797 KB0 KB201020262026Dinçer, HasanEti, SerkanYigitbaşi Aktaş, BeyzaYu¨ksel, Serhatštevilka:1letnik:17Str. 81-106ISSN:1855-931XCOBISSID_HOST:281521923URN:URN:NBN:SI:doc-V0ON5IUHenGEA CollegeAdvances in business related scientific research journalalgorithmic competitionartificial intelligence pricingdecision making modelsdigital market regulationpricing strategy optimizationMitigating competitive risks of artificial intelligence driven pricing in digital platforms|This study aims to identify priority strategies to minimize the risks created by AI-based pricing algorithms. The lack of consensus in the current literature on this topic is considered a significant research gap. Accordingly, a new decision-making model is created to determine priority criteria and strategies. Within this framework, the IDOCRIW technique is considered in calculating the criterion weights. On the other hand, the RAM approach is used to determine the most effective strategies. In addition, newly developed behavioral leadership fuzzy numbers are integrated into the model. This significantly reduces uncertainties in the decision-making process and increases the originality of the model. The application of reinforcement learning-based pricing algorithms with appropriate constraints is identified as the most prioritized strategy. Similarly, static rule-based pricing can also be considered in this processTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaGEA College