[A quite significant announcement last October from TOMACS that I had missed:]
To improve the reproducibility of modeling and simulation research, TOMACS is pursuing two strategies.
Number one: authors are encouraged to include sufficient information about the core steps of the scientific process leading to the presented research results and to make as many of these steps as transparent as possible, e.g., data, model, experiment settings, incl. methods and configurations, and/or software. Associate editors and reviewers will be asked to assess the paper also with respect to this information. Thus, although not required, submitted manuscripts which provide clear information on how to generate reproducible results, whenever possible, will be considered favorably in the decision process by reviewers and the editors.
Number two: we will form a new replicating computational results activity in modeling and simulation as part of the peer reviewing process (adopting the procedure RCR of ACM TOMS). Authors who are interested in taking part in the RCR activity should announce this in the cover letter. The associate editor and editor in chief will assign a RCR reviewer for this submission. This reviewer will contact the authors and will work together with the authors to replicate the research results presented. Accepted papers that successfully undergo this procedure will be advertised at the TOMACS web page and will be marked with an ACM reproducibility brand. The RCR activity will take place in parallel to the usual reviewing process. The reviewer will write a short report which will be published alongside the original publication. TOMACS also plans to publish short reports about lessons learned from non-successful RCR activities.
[And now the first paper reviewed according to this protocol has been accepted:]
The paper Automatic Moment-Closure Approximation of Spatially Distributed Collective Adaptive Systems is the first paper that took part in the new replicating computational results (RCR) activity of TOMACS. The paper completed successfully the additional reviewing as documented in its RCR report. This reviewing is aimed at ensuring that computational results presented in the paper are replicable. Digital artifacts like software, mechanized proofs, data sets, test suites, or models, are evaluated referring to ease of use, consistency, completeness, and being well documented.
We present a novel method for averaging a sequence of histogram states visited by a Metropolis-Hastings Markov chain whose stationary distribution is the posterior distribution over a dense space of tree-based histograms. The computational efficiency of our posterior mean histogram estimate relies on a statistical data-structure that is sufficient for non-parametric density estimation of massive, multi-dimensional metric data. This data-structure is formalized as statistical regular paving (SRP). A regular paving (RP) is a binary tree obtained by selectively bisecting boxes along their first widest side. SRP augments RP by mutably caching the recursively computable sufficient statistics of the data. The base Markov chain used to propose moves for the Metropolis-Hastings chain is a random walk that data-adaptively prunes and grows the SRP histogram tree. We use a prior distribution based on Catalan numbers and detect convergence heuristically. The L1-consistency of the the initializing strategy over SRP histograms using a data-driven randomized priority queue based on a generalized statistically equivalent blocks principle is proved by bounding the Vapnik-Chervonenkis shatter coefficients of the class of SRP histogram partitions. The performance of our posterior mean SRP histogram is empirically assessed for large sample sizes simulated from several multivariate distributions that belong to the space of SRP histograms.
