GENERATIV AI ASOSIDAGI ALGORITMIK NARX BELGILASH MEXANIZMLARI: RAQOBAT IQTISODIYOTIDA MONOPOLLASHUV XAVFI, BAHO BO‘YICHA SOZLASHUV MUAMMOLARI VA ANTIMONOPOL TARTIBGA SOLISH
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https://doi.org/10.5281/zenodo.20848730##article.subject##:
algoritmik narx belgilash, yashirin kelishuv (tacit collusion), generativ sun’iy intellekt, antimonopol tartibga solish, Q-learning, katta til modellari, oligopoliya, Nash muvozanati, EU AI Act, RealPage, raqamli iqtisodiyot, raqobat siyosati, supraraqobatbardosh narxlar.##article.abstract##
Generativ sun’iy intellekt (GenAI) va mashinali o‘qitishga asoslangan algoritmik narx belgilash (AP — Algorithmic
Pricing) mexanizmlarining raqobat iqtisodiyotida yuzaga keltirayotgan monopollashuv va baholar bo‘yicha yashirin
kelishuv (tacit collusion) xavflari hamda ularni antimonopol tartibga solish masalalari kompleks tahlil qilinadi. Q-learning,
chuqur mustahkamlash orqali o‘qitish (Deep Reinforcement Learning — Deep RL) va katta til modellari (Large Language
Models — LLM) asosidagi AP algoritmlarining iqtisodiy ta’siri baholanadi. Calvano et al. (2020, AER), Assad et al.
(2024, JPE), Fish et al. (2024) tadqiqotlari hamda AQSh Adliya vazirligining RealPage kompaniyasiga qarshi ishi (DOJ
v. RealPage, 2024–2025) misolida AP tizimlarining Nash muvozanatidan yuqori bo‘lgan supraraqobatbardosh narxlarni
shakllantirishi va ikki tomonlama bozorlarda tarmoq tashqi ta’sirlari orqali yashirin kelishuvlarni kuchaytirishi empirik dalillar
asosida yoritiladi. Antimonopol tartibga solishning to‘rtta asosiy yondashuvi qiyosiy tahlil qilinib, O‘zbekiston raqamli
iqtisodiyoti sharoitida qo‘llash mumkin bo‘lgan to‘rt bosqichli antimonopol nazorat va tatbiq etish modeli taklif qilinadi.
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