BRIJMOHAN SINGH,DR.LALIT SACHDEVA,MANVI PANT
DOI: https://doi.org/In mechanical design contexts where engineers continually confront complex permutations of uncertainty and boundary conditions, the roles of cognitive risk perception and decision error, while undeniably influential, attract minimal explicit scrutiny. This investigation interrogates the extent to which such cognitive, yet tacit, variables distort design deliberations and ultimate judgments. Employing a mixed-methods paradigm, the inquiry integrates quantitative and qualitative data extracted from controlled simulations, guided interviews, and calibrated psychometric inventories calibrated upon a cohort of seasoned mechanical practitioners. Participants sequentially engaged in design trade-off scenarios calibrated to variegated risk spectra, each encompassing trade-offs among safety, cost, and nascent innovation domains. Results reveal that individual risk construction is dominantly mediated by autobiographical experience, habitual cognitive shortcuts, and discipline-specific heuristics. Compounding cognitive distortions—most notably anchoring, availability, and unwarranted overconfidence—systematically dislocated normative rational evaluation. Complementary ocular-tracking and temporal-task analytics corroborated that suboptimal selections coalesced with truncated decision latencies and constricted visual exploratory behaviour. On the basis of these findings, the article articulates a provisional conceptual edifice linking individual risk propensity to discretely observable classes of systematic error within mechanical design workflows.
The findings reported herein inform the development of engineering curricula, the organization of multidisciplinary design teams, and the architecture of adaptive decision-support systems that counteract the effects of risk-related biases. Future research is encouraged to prototype instructional units that explain the mechanics of cognitive biases, alongside analytic tools that embed these principles within computer-aided design (CAD) environments, thereby fostering designs that converge more rigorously upon reliability and validity criteria. Ethical considerations surrounding the unobtrusive monitoring of cognitive states and the realistic modeling of social dynamics within design teams are critically examined. Collectively, this study contributes to the discipline of human-centered engineering by positioning cognitive diversity as a steering variable within adaptive design processes, thus enhancing the resilience and validity of engineered systems.
