Heider's balance theory emphasizes cognitive consistency in assessing others, as expressed by the phrase “The enemy of my enemy is my friend.” At the same time, the theory of indirect reciprocity provides us wit...
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Heider's balance theory emphasizes cognitive consistency in assessing others, as expressed by the phrase “The enemy of my enemy is my friend.” At the same time, the theory of indirect reciprocity provides us with a dynamical framework to study how to assess others based on their actions as well as how to act toward them based on the assessments. Well known are the “leading eight” from L1 to L8, the eight norms for assessment and action to foster cooperation in social dilemmas while resisting the invasion of mutant norms prescribing alternative actions. In this work, we begin by showing that balance is equivalent to stationarity of dynamics only for L4 and L6 (stern judging) among the leading eight. Stern judging reflects an intuitive idea that good merits reward, whereas evil warrants punishment. By analyzing the dynamics of stern judging in complete graphs, we prove that this norm almost always segregates the graph into two mutually hostile groups as the graph size grows. We then compare L4 with stern judging: The only difference of L4 is that a good player's cooperative action toward a bad one is regarded as good. This subtle difference transforms large populations governed by L4 to a “paradise” where cooperation prevails and positive assessments abound. Our study thus helps us understand the relationship between individual norms and their emergent consequences at a population level, shedding light on the nuanced interplay between cognitive consistency and segregation dynamics.
Project Portfolio Management (PPM) is essential for organizations aiming to align projects with strategic goals. Different organizations adopt diverse PPM frameworks and standards to manage project portfolios each emp...
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The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to i...
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The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.
The use of machine learning and Natural Language Processing (NLP) technologies can assist in the preservation and revitalization of indigenous languages, particularly those classified as "low-resource". Give...
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The incorporation of artificial intelligence (AI) into power-related applications signifies a new and unexplored domain in machine learning for predicting power generation. This novel method utilizes prediction models...
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Analyzing program similarity is useful in automated assessment grading, plagiarism detection, or proving refactor equivalence. The precondition of existing approaches to program similarity is that the programs to comp...
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The field of Social Robotics focuses on developing autonomous robots that can interact socially and assist with various tasks. However, the design and execution of such robots are complex they need to understand their...
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作者：
Liu, YunpengQu, DanSchool of Information Systems Engineering
University of Information Engineering Laboratory For Advanced Computing and Intelligence Engineering Department of Artificial Intelligence Henan China Systems Engineering
University of Information Engineering Laboratory For Advanced Computing and Intelligence Engineering Henan China
Limited data availability remains a significant challenge for Whisper's low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully re...
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The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there ...
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Object tracking based on deep learning is a challenging task. Accurate object detection and tracking requires a neural network that is deeper than a typical network. Therefore, it requires more levels of processing an...
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