Consensus Theory Research Articles

The Paradox of Situated Knowledge: Toward an Existential Embedment Theory of Perceptual Truth

ABSTRACTTruth is a thorny issue for the sociology of knowledge. In emphasizing social influence on knowledge production, sociologists tend to disavow the objectivity of truth and slide down the slope of the consensus theory and the nihilism of post‐truth. In fighting science denial, on the other hand, sociologists often find themselves aligning with the correspondence theory and the naïve notion of naked truth. The aim of this article is to advance a position that recognizes social influence on knowledge attainment without obviating the objectivity in truth. The key to this argument lies in the concept of existential embedment that unifies objectivity and subjectivity in human struggles for survival and prosperity, from which truth originates and to which truth contributes. Existential embedment also anchors knowledge by providing the foundation for truth validation. Depending on the modalities of knowledge attainment, truth is divided into four types with varying degrees of veridical certitude, each having its own criterion for validity assessment. Knowledge that passes the validation test is accepted as truth within the horizon of the given realm of existential embedment, which may change as the existential activity of the knower changes.

Consensus-based methods for distributed systems, blockchain, and voting: a survey

ABSTRACT Condorcet and Borda first introduced the consensus theory in the eighteenth century when they studied voting systems. Consensus problems have been investigated for a long time in many fields such as economics, sociology, computer science, bioinformatics, collective intelligence, and automation. They are fundamental to developing distributed systems. In recent years, with blockchain's great applications and potential, consensus problems have received significant attention from many researchers. The consensus method allows a blockchain network to function in a distributed system, especially IoT (Internet of Things). Voting consensus has been applied in distributed systems and blockchain, and consensus in distributed systems is also used in blockchain systems. This study reviews the consensus methods in voting, distributed systems, and blockchain.

From employee socialization to co‐evolution: A lifespan multidisciplinary conceptualization

AbstractVarious theories have highlighted how employees evolve in their organization and how organizations influence this process, but only portray part of the complex relations among these interacting social entities. We thus propose a meta‐theory to unify these multiple theories, including symbolic interactionism, employee/organizational socialization theory, human resource management (HRM) systems theory, cultural consensus theory, and self‐determination theory. This integration seeks to increase our understanding of the co‐evolution process unfolding over time between individuals and the organizations to which they belong. We first propose a multilevel expansion of the symbolic interactionist framework typically used to described employee socialization. In doing so, we integrate organizational culture, climate, identity, image, reputation, and HRM systems as distinct meso‐social phenomena that can be simultaneously considered in the co‐evolution process and themselves be influenced by macro‐social processes. We then outline how this proposed framework can explain the dynamic co‐evolution occurring between employees and the organization, hoping to spur research on the improvement of social entities through psychological means.

HGEE: Learning for Trajectory Prediction with Heterogeneous Graph Interaction and External Embedding of Unmanned Swarm Systems in Adversarial Environment

Trajectory prediction of unmanned swarm systems, serving as the foundation for behavioral and intentional cognition, has attracted extensive attention and made considerable progress in adversarial research. The influence of heterogeneous interaction relationships and external factors is crucial for trajectory prediction. Consequently, this paper proposes the Heterogeneous Graph with External Embedding (HGEE) network. We model the latent variables as multi-layer heterogeneous graphs based on prior knowledge of different interaction relationships and propose a method for calculating edge embeddings for heterogeneous graphs. Furthermore, we introduce a method that combines external environmental feature with historical observational trajectory data as the input for the decoder, enabling the model to learn the impacts of obstacles, targets, and desired formations on trajectories. We demonstrate that our approach surpasses state-of-the-art models in interaction inference and trajectory prediction through experiments on our proposed formation datasets based on consensus theory, across five evaluation metrics.

Cooperative control of marine vehicles using the receding horizon concept and consensus theory

Cooperative control of marine vehicles using the receding horizon concept and consensus theory

Gender assignment in doctoral theses: revisiting Teseo with a method based on cultural consensus theory

Gender assignment in doctoral theses: revisiting Teseo with a method based on cultural consensus theory

Finite-time consensus of second-order multi-agent connectivity preserving based on adaptive sliding mode control

This study addresses the problem of robust finite-time connectivity preserving consensus for second-order multi-agent systems (MASs) with a limited communication range. Considering that the communication ability of agents in practical applications is limited, this study introduces an innovative approach to the consensus protocol, which involves the incorporation of a potential function aimed at ensuring sustained communication connection within a MAS. In addition, a finite-time fault-tolerant consensus protocol for a second-order MAS with actuator additive faults and external disturbances is designed by combining sliding mode control (SMC) and adaptive control. Following the homogeneous theorem and the finite-time consensus theory, this study concludes that, under specific conditions, a MAS can achieve finite-time consensus. Moreover, an advanced control protocol named a super-twisting sliding mode control protocol is introduced to mitigate the undesirable chattering phenomenon in the SMC. Finally, the effectiveness and superiority of the proposed method are verified by numerical examples.

Cooperative Control and Performance Evaluation of a Linear MIMO Parabolic Spatiotemporal Dynamic System on Directed and Switching Topologies.

The current work concerns on cooperative control of exponential stabilization and control performance improvement in the spatial domain for a linear spatiotemporal dynamic system associated with multiple control actuators and multiple collaborative measurement sensors. By assuming that the system dynamics is modeled by a MIMO parabolic partial differential equation (PPDE) and each sensor can share measurement information with its topological neighbors in directed and switching topological networks, a cooperative control protocol is proposed to achieve the control aim of this article. With the help of a combination of multiagent consensus theory, Lyapunov's method, and integral inequality technique, sufficient conditions are presented for the closed-loop exponential stability of the PPDE in the norm |·|2 . Moreover, some performance indexes are defined to evaluate the closed-loop control performance improvement in the spatial domain. Extensive simulation results are finally presented for a simple numerical example and a practical heat treatment process to verify the effectiveness and performance improvement capability of the proposed cooperative control protocol.

Distributed Cooperative Quantum Learning for Discrete-Time Multiagent Source Exploration With Information Prompts.

In light of optimization theory and swarm evolutionary schemes, under multiple single-integrator mobile agents equipped with sensors and prompters, this article addresses a discrete-time multiagent source exploration problem with information prompts. Regarding information prompts as constraints on the unknown target, by virtue of penalty function skills (PFSs) and sequential unconstrained minimization techniques (SUMTs), the agents are driven toward the source under the guidance of the control strategy. In two cases of available and unavailable gradient information, a quantum potential well, an average optimal position estimator (AOPE), and a global optimal position estimator (GOPE) are introduced into swarm evolutionary schemes with a periodically oscillating weight, such that distributed cooperative quantum learning (DCQL) policy is proposed as a control strategy under communication restrictions, where AOPE and GOPE are developed relying on distributed consensus theory. In particular, when the gradient is unavailable, we put forth an adaptive generalized Bernstein neural network (AGBNN) to replace it based on excellent properties of Bernstein polynomials and adaptive approaches. Further, a performance analysis for the proposed policy is executed on the convergence and computational complexity, which ensures the accuracy and efficiency of the source exploration in theory. Ultimately, a simulation test is carried out, and the results validate the practicability and effectiveness of the offered method.

Target encirclement for multiple quadrotors with visual detection and localization

Target encirclement is widely used in the field of unmanned aerial vehicles(UAVs), which can effectively monitor and intercept external threats. However, the integration from target detection, localization to final tracking is difficult or costly. This article proposes a complete and inexpensive framework of the target encirclement for multiple quadrotors. The framework consists of three modules: object detection, target localization and formation tracking. Firstly, a one-stage object detector based on a convolutional neural network is used to achieve fast and accurate object detection. Then, combined with the position and attitude states of the quadrotor, a 3D target localization scheme to locate the target position is proposed. Based on consensus theory, a time-varying formation tracking control protocol is proposed. Finally, a multiple quadrotor platform composed of one reconnaissance quadrotor and four hunter quadrotors is built with self-organizing network communication, which avoids the expensive cost of deploying object detection modules on each quadrotor platform. We deployed the framework on the multiple quadrotor platform and conducted static and dynamic localization and encirclement experiments with a minibus as the target. The result shows that the reconnaissance quadrotors can detect and accurately locate targets over 30 fps, and the average deviation of locating the target minibus could reach a minimum of 0.0712 m. The hunter quadrotors could track and encircle the dynamic moving target minibus in a time-varying formation. Experiments demonstrate the effectiveness and practicality of the proposed framework of the target encirclement for multiple quadrotors.

A Dual-Stage Semi-Supervised Pre-Training Approach for Medical Image Segmentation

Deep neural networks have played a vital role in developing automated methods for addressing medical image segmentation. However, their reliance on labeled data impedes the practicability. Semi-supervised learning is gaining attention for its intrinsic ability to extract valuable information from labeled and unlabeled data with improved performance. Recently, consistency regularization methods have gained interest due to their efficient learning procedures. They are, however, confined to data or network-level perturbations, negating the benefit of having both forms in a single framework. In light of this, we ask an intriguing but unexplored question: can we have both network-level and data-level perturbation in the semi-supervised framework? To this end, we present a holistic approach that integrates data-level perturbation in the model pre-training stage, followed by implicit network-level perturbation in the fine-tuning stage. Furthermore, we incorporate networks with manifold learning paradigms throughout the training to facilitate the formation of robust data representations by ensuring local and global semantic affinities adhering to the theory of consensus. Notably, this may be the first attempt in the semi-supervised medical image segmentation archetype to use data and network-level perturbation with a model pre-training strategy. We extensively validated the efficacy of the proposed framework on three benchmark datasets, namely the Automated Cardiac Diagnosis Challenge, ISIC-2018, and Left Atrial Segmentation Challenge datasets, subjected to severely low-sampled labeled data. Notably, in ACDC (4%), ISIC-2018 (5%), and LA (6%) labeled cases, the proposed method outperforms the second-best method by 2.95%, 1.31%, and 0.71% in the Dice Similarity Metric.

Augmenting predictive models in forensic psychiatry with Cultural Consensus Theory

Abstract Forensic psychiatric hospitals regularly monitor the mental health and forensic risk factors of their patients. As part of this monitoring, staff score patients on various items. Common practice is to aggregate these scores across staff members. However, this is suboptimal because it assumes that assessors are interchangeable and that patients are independent. An improvement over averaging scores is the use of Cultural Consensus Theory (CCT), which imposes a hierarchical model across patients, staff members, and items. While accounting for differences between patients and staff members, CCT estimates a ‘true’ score for each patient on each item based on the consensus among staff members. Here, we apply a CCT model to data from a Dutch maximum-security forensic psychiatric centre and use the inferences to predict violent behaviour in patients. The CCT model outpredicts several alternatives, such as random forest and boosted regression trees, albeit by a small margin. We discuss practical limitations and directions for how future monitoring of patients could be adapted to maximize the added value of a CCT-based approach.

Distributed continuous-time accelerated neurodynamic approaches for sparse recovery via smooth approximation to [formula omitted]-minimization

This paper develops two continuous-time distributed accelerated neurodynamic approaches for solving sparse recovery via smooth approximation to L1-norm minimization problem. First, the L1-norm minimization problem is converted into a distributed smooth optimization problem by utilizing multiagent consensus theory and smooth approximation. Then, a distributed primal–dual accelerated neurodynamic approach is designed by using Karush–Kuhn–Tucker (KKT) condition and Nesterov’s accelerated method. Furthermore, in order to reduce the structure complexity of the presented neurodynamic approach, based on the projection matrix, we eliminate a dual variable in the KKT condition and propose a distributed accelerated neurodynamic approach with a simpler structure. It is proved that the two proposed distributed neurodynamic approaches both achieve O(1t2) convergence rate. Finally, the simulation results of sparse recovery are given to demonstrate the effectiveness of the proposed approaches.

A New Neural Dynamic Learning Framework for Discrete-Time Strict-Feedback Systems: Internal Interaction-Based Weight Adaptive Laws.

This article investigates internal interaction-based dynamic learning control (LC) for uncertain discrete-time strict-feedback systems. On the basis of predict technology, the original system is converted into a common n -step-ahead input-output predict model. The predict model causes every estimated neural weight to converge to n different constants using the existing control framework. To solve such a problem, the predict model is further decomposed into n one-step-ahead subsystems, which can be viewed as n independent agents. Subsequently, the distributed cooperative weight adaptive laws are designed by introducing an undirected and connected interconnection topology among subsystems. By constructing the variable relationship between the subsystems and the n -step-ahead predict model, a new internal weight interaction-based neural dynamic LC framework is proposed for the whole closed-loop system, in which estimated weights at different times share their weight knowledge. The proposed framework ensures the ultimately uniform boundedness of the closed-loop system and achieves the excellent control performance. By combining the consensus theory and a cooperative persistent excitation condition, every estimated weight along the neural input orbit is verified to exponentially converge to a close vicinity of a unique ideal constant, rather than n different constants. Consequently, the developed LC framework facilitates constant weights storage, saves the knowledge storage space, and improves the robustness of knowledge utilization. These characteristics are verified by simulation results.

Exploring Public Opinion on Responsible AI Through The Lens of Cultural Consensus Theory

Exploring Public Opinion on Responsible AI Through The Lens of Cultural Consensus Theory

Adaptive Distributed Fixed-Time Cooperative Three-Dimensional Guidance Law for Multimissiles against Manoeuvring Target

The problem of cooperative interception of the manoeuvring target is investigated in this paper. Firstly, in light of fast fixed-time consensus theory, time-to-go, and undirected topologies, adaptive cooperative guidance along the line-of-sight (LOS) direction is proposed to guarantee impact time synchronization. Next, novel nonsingular terminal sliding mode (NTSM) is designed to establish adaptive fixed-time guidance law for steering LOS angular rates to the origin or its small neighbourhood. Without the knowledge of target manoeuvre, the proposed cooperative guidance law can be provided by lateral and longitudinal accelerations of each missile, while more reasonable and rigorous analysis of fixed-time stability is carried out through the Lyapunov theory. Within the specified time, both control tasks of simultaneous attack and the desired impact angles can be completed before the final time of the guidance process. Finally, numerical simulations demonstrate the feasibility and effectiveness of the proposed scheme.

Inverse-free distributed neurodynamic optimization algorithms for sparse reconstruction

This article proposes three novel inverse-free distributed neurodynamic optimization algorithms to reconstruct sparse signal and image by addressing the L1-minimization problems. Based on multi-agent consensus theory, we successfully transform the original L1-minimization problem into a distributed optimization model. To tackle such model, a three-layer inverse-free distributed algorithm is proposed by using projection operator and derivative feedback, which enjoys global convergence. To simplify the structure of this three-layer distributed algorithm, a time-varying parameter-based two-layer inverse-free distributed algorithm is designed, which has global convergence. Moreover, to accelerate convergence and further simplify the structure of this two-layer distributed algorithm, we develop a Tikhonov-like regularization-based single-layer inverse-free distributed algorithm, which achieves consensus within finite time for any given initial point and possesses an O(1/ξ(t)) convergence rate of the linear-equality constraint function. Finally, experimental results on signal and image reconstruction are presented to illustrate the efficiency of our inverse-free distributed algorithms.

The Illusion of Supremacy: A Theory of Indifference

The Supremacy Clause establishes that federal law takes precedence over conflicting state law and can consequently nullify state legislation. However, the inconsistent enforcement of the Supremacy Clause regarding the coexistence of conflicting state and federal cannabis laws raises questions about the legitimacy of the federal government. By drawing on the contrasting views of social conflict theory and consensus theory, a new indifference theory better encompasses the rare phenomenon of the federal government choosing not to invoke the Supremacy Clause when it has the means to. This highlights the potential for delegitimization in the federal government due to the inconsistency in enforcing the Supremacy Clause, leading to the public’s potential noncompliance of federal law. The federal government must address these inconsistencies to retain a strong legitimate legal system.

Contributed Session I: Human cone response models for optoretinography with FF-SS-OCT and adaptive optics.

Recent work has shown that human rod and cone outer segments (ROS and COS, respectively) deform in response to visual stimuli. In phase-based optoretinography (ORG) the phases of backscattered light from the inner/outer segment junction (IS/OS) and the COS/ROS tips (COST/ROST), is measured, which allows observation of stimulus-evoked, nanometer-scale changes in the OS length. In this work, we used a full-field swept-source OCT with AO that allowed up to kHz volume rates. ORG responses were recorded in two healthy volunteers, with photopigment bleaching levels in the range of 1-60 %, and modeled using an exponential sum. The proposed harmonic oscillator-based response model allowed us to describe the shape of the cone's ORG responses by amplitudes of deflection and relaxation times. Our preliminary results show that responses to complex stimuli were consistent with photopigment availability, which in the context of the consensus theory that adaptation in cones is mediated by photopigment suggests that the ORG may be a useful way to probe light adaptation in cones. The development of simple quantitative parameters describing the ORG response should benefit future clinical applications and help to track the progress of blinding diseases.

Using a cognitive model to understand crowdsourced data from citizen scientists.

Threatened species monitoring can produce enormous quantities of acoustic and visual recordings which must be searched for animal detections. Data coding is extremely time-consuming for humans and even though machine algorithms are emerging as useful tools to tackle this task, they too require large amounts of known detections for training. Citizen scientists are often recruited via crowd-sourcing to assist. However, the results of their coding can be difficult to interpret because citizen scientists lack comprehensive training and typically each codes only a small fraction of the full dataset. Competence may vary between citizen scientists, but without knowing the ground truth of the dataset, it is difficult to identify which citizen scientists are most competent. We used a quantitative cognitive model, cultural consensus theory, to analyze both empirical and simulated data from a crowdsourced analysis of audio recordings of Australian frogs. Several hundred citizen scientists were asked whether the calls of nine frog species were present on 1260 brief audio recordings, though most only coded a fraction of these recordings. Through modeling, characteristics of both the citizen scientist cohort and the recordings were estimated. We then compared the model's output to expert coding of the recordings and found agreement between the cohort's consensus and the expert evaluation. This finding adds to the evidence that crowdsourced analyses can be utilized to understand large-scale datasets, even when the ground truth of the dataset is unknown. The model-based analysis provides a promising tool to screen large datasets prior to investing expert time and resources.