# Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning
## 2 Literature Review
Ethical challenges arise when pricing algorithms exploit consumer behavior or vulnerabilities, prompting calls for greater scrutiny and regulation [25]. Additionally, dynamic pricing in essential sectors like electricity has faced criticism for potential inequities, as time-of-use rates may disproportionately impact low-income consumers who are less flexible in their consumption patterns [24]. From a consumer perspective, the ethicality of dynamic pricing depends heavily on perceived fairness and transparency. Research suggests that consumers often view dynamic pricing strategies negatively if they feel deceived or if the pricing lacks a clear justification [26]. Transparency in pricing mechanisms and ensuring that customers are aware of how and why prices vary can mitigate ethical concerns. Moreover, the introduction of algorithms and artificial intelligence in pricing decisions has amplified the ethical debate, with concerns over privacy violations and the potential manipulation of consumer behavior [18]. Addressing these ethical dilemmas requires robust regulatory frameworks and adherence to ethical business practices that prioritize consumer trust and equity. Dynamic pricing models can be classified based on the pricing mechanism used. According to this categorization, several types of models can be distinguished. The first are rule-based models, which rely on predefined rules and criteria established by the company. Prices are adjusted according to specific principles, such as the time of day, day of the week, inventory levels, or historical sales data [27]. The next are algorithm-based models. These models use mathematical and statistical algorithms to predict demand and optimize prices. The algorithms can consider various variables, such as sales trends, competition, weather conditions, and special events [28]. The literature also identifies dynamic pricing models that use machine learning. These models analyze large datasets and predict future demand. In recent years, various dynamic pricing models have been developed, finding applications across different industries and decision-making contexts:  
1. Dynamic Toll Pricing Model: This model focuses on the design, simulation, implementation, and evaluation of dynamic toll pricing systems. The goal is to optimize traffic flow by adjusting toll rates in real time, leading to the more efficient management of transportation infrastructure [29]. 2. Dynamic Pricing with Demand Learning and Reference Effect: In this approach, sellers learn demand patterns based on historical sales and pricing data while also considering consumer reference effects. This allows for dynamic price adjustments to maximize profits while accounting for changing customer preferences [30]. 3. Deep Reinforcement Learning-Based Dynamic Pricing Strategy: This model uses deep reinforcement learning algorithms to automatically adjust prices in real time. The goal is to optimize inventory and revenues in pre-sale environments, taking into account demand uncertainty and other market factors [31]. Each of these models offers a unique approach to dynamic pricing, tailored to specific market conditions and business objectives. The algorithms learn from historical data and adjust prices based on patterns detected in the data [32]. Another group of models is demand-based models. These models adjust prices in response to changing demand. Prices increase when demand is high and decrease when demand is low. The goal is to maximize revenue by leveraging demand fluctuations. Using this classification, another group can be identified: price elasticity models [33]. These models analyze the price elasticity of demand, which is how changes in price affect the demand for a product. Prices are adjusted to achieve the optimal point where revenues are maximized. In his systematic literature review on the dynamic pricing process, [PERSON] [7] identified eight leading research trends, which include the following:  
1. Moderating factors influencing the impact of dynamic pricing on consumer behavior. 2.