arXiv subjects

Nina Bočková

Publications and source records attributed to Nina Bočková.

3 recordsLinked to original sources

arXiv2026

Optimisation of complex product innovation processes based on trend models with three-valued logic

This paper investigates complex product-innovation processes using models grounded in a set of heuristics. Each heuristic is expressed through simple trends -- increasing, decreasing, or constant -- which serve as minimally information-intensive quantifiers, avoiding reliance on numerical values or rough sets. A solution to a trend model is defined as a set of scenarios with possible transitions between them, represented by a transition graph. Any possible future or past behaviour of the system under study can thus be depicted by a path within this graph.

cs.AI↗

arXiv2025

Information-Nonintensive Models of Rumour Impacts on Complex Investment Decisions

This paper develops a qualitative framework for analysing the impact of rumours on complex investment decisions (CID) under severe information constraints. The proposed trend-based models rely on minimal data inputs in the form of increasing, decreasing, or constant relations. Sets of trend rules generate scenarios, and permitted transitions between them form a directed graph that represents system behaviour over time. The approach is applied in three interconnected models: financial CID, rumour-spreading dynamics, and their integration.

econ.GN↗

arXiv2025

SME Gender-Related Innovation: A Non-Numerical Trend Analysis Using Positive, Zero, and Negative Quantities

This paper addresses gender-related aspects of innovation processes in Small and Medium Enterprises (SMEs). Classical analytical and statistical approaches often struggle with the high complexity and insufficient data typical of gender-related innovation studies. We propose a trend-based modelling framework that requires minimal information and uses non-numerical quantifiers: increasing, constant, and decreasing. This approach enables the analysis of ten-dimensional models including variables such as Gender, Product Innovation, Process Innovation, and High-Risk Tolerance. Using trend-based artificial intelligence methods, we identify 13 distinct scenarios and all possible transitions between them. This allows for the evaluation of queries like: Can exports increase while gender parameters remain constant? Two versions of the GASI trend model are presented: the original and an expert-modified version addressing critiques related to scenario transitions. The final model confirms stability and supports the assumption that "no tree grows to heaven." Trend-based modelling offers a practical, interpretable alternative for complex, data-scarce systems.

econ.GN↗