Evaluation and Adaptive Complexity of Cognitive Information Systems

Attila Márton Putnoki; Tamás Orosz

doi:10.14513/actatechjaur.00806

Authors

Attila Márton Putnoki ELTE Eötvös Loránd University, Pázmány Péter 1/C, 1117 Budapest, Hungary
Tamás Orosz ELTE Eötvös Loránd University, Pázmány Péter 1/C, 1117 Budapest, Hungary

DOI:

https://doi.org/10.14513/actatechjaur.00806

Keywords:

Artificial Intelligence, Cognitive Information Systems, Cognitive Resonance, Personalization, Decision-Making, Human-Computer Interaction

Abstract

Contemporary business Decision-Making (DM) requires adaptive systems capable of aligning Artificial Intelligence (AI) with human cognitive reasoning. Traditional Decision-Support Systems (DSS) struggle to address the multidimensional and subjective nature of modern decisions. Cognitive Information Systems (CIS) aim to bridge this gap by enabling continuous, adaptive interaction between human (Carbon) and system (Silicon) agents. By leveraging AI, Generative AI (GenAI), and automation, CIS can enhance cognitive alignment, support personalized decision environments, and sustain system-user trust even in dynamic, uncertain conditions. Cognitive Resonance is a measurable attribute of CIS that reflects the degree of alignment between system outputs and user cognitive feedback during dynamic interaction. It captures how the reasoning structures of Carbon and Silicon agents become synchronized through iterative DM. The Cognitive and Artificial Intelligence Evaluation (CAIE) model offers a structured framework to assess cognitive system maturity across six key domains. These components enable CIS to sustain cognitive alignment and effective decision support, even under evolving and unpredictable organizational conditions.

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