Impact of different belief facets on agents' decision -- a refined cognitive architecture to model the interaction between organisations' institutional characteristics and agents' behaviour

• URL: http://arxiv.org/abs/2004.11858v2
• Date: Fri, 7 Aug 2020 07:04:24 GMT
• Title: Impact of different belief facets on agents' decision -- a refined cognitive architecture to model the interaction between organisations' institutional characteristics and agents' behaviour
• Authors: Amir Hosein Afshar Sedigh, Martin K. Purvis, Bastin Tony Roy Savarimuthu, Christopher K Frantz, and Maryam A. Purvis
• Abstract summary: We investigate the impact of personality and the way that an agent weights its internal beliefs and social sanctions on an agent's actions. The study also uses the concept of cognitive dissonance associated with the fairness of institutions to investigate the agents' behaviour.
• Score: 0.8563354084119061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a conceptual refinement of agent cognitive architecture inspired from the beliefs-desires-intentions (BDI) and the theory of planned behaviour (TPB) models, with an emphasis on different belief facets. This enables us to investigate the impact of personality and the way that an agent weights its internal beliefs and social sanctions on an agent's actions. The study also uses the concept of cognitive dissonance associated with the fairness of institutions to investigate the agents' behaviour. To showcase our model, we simulate two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company. The results demonstrate the importance of internal beliefs of agents as a pivotal aspect for following institutional rules.

Related papers

• Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
  arXiv  Detail & Related papers  (2025-03-12T21:39:38Z)
• Exploring the Impact of Reflexivity Theory and Cognitive Social Structures on the Dynamics of Doctor-Patient Social System [0.0]
  We create two different models for a doctor-patient system. One retains the established assumptions, while the other incorporates principles of reflexivity theory and cognitive social structures. We utilize a microbial genetic algorithm to optimize the behaviour of the physician and patient agents in both models.
  arXiv  Detail & Related papers  (2024-11-08T23:23:13Z)
• The Essential Role of Causality in Foundation World Models for Embodied AI [102.75402420915965]
  Embodied AI agents will require the ability to perform new tasks in many different real-world environments. Current foundation models fail to accurately model physical interactions and are therefore insufficient for Embodied AI. The study of causality lends itself to the construction of veridical world models.
  arXiv  Detail & Related papers  (2024-02-06T17:15:33Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• RealBehavior: A Framework for Faithfully Characterizing Foundation Models' Human-like Behavior Mechanisms [45.97077960079147]
  We introduce a framework, RealBehavior, which is designed to characterize the humanoid behaviors of models faithfully. Our findings suggest that a simple application of psychological tools cannot faithfully characterize all human-like behaviors.
  arXiv  Detail & Related papers  (2023-10-17T12:58:17Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• Diversifying Agent's Behaviors in Interactive Decision Models [11.125175635860169]
  Modelling other agents' behaviors plays an important role in decision models for interactions among multiple agents. In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model prior to their interactions.
  arXiv  Detail & Related papers  (2022-03-06T23:05:00Z)
• Metacognitive Agents for Ethical Decision Support: Conceptual Model and Research Roadmap [0.0]
  An ethical value-action gap exists when there is a discrepancy between intentions and actions. This paper outlines a roadmap for translating cognitive-affective models into assistant agents to help make value-aligned decisions.
  arXiv  Detail & Related papers  (2022-02-24T11:39:57Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)
• Impact of meta-roles on the evolution of organisational institutions [0.8563354084119061]
  The study embeds agents' meta-roles in the BDI architecture. The study scrutinises the impact of cognitive dissonance in agents due to unfairness of institutions. Results show how change in roles of agents coupled with specific institutional characteristics leads to changes of the rules in the system.
  arXiv  Detail & Related papers  (2020-08-07T07:00:00Z)
• On the Relationship Between Active Inference and Control as Inference [62.997667081978825]
  Active Inference (AIF) is an emerging framework in the brain sciences which suggests that biological agents act to minimise a variational bound on model evidence. Control-as-Inference (CAI) is a framework within reinforcement learning which casts decision making as a variational inference problem.
  arXiv  Detail & Related papers  (2020-06-23T13:03:58Z)

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.