Dynamic Pricing on the Internet: Theory and Simulations

As is the case with traditional markets, the sellers on the Internet do not usually know the demand functions of their customers. However, in such a digital environment, a seller can experiment different prices in order to maximize his profits. In this paper, we fit the dynamic pricing model of Rothschild (1974) to match the pricing problem of a Web-store. In this setting, we define the optimization problem of a Web-store and by simulations we study the price dynamics that can appear when all the sellers on a given market follow an optimal pricing policy.     

Dynamic Consumer Profiling and Tiered Pricing Using Software Agents

Shopbots or software agents that enable comparison shopping of items from different online sellers have become popular for quick and easy shopping among online buyers. Rapid searches and price comparison by shopbots have motivated sellers to use software agents called pricebots to adjust their prices dynamically so that they can maintain a competitive edge in the market. Existing pricebots charge the same price for an item from all of their customers. Online consumers differ in their purchasing preferences and, therefore, a seller's profit can be increased by charging two different prices for the same good from price-insensitive and price-sensitive consumers. In this paper, we present an algorithm that partitions the buyer population into different segments depending on the buyers' purchase criteria and then charges a different price for each segment. Simulation results of our tiered pricing algorithm indicate that sellers' profits are improved by charging different prices to buyers with different purchase criteria. Price wars between sellers that cause regular price fluctuations in the market, are also prevented when all the sellers in the market use a tiered pricing strategy.     

Learning Agents in an Artificial Power Exchange: Tacit Collusion, Market Power and Efficiency of Two Double-auction Mechanisms

This paper investigates the relative efficiency of two double-auction mechanisms for power exchanges, using agent-based modeling. Two standard pricing rules are considered and compared (i.e., “discriminatory” and “uniform”) and computational experiments, characterized by different inelastic demand level, explore oligopolistic competitions on both quantity and price between learning sellers/producers. Two reinforcement learning algorithms are considered as well—“Marimon and McGrattan” and “Q-learning”—in an attempt to simulate different behavioral types. In particular, greedy sellers (optimizing their instantaneous rewards on a tick-by-tick basis) and inter-temporal optimizing sellers are simulated. Results are interpreted relative to game-theoretical solutions and performance metrics. Nash equilibria in pure strategies and sellers’ joint profit maximization are employed to analyze the convergence behavior of the learning algorithms. Furthermore, the difference between payments to suppliers and total generation costs are estimated so as to measure the degree of market inefficiency. Results point out that collusive behaviors are penalized by the discriminatory auction mechanism in low demand scenarios, whereas in a high demand scenario the difference appears to be negligible.     

Learning market prices in real-time supply chain management

This paper proposes a model for dynamic pricing that combines knowledge of production capacity and existing commitments, reasoning about uncertainty and learning of market conditions in an attempt to optimise expected profits. In particular, the market conditions are represented as a set of probabilities over the success rate of product prices, and those prices are learned online as the market develops. The dynamic pricing model is integrated into a real-time supply chain management agent using the Trading Agent Competition Supply Chain Management game as a test framework. We evaluate the agent experimentally in competition with other supply chain agents, and demonstrate the benefits of incorporating more market data into the dynamic pricing mechanism.     

Pricing with local interactions on agent-based electronic marketplaces

This paper investigates the optimal pricing strategies of a selling agent that is randomly matched with several heterogeneous buying agents whose reservation prices are initially unknown. The seller perceives the behaviors of the buying agents through a logistic distribution with unknown parameters. We study the optimal learning by experimentation model of the logistic distribution. We extend this framework to a dynamic pricing model in which the selling agent is randomly matched with buying agents that are able to communicate their purchase experience to other buying agents. We carry out multi-agent system simulations of this dynamic pricing decision problem and we discuss some properties of the price dynamics one can observe on such marketplaces.     

Analyzing strategies of mobile agents on malicious cloud platform with Agent-Based Computational Economic Approach

Cloud computing is a service model that enables resource-limited mobile devices to remotely execute tasks from a server. The mobile agent is a software program installed in the mobile device to negotiate a diversity of commerce transactions with other mobile agents in the cloud. However, the negotiation plans carried by mobile agents are easily be eavesdropped on by the malicious cloud platforms, since the codes of mobile agents are read and executed by the cloud platform. Thus, sellers can tailor the negotiation plans to cheat buyers for seizing buyers’ profits in negotiations after eavesdropping on buyers’ negotiation plans. In this paper, we consider the buyers can take actions to resist the sellers’ cheatings, which is that the buyers can tailor their plans with extremely low demand to decrease sellers’ profits before migrate to the hosts. In this paper, we consider actions that buyers can take to resist sellers’ cheatings, that is the buyers can tailor their plans with extremely low demands before migrate to the cloud platform. Above situations between buyers and sellers are modeled as a mathematical model called Eavesdropping and Resistance of Negotiation (ERN) Game. The strategies of the buyers and sellers playing the ERN Game are analyzed by the Agent-Based Computational Economic approach. The simulation results show the cooperative strategies will be emerged between buyers and sellers in the ERN Game.     

Competitive pricing with dynamic asymmetric price effects

We model the temporal pricing strategies for two firms with asymmetric costs and differing market power (i.e. big‐box retailer versus smaller local merchant). A firm's demand is a function of its price, a reference price and its competitor's price. Price effects may be asymmetric, i.e. consumers respond differently if they perceive a good to be over‐priced versus under‐priced. We derive analytical results for optimal prices. We show through a series of numerical examples under what settings firms choose various pricing strategies as well as profit implications for firms with differing costs.     

Commitment decisions with demand information updating and a capital‐constrained supplier

In this paper, we study a single two‐echelon supply chain with a capital‐constrained supplier (manufacturer) and a retailer. The supply chain faces a stochastic demand. As the production lead time is long, the market demand is updated during the supplier's production lead time. The supplier needs to determine the production quantity based on the original demand forecast, and the retailer needs to determine the time and quantity to order. The retailer can place an order before the supplier's production (preorder) or after the supplier's production (regular order). We prove the existence of optimal equilibrium solutions under both preorder and regular order strategies. We analytically investigate the order strategies for the supply chain agents under perfect and worthless market information updating. Moreover, we numerically analyze the impact of the information updating quality on the order strategy selection, and the effect of exogenous shocks on the supply chain agents.     

Market power and efficiency in a computational electricity marketwith discriminatory double-auction pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth-Erev individual reinforcement learning algorithm (1995) to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained and that market microstructure is strongly predictive for the relative market power of buyers and sellers, independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms     

Shopbot Economics

Shopbots are Internet agents that automatically search for information pertaining to the price and quality of goods and services. As the prevalence and usage of shopbots continues to increase, one might expect the resultant reduction in search costs to alter market behavior significantly. We explore the potential impact of shopbots upon market dynamics by proposing, analyzing, and simulating a model that is similar in form to some that have been studied by economists investigating the phenomenon of price dispersion. However, the underlying assumptions and methodology of our approach are different, since our ultimate goal is not to explain human economic behavior, but rather to design economic software agents and study their behavior. We study markets consisting of shopbots and other agents representing buyers and sellers in which (i) search costs are nonlinear, (ii) some portion of the buyer population makes no use of search mechanisms, and (iii) shopbots are economically motivated, strategically pricing their information services so as to maximize their own profits. Under these conditions, we find that the market can exhibit a variety of hitherto unobserved dynamical behaviors, including complex limit cycles and the co-existence of several buyer search strategies. We also demonstrate that a shopbot that charges buyers for price information can manipulate markets to its own advantage, sometimes inadvertently benefitting buyers and sellers.