriving for slaughter. The conditional (municipality specific) probability of selection of a herd for testing was modeled given the underlying true infection status of the herd and information about the general sampling activity in the specific region. The model also accounted for the overall sensitivity of the sampling methods, both at the herd and at the animal level. In 1999, the 95% posterior probability intervals of true salmonella prevalence in the herd population, in individual cattle, and in slaughter animal populations were [0.54%, 1.4%] (mode 0.8%), [0.15%, 0.39%] (mode 0.2%), and [0.12%, 0.36%] (mode 0.2%), respectively. The results will be further exploited in other risk assessments focusing on the subsequent parts of the beef production chain, and in evaluation of the FSCP.     

Bayesian model learning based on a parallel MCMC strategy

We introduce a novel Markov chain Monte Carlo algorithm for estimation of posterior probabilities over discrete model spaces. Our learning approach is applicable to families of models for which the marginal likelihood can be analytically calculated, either exactly or approximately, given any fixed structure. It is argued that for certain model neighborhood structures, the ordinary reversible Metropolis-Hastings algorithm does not yield an appropriate solution to the estimation problem. Therefore, we develop an alternative, non-reversible algorithm which can avoid the scaling effect of the neighborhood. To efficiently explore a model space, a finite number of interacting parallel stochastic processes is utilized. Our interaction scheme enables exploration of several local neighborhoods of a model space simultaneously, while it prevents the absorption of any particular process to a relatively inferior state. We illustrate the advantages of our method by an application to a classification model. In particular, we use an extensive bacterial database and compare our results with results obtained by different methods for the same data.