Performance of software agents in non-transferable payoff group buying

Abstract

Software agents can be useful in forming buyers’ groups since humans have considerable difficulties in finding Pareto-optimal deals (no buyer can be better without another being worse) in negotiation situations. What are the computational and economical performances of software agents for a group buying problem? We have developed a negotiation protocol for software agents which we have evaluated to see if the problem is difficult on average and why. This protocol probably finds a Pareto-optimal solution and, furthermore, minimizes the worst distance to ideal among all software agents given strict preference ordering. This evaluation demonstrated that the performance of software agents in this group buying problem is limited by memory requirements (and not execution time complexity). We have also investigated whether software agents following the developed protocol have a different buying behaviour from that which the customer they represented would have had in the same situation. Results show that software agents have a greater difference of behaviour (and better behaviour since they can always simulate the obvious customer behaviour of buying alone their preferred product) when they have similar preferences over the space of available products. We also discuss the type of behaviour changes and their frequencies based on the situation.