Note: Evolutionary Game Theory Focus Informational Health: The Cocktail Party Effect Through Werewolfgame under Incomplete Information and ESS Search Method Using Expected Gains of Repeated Dilemmas

• URL: http://arxiv.org/abs/2402.18598v1
• Date: Tue, 27 Feb 2024 14:10:34 GMT
• Title: Note: Evolutionary Game Theory Focus Informational Health: The Cocktail Party Effect Through Werewolfgame under Incomplete Information and ESS Search Method Using Expected Gains of Repeated Dilemmas
• Authors: Yasuko Kawahata
• Abstract summary: We explore the state of information disruption caused by the cocktail party effect within the framework of non-perfect information games. We mathematically model and analyze the effects on the gain of each strategy choice and the formation process of evolutionary stable strategies (ESS) under the assumption that the pollution risk of fake news is randomly assigned.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We explore the state of information disruption caused by the cocktail party effect within the framework of non-perfect information games and evolutive games with multiple werewolves. In particular, we mathematically model and analyze the effects on the gain of each strategy choice and the formation process of evolutionary stable strategies (ESS) under the assumption that the pollution risk of fake news is randomly assigned in the context of repeated dilemmas. We will develop the computational process in detail, starting with the construction of the gain matrix, modeling the evolutionary dynamics using the replicator equation, and identifying the ESS. In addition, numerical simulations will be performed to observe system behavior under different initial conditions and parameter settings to better understand the impact of the spread of fake news on strategy evolution. This research will provide theoretical insights into the complex issues of contemporary society regarding the authenticity of information and expand the range of applications of evolutionary game theory.

Related papers

• Evolutionary Reinforcement Learning: A Systematic Review and Future Directions [18.631418642768132]
  Evolutionary Reinforcement Learning (EvoRL) is a solution to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving. EvoRL integrates EAs and reinforcement learning, presenting a promising avenue for training intelligent agents. This systematic review provides insights into the current state of EvoRL and offers a guide for advancing its capabilities in the ever-evolving landscape of artificial intelligence.
  arXiv  Detail & Related papers  (2024-02-20T02:07:57Z)
• Evolutionary game theory: the mathematics of evolution and collective behaviours [1.4685355149711299]
  It summarises some of my recent research directions using evolutionary game theory methods. It includes the analysis of statistical properties of the number of (stable) equilibria in a random evolutionary game. It also includes the modelling of safety behaviours' evolution and the risk posed by advanced Artificial Intelligence technologies in a technology development race.
  arXiv  Detail & Related papers  (2023-11-24T13:42:55Z)
• Interpretable Imitation Learning with Dynamic Causal Relations [65.18456572421702]
  We propose to expose captured knowledge in the form of a directed acyclic causal graph. We also design this causal discovery process to be state-dependent, enabling it to model the dynamics in latent causal graphs. The proposed framework is composed of three parts: a dynamic causal discovery module, a causality encoding module, and a prediction module, and is trained in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-09-30T20:59:42Z)
• Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies [50.10277748405355]
  Noise-Reuse Evolution Strategies (NRES) is a general class of unbiased online evolution strategies methods. We show NRES results in faster convergence than existing AD and ES methods in terms of wall-clock time and number of steps across a variety of applications.
  arXiv  Detail & Related papers  (2023-04-21T17:53:05Z)
• eVAE: Evolutionary Variational Autoencoder [40.29009643819948]
  We propose a novel evolutionary variational autoencoder (eVAE) building on the variational information bottleneck (VIB) theory. eVAE integrates a variational genetic algorithm into VAE with variational evolutionary operators including variational mutation, crossover, and evolution. eVAE achieves better reconstruction loss, disentanglement, and generation-inference balance than its competitors.
  arXiv  Detail & Related papers  (2023-01-01T23:54:35Z)
• Latent Variable Representation for Reinforcement Learning [131.03944557979725]
  It remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of model-based reinforcement learning. We provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models.
  arXiv  Detail & Related papers  (2022-12-17T00:26:31Z)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [77.14319095965058]
  We formulate the OOD problem for node-level prediction on graphs. We develop a new domain-invariant learning approach, named Explore-to-Extrapolate Risk Minimization. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)
• A Critical Review of Information Bottleneck Theory and its Applications to Deep Learning [0.0]
  Deep neural networks have seen unparalleled improvements that continue to impact every aspect of today's society. The information bottleneck theory has emerged as a promising approach to better understand the learning dynamics of neural networks. The goal of this survey is to provide a comprehensive review of IB theory covering it's information theoretic roots and the recently proposed applications to understand deep learning models.
  arXiv  Detail & Related papers  (2021-05-07T14:16:38Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)
• A Theoretical Framework for Target Propagation [75.52598682467817]
  We analyze target propagation (TP), a popular but not yet fully understood alternative to backpropagation (BP) Our theory shows that TP is closely related to Gauss-Newton optimization and thus substantially differs from BP. We provide a first solution to this problem through a novel reconstruction loss that improves feedback weight training.
  arXiv  Detail & Related papers  (2020-06-25T12:07:06Z)
• EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning [41.42230144157259]
  We propose a generic trajectory forecasting framework with explicit relational structure recognition and prediction via latent interaction graphs. Considering the uncertainty of future behaviors, the model is designed to provide multi-modal prediction hypotheses. We introduce a double-stage training pipeline which not only improves training efficiency and accelerates convergence, but also enhances model performance.
  arXiv  Detail & Related papers  (2020-03-31T02:49:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.