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on Knowledge Management and Knowledge Economy |
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Issue of 2025–07–21
three papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Moreno, William Fernando |
| Abstract: | Representing and reasoning with complex, uncertain, context-dependent, and value-laden knowledge remains a fundamental challenge in Artificial Intelligence (AI) and Knowledge Representation (KR). Existing frameworks often struggle to integrate diverse knowledge types, make underlying assumptions explicit, handle normative constraints, or provide robust justifications for inferences. This preprint introduces the Conditional Reasoning Framework (CRF) and its Orthogonal Knowledge Graph (OKG) as a novel computational and conceptual architecture designed to address these limitations. The CRF operationalizes conditional necessity through a quantifiable, counterfactual test derived from a generalization of J.L. Mackie's INUS condition, enabling context-dependent reasoning within the graph-based OKG. Its design is grounded in the novel Theory of Minimal Axiom Systems (TOMAS), which posits that meaningful representation requires at least two orthogonal (conceptually independent) foundational axioms; TOMAS provides a philosophical justification for the CRF's emphasis on axiom orthogonality and explicit context (W). Furthermore, the framework incorporates expectation calculus for handling uncertainty and integrates the "ought implies can" principle as a fundamental constraint for normative reasoning. By offering a principled method for structuring knowledge, analyzing dependencies (including diagnosing model limitations by identifying failures of expected necessary conditions), and integrating descriptive and prescriptive information, the CRF/OKG provides a promising foundation for developing more robust, transparent, and ethically-aware AI systems. |
| Date: | 2025–05–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:zwpnv_v7 |
| By: | Wohlschlegel, Julian; Jussupow, Ekaterina; Pumplun, Luisa; Dittrich, Janek |
| Abstract: | The work of knowledge brokers comprises the transfer, translation, and transformation of knowledge between individuals who are unlikely to interact efficiently because of knowledge boundaries. In an extension of this theory, algorithmic brokers are defined as individuals performing these practices with artificial intelligence (AI) output to enable a community to leverage this output in their work. However, with the introduction of large language models (LLMs), we argue this brokerage role is shifting and that LLMs have the potential to broker knowledge between humans. We conducted a case study with domain experts in a Research and Development (R&D) department of a large multinational science and technology company who regularly use a recently developed domain-specific R&D-LLM. Our preliminary findings show that the R&D-LLM is reshaping interactions between human experts through three knowledge brokerage practices of varying complexity, assisting in simple knowledge recall, enabling the approach to experts and being a simulated counterpart. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:155585 |
| By: | Vojtech Bartos; Urlich Glogowsky; Johannes Rincke |
| Abstract: | We demonstrate that racial biases against tutors hinder learning. In e-learning experiments, U.S. conservatives are more likely to disregard advice from Black tutors, resulting in reduced performance compared to learners taught by white tutors. We show that the bias is unconscious and, consequently, does not skew tutor selection. In line with our theory, the bias disappears when the stakes are high. In contrast, liberals favor Black tutors without experiencing learning disparities. Methodologically, we contribute by using video post-production techniques to manipulate tutor race without introducing typical confounds. Additionally, we develop a novel two-stage design that simultaneously measures tutor selection, learning, and productivity. |
| Keywords: | Discrimination; racial bias; advice-seeking; online experiment |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:jku:econwp:2025-08 |