Balanced matching of buyers and sellers in e-marketplaces

Abstract

In this paper, we describe the operation of barter trade exchanges by identifying key techniques used by trade brokers to stimulate trade and satisfy member needs, and present algorithms to automate some of these techniques. In particular, we develop algorithms that emulate the practice of trade brokers by matching buyers and sellers in such a way that trade volume is maximized while the balance of trade is maintained as much as possible. We show that the buyer/seller matching and trade balance problems can be decoupled, permitting efficient solution as well as numerous options for matching strategies.We model the trade balance problem as a minimum cost circulation problem (MCC) on a network. When the products have uniform cost or when the products can be traded in fractional units, we solve the problem exactly. Otherwise, we present a novel stochastic rounding algorithm that takes the fractional optimal solution to the trade balance problem and produces a valid integer solution. We then make use of a greedy heuristic that attempts to match buyers and sellers so that the average number of suppliers that a buyer must use to satisfy a given product need is minimized.We present results on the empirical evaluation of our algorithms on test problems and simulations. Experiments show that our algorithm (MCC + stochastic rounding) runs in a fraction of the time of a commercial mixed integer programming (MIP) package while producing solutions that are always within 0.7% of the MIP solution. We evaluate the effectiveness of our algorithm on maintaining balance and on stimulating trade using two different simulation techniques, both based on transaction history data from a trade exchange. The simulation results support the barter trade exchange rule of thumb that maximizing single-period trade volume while maintaining balance of trade helps to maximize trade volume over the long run.

Bartering Books to Beers

“…For Ratebeer and /r/gameswap, the transactional information was extracted from textual submissions of users, containing information about their completed transactions (i.e., the transacting parties, along with the beers and games they exchanged, respectively). 8 The datasets are long-tailed. The distribution of the size of users' wish lists and give-away lists, as well as the popularity of each item (both in terms of how many users own it and of how many users desire it), are depicted in Figure 2, as are the Cumulative Distribution Functions for the number of transactions that each user has taken part in (right column).…”

“…The problem is solved by Roth et al [22], using the The Top Trading Cycles and Chains mechanism. Another relevant work is that of Haddawy et al [8], as it solves the problem of determining a balanced matching of buyers and sellers in the context of barter trade exchanges. The trades are managed by an intermediary, and parties are matched based on supply and demand information, as well as their credit in terms of a private label currency.…”

Data-driven agent-based simulation of commercial barter trade

“…The barter pool absolute balance is defined as the sum of the absolute values of the balances of the companies in the pool. It is a measure of the balance of trade in the barter pool [14]. An example of the TRADES output screen is shown in Figure 4.…”

“…Details of the brokering algorithm can be found in [14]. We were interested in seeing whether the strategy of maintaining trade balance helps to maximize trade volume over the long run.…”

Peer-to-Peer Bartering: Swapping Amongst Self-interested Agents

“…The market approach to re-source allocation in the human society has inspired the Multi-Agent Systems community to construct similar concepts for MAS, where the trade is performed between computational agents on computational markets it is know as market oriented programming. See [191], [192], [84], [85], [47].…”