WO2002021395A9 - Agents, system and method for dynamic pricing in a reputation-brokered, agent-mediated marketplace - Google Patents
Agents, system and method for dynamic pricing in a reputation-brokered, agent-mediated marketplaceInfo
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- WO2002021395A9 WO2002021395A9 PCT/US2001/027671 US0127671W WO0221395A9 WO 2002021395 A9 WO2002021395 A9 WO 2002021395A9 US 0127671 W US0127671 W US 0127671W WO 0221395 A9 WO0221395 A9 WO 0221395A9
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- seller
- reputation
- agents
- buyer
- sellers
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
Definitions
- This invention relates to electronic marketplaces where products and services are bought and sold.
- it relates to agent-mediated marketplaces wherein buyers and sellers act through software agents to effectuate transactions and pricing may be altered dynamically by sellers in response to market conditions including the parties' reputations.
- an electronic exchange is any exchange between two or more entities over an electronic network (i.e., not in person) such as, for example, a voice communications network (e.g., POTS or PBX or a data communications network (e.g., LAN or the Internet) or a voice-and-data communications network (e.g., voice-over-IP network).
- Electronic exchanges may include electronic business transactions and electronic cornmunications. Such electronic business transactions may include the negotiation and closing of a sale of goods or services, including solicitation of customers, making an offer and accepting an offer.
- consumer- to-consumer electronic marketplaces e.g., the eBay, OnSale, Yahoo and Amazon marketplaces found on the global Internet at the following respective URLs: www.ebay.com, www.onsale.com, www.yahoo . com, and www.amazon.com
- entities may transact for the sale and purchase of goods or services.
- Electronic communications also may include cornmunications in on-line communities such as mailing lists, news groups, or web-based message boards and chat rooms, where a variety of sensitive personal information may be exchanged, including health-related data, financial investment data, help and advise on research and technology-related issues, or even information about political issues.
- an entity may be a person or an electronic agent (e.g., a software agent). Such a person may act as an individual (i.e., on the person's own behalf) or as a representative (e.g., officer or agent) of a corporation, partnership, agency, organization, or other group.
- An electronic agent may act as an agent of an individual, corporation, partnership, agency, organization, or other group.
- an entity's identity may be anonymous to another entity. This anonymity raises several issues regarding trust and deception in connection to these exchanges. For example, an anonymous entity selling goods on-line may misrepresent the condition or worth of a good to a buyer without suffering a loss of reputation, business or other adverse effect, due to the entity's anonymity.
- One solution to the problems regarding trust and deception is to provide a reputation mechanism to determine and maintain a reputation or reliability rating of an entity.
- a reputation mechanism is intended to provide an indication of how reliable an entity is, i.e., how truly its actions correspond to its representations, based on feedback by other entities that have conducted an electronic exchange with the entity.
- Such feedback typically is provided by another entity in the form of evaluations in a numerical (e.g., 1-5) or Boolean (e.g., good or bad) form.
- a numerical e.g., 1-5
- Boolean e.g., good or bad
- an average of the evaluations provided by other entities are calculated to produce the reputation rating of the entity.
- Such reputation mechanisms typically represent the reputation of an entity witn a scalar value.
- Typical reputation mechanisms suffer from susceptibility to frauds or deceptions.
- a first typical fraud occurs when an anonymous entity, after developing a poor reputation over time in an on-line community, reenters the community with a new anonymous identity (i.e., on-line name), thereby starting anew with a higher reputation than the entity's already earned poor reputation.
- a second typical fraud to which typical reputation mechanisms are susceptible, occurs when two or more entities collude to provide high ratings for each other on a relatively frequent basis, such that the reputations of these entities are thereby artificially inflated.
- Sporas is ⁇ a reputation mechanism for loosely-connected communities (i.e., one in which many entities may not have had an electronic exchange with one another and thus not have rated one another.)
- a reputation may be calculated for an entity by applying the following equation:
- i? ⁇ -7 is the initial reputation of the entity
- C is an effective number of ratings
- 1/C is the change rate factor, named as such because it impacts the rate at which the reputation changes
- damp (R i) is a damping function
- R i other is the reputation of another entity providing the rating
- W t is the rating of the entity provided by the other entity
- Ej is the expected value of the rating
- R is the reputation of the entity.
- the Sporas technique implements an entity reputation mechanism based on the following principles.
- new entities start with a minimum reputation value, and build-up their reputations as a result of their activities on the system. For example, if a reputation mechanism has a rating range from 1 to 100, then an entity may start with an initial reputation value, Ro, of 1.
- Ro initial reputation value
- Sporas reduces the incentive to, and effectively eliminates, that ability of an entity with a low reputation to improve the entity's reputation by reentering the system as a new anonymous identity.
- the reputations of each of the two or more entities involved are updated according to the feedback or ratings provided by the other entities, where the feedback or ratings represent the demonstrated trustworthiness of the two or more entities in the latest exchange. For example, referring to Equation 1 above, the ratee reputation Rj of an entity is updated for each new rating, W ⁇ .
- two entities may rate each other only once within a predetermined number of consecutive ratings. If two entities exchange more than once, then, for each entity, the reputation mechanism only applies the most recently submitted rating to determine the reputation of the rated entity. This fourth principle prevents two or more entities from fraudulently inflating their reputations, as describe above, by frequently rating each other with artificially high ratings.
- Equations 1 and 2 above The damping function increases as the ratee reputation of the rated entity decreases, and decreases as the ratee reputation of the rated entity increases. Thus, a high reputation is less susceptible to change by a single poor rating provided by another entity.
- the reputation mechanism adapts to changes in an entity's behavior. For example, a reputation may be discounted over time so that the most recent ratings of an entity have more weight in determining the ratee reputation of the entity. For example, in Equation 1, above, ratings are discounted over time by limiting the effective number of ratings considered, C.
- the Sporas reputation mechanism also weights the reputation of a rated entity according to the reputation, R other , of another entity providing the rating, where this reputation of the other entity may be determined by applying Equation 1. Therefore, ratings from entities having relatively higher reputations have more of an impact on the reputation of the rated entity than ratings from entities having relatively lower reputations.
- Histos is a reputation mechanism better-suited for a highly-connected community, where entities have provided ratings for a significant number of the other entities. Histos determines a personalized reputation of a first entity from a perspective of a particular entity.
- Histos represents the principle that a person or entity is more likely to trust the opinion of another person or entity with whom she is familiar than trust the opinion of another person or entity who she does not know. Unlike Sporas, a reputation of first entity in Histos depends on the second entity from whose perspective the determination is made, and other ratings of the second entity provided by other users in an on-line community or population.
- Fig. 1 is a block diagram illustrating a representation of an on-line community or population 300 of entities Ai-A ⁇ interconnected by several rating links, including rating links 302, 303, 304, 306, 308 and 310.
- Each rating link indicates a rating of a rated entity (i.e., a ratee) by a rating entity (i.e., a rater) with an arrowhead pointing from the rating entity to the rated entity.
- a ratee is an entity in a position of being rated by one or more other entities
- a rater is an entity in a position of rating one or more other entities.
- rating link 302 represents a rating of 0.8 for ratee A 3 by rater A ls and rating link 303 represents a rating of 0.9 for ratee A t by rater A 3 .
- each rating link only indicates a single rating, it is possible tnat an entity has provided more than one rating for another entity.
- the Zacharia reference discloses that if an entity has provided more than rating for another entity, the most recent rating should be selected to determine a personalized reputation of a first entity from the perspective of a second entity.
- a rating may be multi-dimensional, also, rather than one-dimensional.
- dimensions may include promptness of shipment, correspondence between advertised and delivered quality of goods, warranty terms, and so forth.
- Buyers may be viewed as users with questions and sellers as users with answers. In one regard, it is reputation that answers many of the questions, particularly comparative analysis of reputation as between two or more would-be sellers. Reputation is thus a potentially more important factor in a service-provider's profitability than it is for a goods merchant, both in conventional markets and for electronic commerce.
- a service provider with a strong reputation can close more sales at higher prices (up to some limits) than can a service provider with a significantly lower reputation.
- service providers often conduct customer satisfaction surveys in order to assess their reputations and find ways to improve them.
- a service provider that receives a high rating from customers may feel comfortable raising its prices, while a service provider who receives low ratings may feel compelled to lower its prices to generate more business.
- Service providers thus strive to keep their workers as busy as possible, lowering prices when necessary to do so.
- a buyer who can be patient and wait for a particular provider to be idle may be able to hire that service provider at a low price, even if the seller (service provider) has an excellent reputation.
- Kasbah which was implemented using a so-called MarkefMaker infrastructure, allows users to trade intangibles such as services.
- price negotiation is based on a limited number of predefined negotiation strategies provided by the system. Agents created with these strategies cannot adjust a negotiation behavior according to the market conditions and the user has to make sure that his/her/its price ranges are close to the market prices.
- software agents that automate the task of monitoring market conditions for their users.
- the concept of utility enters economic analysis typically via the concept of a utility function which itself is just a mathematical representation of an individual's preferences over alternative bundles of consumption goods (or, more generally, over goods, services, and leisure). If the individual's preferences are complete, reflexive, transitive, and continuous, then they can be represented by a continuous utility function. In this sense, utility itself is an almost empty concept: It is just a number associated with some consumption bundle.
- a method and system for use in that method and system, for time-varying pricing of transactions between buyers and sellers, particularly as related (but not limited) to transactions for services.
- seller agents may offer services at prices that vary over time, based on past experiences.
- Buyer agents may be configured by their users according to time and constraints, budget and the importance of a specific task (also called a job, project or contract). The buyer agents created this way try, probabilistically, to maximize their owners' utilities. They do so, in part, by estimating the expected performance of each seller based on the reputation of that seller in the relevant marketplace (i.e., a seller could have different reputations in different marketplaces).
- the buying agents may reveal only their time constraints and descriptions of the tasks (services) desired to the sellers, in order to achieve their goal.
- the budget constraints and the importance of the task for the buyer are not revealed since they reduce the negotiating power of the buyer.
- Seller (selling) agents respond to buying (buyer) agents by bidding on behalf oi tneir owners for the available (i.e., proposed or offered) tasks.
- the bids of the sellers may be based in part on their owners' reputations, their time availability, the difficulty of the task and the current demand on the marketplace, or some one of such factors or other combination thereof, with or without other not-listed factors.
- the seller reputations are updated in this marketplace in a collaborative fashion (i.e., with all or most buyers contributing their evaluations), based on the performance of the sellers in their delegated tasks (i.e., the tasks required in the contracts they win from buyers).
- a seller's agent for use in an agent-mediated marketplace, the seller's agent using a reputation follower strategy to set a bid price for responding to a buyer's offer to purchase, and responsive to seller reputation information.
- the reputation information may include reputation values for all sellers bidding in response to the buyer's offer.
- the reputation follower strategy may (preferably) be a profit maximizing reputation follower strategy as described below.
- the seller's agent may evaluate its resulting abilities and withdraw from bidding on any further buyers' offers it will not be able to satisfy as a result of the contractual demands on the seller until the contract has been completed and the seller's associated resources are again available.
- Another aspect of the invention is a method for a seller's agent to formulate a bid price in response to a buyer's offer to purchase via an agent-mediated marketplace. The seller's agent examines the buyer's offer and receives information about the seller's reputation and the reputations of other sellers of services requested by the buyer.
- the seller's agent Based on the buyer's offer, the reputation information, and the seller's history of success, the seller's agent formulates a bid price and conveys the bid price to the buyer.
- the bid formulation may be based on a reputation follower or profit maximizing reputation follower strategy.
- Still another aspect of the invention is a system for effecting electronic contracts between buyers and sellers.
- the system includes a plurality of seller agents, a plurality of buyer agents, a marketplace server, and a seller reputation data source.
- the buyer agents place on the marketplace server offers to purchase; the seller agents evaluate the offers to purchase and selective bid to meet an offer when a seller has the ability to do so, a price included in the bid being based at least in part on a seller reputation value obtained from the seller reputation data source.
- buying agents may evaluate bids from sellers at least in part in consideration of seller reputation values from the seller reputation data source, a seller's price bid and an importance the buying attaches to the purchase.
- the selling agents use a reputation follower strategy, preferably a profit-maximizing reputation follower strategy, to set a bid price.
- Fig. 1 is a block diagram illustrating a representation of an on-line community, showing rating links between various entities
- Fig. 2 is a diagrammatic illustration of a system platform for an agent-mediated marketplace for dynamic pricing in response to reputation changes;
- Fig. 3 is a graph of the seller's available offer space as a function of the seller's reputation
- Fig. 4 is a graph showing the results of a simulation of the performance of three types of seller agents in the absence of competition among them
- Fig. 5 is a graph showing the results of a simulation of the performance of three types of seller agents in the presence of competition among them;
- Fig. 6 is a graph showing the results of a simulation to compare the profits achieved by Reputation Follower seller agents with the profits achieved by Derivative Follower seller agents in unemployment;
- Fig. 7 is a graph showing the results of a simulation of the performance of three types of seller agents in the absence of competition among them;
- Fig. 8 is a graph showing the results of a simulation of the performance of three types of seller agents in the presence of competition among them;
- Fig. 9 is a graph showing the results of a simulation to compare the profits achieved by Reputation Follower seller agents with the profits achieved by Derivative Follower seller agents in overemployment;
- Figs. 10 - 13 are charts listing experimental results obtained with so-called Optimal Sellers as described herein;
- Fig. 14 is a table which sets forth the logic for a Profit Maximizing Reputation
- Figs. 15 - 17 are charts listing experimental results obtained with agent logic of Fig. 14.
- Described below is a method and system, and agents for use in that method and system, for time-varying pricing of transactions between buyers and sellers, particularly as related to transactions for services in an electronic marketplace. That is, sellers may offer services at prices that vary over time, based on past experiences.
- dynamic pricing is described below primarily in connection with pricing of transactions on electronic exchanges, such pricing may be applied to any of a variety of situations, regardless of whether the transaction is on an electronic exchange. Solely for purposes of illustration, as an example and not to be limiting, the dynamic pricing agents and system will be shown in the context of an electronic marketplace accessed by users via the global Internet.
- the ratings used by the dynamic pricing mechanisms discussed herein may come from any usable source or system, including, but not limited to, the systems disclosed in any of the above-referenced patent applications or commercially avauaoie from U j ⁇ eru un ⁇ , Inc. of Boston, MA, USA.
- buyers are users who need certain goods or services that sellers can provide.
- buyers have to face complexities such as measuring seller competency and performance. This is very similar to a marketplace for tangible goods wherein a seller is concerned with measuring the creditworthiness of the buyer and the buyer is concerned with measuring the reliability of the stated delivery time of the seller and the seller's history of complaint resolution, as well as other factors.
- Collaborative reputation mechanisms are employed to estimate the sellers' performance based on their past transactions, and the process of matchmaking and pricing of the services is automated.
- FIG. 2 there is shown a diagrammatic illustration of a "platform" 10 for an agent-mediated marketplace wherein the present invention may be used.
- the platform includes a server computer 12, a number of buyer client computers 14 (only one being shown), a number of seller client computers 16 (only one being shown), and the global Internet 18 to interconnect them.
- the buyer agents and seller agents are software program modules that may reside on any of the computers; for purposes of illustration only, and without any intended loss in generality, buyer agents (BA) 22 and seller agents (SA) 24 are ' shown as executing on server 12.
- the server computer or other computer(s) executing the agents receive reputation information from a reputation database (RDB) on a reputation server 32.
- the reputation server may operate in accordance with any suitable algorithm, including, but not limited to, the various reputation-generating systems of the above-identified co-pending applications.
- Other software such as the operating system and an electronic marketplace engine, are not shown in order to avoid obfuscating the invention.
- the electronic marketplace engine may have various suitable forms. For example, it may be an electronic bulletin board on which buyer agents post their offers to purchase and which buyer agents survey to look for opportunities to do business.
- Buyer Agents may be an electronic bulletin board on which buyer agents post their offers to purchase and which buyer agents survey to look for opportunities to do business.
- the buyers configure their agents with the buyers' budgets and the importance the buyers ascribe to specific tasks (jobs). (This may be done in any convenient way.
- a web site may be configured on the server computer, with forms for creating and configuring buyer and seller agents.
- the buyers and sellers may use any Internet-connected client computer to access the web site and set up their agents.)
- These buyer agents try to maximize their owners' utilities (defined elsewhere herein).
- the buyer agents estimate the expected performance of each seller based on the reputation of that seller in the marketplace, as well as the sellers' price, and choose the seller that maximizes their expected utility.
- Selling agents respond to buyers by bidding on behalf of their owners for the available tasks based on their owners' reputations.
- the equilibria of this marketplace are evaluated for two different scenarios: unemployment (i.e., less demand than supply), and overemployment (i.e., more demand than supply). Since the number of buyers and sellers is kept fixed, the scenarios are created by changing the rate of creation of tasks for each buyer. In particular, we consider the operation of the market over successive defined intervals, or periods, of time. In every period, each buyer has a probability P to generate a problem. Once a problem is generated, the buying agent dispatches a request for bids to all sellers. Upon receipt of this query, all available seller agents respond with a price bid and wait for the buyer's decision. Optionally, if a selling agent is already engaged in another task, it cannot undertake another one, so it does not respond. However, the buying agents may have multiple tasks served at the same time.
- the buyer evaluates the expected utility function for each bid and picks the available seller that offers the highest expected utility. The buyer is allowed to reject all bids. Once the buyer makes its selection, the buyer delegates the task of service completion to (i.e., engages) the chosen seller. Optionally, a seller may become unavailable for some periods in order to perform a delegated task. Tasks mav be assumed to take the same amount of time or they may be assigned varying amounts of time. This process is completed for each buyer in the market and, for each buyer, for each transaction that the buyer wishes to complete.
- the importance / is a uniformly distributed random variable from 0 to 1. If a problem is generated, the buyer will request bids from the seller without providing information about the importance of the task, so that it does not lose its bargaining power.
- the sellers have uniformly distributed abilities A ranging from 0 to 1.
- the outcomes of all tasks performed by a seller i.e., the evaluations of their performance
- follow a normal distribution if the outcome comes out with a mean value that is negative or greater than 1, it is truncated to 0 or 1, respectively.
- the seller's reputation is updated over time based on the seller's ability, as discussed below.
- a user with reputation Rj_ ⁇ is rated with a score W; which is a random value normally distributed around the user's ability A, truncated between 0 and 1.
- E; R ⁇ /D, where D is the reputation range.
- Ej can be interpreted as the expected value of W;, which is the ability A of the user, though early in a user's activity it will be an estimate.
- the parameter ⁇ controls the damping function ⁇ so that the reputations of highly probable users are less sensitive to rating fluctuations.
- the initial reputation of the agents may be chosen to be minimal; for example, the initial reputation value may be 0.01.
- the objective of a buying agent is to pick the most suitable seller for a given task. It does so by maximizing its predetermined utility function.
- a suitable utility function is the Cobb-Douglas utility function: Equation 7: where P is the price the buyer will pay normalized by his budget cap (i.e., where P ac tuai is the actual price to be paid and P cap is the maximum price the buyer is willing to pay) so that it is between 0 and 1 ; I is the importance of the problem to the buyer, and O is the outcome of the problem in the range of 0 to 1 , where 1 is a perfect outcome and 0 is the worst possible outcome.
- This utility function is appropriate because it has properties consistent with two points: (1) for an important problem, the buyer is willing to spend more and (2) for an unimportant problem, the buyer will sacrifice quality for price.
- a buyer agent may treat the performance of the seller as a deterministic variable, represented by the value of the seller's reputation. Thus, they evaluate their utility functions using the assumption that the outcome, O, is equal to the reputation of the seller which, as noted above, changes over time.
- Selling agents may be of several kinds. Certain basic kinds of selling agents will be discussed as well as some using more advanced dynamic pricing methods, it being understood that the development of increasingly more intelligent selling agents will result in other candidates in the future.
- Derivative Followers are selling agents who decide their next bid according to the success of their preceding bid. Therefore, these sellers focus on increasing their prices from one contract to the next so long as they can get the contracts. Likewise, they decrease their bids after having offered a bid and failed to win a contract.
- An assumption may be made that Derivative Followers increase their bid prices by a fixed step S up multiplied by a random number picked from a uniform distribution with range [0,1] for the next (inertia+1) periods. The random number is different every time the agent offers a bid.
- Preliminary experiments have shown that the value of the variable inertia does not have much effect on the results because there are no local maxima or minima in the profit landscapes of the Derivative Follower sellers.
- a Derivative Follower fails to receive a contract (i.e., be engaged by the buyer), it will start decreasing its price bids, which with each successive decrease being S dn * random, where "random” denotes the value of a random variable with a uniform distribution in the range [0,1].
- S dn * random the offer by the Derivative follower will be given by:
- LastContractPrice is, as implied, the price bid on the last offered contract.
- This algorithm allows the selling agents to respond fast to changes in their reputations.
- Reputation Followers set bids that follow their received reputation patterns (and eventually their actual performance and abilities) better than do the Derivative Followers. In a sense, these Reputation Followers are Derivative Followers but with a step that depends on the seller's reputation, which changes dynamically. Selling agents with low reputation change their prices slowly. Therefore, in the case of unemployment, it can be expected that they will perform better than low reputation Derivative Followers, since they will undercut the latters' offers.
- Random Sellers are agents having no pricing or bidding strategies. They just bid random prices. Naturally, these agents do not perform particularly well, but they provide a measure to use for comparison purposes.
- the maximum price that the seller can charge is a function of a given reputation J , the available external market price Pm, and the importance I of the proposed transaction.
- the maximum price, P ms ⁇ can be modeled as:
- Equation 9 P max ⁇
- a seller initially has a very low reputation. Therefore, at the outset it can only receive low importance jobs. Even if it bid for 0 price, it can only get a contract if the following relationship holds true:
- Equation 10 R 1 ⁇ (l-Pj '1 *> / ⁇ lo ⁇ ⁇ ⁇ (l - p > where 7 is the importance of the job, and R is the initial reputation of the selling agent. This is expected since agents will opt to build reputation, in order to bid actively for a larger share of the contracts.
- Fig. 3 depicts the seller's available offer space and shows the range of bids allowed for a seller as his reputation increases. Sellers have a chance of receiving a contract only if they bid below the curve 34 corresponding to their current reputation value. Of course, the bid also must not exceed the importance the buyer attaches to the problem, which the seller does not know when it places its bid.
- Fig. 6 portrays the average difference between the profits of the Reputation Followers and the Derivative Followers over time.
- the y-axis values represent the difference in average profit of an RF and a DF divided by the number of trade iterations (i.e., periods), with the x-axis being the number of trade iterations.
- the set of buyers will be represented as ⁇ Bi, B 2 , B 3 , . . . , B m ⁇ , sorted by quality sensitivity, I(B j ), such that I(B >I(B 2 )>I(B 3 )> . . . I(B m ).
- Unemployment conditions exist when n>m; full employment, when n m: and overemployment, when n ⁇ m.
- Figs. 10 and 11 show the equilibrium reached for derivative and Reputation Followers.
- the first column is the seller identification index (sellers Si through S 10 ); the second and third columns show the average buyer identification index with whom each seller traded the first 50 iterations (-1 if the seller made no trades), and the total number of trades made by each seller during these iterations.
- seller Si traded with "buyer" B 1 . 5 (this is simply the arithmetic average of the identification indices of the buyers with whom seller St trades), and had a total of forty trades.
- Seller S 6 traded only with buyer B 3 a total of thirteen trades.
- the fourth and fifth columns show the average buyer identification index and total number of trades during iterations 100-150; and the sixth and seventh columns, the same for iterations 750-800.
- the sellers oscillate their prices, optimizing for two consecutive buyers rather than one the same ranking of quality sensitivity (i.e., buyers Bi, B 2 and B 3 buy from sellers (S ⁇ ,S 2 ), (S 3 ,S ), and (S 5 ,S 6 ), instead of Si, S 2 and S 3 ). Therefore, we need a pricing mechanism that allows the sellers to escape from local maxima and learn the optimal prices for their abilities.
- dynamic pricing sellers have been designed that not only take into account their prices and reputations, but also their profits; they also compare prices, profits, and reputations over a period of time so that, in a sense, they have "memory" of the past.
- the profit followers w ⁇ m mcmui u ⁇ u»» follows: for a given time window, say of length of 10 iterations, they measure their average prices, profit, and reputation over the most recent 10 iterations and of the previous 10 iterations (i.e., from 20 iterations ago until 10 iterations ago). They then decide their next price bid based on the relative changes of their reputation, prices, and profits over these two periods. For example, if the profits, the prices, and the reputations increased relative to their values from 20 to 10 iterations ago, the agents further increase their prices. If the profits decreased while the prices increased and the reputations decreased, they decrease their next price.
- the decision logic of, and hence the actions taken by, these agents under all market circumstances are set forth in Fig. 14.
- the agents choose the average price of the past 2t iterations. These agents are referred to as Profit Maximizing Reputation Followers (PMRFs).
- PMRFs Profit Maximizing Reputation Followers
- the PMRF agents implement a divide and conquer approach, by choosing the mean of the average price during the two consecutive periods. A more optimal value than the mean may be selected, based on the incremental impact of prices and reputations on the buyers' perceived utilities. Such an approach requires that the sellers maintain a model for the utility functions of the buyers.
- Fig. 15 shows the equilibrium results with stationary reputations
- Figs. 16 and 17 show the equilibrium results with changing reputations.
- the sellers end up optimizing their pricing in order to trade with their respective buyers, in both the experiments with stationary, and non stationary reputations.
- Fig. 17 shows that the final seller prices are slightly higher than the optimal for all the sellers who are able to make transactions except the first one.
- a contract between a buyer and a seller may be formed as follows: Buying agents 22 post to a bulletin board 26 on server 12 a list of services they desire to purchase, along with the related conditions. Conditions may be expressed by completing a web form supplied by the server.
- the form may include check boxes, pull down menus and the like, to facilitated automated matching to a seller's offerings, and perhaps may also include a text field for other comments. Pull down menus and check boxes can be based on an ontology compiled u . address the appropriate service descriptions for a particular field or fields. The same ontology would be used by the sellers to express their competencies and for the buyers to express services desired when the respective agents are created or modified.
- Sellers' agents scan the listings from the buyers looking for listings on which to submit bids. When a seller agent finds a suitable listing matching its abilities, it submits a bid, provided that it can do so while still generating a profit for the seller (a break-even limit having been established by the seller when it set up its agent). Buyers (i.e., their agents) evaluate the sellers' reputations against their own minimum requirements, if any, and evaluate the bids of acceptable sellers to determine if there is one to accept. If a buyer accepts a bid, it sends a message to the seller so indicating. Typically, it is the responsibility of the seller to notify the marketplace operator for the purpose of fulfilling a contract to pay a fee to the operator.
- the seller then reviews its inventory to determine whether fulfilling the contract will use up its available resources (abilities), in which case it must make itself unavailable to bid on buyer offers to purchase that it would be unable to fulfill. (Otherwise, if the seller overcommits, it is comrnitting commercial suicide because the buyer will be disappointed and provide a low rating to the reputation service provider.
- the reputation server sends the buyer a questionnaire and obtains performance rating responses which can then be used to augment and update the reputation report on the seller.) If the contract calls for delayed delivery of services, the period of unavailability, if any, may not begin immediately.
- a seller agent designed to maximize profit for the seller would then require an additional algorithm to make decisions among the posted buyer offers to purchase.
- reputation may be multi-dimensional, such that a vector representation is more appropriate. For example, if a seller offers three tiers of quality, separate reputation values for each tier would make sense. Also, different aspects of performance might warrant separate values, such as a value for timeliness, a value for quality, and a value for responsiveness. Buyers could then specify minimum value requirements for each reputation component category when seeking out acceptable sellers.
Abstract
L'invention concerne un procédé et un système de commerce soumis à la médiation d'agents et ainsi que les agents associés à ce procédé et à ce système. Des agents de vente peuvent offrir des services à des prix qui varient dans le temps, sur la base d'expériences passées. Des agents d'achat peuvent être configurés par leurs utilisateurs en fonction du temps et de contraintes, du budget et de l'importance d'une tâche spécifique. Ces agents d'achat essaient, selon des méthodes probabilistes, de maximiser les services de leur propriétaire, en partie, en estimant le rendement espéré de chaque vendeur sur la base de la réputation de ce vendeur dans la place de marché appropriée. Ces agents d'achat peuvent ne révéler aux vendeurs que leurs contraintes et les descriptions des tâches (services) souhaitées. Les agents de vente font des offres pour les tâches offertes et basent leurs offres au moins partiellement sur les réputations de leurs propriétaires, leur disponibilité, la difficulté de la tâche et la demande courante sur le marché. Les réputations des vendeurs sont mises à jour de façon coopérative sur la base du rendement du vendeur. Les agents de vente emploient des mécanismes de tarification dynamiques, y compris des suiveurs de réputation qui recherchent spécifiquement la maximisation de leur bénéfice.The invention relates to a process and a system of trade mediated by agents and also to the agents associated with this process and this system. Sales agents can offer services at prices that vary over time, based on past experience. Purchasing agents can be configured by their users according to time and constraints, budget and the importance of a specific task. These purchasing agents try, using probabilistic methods, to maximize the services of their owner, in part, by estimating the expected performance of each seller based on the reputation of that seller in the appropriate marketplace. These purchasing agents may reveal to the sellers only their constraints and the descriptions of the tasks (services) desired. Sales agents make offers for the tasks offered and base their offers at least partially on the reputations of their owners, their availability, the difficulty of the task and current market demand. Sellers' reputations are updated cooperatively based on the seller's performance. Sales agents employ dynamic pricing mechanisms, including reputation followers who specifically seek profit maximization.
Description
Claims
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU2001288833A AU2001288833A1 (en) | 2000-09-06 | 2001-09-06 | Agents, system and method for dynamic pricing in a reputation-brokered, agent-mediated marketplace |
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
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US23035500P | 2000-09-06 | 2000-09-06 | |
US23027300P | 2000-09-06 | 2000-09-06 | |
US60/230,273 | 2000-09-06 | ||
US60/230,355 | 2000-09-06 |
Publications (3)
Publication Number | Publication Date |
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WO2002021395A2 WO2002021395A2 (en) | 2002-03-14 |
WO2002021395A8 WO2002021395A8 (en) | 2002-07-11 |
WO2002021395A9 true WO2002021395A9 (en) | 2003-03-27 |
Family
ID=26924080
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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PCT/US2001/027671 WO2002021395A2 (en) | 2000-09-06 | 2001-09-06 | Agents, system and method for dynamic pricing in a reputation-brokered, agent-mediated marketplace |
Country Status (3)
Country | Link |
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US (1) | US20020138402A1 (en) |
AU (1) | AU2001288833A1 (en) |
WO (1) | WO2002021395A2 (en) |
Families Citing this family (54)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8290809B1 (en) | 2000-02-14 | 2012-10-16 | Ebay Inc. | Determining a community rating for a user using feedback ratings of related users in an electronic environment |
US9614934B2 (en) | 2000-02-29 | 2017-04-04 | Paypal, Inc. | Methods and systems for harvesting comments regarding users on a network-based facility |
US7428505B1 (en) | 2000-02-29 | 2008-09-23 | Ebay, Inc. | Method and system for harvesting feedback and comments regarding multiple items from users of a network-based transaction facility |
US7231353B1 (en) * | 2000-07-13 | 2007-06-12 | Infoshop Llc | System and method for recording and reporting consumer monetary commentary |
US20020078152A1 (en) | 2000-12-19 | 2002-06-20 | Barry Boone | Method and apparatus for providing predefined feedback |
US20080215452A1 (en) * | 2001-02-28 | 2008-09-04 | Digonex Technologies, Inc. | Digital online exchange for sending prices to customers |
WO2002069107A2 (en) * | 2001-02-28 | 2002-09-06 | Musicrebellion Com, Inc. | Digital online exchange |
US20030130932A1 (en) * | 2002-01-08 | 2003-07-10 | Wong Kwok D. | Method of selling items using a computer and a communication network |
JP2003223322A (en) * | 2002-01-30 | 2003-08-08 | Mitsubishi Electric Corp | Device for analyzing combinatorial optimization problem |
US20050139662A1 (en) * | 2002-02-27 | 2005-06-30 | Digonex Technologies, Inc. | Dynamic pricing system |
US20050149458A1 (en) * | 2002-02-27 | 2005-07-07 | Digonex Technologies, Inc. | Dynamic pricing system with graphical user interface |
US20030187773A1 (en) * | 2002-04-02 | 2003-10-02 | Santos Cipriano A. | Virtual marketplace agent technology |
US6805277B1 (en) * | 2003-04-16 | 2004-10-19 | Lotes Co., Ltd. | Process for soldering electric connector onto circuit board |
US7685028B2 (en) * | 2003-05-28 | 2010-03-23 | Gross John N | Method of testing inventory management/shipping systems |
GB0401570D0 (en) * | 2004-01-24 | 2004-02-25 | Guaranteed Markets Ltd | A transaction management system and method |
US20080189204A1 (en) * | 2004-05-26 | 2008-08-07 | Hansford Brendon N | Method and apparatus for providing home equity financing without interest payments |
US8458060B2 (en) * | 2004-05-28 | 2013-06-04 | Vendavo, Inc. | System and method for organizing price modeling data using hierarchically organized portfolios |
US20050278227A1 (en) * | 2004-05-28 | 2005-12-15 | Niel Esary | Systems and methods of managing price modeling data through closed-loop analytics |
US7945469B2 (en) * | 2004-11-16 | 2011-05-17 | Amazon Technologies, Inc. | Providing an electronic marketplace to facilitate human performance of programmatically submitted tasks |
US20060253582A1 (en) * | 2005-05-03 | 2006-11-09 | Dixon Christopher J | Indicating website reputations within search results |
US7562304B2 (en) | 2005-05-03 | 2009-07-14 | Mcafee, Inc. | Indicating website reputations during website manipulation of user information |
US8566726B2 (en) * | 2005-05-03 | 2013-10-22 | Mcafee, Inc. | Indicating website reputations based on website handling of personal information |
US9384345B2 (en) | 2005-05-03 | 2016-07-05 | Mcafee, Inc. | Providing alternative web content based on website reputation assessment |
US7765481B2 (en) * | 2005-05-03 | 2010-07-27 | Mcafee, Inc. | Indicating website reputations during an electronic commerce transaction |
US8438499B2 (en) | 2005-05-03 | 2013-05-07 | Mcafee, Inc. | Indicating website reputations during user interactions |
US7822620B2 (en) * | 2005-05-03 | 2010-10-26 | Mcafee, Inc. | Determining website reputations using automatic testing |
US8949338B2 (en) | 2006-03-13 | 2015-02-03 | Ebay Inc. | Peer-to-peer trading platform |
US7877353B2 (en) * | 2006-03-13 | 2011-01-25 | Ebay Inc. | Peer-to-peer trading platform with relative reputation-based item search and buddy rating |
US7958019B2 (en) * | 2006-03-13 | 2011-06-07 | Ebay Inc. | Peer-to-peer trading platform with roles-based transactions |
US8335822B2 (en) * | 2006-03-13 | 2012-12-18 | Ebay Inc. | Peer-to-peer trading platform with search caching |
US8701196B2 (en) | 2006-03-31 | 2014-04-15 | Mcafee, Inc. | System, method and computer program product for obtaining a reputation associated with a file |
US20080126264A1 (en) * | 2006-05-02 | 2008-05-29 | Tellefsen Jens E | Systems and methods for price optimization using business segmentation |
WO2007133748A2 (en) * | 2006-05-15 | 2007-11-22 | Vendavo, Inc. | Systems and methods for price setting and triangulation |
US8615440B2 (en) * | 2006-07-12 | 2013-12-24 | Ebay Inc. | Self correcting online reputation |
US7848979B2 (en) * | 2006-08-21 | 2010-12-07 | New York University | System, method, software arrangement and computer-accessible medium for incorporating qualitative and quantitative information into an economic model |
US7680686B2 (en) * | 2006-08-29 | 2010-03-16 | Vendavo, Inc. | System and methods for business to business price modeling using price change optimization |
US7860752B2 (en) * | 2006-08-30 | 2010-12-28 | Ebay Inc. | System and method for measuring reputation using take volume |
US20080109244A1 (en) * | 2006-11-03 | 2008-05-08 | Sezwho Inc. | Method and system for managing reputation profile on online communities |
US20080109245A1 (en) * | 2006-11-03 | 2008-05-08 | Sezwho Inc. | Method and system for managing domain specific and viewer specific reputation on online communities |
US20080137537A1 (en) * | 2006-11-22 | 2008-06-12 | Bader Al-Manthari | Method for optimal packet scheduling for wireless and mobile communications networks |
US7711684B2 (en) | 2006-12-28 | 2010-05-04 | Ebay Inc. | Collaborative content evaluation |
US20080162236A1 (en) * | 2006-12-28 | 2008-07-03 | Peter Sommerer | Method for trust management in complex organizations |
US20080294501A1 (en) * | 2007-05-21 | 2008-11-27 | Steven Carl Rennich | Collecting and providing information about vendors, products and services |
US7831611B2 (en) | 2007-09-28 | 2010-11-09 | Mcafee, Inc. | Automatically verifying that anti-phishing URL signatures do not fire on legitimate web sites |
US8108910B2 (en) * | 2007-10-16 | 2012-01-31 | International Business Machines Corporation | Methods and apparatus for adaptively determining trust in client-server environments |
US20090199185A1 (en) * | 2008-02-05 | 2009-08-06 | Microsoft Corporation | Affordances Supporting Microwork on Documents |
US10395187B2 (en) | 2008-02-11 | 2019-08-27 | Clearshift Corporation | Multilevel assignment of jobs and tasks in online work management system |
US8392266B2 (en) * | 2009-11-13 | 2013-03-05 | Omnione Usa, Inc. | System and method for certifying information relating to transactions between a seller and a purchaser |
US20130006712A1 (en) * | 2011-07-01 | 2013-01-03 | Nabil Behlouli | Method and system for revenue management system based on market pricing |
US10380656B2 (en) | 2015-02-27 | 2019-08-13 | Ebay Inc. | Dynamic predefined product reviews |
MX2018010083A (en) * | 2016-02-22 | 2019-06-06 | Tata Consultancy Services Ltd | Systems and methods for resolving conflicts in order management of data products. |
US11049150B2 (en) * | 2018-06-22 | 2021-06-29 | Criteo Sa | Generation of incremental bidding and recommendations for electronic advertisements |
CN113269572B (en) * | 2021-07-01 | 2023-12-12 | 广西师范大学 | Credibility-based blockchain agricultural product traceability trusted data uploading method |
WO2023203375A1 (en) * | 2022-03-01 | 2023-10-26 | Patrick Joseph Byrne | Evolutionary computational modular neural networks, structures and methods, incorporating evolutionary computational economic data systems, and adaptive, emergent, evolutionary augmented economic data system machine learning |
Family Cites Families (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4870579A (en) * | 1987-10-01 | 1989-09-26 | Neonics, Inc. | System and method of predicting subjective reactions |
US4996642A (en) * | 1987-10-01 | 1991-02-26 | Neonics, Inc. | System and method for recommending items |
US5550746A (en) * | 1994-12-05 | 1996-08-27 | American Greetings Corporation | Method and apparatus for storing and selectively retrieving product data by correlating customer selection criteria with optimum product designs based on embedded expert judgments |
US5732400A (en) * | 1995-01-04 | 1998-03-24 | Citibank N.A. | System and method for a risk-based purchase of goods |
US5768142A (en) * | 1995-05-31 | 1998-06-16 | American Greetings Corporation | Method and apparatus for storing and selectively retrieving product data based on embedded expert suitability ratings |
US6112186A (en) * | 1995-06-30 | 2000-08-29 | Microsoft Corporation | Distributed system for facilitating exchange of user information and opinion using automated collaborative filtering |
US6415264B1 (en) * | 1997-07-08 | 2002-07-02 | Walker Digital, Llc | System and method for determining a posting payment amount |
JP3357812B2 (en) * | 1997-03-18 | 2002-12-16 | 株式会社東芝 | Mutual credit server device and distributed mutual credit system |
US6362837B1 (en) * | 1997-05-06 | 2002-03-26 | Michael Ginn | Method and apparatus for simultaneously indicating rating value for the first document and display of second document in response to the selection |
US6275811B1 (en) * | 1998-05-06 | 2001-08-14 | Michael R. Ginn | System and method for facilitating interactive electronic communication through acknowledgment of positive contributive |
US6487541B1 (en) * | 1999-01-22 | 2002-11-26 | International Business Machines Corporation | System and method for collaborative filtering with applications to e-commerce |
US6708155B1 (en) * | 1999-07-07 | 2004-03-16 | American Management Systems, Inc. | Decision management system with automated strategy optimization |
US6347332B1 (en) * | 1999-12-30 | 2002-02-12 | Edwin I. Malet | System for network-based debates |
US20010034631A1 (en) * | 2000-01-21 | 2001-10-25 | Kiselik Daniel R. | Method and apparatus for the automatic selection of parties to an arrangement between a requestor and a satisfier of selected requirements |
-
2001
- 2001-09-06 AU AU2001288833A patent/AU2001288833A1/en not_active Abandoned
- 2001-09-06 US US09/948,082 patent/US20020138402A1/en not_active Abandoned
- 2001-09-06 WO PCT/US2001/027671 patent/WO2002021395A2/en active Application Filing
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WO2002021395A2 (en) | 2002-03-14 |
WO2002021395A8 (en) | 2002-07-11 |
AU2001288833A1 (en) | 2002-03-22 |
US20020138402A1 (en) | 2002-09-26 |
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