Making Consensus Tractable

Characteristic-function-based independent component analysis

Common Business Big Data Management and Decision Model

We address the problem of identifying the structure of an undirected graph from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. The fresh look advocated here leverages concepts from convex optimization and stationarity of graph signals, in order to identify the graph shift operator (a matrix representation of the graph) given only its eigenvectors . These spectral templates can be obtained, e.g., from the sample covariance of independent graph signals diffused on the sought network. The novel idea is to find a graph shift that, while being consistent with the provided spectral information, endows the network with certain desired properties such as sparsity. To that end, we develop efficient inference algorithms stemming from provably tight convex relaxations of natural nonconvex criteria, particularizing the results for two shifts: the adjacency matrix and the normalized Laplacian. Algorithms and theoretical recovery conditions are developed not only when the templates are perfectly known, but also when the eigenvectors are noisy or when only a subset of them are given. Numerical tests showcase the effectiveness of the proposed algorithms in recovering synthetic and real-world networks.

Interval-parameter chance-constrained programming model for uncertainty-based decision making in tire retreading industry

The use of machine learning models in consequential decision making often exacerbates societal inequity, in particular yielding disparate impact on members of marginalized groups defined by race and gender. The area under the ROC curve (AUC) is widely used to evaluate the performance of a scoring function in machine learning, but is studied in algorithmic fairness less than other performance metrics. Due to the pairwise nature of the AUC, defining an AUC-based group fairness metric is pairwise-dependent and may involve both intra-group and inter-group AUCs. Importantly, considering only one category of AUCs is not sufficient to mitigate unfairness in AUC optimization. In this paper, we propose a minimax learning and bias mitigation framework that incorporates both intra-group and inter-group AUCs while maintaining utility. Based on this Rawlsian framework, we design an efficient stochastic optimization algorithm and prove its convergence to the minimum group-level AUC. We conduct numerical experiments on both synthetic and real-world datasets to validate the effectiveness of the minimax framework and the proposed optimization algorithm.

Aiming at the problems of slow network convergence, poor reward convergence stability, and low path planning efficiency of traditional deep reinforcement learning algorithms, this paper proposes a BiLSTM-D3QN (Bidirectional Long and Short-Term Memory Dueling Double Deep Q-Network) path planning algorithm based on the DDQN (Double Deep Q-Network) decision model. Firstly, a Bidirectional Long Short-Term Memory network (BiLSTM) is introduced to make the network have memory, increase the stability of decision making and make the reward converge more stably; secondly, Dueling Network is introduced to further solve the problem of overestimating the Q-value of the neural network, which makes the network able to be updated quickly; Adaptive reprioritization based on the frequency penalty function is proposed. Experience Playback, which extracts important and fresh data from the experience pool to accelerate the convergence of the neural network; finally, an adaptive action selection mechanism is introduced to further optimize the action exploration. Simulation experiments show that the BiLSTM-D3QN path planning algorithm outperforms the traditional Deep Reinforcement Learning algorithm in terms of network convergence speed, planning efficiency, stability of reward convergence, and success rate in simple environments; in complex environments, the path length of BiLSTM-D3QN is 20 m shorter than that of the improved ERDDQN (Experience Replay Double Deep Q-Network) algorithm, the number of turning points is 7 fewer, the planning time is 0.54 s shorter, and the success rate is 10.4% higher. The superiority of the BiLSTM-D3QN algorithm in terms of network convergence speed and path planning performance is demonstrated.

Decision models are essential tools for coastal groundwater management (CGM). A combined simulation-optimization framework is employed to develop these models. One of the main barriers in the widespread application of these models for real-world cases is their large computational burden. Recent advances in efficient computational approaches and robust optimization methods can crack this barrier. This study surveys the scientific basis of CGM to provide an overview on this subject and reviews the-state-of-the-art to clarify recent developments and to outline ideas for improving the computational performance. Key details are presented on the performance and choice of possible robust tools such as efficient evolutionary algorithms (EAs), surrogate models, and parallel processing techniques. Then, the potential challenges remaining in this context are scrutinized, demonstrating open fields for further research, which include issues related to advances in simulating and optimizing phases such as introducing new robust algorithms and considering multi-objective purposes, implementing novel and high-performance tools, considering global concerns (e.g. climate change impacts), enhancing the existing models to fit the real world, and taking into account the complexities of real-world applications (e.g. uncertainties in the modeling parameters, and data acquisition). Finally, the outcomes of the systematic review are applied to solve a real-world CGM problem in Iran, to quantitatively examine the performance of combined implementation of some of the suggested tools. It is revealed that the required computational time is considerably reduced by as much as three orders of magnitude when correct combinations of robust EAs, surrogate model, and parallelization technique are utilized.

A concise consensus support model for group decision making with reciprocal preference relations based on deviation measures

Due to the inevitable focus on core competencies, even small- and medium-sized companies are increasingly forced to form supply chain (SC) networks. However, their specific situation is often characterized by a lack of equity and limited access to capital markets, so that bank loans must then be used to initiate production and distribution. Within a short-term multi-day planning horizon, both operations and finance must be scheduled precisely in order to obtain practical instructions for each network partner and the network managers. For this purpose, continuous-time modeling is required. Additionally, a coordination of monetary consequences resulting from both site-specific operational events and network-wide financial transactions is necessary to prevent insolvency. As bank overdrafts can be used to overcome financial imbalances during short periods (e.g., days or even hours), appropriate time intervals for liquidity management should be determined. The implementation of these intervals requires discrete-time modeling. In this context, the main challenge is to combine both of the aforementioned modeling techniques within a common decision model. To address this problem, a novel mixed-integer nonlinear program (MINLP) is developed, which enables exact planning and scheduling of SC operations as well as related financial transactions on the one hand, and periodic liquidity balancing on the other hand. A numerical analysis was based on a test scenario with randomly generated data. As we found out that even small problem instances of the MINLP, e.g., a three-stage supply chain with three sites in each stage, were not computable with high-performance hardware and a commercial nonlinear standard solver, we additionally propose an equivalent linearized version of the decision model. The latter could be optimized within acceptable computation time using the CPLEX solver.

A decision model on human-robot collaborative routing for automatic logistics

Risk aversion has formed an integral part in most applied decision models under uncertainty, where the latter is generated by a probabilistic mechanism. This mechanism is not completely known, so that the environment under which decisions are to be made is random. However, the source of randomness may differ from one application to another, hence the decision-making response may vary. For example, in micro-economic models, consumer’s or producer’s response behavior is analyzed under conditions of price or cost parameter uncertainty and this behavior is compared with the case when there is no uncertainty. In engineering systems having a number of channels, the reliability levels may vary between channels and the noise elements may vary: the decision problem is one of maximizing the system reliability. In quality control, a sample inspection plan seeks to determine an acceptable quality level (AQL) of a product lot by minimizing thecost of making wrong decisions, where the latter involves producer’s risk (α) and consumer’s risk (β). The probability that a sampling plan will reject AQL is called the producer’s risk a the Type I error, whereas the probability that the plan will accept fraction-defective levels (FDL) is called consumer’s risk β the Type II error. Usually, if the lot quality is equal to or better than AQL quality, pa is equal to or greater than (1−α) where pa denotes the probability of acceptance which is usually 0.95 α = 0.05. Similarly, if the lot quality is equal to or worse than FDL quality, pa is equal to or less than β, the Type II decision error and pa is usually 0.10.

European Association of Nuclear Medicine (EANM) Focus 4 consensus recommendations: molecular imaging and therapy in haematological tumours

An investigation of the role of leadership in consensus decision-making

Computational models for human decision making are typically based on the properties of bistable dynamical systems where each attractor represents a different decision. A limitation of these models is that they do not readily account for the fragilities of human decision making, such as “choking under pressure”, indecisiveness and the role of past experiences on current decision making. Here we examine the dynamics of a model of two interacting neural populations with mutual time–delayed inhibition. When the input to each population is sufficiently high, there is bistability and the dynamics is determined by the relationship of the initial function to the separatrix (the stable manifold of a saddle point) that separates the basins of attraction of two co–existing attractors. The consequences for decision making include long periods of indecisiveness in which trajectories are confined in the neighborhood of the separatrix and wrong decision making, particularly when the effects of past history and irrelevant information (“noise”) are included. Since the effects of delay, past history and noise on bistable dynamical systems are generic, we anticipate that similar phenomena will arise in the setting of other physical, chemical and neural time–delayed systems which exhibit bistability.

A consensus model for group decision making under additive reciprocal matrices with flexibility