( Gaurav Tewari, Pattie Maes [MIT Media Labs])
ABSTRACT
MARI
( Multi Attribute Resource Intermediary ) is proposed to improve online
marketplaces , specifically those involve buying and selling of non-tangible
goods and services. It is an agent based intermediary architecture
intended as a generalized platform for the specification and brokering
of heterogeneous goods and services. It basically comes under three areas
- multi- agent system
- highly mediated communications
- electronic commerce .
But it is
primarily positioned from a "highly mediated communications" perspective
. MARI builds upon multi-attribute utility theory formulations to
model relative user preferences and quantify tradeoffs. Here the user can
specify the range of attributes he can use and also the Utility Function
for each attribute. Due to this a buyer is made to show his hidden preferences
through his preferences. For example, if user's thinks that he wants to
get a product for a perticular price only, then he will give almost equal
values for maximum price and minimum price, which inturn used by the MARI
to match it to a seller whose selling price is almost same as buyer's wish.
NEED FOR MARI
Unlike
most online shoppeing systems which generally operates in only one stage
of the online process it operates in three core stages of online shopping
- product brokering
- merchant brokering
- negotiation
MARI not only relates to most of the classical models it also improves them As follows :
It relates to first generation price-comparison system but does more than the rudimentary functionality afforded by such tools . It goes beyond just bid and ask prices to include the attributes of transaction parties as dimension for consideration and differentiation .
MARI relates to second generation value comparison shopping systems where it offers advanced decision suport engine based upon multi-attribute utility theory , that meaningfully faciliatetes the exchange of comples and heterogeneous products. It improves upon this model by
1. allow both parties (buyers and sellers) to search for an optimal transaction partner
2. Automates the match making between buyers and sellers .
Also it supports a non-linear and iterative user interaction model , that accurately reflects the true nature of real-life transactions.
It relates to online negotation systems and auctions , such as Kasbah, and differs from them in proposing an integrative negotiation protocol and interaction model.
MARI relates to dynamic pricing of inventorie, where sellers dynamically shift their valuations when demand is price sensitive and stochastic and seller's objective is to maximize expected revenues. It is based on flow algorithms encountered in combinatorial optimization and network theory. Also uses market-oriented allocation mechanisms and economic theory in general and game theory in particular . To formulate MARI problem in economics terminology with optimization heuristics , such as maximization of aggregate surplus that drives directly from the literature .
MARI is idle model when negotiations will be hightly complex and participants will engage in integrative negotiation over various aspects of a transaction price being only of the many considerations .
In MARI each
seller has the ability to differentiate her product or services from those
of other sellers . The market structure is monopolistic in the sense that
each seller has ability to set her/his own price . On the other hand
, each seller still competes in terms of price and range of product offered
, with other sellers since they are all effectively trying to find transaction
partners from a common underlying set of buyers . Moreover there are no
barriers to entry, and new sellers are free to enter the market. In this
way, the market structure also resembles that of a competitive industry.
CRITICAL ASPECTS OF MARI
- MARI trades non-tangible goods and single item
- Type of bid it uses is sealed bid
- There are multiple rounds of negotiations in MARI
- Revenue of any one ( buyer, seller, market) can be maximized
(In our implementation we have maximized Market Revenue, which in turn maximizes Revenues of buyers and sellers together)
- Optimization problem has been handled using Matching Algorithm
MAJOR FUNCTIONAL COMPONENTS OF MARI
Attend : User Interface manager is handles all intractions between user and MARI. It presents the appropriate interface to the user , such that the system is able to adequately capture all relevant parameters .
dataBuyer, dataSeller : Each buyer or seller is represented within MARI by an agent . Each agents is customized to the needs and desires of its owner , and attempts to advocate on the owner's behalf when finding suitable transaction partners. These agents are then used by the system to coordinate the preferences and intrests of each party involved .
MariServer : It creates an instantiation of MariAlgo specific to their product domain . When instantiating MariAlgo, the market maker specifies the product ontology as well as set of parameters which determine how user utility functions are modeled and what heuristics are used in match process .
MariAlgo
: It finds the optimal pairing such that Market Revenue is maximized. For
that to happen number of matchings will be maximized, which inturn maximizes
the revenue of both buyer and seller.
HOW MARI WORKS ?
Step 1: Buyer/Seller specifies all of his preferences to MariServer using interface provided
Step 2: MariServer collects all those requests and stores them.
Step 3: After every time T it instantiates MariAlgo for each product
Step 4: Then it collects result and updates the corresponding entries
Details of MariAlgo:
Input: List of buyerAgent ds and List of sellerAgent db
Step 1: For each buyer find the hidden weights for each attribute
Relative Weight(i) =1- (Permissible Range of attribute i/Possible range
of attribute i)
Weight(i) = Relative Weight(i)/( Sum Relative Weight(j) for all j)
Step 2: A table A is created such that
A[i][j] = incompatibility( buyer i, seller j)
incompatibility(i,j) = Sum of [ cost of each attribute of j, using
referential function of i ]
Step 3: Find optimal pairings such that total cost is minimized
Step 4: Return optimal pairings
The optimization problem:
Minimize Sum [ CijXij ]
Subject to
Sum[Xij] = 1 for all i
Sum[Xij] = 1 for all j
Xij = {0,1}
Kasbah:
Authors:
A Chevez, P Maes
This paper proposes a E-Market system which can improve online trading to a large extent. In this users can create autonomous agents which will do the trading for them. This approach is more natural compared to other online trading systems.
WHenever a User creates a agent(buying/selling) he enters some parameters like BestPrice, WorstPrice, Time, NegotiationStrategy. Once created, the agent will look for potential Traders (whose list will be provided by the server) and starts negotiating with them. While negotiating it uses the strategy provided by the User.
NOTE: As a part of Programming Assignment we made the following improvements:
1. Agent1 will stop when they get a fellow Agent2 who meets its Price.
But in our implementation if the number of Agents who meets CurrentPrice
is more than some Threshold, Agent1 will "Improve" it's CurrentPrice.
2. When NegotiationStrategy is Quadratic or Cubic, the rate
of change will only depend on time. But in our implementation rate will
change according to | No of times CurrentPrice has been
Improved- No of times it has been Worsened|
Agents that automate the work of humans have been used in many fields to decrease the overload on humans. Recent Usage of these Agents in Ecommerce has revolutionised the way the transactions happen in the E-market place. Here a survey of various buying and selling agent technologies and e-commerce systems that use these automated agents in a general buying model is taken up.
With the evolution of Internet and World Wide Web the number of transactions on the Internet have increased phenominally. But due to human factor involved in the transactions the throughput is less. So with the automation of some tasks which now involve human interaction, the cost of transactions can be decreased. Software agent technologies differ from traditional software by being personalized, semi-autonomous, continuously running. These characteristics optimize the whole buying experience and revolutionize e-commerce. These automatically collect required information about products and vendors which satisfy the given constraints, evaluate the offers, take decisions about the products and vendors, negotiate the transaction, make orders and finally make payments.
The agents
have a major role as Mediators in Ecommerce under the Common Buying Behaviour
model. Even though the approach is limited it offers an insight in the
role to be played by these agents. There are many Buying Behaviour models
but all of them contain six main activities. These activities and the role
of agents played in these are described below:
Need Identification:-
This Stage
involves finding of products that satisfy the buyers requirements and he
being informed on their availability. Here agents can be useful in requirements
that are repetitive or predictable. These agents are like monitors that
run continuously and monitor a set of activities and take actions to activities
that satisfy a certain pre-requisite. Examples include Amazon.com,Stock
Market and Ecommerce sites, which inform potential buyers about the arrival
of new product which has been specified the customers.
Product Brokering:-
This Stage
involves evaluating different product offers and making a decision on what
to buy given a set of constraints by the user. Here agents can be given
a set of constraints and they take the optimal decision. The agents can
be divided into mainly constraint satisfaction engines or collaborative
filtering techniques. The constraint satisfaction engines search through
a large feature space and find the set of offers that satisfy a set of
hard constraints and in the order of satisfaction of soft constraints.
The collaborative filtering techniques work through collaboration of different
agents and filtering out the useful offers. Other techniques involve Data
Mining and Rule Based Systems which make patterns in customer behaviour
and personalize the buying experience.
Merchant Brokering:-
This Stage
involves using the set in the before stage and gauging the merchant information
to find the best merchant for the offer. The Agents can be made to find
an optimal alternative out of the possible merchant alternatives. The limitations
of agents here is that they donot take into account various value added
services in account and possibility of vendors detecting an agent and blocking
its requests. These limitations can be avoided by using wrappers and other
techniques.
Negotiation:-
This Stage
involves deciding the terms of the transaction. The negotiations vary in
duration and complexity of the market. The need to automate this stage
comes due to frustration and time consuming nature of negotiations and
the costs incured. The Agents can negotiate to any length of time and they
can be set to optimize certain terms of the transaction. These agents can
be personalized to each transaction and to each customer. These agents
can be even set to negotiate even value added services along with price
and different additions can be made to make the decision process better
like combining both product and merchant information to select the best
product and merchant at the same time.
Purchase and Delivery:-
Once the terms
of the transaction are agreed, the purchase and delivery is taken up. The
mode of purchase and delivery might influence the product and merchant
brokering. The agents can be made to make offers depending upon the needs
of the customer.
Product Service and Evaluation:-
This Stage
involves product service, customer service and evaluation of overall buying
experience and decision. The agents can be used to do analysis in post-purchase
phase to evaluate the overall experience of both the customer and seller.
Basically
these stages are an approximation of behaviour of real world system. Thus
can be seen that software agents provide a way of decreasing the workload
and make complex decisions. The Agents have reduced transaction costs and
have revolutionized certain areas of business. In future the agents may
interact and make decisions on dynamic behaviour to make complex offers,
thus revolutionising the whole way transactions are done on the Internet
and World Wide Web.
MARI and KASBAH are a good steps in the direction of a very effective, global, frictionless, completely automated marketplaces. But still a lot of work has to be done in the following areas:
* Adding more intelligence in Agents
* Automating all the processes in a transaction
* Better payment systems
* Combinotorial Trading, where a agent can sell/buy more than one item
* Better algorithms to check monopoly
A dream Marketplace
would look like this:
* A generic markeplace which just acts as communicator
* There should be a lot of options ( better to say, real lifelike options)
while creating a Agent
* A capable end user should be allowed to create his own agents ( with
his own negotiation algorithm )
[1] Design and implementation of an Agent-Based Intermediary
Infrastructure for Electronic Commerce. ( G Tiwari, P Maes)
[2] Kasbah: An Agent Marketplace for Buying and Selling
Goods ( A Chavez, P Maes)
[3] Adaptation in Natural and Artificial Systems
(Holland)
[4] Agents that Buy and Sell:Transforming Commerce as
We Know It ( Pattie Maes, Robert H Guttman and Alexandros G Moukas)