Abstract
In current markets of perishable goods such as fish and vegetables, sellers are typically in a weak bargaining position, since perishable products cannot be stored for long without losing their value. To avoid the risk of spoiling products, sellers have few alternatives other than selling their goods at the prices offered by buyers in the markets. The market mechanism needs to be reformed in order to resolve unfairness between sellers and buyers. Double auction markets, which collect bids from both sides of the trades and match them, allow sellers to participate proactively in the price-making process. However, in perishable goods markets, sellers have an incentive to discount their bid gradually for fear of spoiling unsold goods. Buyers can take advantage of sellers’ discounted bids to increase their profit by strategic bidding. To solve the problem, we incrementally improve an online double auction mechanism for perishable goods markets, which promotes buyers’ truthful bidding by penalizing their failed bids without harming their individual rationality. We evaluate traders’ behavior under several market conditions using multi-agent simulations and show that the developed mechanism achieves fair resource allocation among traders.
Highlights
IntroductionIn the research of multi-agent simulations, several types of auction mechanisms have been investigated extensively to solve large-scale distributed resource allocation problems [1] and several applications have been proposed in different kinds of markets [2,3,4]
In the research of multi-agent simulations, several types of auction mechanisms have been investigated extensively to solve large-scale distributed resource allocation problems [1] and several applications have been proposed in different kinds of markets [2,3,4].In the auctions, resources are generally supposed to have clear capacity limitations, and in some cases they have explicit temporal limitations on their value [5]
From the experimental results we find that the naive double action (DA) mechanism cannot satisfy quasi incentive compatibility for buyers in perishable goods markets
Summary
In the research of multi-agent simulations, several types of auction mechanisms have been investigated extensively to solve large-scale distributed resource allocation problems [1] and several applications have been proposed in different kinds of markets [2,3,4]. Since values of the perishable goods decrease progressively, sellers have an incentive to discount their price for fear of spoiling unsold goods Taking advantage of such an incentive of the sellers, buyers can increase their surplus by bidding a lower price. Sellers have an incentive to discount their price for fear of spoiling unsold goods In this study, extending our previous research, we incrementally develop a heuristic online DA mechanism for perishable goods with a standard greedy price-based allocation policy and achieve fair resource allocation by penalizing buyers’ untruthful bids while maintaining their individual rationality.
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