Concern Solving Not Problem Solving

The object of this study is problem solving. The authors believe that considerable advantage can be gained from designers talking about clients’ concerns, rather than their problems. Using Mitroff and Linstone’s (1993) division of the knowing world into objective, subject and personal, the authors are suggesting need for a more personal perspective. Further, their and Checkland’s [1999] call for perspectives thinking can be used to very usefully separate the problem into object-like and subjective-like elements. The thing being studied is separated from the client’s concerns about that thing (treated as an object). The evidence to support this conclusion includes the multiple perspectives literature, and the first author’s many years of experiences in problem solving both is IS and in research design. A simple graphical tool is presented that the author has found useful to assist group discussion about separating the object under consideration from the client’s concerns

Fitting the Phenomenological MSSM

We perform a global Bayesian fit of the phenomenological minimal supersymmetric standard model (pMSSM) to current indirect collider and dark matter data. The pMSSM contains the most relevant 25 weak-scale MSSM parameters, which are simultaneously fit using `nested sampling' Monte Carlo techniques in more than 15 years of CPU time. We calculate the Bayesian evidence for the pMSSM and constrain its parameters and observables in the context of two widely different, but reasonable, priors to determine which inferences are robust. We make inferences about sparticle masses, the sign of the $\mu$ parameter, the amount of fine tuning, dark matter properties and the prospects for direct dark matter detection without assuming a restrictive high-scale supersymmetry breaking model. We find the inferred lightest CP-even Higgs boson mass as an example of an approximately prior independent observable. This analysis constitutes the first statistically convergent pMSSM global fit to all current data.Comment: Added references, paragraph on fine-tunin

Cosmological parameter estimation with large scale structure and supernovae data

Most cosmological parameter estimations are based on the same set of observations and are therefore not independent. Here, we test the consistency of parameter estimations using a combination of large-scale structure and supernovae data, without cosmic microwave background (CMB) data. We combine observations from the IRAS 1.2 Jy and Las Campanas redshift surveys, galaxy peculiar velocities and measurements of type Ia supernovae to obtain h=0.57_{-0.14}^{+0.15}, Omega_m=0.28+/-0.05 and sigma_8=0.87_{-0.05}^{+0.04} in agreement with the constraints from observations of the CMB anisotropies by the WMAP satellite. We also compare results from different subsets of data in order to investigate the effect of priors and residual errors in the data. We find that some parameters are consistently well constrained whereas others are consistently ill-determined, or even yield poorly consistent results, thereby illustrating the importance of priors and data contributions.Comment: (1) Astrophysics Group, Cavendish Laboratory, Cambridge Unviersity, UK (2) Dipartimento di Fisica, Universita di Roma "La Sapienza", Ital

Challenges of Profile Likelihood Evaluation in Multi-Dimensional SUSY Scans

Statistical inference of the fundamental parameters of supersymmetric theories is a challenging and active endeavor. Several sophisticated algorithms have been employed to this end. While Markov-Chain Monte Carlo (MCMC) and nested sampling techniques are geared towards Bayesian inference, they have also been used to estimate frequentist confidence intervals based on the profile likelihood ratio. We investigate the performance and appropriate configuration of MultiNest, a nested sampling based algorithm, when used for profile likelihood-based analyses both on toy models and on the parameter space of the Constrained MSSM. We find that while the standard configuration is appropriate for an accurate reconstruction of the Bayesian posterior, the profile likelihood is poorly approximated. We identify a more appropriate MultiNest configuration for profile likelihood analyses, which gives an excellent exploration of the profile likelihood (albeit at a larger computational cost), including the identification of the global maximum likelihood value. We conclude that with the appropriate configuration MultiNest is a suitable tool for profile likelihood studies, indicating previous claims to the contrary are not well founded.Comment: 21 pages, 9 figures, 1 table; minor changes following referee report. Matches version accepted by JHE