Metacognitive Agents for Ethical Decision Support: Conceptual Model and
Research Roadmap
- URL: http://arxiv.org/abs/2202.12039v1
- Date: Thu, 24 Feb 2022 11:39:57 GMT
- Title: Metacognitive Agents for Ethical Decision Support: Conceptual Model and
Research Roadmap
- Authors: Catriona M. Kennedy
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: An ethical value-action gap exists when there is a discrepancy between
intentions and actions. This discrepancy may be caused by social and structural
obstacles as well as cognitive biases. Computational models of cognition and
affect can provide insights into the value-action gap and how it can be
reduced. In particular, metacognition ("thinking about thinking") plays an
important role in many of these models as a mechanism for self-regulation and
reasoning about mental attitudes. This paper outlines a roadmap for translating
cognitive-affective models into assistant agents to help make value-aligned
decisions.
Related papers
- Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilities [101.77467538102924]
Recent advancements in Large Reasoning Models (LRMs) have demonstrated remarkable performance in specialized reasoning tasks.
We show that acquiring deliberative reasoning capabilities significantly reduces the foundational capabilities of LRMs.
We demonstrate that adaptive reasoning -- employing modes like Zero-Thinking, Less-Thinking, and Summary-Thinking -- can effectively alleviate these drawbacks.
arXiv Detail & Related papers (2025-03-23T08:18:51Z) - The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks [96.27754404942364]
Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving capabilities, but their effectiveness in interactive environments can be limited.
This paper introduces and analyzes overthinking in LRMs.
We observe three recurring patterns: Analysis Paralysis, Rogue Actions, and Premature Disengagement.
arXiv Detail & Related papers (2025-02-12T09:23:26Z) - 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) - Why not both? Complementing explanations with uncertainty, and the role
of self-confidence in Human-AI collaboration [12.47276164048813]
We conduct an empirical study to identify how uncertainty estimates and model explanations affect users' reliance, understanding, and trust towards a model.
We also discuss how the latter may distort the outcome of an analysis based on agreement and switching percentages.
arXiv Detail & Related papers (2023-04-27T12:24:33Z) - Exploring the Trade-off between Plausibility, Change Intensity and
Adversarial Power in Counterfactual Explanations using Multi-objective
Optimization [73.89239820192894]
We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances.
We present a novel framework for the generation of counterfactual examples.
arXiv Detail & Related papers (2022-05-20T15:02:53Z) - Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty [0.0]
In Part I, we discussed methods which generate a model of behavior from exploration of the system and feedback based on the exhibited behavior.
In this work, we will continue the discussion from the perspective of methods which focus on the assumed cognitive abilities, limitations, and biases demonstrated in human reasoning.
arXiv Detail & Related papers (2022-05-13T07:29:15Z) - 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) - Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards
Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
Key challenge for robotic systems is to figure out the behavior of another agent.
Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally.
We propose equipping robots with the necessary tools to conduct observational studies on people.
arXiv Detail & Related papers (2022-01-27T22:15:56Z) - Differentiating Approach and Avoidance from Traditional Notions of
Sentiment in Economic Contexts [0.0]
Conviction Narrative Theory places Approach and Avoidance sentiment at the heart of real-world decision-making.
This research introduces new techniques to differentiate Approach and Avoidance from positive and negative sentiment on a fundamental level of meaning.
arXiv Detail & Related papers (2021-12-05T16:05:16Z) - 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) - Individual Explanations in Machine Learning Models: A Survey for
Practitioners [69.02688684221265]
The use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise.
Many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways.
Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models.
arXiv Detail & Related papers (2021-04-09T01:46:34Z) - Models we Can Trust: Toward a Systematic Discipline of (Agent-Based)
Model Interpretation and Validation [0.0]
We advocate the development of a discipline of interacting with and extracting information from models.
We outline some directions for the development of a such a discipline.
arXiv Detail & Related papers (2021-02-23T10:52:22Z)
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.