A Method for Solving Distributed Service Allocation Problems

We present a method for solving service allocation problems in which a set of services must be allocated to a set of agents so as to maximize a global utility. The method is completely distributed so it can scale to any number of services without degradation. We first formalize the service allocation problem and then present a simple hill-climbing, a global hill-climbing, and a bidding-protocol algorithm for solving it. We analyze the expected performance of these algorithms as a function of various problem parameters such as the branching factor and the number of agents. Finally, we use the sensor allocation problem, an instance of a service allocation problem, to show the bidding protocol at work. The simulations also show that phase transition on the expected quality of the solution exists as the amount of communication between agents increases

Efficient Methods for Automated Multi-Issue Negotiation: Negotiating over a Two-Part Tariff

In this article, we consider the novel approach of a seller and customer negotiating bilaterally about a two-part tariff, using autonomous software agents. An advantage of this approach is that win-win opportunities can be generated while keeping the problem of preference elicitation as simple as possible. We develop bargaining strategies that software agents can use to conduct the actual bilateral negotiation on behalf of their owners. We present a decomposition of bargaining strategies into concession strategies and Pareto-efficient-search methods: Concession and Pareto-search strategies focus on the conceding and win-win aspect of bargaining, respectively. An important technical contribution of this article lies in the development of two Pareto-search methods. Computer experiments show, for various concession strategies, that the respective use of these two Pareto-search methods by the two negotiators results in very efficient bargaining outcomes while negotiators concede the amount specified by their concession strategy

An Evolutionary Framework for Determining Heterogeneous Strategies in Multi-Agent Marketplaces

We propose an evolutionary approach for studying the dynamics of interaction of strategic agents that interact in a marketplace. The goal is to learn which agent strategies are most suited by observing the distribution of the agents that survive in the market over extended periods of time. We present experimental results from a simulated market, where multiple service providers compete for customers using different deployment and pricing schemes. The results show that heterogeneous strategies evolve and co-exist in the same market.marketing;simulation;multi-agent systems;complexity economics;trading agents

Online Bargaining as a Form of Dynamic Pricing and the Sellers\u27 Advantage from Information Assymmetry

Among the means of implementing dynamic pricing strategies in e-commerce, online bargaining is found to be better than revenue management and online auction, because each deal actually reaches a “win-win” situation for both the buyer and the seller in the sense that the mutually agreed deal price is higher than the seller’s reserved price but lower than the buyer’s reserved price. Such feature brings profit to the seller, as well as savings to the buyer. Meanwhile when bargaining online, there is an information asymmetry between the seller side, i.e. the company side, and the buyer side, which grants a great advantage to the sellers over the buyers. This information asymmetry can be captured and exploited for financial gains through adopting a properly designed online bargaining algorithm

Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.dynamic pricing;machine learning;market forecasting;Trading agents

The logic behind negotiation : from pre-argument reasoning to argument-based negotiation

The use of agents in Electronic Commerce environments leads to the necessity to introduce some formal analysis and definitions. A 4-step method is introduced for developing EC-directed agents, which are able to take into account non-linearites such as gratitude and agreement. Negotiations that take into account a multi-step exchange of arguments provide extra information, at each step, for the intervening agents, enabling them to react accordingly. This argument-based negotiation among agents has much to gain from the use of Extended Logic Programming mechanisms. Incomplete information is common in EC scenarios; therefore arguments must also take into account the presence of statements with an unknown valuation

Adaptive Strategies for Dynamic Pricing Agents

Dynamic Pricing (DyP) is a form of Revenue Management in which the price of a (usually) perishable good is changed over time to increase revenue. It is an effective method that has become even more relevant and useful with the emergence of Internet firms and the possibility of readily and frequently updating prices. In this paper a new approach to DyP is presented. We design adaptive dynamic pricing strategies and optimize their parameters with an Evolutionary Algorithm (EA) offline while the strategies can deal with stochastic market dynamics quickly online. We design two adaptive heuristic dynamic pricing strategies in a duopoly where each firm has a finite inventory of a single type of good. We consider two cases, one in which the average of a customer population’s stochastic valuation for each of the goods is constant throughout the selling horizon and one in which the average customer valuation for each good is changed according to a random Brownian motion. We also design an agent-based software framework for simulating various dynamic pricing strategies in agent-based marketplaces with multiple firms in a bounded time horizon. We use an EA to optimize the parameters for each of the pricing strategies in each of the settings and compare the strategies with other strategies from the literature. We also perform sensitivity a analysis and show that the optimized strategies work well even when used in settings with varied demand functions