نوع مقاله : مقاله پژوهشی
نویسندگان
1 استادیارآینده پژوهی، مؤسسه تحقیقات سیاست علمی کشور،تهران، ایران
2 دانشجوی دکتری انرژی، دانشگاه صنعتی شریف، تهران، ایران
3 پژوهشگر سیاست گذاری علم، فناوری و نوآوری، پژوهشکده مطالعات بنیادین علم و فناوری، دانشگاه شهید بهشتی، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The accelerating pace of scientific and technological change, increasing interdisciplinary convergence, and growing uncertainty regarding future trajectories have posed fundamental challenges for science and technology policy making. Under such conditions, reliance on purely retrospective approaches and uniform support policies is not only ineffective but may also lead to resource fragmentation and increased policy decision errors. This article examines the role of technology foresight in improving the prioritization of science and technology policies in Iran and demonstrates how structured expert-based assessment can function as an effective decision-support tool under conditions of uncertainty. Drawing on a foresight-oriented framework grounded in expert evaluation, the study analyses transformative technologies across several key technological domains. To enhance analytical robustness, the results are first examined in an aggregated, cross-domain manner and subsequently interpreted in a concise, domain-specific form. The findings reveal limited overlap between short-term priorities and long-term strategic priorities, indicating that gaps between global and national levels of technological maturity necessitate simultaneous attention to institutional, infrastructural, and human capital dimensions. The study also identifies patterns of expert participation as a meaningful institutional signal and argues that the institutionalization of technology foresight requires the genuine integration of analytical tools into science and technology governance processes. Overall, the results suggest that when effectively embedded in policy decision-making, technology foresight can contribute to reducing policy errors, enhancing decision coherence, and enabling more strategic allocation of resources within the national science and technology system.
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