In additional simulations, we investigated a situation in which all five sellers use identical pricing strategies, but one of the sellers resets its price more quickly than the others. We observed that the faster price-setter earns substantially more profit than the others because, for example, in the case of myoptimal agents, it undercuts far more often than it itself is undercut. In the absence of any throttling mechanism, it is advantageous for sellers to re-price their goods as quickly as possible, but this could potentially lead to an arms race in which sellers do so with ever-increasing frequency. In such a world, a human price setter would undoubtedly be too slow and costly, and would be replaced with a pricebot (likely one based on a more sophisticated algorithm than any explored in Section 4!). Almost certainly, this strategy would make use of information about the buyer population, which could be purchased from other agents. Even more likely, however, the strategy would require knowledge of competitors' prices. How would the pricebot obtain this information? From a shopbot, of course!
With each seller seeking to re-price its products faster than its competitors, shopbots would quickly become overloaded with requests. A pricebot representing amazon.com might submit a million or more queries (one per book title) to a shopbot every hour -- or maybe even every minute! Since shopbots must query individual sellers for prices, they would in turn pass this load back to amazon.com's competitors: e.g., barnesandnoble.com, kingbooks.com. The rate of pricing requests made by sellers could easily dwarf the rate at which similar requests would be made by human buyers, eliminating the potential of shopbots to ameliorate market frictions.
A typical solution to an excess demand for shopbot services would be for shopbots to charge pricebots for price information. Today, shopbots tend to make a living by selling advertising space on their Web pages. This appears to be an adequate business model so long as requests are made by humans. Agents, however, are unwelcome customers because they are are not influenced by advertisements; as a result, agents are either barely tolerated or excluded intentionally. By charging for the information services they provide, shopbots would be economically-motivated agents, creating the proper incentives to deter excess demand, and welcoming business from other agents. Once shopbots begin to charge for pricing information, it would seem natural for sellers -- the actual owners of the desired information -- to themselves charge the shopbots for their information. The sellers could use another form of pricebot to dynamically price this information. This scenario illustrates how the need for agents to dynamically price their services could quickly percolate through an entire economy of software agents. The alternative is ``meltdown'' due to overload which could occur as agents become more prevalent on the Internet. Rules of etiquette followed voluntarily today by web crawlers and related programs could be trampled in the rush for competitive advantage.