# Survey of Artificial Intelligence Model Marketplace
## 4 Model Pricing and Trading
### Model Pricing Schemes
#### 4.1.2 Pricing Techniques (X-Axis)  
Pricing techniques are tools used to yield a financial value for models in the marketplace. The tools balance buyers' and sellers' interests and enable the coexistence of the marketplace stakeholders. The techniques are objectively categorized as below:  
**Auction-based:** This is a dynamic pricing strategy where the price of a product or service is determined through a bidding process. In auctions, the bidding process enables buyers and sellers to interact and establish a fair market price based on demand and supply dynamics. Auction theory has been widely employed in both physical and digital commodity markets ranging from economics, big data, spectrum, electricity, and mobile markets, among others. The key components and the processes that are assembled to make up the auction-based pricing in model marketplaces are as follows: (i) _Data Owners_: These auction components offer their data for sale or contribute to model updates in collaborative training to yield a benefit; (ii) _Model Buyers_: They are the auction components that bid for the required models to enhance their use case; (iii) _Broker_: It acts as the intermediary facilitating the auction process by collecting bids, determining winners, and managing payments; (iv) _Bidding_: This can be the case for both forward and reverse auctions in the model marketplace. As for the forward, i.e., from buyers to brokers, then brokers to data owners. For example, the buyer submits bids indicating the maximum price they will pay for a model. The broker activates the auction process to enable data owners to submit their bids, then selects the winners to train the model that satisfies buyers' utility; (v) _Allocation and Payment Functions_: The allocation function identifies the buyers that win the auction based on their bids. This function ranks buyer(s) and allocates the trained model to the highest bidder. The payment function calculates the payment to be made by the winner before the broker releases the model. In most marketplaces, payments are mostly based on the second-highest bid (second-price auction) to encourage truthful bidding. An effective auction-based pricing mechanism is expected to be incentive-compatible, computation-efficient, and preserve individual rationality [91; 94; 95]. In model trading marketplaces, auction-based pricing can be objectively categorized into incentive-based, performance-based, and privacy-based. As for the incentive-based approach, data owners are encouraged to actively participate in model training using their best-quality data. The mechanism achieves its goal by encouraging truthful bidding from the data owners using incentives and preserving individual rationality by the pricing function; this ensures that the data owners do not lose anything by accepting to join the trading process. Related to this, [PERSON] et al. [88] proposed an auction-based strategy that incentivizes data owners' participation in a services market supported by federated learning. In their study, two auction mechanisms were leveraged so that social welfare could be improved in such a market. The first version employs a greedy approach to select data owners based on their bids and data quality, while the enhanced version leverages deep reinforcement learning and graph neural networks to automate the auction process. Both auction mechanisms ensure truthfulness and individual rationality among the participating data owners. The auction mechanism improves social welfare in the market by providing a better way of handling wireless communication and data distribution complexities. As for the performance-based approach, some performance metrics are utilized. In that case, buyers bid based on the expected performance a model should provide, and then data owners are compensated proportionally to the performance their model offers. Concerning this, [PERSON] et al. [94] proposed to incentivize resource-efficient data owners' participation in model training using auction-based pricing.