ABI Approach: Automatic Bias Identification in Decision-Making Under Risk based in an Ontology of Behavioral Economics

• URL: http://arxiv.org/abs/2405.14067v1
• Date: Wed, 22 May 2024 23:53:46 GMT
• Title: ABI Approach: Automatic Bias Identification in Decision-Making Under Risk based in an Ontology of Behavioral Economics
• Authors: Eduardo da C. Ramos, Maria Luiza M. Campos, Fernanda Baião,
• Abstract summary: 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.
• Score: 46.57327530703435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Organizational decision-making is crucial for success, yet cognitive biases can significantly affect risk preferences, leading to suboptimal outcomes. Risk seeking preferences for losses, driven by biases such as loss aversion, pose challenges and can result in severe negative consequences, including financial losses. This research introduces the ABI approach, a novel solution designed to support organizational decision-makers by automatically identifying and explaining risk seeking preferences during decision-making. This research makes a novel contribution by automating the identification and explanation of risk seeking preferences using Cumulative Prospect theory (CPT) from Behavioral Economics. The ABI approach transforms theoretical insights into actionable, real-time guidance, making them accessible to a broader range of organizations and decision-makers without requiring specialized personnel. By contextualizing CPT concepts into business language, the approach facilitates widespread adoption and enhances decision-making processes with deep behavioral insights. Our systematic literature review identified significant gaps in existing methods, especially the lack of automated solutions with a concrete mechanism for automatically identifying risk seeking preferences, and the absence of formal knowledge representation, such as ontologies, for identifying and explaining the risk preferences. The ABI Approach addresses these gaps, offering a significant contribution to decision-making research and practice. Furthermore, it enables automatic collection of historical decision data with risk preferences, providing valuable insights for enhancing strategic management and long-term organizational performance. An experiment provided preliminary evidence on its effectiveness in helping decision-makers recognize their risk seeking preferences during decision-making in the loss domain.

Related papers

• PresAIse, A Prescriptive AI Solution for Enterprises [6.523929486550928]
  This paper outlines an initiative from IBM Research, aiming to address some of these challenges by offering a suite of prescriptive AI solutions. The solution suite includes scalable causal inference methods, interpretable decision-making approaches, and the integration of large language models. A proof-of-concept, PresAIse, demonstrates the solutions' potential by enabling non-ML experts to interact with prescriptive AI models via a natural language interface.
  arXiv  Detail & Related papers  (2024-02-03T03:23:08Z)
• Risk-reducing design and operations toolkit: 90 strategies for managing risk and uncertainty in decision problems [65.268245109828]
  This paper develops a catalog of such strategies and develops a framework for them. It argues that they provide an efficient response to decision problems that are seemingly intractable due to high uncertainty. It then proposes a framework to incorporate them into decision theory using multi-objective optimization.
  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)
• Deep Learning for Systemic Risk Measures [3.274367403737527]
  The aim of this paper is to study a new methodological framework for systemic risk measures. Under this new framework, systemic risk measures can be interpreted as the minimal amount of cash that secures the aggregated system. Deep learning is increasingly receiving attention in financial modelings and risk management.
  arXiv  Detail & Related papers  (2022-07-02T05:01:19Z)
• 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)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)
• Inverse Active Sensing: Modeling and Understanding Timely Decision-Making [111.07204912245841]
  We develop a framework for the general setting of evidence-based decision-making under endogenous, context-dependent time pressure. We demonstrate how it enables modeling intuitive notions of surprise, suspense, and optimality in decision strategies.
  arXiv  Detail & Related papers  (2020-06-25T02:30:45Z)
• Contestable Black Boxes [10.552465253379134]
  This paper investigates the type of assurances that are needed in the contesting process when algorithmic black-boxes are involved. We argue that specialised complementary methodologies to evaluate automated decision-making in the case of a particular decision being contested need to be developed.
  arXiv  Detail & Related papers  (2020-06-09T09:09:00Z)
• Automatic Discovery of Interpretable Planning Strategies [9.410583483182657]
  We introduce AI-Interpret, a method for transforming idiosyncratic policies into simple and interpretable descriptions. We show that prividing the decision rules generated by AI-Interpret as flowcharts significantly improved people's planning strategies and decisions.
  arXiv  Detail & Related papers  (2020-05-24T12:24:52Z)

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.