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This paper makes a theoretical innovation by integrating two key principles – mutual forbearance and the principle of congruity – into one general process model. It examines the micro‐mechanisms underlying the formation of a mutual‐forbearance agreement and explicates the role of time and of individual actions. We further understanding of the process of cooperation building by drawing a parallel between early stages of the formation process of mutual forbearance and cooperation, and argue that mutual forbearance may, under certain conditions, lead to long‐term cooperation or, if mismanaged, completely smother any chances of it. A prospective agreement may be put at risk when potential contributions are evaluated differently by each party and no action to mitigate the consequences is taken; even more so in a mutual‐forbearance context when the parties can only observe their counterparts’ actions through the market. Our model takes into account the micro‐mechanisms associated with the time between the actions of one entity/individual (e.g. the top manager) and the reaction of another entity/individual, the boundary conditions of the background to those actions and the alternative actions available during this time. Propositions for further exploration and implications are drawn.

The task of detecting anomalous data patterns is as important in practical applications as challenging. In the context of spatial data, recognition of unexpected trajectories brings additional difficulties, such as high dimensionality and varying pattern lengths. We aim to tackle such a problem from a probability density estimation point of view, since it provides an unsupervised procedure to identify out of distribution samples. More specifically, we pursue an approach based on normalizing flows, a recent framework that enables complex density estimation from data with neural networks. Our proposal computes exact model likelihood values, an important feature of normalizing flows, for each segment of the trajectory. Then, we aggregate the segments' likelihoods into a single coherent trajectory anomaly score. Such a strategy enables handling possibly large sequences with different lengths. We evaluate our methodology, named aggregated anomaly detection with normalizing flows (GRADINGS), using real world trajectory data and compare it with more traditional anomaly detection techniques. The promising results obtained in the performed computational experiments indicate the feasibility of the GRADINGS, specially the variant that considers autoregressive normalizing flows., Accepted as a conference paper at 2020 International Joint Conference on Neural Networks (IJCNN 2020), part of 2020 IEEE World Congress on Computational Intelligence (IEEE WCCI 2020)

Made available in DSpace on 2020-11-19T09:14:26Z (GMT). No. of bitstreams: 1 Praticas-agroflorestais-no-Rio-de-Janeiro.pdf: 211634 bytes, checksum: 8e234cde64826eadd8531cbde11d71ba (MD5) Previous issue date: 2020 Anais do XI Congresso Brasileiro de Agroecologia, São Cristóvão, Sergipe.

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Gaussian Process Latent Variable Model (GPLVM) is a flexible framework to handle uncertain inputs in Gaussian Processes (GPs) and incorporate GPs as components of larger graphical models. Nonetheless, the standard GPLVM variational inference approach is tractable only for a narrow family of kernel functions. The most popular implementations of GPLVM circumvent this limitation using quadrature methods, which may become a computational bottleneck even for relatively low dimensions. For instance, the widely employed Gauss-Hermite quadrature has exponential complexity on the number of dimensions. In this work, we propose using the unscented transformation instead. Overall, this method presents comparable, if not better, performance than offthe-shelf solutions to GPLVM and its computational complexity scales only linearly on dimension. In contrast to Monte Carlo methods, our approach is deterministic and works well with quasi-Newton methods, such as the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. We illustrate the applicability of our method with experiments on dimensionality reduction and multistep-ahead prediction with uncertainty propagation., 10 pages, currently under review

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