Monte Carlo Simulation versus Sensitivity Analysis

Lorén, Sara; Johannesson, Pär; Maré, Jacques de

doi:10.1002/9780470748794.ch8

Cited by 4 publications

(3 citation statements)

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“…In future work, due to our initial results on the communication quality that call for more sensitivity of the parameters used, together with the recent criticisms of the mathematical basis of the patterns of team dynamics based on Losada's parameters (Brown, Sokal, & Friedman, 2013; see also Hämäläinen, Luoma, & Saarinen, 2014), we will reexamine the results utilizing Monte Carlo sensitivity analysis (e.g., Lorén, Johannesson, & de Maré, 2009). The analysis will estimate, for example, the most sensitive Losada's initial parameters, which make the normalized communication values too uniform or saturated in the context of new learning teams.…”

Section: Discussionmentioning

confidence: 99%

Student teams’ development over time: tracing the relationship between the quality of communication and teams’ performance

Pöysä-Tarhonen

Elen

Tarhonen³

2016

Higher Education Research & Development

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Current discussions in higher education and alumni training acknowledge the challenges training programs face in responding to the authentic needs of the labor market. In addition to academic knowledge, higher education institutions are expected to provide general 21st-century skills, such as problem-solving, critical thinking, collaboration, and interpersonal skills. To meet these challenges, many institutions utilize collaborative pedagogies such as learning in teams. However, teamwork in higher education tends to focus primarily on the task aspects of performance at the expense of the team aspects, and for educators, there may be no feasible way to assess whether the students are learning to work successfully as teams. This paper explores how new student teams (n = 3) that simulate real business teams by taking a challenging entrepreneur assessment, developed over three semesters for general skills (i.e., communication), and whether the improvement in their communication also indicated the teams' improved performance (i.e., financial success). As an analytical tool, the study relies on initial parameters on teams' microdynamics of communication (Losada, 1999) normalized with fuzzy logic. In accordance with the current understanding of team development, the results did not show any linear improvement, but the quality of communication in the teams improved episodically. Further, the results provide evidence of the possible relationship between the improved quality of communication and the teams' collective financial success. However, in future work, due to the lack of sensitivity of the parameters in this context together with the recent criticisms of the mathematical basis of the patterns of team dynamics based on Losada's parameters, they will be reexamined with a Monte Carlo sensitivity analysis.

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Section: Discussionmentioning

confidence: 99%

Student teams’ development over time: tracing the relationship between the quality of communication and teams’ performance

Pöysä-Tarhonen

Elen

Tarhonen³

2016

Higher Education Research & Development

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“…The important consequence is that knowledge about statistical properties beyond mean values and standard deviations is rare. Methods based on full statistical distributions, like probabilistic software integration or Monte Carlo simulations, will in most cases be highly sensitive to subjective choice of input distribution …”

Section: Thinking About Reliabilitymentioning

confidence: 99%

“…Methods based on full statistical distributions, like probabilistic software integration or Monte Carlo simulations, will in most cases be highly sensitive to subjective choice of input distribution. 27 For robust design using demand-capacity modeling, we promote the so-called second-moment approach, which is based on assessing the total uncertainty by adding the uncertainties from all the contributing sources, and we will later discuss its advantages and limitations. This is exactly the backbone of the VMEA method.…”

Section: 23mentioning

confidence: 99%

A Robustness Approach to Reliability

Johannesson

Bergman

Svensson

et al. 2012

Quality & Reliability Eng

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Reliability of products is here regarded with respect to failure avoidance rather than probability of failure. To avoid failures,\ud we emphasize variation and suggest some powerful tools for handling failures due to variation. Thus, instead of technical\ud calculation of probabilities from data that usually are too weak for correct results, we emphasize the statistical thinking that\ud puts the designers focus on the critical product functions.\ud Making the design insensitive to unavoidable variation is called robust design and is handled by (i) identification and\ud classification of variation, (ii) design of experiments to find robust solutions, and (iii) statistically based estimations of proper\ud safety margins.\ud Extensions of the classical failure mode and effect analysis (FMEA) are presented. The first extension consists of identifying\ud failure modes caused by variation in the traditional bottom–up FMEA analysis. The second variation mode and effect analysis\ud (VMEA) is a top–down analysis, taking the product characteristics as a starting point and analyzing how sensitive these\ud characteristics are to variation.\ud In cases when there is sufficient detailed information of potential failure causes, the VMEA can be applied in its most\ud advanced mode, the probabilistic VMEA. Variation is then measured as statistical standard deviations, and sensitivities are\ud measured as partial derivatives. This method gives the opportunity to dimension tolerances and safety margins to avoid\ud failures caused by both unavoidable variation and lack of knowledge regarding failure processe

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2013

Guide to Load Analysis for Durability in Vehicle Engineering

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Monte Carlo Simulation versus Sensitivity Analysis

Cited by 4 publications

References 3 publications

Student teams’ development over time: tracing the relationship between the quality of communication and teams’ performance

Student teams’ development over time: tracing the relationship between the quality of communication and teams’ performance

A Robustness Approach to Reliability

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