Generative AI in Managerial Decision-Making: Redefining Boundaries through Ambiguity Resolution and Sycophancy Analysis

• URL: http://arxiv.org/abs/2603.03970v1
• Date: Wed, 04 Mar 2026 12:10:56 GMT
• Title: Generative AI in Managerial Decision-Making: Redefining Boundaries through Ambiguity Resolution and Sycophancy Analysis
• Authors: Sule Ozturk Birim, Fabrizio Marozzo, Yigit Kazancoglu,
• Abstract summary: This study compares various models on ambiguity detection, evaluating how a systematic resolution process enhances response quality.<n>Using a novel four-dimensional business ambiguity taxonomy, we conducted a human-in-the-loop experiment across strategic, tactical, and operational scenarios.<n>Results reveal distinct performance capabilities. While models excel in detecting internal contradictions and contextual ambiguities, they struggle with structural linguistic nuances.
• Score: 0.45880283710344055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative artificial intelligence is increasingly being integrated into complex business workflows, fundamentally shifting the boundaries of managerial decision-making. However, the reliability of its strategic advice in ambiguous business contexts remains a critical knowledge gap. This study addresses this by comparing various models on ambiguity detection, evaluating how a systematic resolution process enhances response quality, and investigating their sycophantic behavior when presented with flawed directives. Using a novel four-dimensional business ambiguity taxonomy, we conducted a human-in-the-loop experiment across strategic, tactical, and operational scenarios. The resulting decisions were assessed with an "LLM-as-a-judge" framework on criteria including agreement, actionability, justification quality, and constraint adherence. Results reveal distinct performance capabilities. While models excel in detecting internal contradictions and contextual ambiguities, they struggle with structural linguistic nuances. Ambiguity resolution consistently increased response quality across all decision types, while sycophantic behavior analysis revealed distinct patterns depending on the model architecture. This study contributes to the bounded rationality literature by positioning GAI as a cognitive scaffold that can detect and resolve ambiguities managers might overlook, but whose own artificial limitations necessitate human management to ensure its reliability as a strategic partner.

Related papers

• Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning [49.28548464288051]
  Composed Image Retrieval (CIR) enables image search by combining a reference image with modification text.<n>In intrinsic noise in CIR triplets incurs intrinsic uncertainty and threatens the model's robustness.<n>This paper introduces a Heterogeneous Uncertainty-Guided (HUG) paradigm to overcome these limitations.
  arXiv  Detail & Related papers  (2026-01-16T16:05:49Z)
• Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios [1.5367554212163714]
  This paper presents a Case-Based Reasoning Augmented Large Language Model (CBR-LLM) framework for evasive maneuver decision-making in complex risk scenarios.<n>Our approach integrates semantic scene understanding from dashcam video inputs with the retrieval of relevant past driving cases.<n>Experiments show that our framework improves decision accuracy, justification quality, and alignment with human expert behavior.
  arXiv  Detail & Related papers  (2025-06-25T15:19:25Z)
• Adversarial Testing in LLMs: Insights into Decision-Making Vulnerabilities [5.0778942095543576]
  This paper introduces an adversarial evaluation framework designed to systematically stress-test the decision-making processes of Large Language Models.<n>We apply this framework to several state-of-the-art LLMs, including GPT-3.5, GPT-4, Gemini-1.5, and DeepSeek-V3.<n>Our findings highlight distinct behavioral patterns across models and emphasize the importance of adaptability and fairness recognition for trustworthy AI deployment.
  arXiv  Detail & Related papers  (2025-05-19T14:50:44Z)
• Agentic LLM Framework for Adaptive Decision Discourse [2.4919169815423743]
  This study introduces a real-world inspired agentic Large Language Models (LLMs) framework.<n>Unlike traditional decision-support tools, the framework emphasizes dialogue, trade-off exploration, and the emergent synergies generated by interactions among agents.<n>Results reveal how the breadth-first exploration of alternatives fosters robust and equitable recommendation pathways.
  arXiv  Detail & Related papers  (2025-02-16T03:46:37Z)
• Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy [59.64384863882473]
  We study the problem of interactive decision making in which the underlying environment changes over time subject to given constraints.<n>We propose a framework, which provides an complexity between the complexity and adversarial settings of decision making.
  arXiv  Detail & Related papers  (2025-01-24T21:31:50Z)
• ABI Approach: Automatic Bias Identification in Decision-Making Under Risk based in an Ontology of Behavioral Economics [46.57327530703435]
  Risk seeking preferences for losses, driven by biases such as loss aversion, pose challenges and can result in severe negative consequences. This research introduces the ABI approach, a novel solution designed to support organizational decision-makers by automatically identifying and explaining risk seeking preferences.
  arXiv  Detail & Related papers  (2024-05-22T23:53:46Z)
• LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
  Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly. We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models. It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning [72.80902932543474]
  Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeling a decision-maker's policy is challenging. We propose a data-driven representation of decision-making behavior that inheres transparency by design, accommodates partial observability, and operates completely offline.
  arXiv  Detail & Related papers  (2023-10-28T13:06:14Z)
• On strategies for risk management and decision making under uncertainty shared across multiple fields [55.2480439325792]
  The paper finds more than 110 examples of such strategies and this approach to risk is termed RDOT: Risk-reducing Design and Operations Toolkit.<n>RDOT strategies fall into six broad categories: structural, reactive, formal, adversarial, multi-stage and positive.<n>Overall, RDOT represents an overlooked class of versatile responses to uncertainty.
  arXiv  Detail & Related papers  (2023-09-06T16:14:32Z)
• On solving decision and risk management problems subject to uncertainty [91.3755431537592]
  Uncertainty is a pervasive challenge in decision and risk management. This paper develops a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them.
  arXiv  Detail & Related papers  (2023-01-18T19:16:23Z)
• On the Complexity of Adversarial Decision Making [101.14158787665252]
  We show that the Decision-Estimation Coefficient is necessary and sufficient to obtain low regret for adversarial decision making. We provide new structural results that connect the Decision-Estimation Coefficient to variants of other well-known complexity measures.
  arXiv  Detail & Related papers  (2022-06-27T06:20:37Z)

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.