Agile Decision-Making in Unstable Markets: A Soft Set Paradigm with Temporal Weighting

Navigating volatile financial markets requires decision-making frameworks that synergize analytical rigor with real-time adaptability. Traditional models, reliant on static criteria and fixed weights, falter in dynamically evolving economic landscapes where the relevance of parameters like risk, liquidity, and inflation sensitivity shifts rapidly. This study bridges this critical gap by introducing a novel decision-making model founded on soft set theory and augmented with a temporal weighting mechanism. The proposed framework incorporates a time-dependent decision core, $A(t)$, which captures the set of relevant alternatives in real-time. This dynamic core drives the construction of temporal equivalence relations, $R(t)$, that cluster parameters based on their current influence, and enables the computation of dynamic, support-based weights, $w(t,p)$. These elements culminate in a responsive scoring function, $S(u,t)$, that adapts to fluctuating market conditions. The model is rigorously validated through a comparative application to investment decision-making, demonstrating its superior ability to recalibrate parameter importance and optimize choices during regime shifts—such as from economic stability to inflationary crisis—compared to static soft set and fuzzy TOPSIS benchmarks. Theoretical analyses confirm the model's stability, computational efficiency, and robustness. Our approach thus offers a powerful, interpretable, and mathematically sound tool for agile financial decision support in complex, unstable environments.