TRUST MODEL FOR CONSUMER

PROTECTION (TMCP) PhD. Student Florentina Loredana Tache University "Dunarea de Jos" of Galati

Domneasca 111, 800008, Galati,Romania Florentina. Tache@profmfo.edu.ro

Abstract In the future, in e-commerce, the main actors will be the intelligent agents, who will dramatically reduce online shopping by human factors because of their reactive, proactive and social characteristics. Internet technology nowadays determines us to be less prepared to control and sanction the increasing number of users and of those service providers who have a doubtful behavior. The model developed in this paper greets the ethical behavior of the online consumer and attempts to delineate the basis of a juridical protection of the consumer according to effectual European normative laws and not to overshadow the financial profit of the electronic trade participants. Trust model for consumer protection brings its contribution by developing components both for the system's agents and for the consultant agent, components which, by the introduction of some factors with greater relevance and of a new reputation element represented by the abusive clauses in contracts, offers greater robustness and accuracy to the consumer protection model and at the same time does not create the false impression that sellers and buyers might not obtain profit out of this trust system

I.INTRODUCTION

In the future, in e-commerce, the main actors will be the intelligent agents, who will dramatically reduce online shopping by human factors because of their reactive, proactive and social characteristics. These agents have to be capable to determine with what other agents they can interact anytime with trust. These agents will try to accomplish good businesses negotiating with providers, to offer counseling in the process of making a decision, to make transactions, all of this in the name of the human representative. This being the image of a not so distant future, the words of Dasgupta[3] "trust is vital to all transactions" will still be topical because as the e-commerce system exists, in order to facilitate the transactions, it can be said that trust is the key to all online commercial environments.

The model developed in this paper greets the ethical behavior of the online consumer and attempts to delineate the basis of a juridical protection of the consumer according to effectual European normative laws [12] and not to overshadow the fmancial profit of the electronic trade participants. It is observed that the collaborative mechanisms which evaluate the behavior against the ethical norms of the members of a community make possible, in a small measure, both the identification and the sanctioning of those participants who break the ethical norms and also the recognition and awarding of those members who adhere to these norms.

II.TRUST MODEL FOR CONSUMER PROTECTION

The subjective and social qualities correspondent to trust and reputation introduce however, a complex side to the development of a model on which you can rely. Few models have offered a solution for the development of an initial set of counselors to which one can go in order to fmd out about some levels of reputation and only few models have taken into consideration as many social criteria so as to determine trust. The trust model brings its contribution by the development of two components of FIRE [6]: trust interaction and witness reputation used both by system agents and by the consultant agent components which, by the introduction of some factors with greater relevance and of a new reputation element represented by the abusive clauses in contracts, offers greater robustness and accuracy to the consumer protection model and at the same time does not create the false impression that sellers and buyers might not obtain profit out of this trust system.

A.MODEL TOPOLOGY In order to have a successful e-commerce it is

necessary that efficient models of trust and reputation are built. The greatest challenge in the developing of an efficient trust model is the fact that trust is subjective [2]. Being given the importance and complexity of the problem, a number of researchers have tried to diminish it and have proposed systems which evaluate trust using different methods and parameters [4],[5],[7], [8], [9], [10]. How much psychological and social aspects manager to be transposed into the electronic transactions remains a topical problem in the academic area. A successful attempt was accomplished by McKnight and Chemany (2001) which group the types of construction of trust in three major categories: interpersonal trust, disposition trust and personal trust, characterized by 14 attributes which defme them and are presented in figure 1.

CHARITY 4

\ \ �THOUT PREJUDICE

COMPETENCE

CREDIBILITY

MORALITY

Fig.! Attributes of trust.

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SOFA 2010· 4th International Workshop on Soft Computing Applications · 15-17 July, 2010 - Arad, Romania

Mui took some of the trust attributes and built a typology of trust present in trust models. In conformity with Mui's topology, reputation is a contextual quantity, idea to which TMPC also subscribes. Figure 2 will present the dependences of the reputation which provides values that are taken into consideration in the calculation of the trust coefficient of the model so as its topology will serve the purpose of a more realistic protection of the online buyer.

Reputation

Personal Reputation PR

Fig.2.Topology of TMCP.

B. MODELING OF DIRECT TRUST

Indirect Reputation IR

Direct Reputation DR

Legal Reputation LR

I mention the fact that a new model of trust will not be built in interaction, but the interaction trust component of the FIRE model will be used with a few adaptations which demonstrate the obtaining of a model with better performances. TMCP is defining because it uses only the main transaction information in the actual e-market such as: price, the volume of commercialized products, feedback rating and time stamp. The estimation of direct trust is made by taking into consideration three factors: time, impact of the rating and price-quantity.

TEMPORAL EFFECT FACTOR OF THE RATING

In e-commerce there are a few models who take into consideration the time factor. Old evaluations of a commercial transaction are less important than recent ratings. Sabater and Sierra (200 I) in the REGRET model introduced the time factor using a function dependent of time which offers more relevance to recent evaluations. In Huang's (2004) trust model there are 5 temporal functions which are discussed in relation to the level of socialization, of adaptability to the dependence of environment, and Huynh, Jennings and Shadbolt (2006) use an exponential function to accomplish the temporal effect of the evaluations. The model uses 0 negative exponential function that simulates the effect of the time factor as follows:

- c [ I - I 1 w (t)=l-e 1m i 0

1m

In which:

(I)

w,m(ttthe importance of every evaluation at the time in which the evaluation was provided; Ctm -constant set to control the form of the exponential function; ti-to -the difference of time between the evaluation time and the starting time.

THE IMPACT FACTOR OF THE RATING

Many of these models propose using a direct trust rating in contrast with the reputation rating obtained from the consultant agents [1],[2],[3],[4],[5],[8],[9],[10],[11]. A few of these models based on the experience from evidence, especially ReGret[lO], FlRE[5], Certain Trust[9] as well as the evidence based model of Huang[12] takes into consideration the number of experiences on which a rating is based in the computerization of aggregated systems of rating. This is important, in Huang's opinion, because when a single rating value is estimated as a probability, it is impossible to know if the rating is based on few or many experiences. The schemes for actualizing trust have the tendency of coupling the positive and negative values of the evaluations. An exact weighting of positive and negative evaluations has not been found and accepted by researchers in the scientific world. A lot of work is still necessary to determine precisely the weight of an evaluation. In this sense, a subjective function of rating measurement is adopted in the model as follows:

w = m " n' n p {ap,daca,ra a (t) > 0 CU 0< a a > a (2) pn an,daca,ra•a" (I) < 0

Where: a p -the weight of the pozitive rating; an-the weight of the negative rating;

THE PRICE-QUANTITY FACTOR

The number of commercialized products is a relevant criterion of trust because there are selling strategies in which sellers begin to cheat after they create a reputation for themselves. The researcher Axelord (1984) shows that the price and quantity of the products commercialized by a commercial agent can say much about the agent. For example, if a merchant is stable on the market, sells a constant volume of products, practicing a small or large variation of prices are information which bring a plus or a minus of trust in transaction and therefore in the merchant. In order to discourage this type of behavior and to better the theory of trust in this model, the price-volume of ratings factor is introduced. A negative exponential function, similar to that which calculates the time effect is adopted in order to calculate the influence of the price-volume factor as follows:

W (t)=l_e-cppxpretxnrp

Pp (3)

where: cpp - constant selected to control the slope of the exponential curve;pret-the price of the product, nrp- the number of products which correspond the transaction. The price of the products has to be reflected in the merchant's reputation and at the same time has to guarantee the consumer's protection. Although the cost of a product is calculated after the economic rules set by the legislation in force, the introduction of the price-volume factor in the calculation of direct trust will determine a discouragement of abusive practices.

108

, , , �::rama, (i)lI\m(i) Lrama, (i)wpn(i) Lrama, (i)wpp(i) 1-0

, + 1=0

, +-'.:1-:::.0---,--,

__ _

LlI\m(i) LWpn(i) LWpp(i) j-O i-O i=O

3 (4)

Where: raman 0) -rating of the agent am about the agent a",

Wtm{i)- the weight of every evaluation based on the moment in which the evaluation was provided, wpnO)- the weight of every rating according to the pozitive or negative evaluation, wpp{i)- the weight of every rating provided by the price-volume factor.

C. THE MODELING OF INDIRECT REPUTATION

In order to build a value of reputation, an agent must consult other agents of the society, to collect their observations regarding the target agent (the agent whose reputation is evaluated). Indirect reputation (lR) and interaction trust have a tight connection. The trust from the interactions which an agent gives to another after an interaction is reflected in the correspondent notes of that agent. Because an agent's reputation is built according to another agent's observations, it can be said that the reputation of that agent is built from the interaction trust offered by other individuals of the society (if its reported correctly). On the other hand, interaction trust between two agents can be seen as reputation at an individual level (like in the case of ReGret, FIRE). The collecting of witness observations for TMCP is made in a hybrid approach. As the name suggests, this approach is both centralized and distributed within the system. Indirect reputation (lR) is the value represented by the estimation of the reputation of the evaluated agents based on the confessions of other agents who have made direct transactions with the evaluated agent, and its aggregation is made through a method based on shares, with the emphasis put on the way in which these shares can be chosen in order to reflect the credibility of each confession. Similar to the modeling of direct trust, the time and price-volume factors are taken into consideration for evaluating indirect trust. The reason for which the impact factor is omitted is owed to its subjectivity. If this factor is taken into consideration, then the risk of entering in the complexity of modeling the opinion of the opinion exists. In this research, the agent remains an international system. This means that the agents have beliefs and wishes, but don't have beliefs and wishes about beliefs and wishes (Dennet, 1987). The average of the results obtained from the two factors is used to calculate the value of every witness's indirect reputation, as follows:

, l li>a,am (i)w,m(i) :i:>a,a, (i)WPV(i). W (t)=- ,=0 +-",=0"'--__ _ °zOn 2 t � L w'm (i) L.. W pv (i)

i=O i=O

(5)

F. L. Tache· Trust Model for Consumer Protection (TMCP)

Where: r (i) -the rating of the agent az about the agent a,,; ayan Wtm{i)-the weight of every evaluation based on the moment when the evaluation was provided; wpv(i)- the weight of every rating provided by the price-volume factor. The sum of witness reputation values of all the confessions available is the component of indirect reputation of the trust model :

LW�,am (t) aeA (6) Repw'm", (t) countWC(ra,am)

In which countWC( raman ) is a function which counts the number of witnesses.

D.MODELING OF LEGISLATIVE REPUTATION TMPC starts from the following premise: there

cannot exist a well appreciated reputation and trust if abusive practices and clauses are tolerated in online contracts. A poll of the trust and reputation systems presents an actual state which sanctions only abusive practices: inappropriate quality, broken delivery deadline, but do not signal and sanction the abusive clauses. The legislative reputation is represented in the model by the abusive clauses, which although legislatively regulated by the consumer's protection, have much to do with the user's profile. This fact determines me to state that the following defmition of legislative reputation will be accepted: Legislative reputation is the quality of the person who respects the legislation in force as it is seen or judged by the others in general. Abusive clauses are perceived differently by the consumers and this degree of cognitive dissonance is reflected in the model through a particular importance WAC associated to the abusive clause. The temporal effect on the abusive clauses does not have relevance in the model presented because it does not sustain the legislative side of e-commerce and does not take into account the eventual sudden changes of behavior of the agents caused by changes of belief (today the agent accepts to keep the package of a product and tomorrow he does not have the same opinion). Temporal modifications can bring only the retreat of the abusive clauses from contracts. Reputation resulting from the abusive clauses is calculate as the weighted average of user's profile characteristics, as follows:

:t WAC (i)· countAC RepAC (an )(t) = ..!:i-:!...1 ----­

nr E. THE TRUST FORMULA OF THE MODEL

(7)

The environment of the electronic market in the proposed model supplies direct reputation, indirect reputation and that of abusive clauses. Usually, from the evaluator's point of view, DT of an agent is more accurate that his indirect reputation, since DT is based on direct evaluation of the agent from the personal transaction with him. That is why, the model is built in an environment in which all agents share

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SOFA 2010· 4th International Workshop on Soft Computing Applications · 15-17 July, 2010 - Arad, Romania

the information and tell the truth, and so, the reputation delivered by the witnesses should also be reliable. Indirect reputation and legislative reputation may converge to the most efficient way of evaluating when estimating an unknown transaction partner, since the evaluating agent can obtain a correct estimation of the evaluated agent based on the reputation of the witnesses and the legislative one without having to have direct transactions with the evaluated agent. But in an environment where agents lie in their statements, or are not willing to share their negative experiences, IR can be of lower trust. In the proposed model, agents learn how to adapt the values of DT and IR in order to be able to evaluate others' reputation with better precision and adapting. They incrementally adjust the components of their model as transactions appear on the market and they determine how heavy the reputations are in computing esteeming thrust in the future transaction. After each transaction, the buyer or the evaluating agent give a rating to the transaction, or the buyer or the evalluated agent respectively. The difference between the current value of the rating and that of the reputation with which it entered in the commercial trade represents the weight or the importance with which the evaluating agent will learn how to estimate the reputation of next transaction. The difference of direct trust is calculated after each transaction. The equation for difference of direct trust is:

dDT aman (t) = raman (t) - RePaman (t) (8)

where: dDTaman (t).- difference of direct trust of the

transaction at time t; ra a (t) -current value of the rating. m n RePaman - current value of reputation of the evaluated agent.

This difference is used by the evaluating agent to learn the weight of direct trust using the equation:

(9)

Where: a DT (t + 1) - weight of component DT in estimating thrust in future transactions; a DT (t )-current weight of component DT.17 D -current weight of component DT. The difference of indirect reputation is the difference between the average value of liz agent's ratings offered for the evaluation of the agent an and w (t)at the time t. The

aman

following equation is used to calculate the difference of indirect reputation:

(10)

Where: dIR •••

,(trthe difference of indirect reputation at the

time t; ra a (t)- the average value of az agent's ratings n n

offered for the evaluation of agent au; RePw (t) -the witness Oman

reputation of agent au at the moment t. This difference of indirect reputation is used by the evaluating agent am so as to learn the weight of the witnesses' statements using the following equation:

where: a DT (/)- current weight of component DT.FiabDT(t) -the reliability of component DT;aIR(t) -current weight of component IR; Pw (t Lstandard deviation of reputation IR; R,pw.m •• (t) -witness reputation of component IR. R,p,e

an (t)_

legislative reputation of agent an.

III. SIMULATION PLANS OF TMPC

The simulation of the modeling is made with the help of the Repast J instrument, (Fig. 1 ) TMPC being compared to the traditional reputation model and with the two components of FIRE developed by Huyhn .

Fig.3 Repast simulation.

There are three simulation registers: 1. PR level which establishes the number of agents on the market who offer rating (50%,80%,100%) through the help of the PostRating parameter; 2.level of noise: 0, 0.2, 0.5; 3. Type of product: only expensive or only cheap products can be bought from a single type of list, or products from the two types of lists. In each round, the last 100 gains of the buyer are collected, their average is calculated and it represents the general gain GGB of the buyers. The average of GGB in the 10 rounds represents the gain of the buyers in a FGB scenery. Comparing the FGB of different models at different levels of noise and PR levels , the proposed model is being compared with competition models from the buyer's performance point of view.

1 10

IV. RESULTS

When there are two types of items(products, services) in the electronic market, one with a high price and the other with a low price, experiments lead to a comparison of the buyers' performance with Huynh' s(H) traditional model(T) at different levels of noise and PR.

As it is indicated in table 1, in the same noise conditions, the gains of the buyers in the TMCP model are greater than those of competitive models. It is observed that for the proposed model, there isn't such a great difference between the gained profit compared to the noise levels. This indicates that the model is robust in what concerns noise both at the acquisition of a type of product and at the buying of two types of products or services.

TABLE L THE GAIN OF THE BUYER AT NOISE

One tip item

Noise

T H TMPC

FGB 51.29057 64.53044 71.83983

0

STD 20.4388 13.5494 11.6571

FGB 50.74943 58.5793 71.56415

0.20

STD 19.4565 19.9839 12.5913

FGB 51.85883 56.73673 73.35483

0.50

STD 21.7122 22.5624 11.434

Two tip items

Noise

T H TMPC

FGB 72.85065 90.63083 102.2119

0

STD 29.3056 18.6259 16.8652

FGB 73.26401 86.48116 101.331

0.20

STD 27.4174 22.5109 17.6052

FGB 74.16427 76.44885 104.8834

0.50

STD 31.2021 46.1071 15.3445

Again from table 1 it results the traditional model is relatively stable in what concerns the buyers' profit, the lack of information or noise from the data do not influence the buyer's gain. However, Huynh's model registers a lower gain for the buyer as the level of noise grows.

On the whole, all three models act more robustly when two items are bought. As far as the STD,standard deviation registered by the models is concerned, TMCP

1 1 1

F. L. Tache· Trust Model for Consumer Protection (TMCP)

accomplishes a gain for the buyers with a deviation that oscillates very little. The same phenomenon can be observed in the case of the traditional model, in the simulation plan with one type of item. Huynh's model has greater deviations as the noise level grows to 0.20 and 0.50 both in the simulation plan with one item and in the plan with two items.

TABLE 2. THE GAIN OF THE BUYER AT PR LEVEL

Two tip items

PR

T H TMPC

FGB 72.85065 90.63083 102.2119

100%

STD 29.3056 18.6259 16.8652

FGB 73.26401 86.48116 101.331

80%

STD 27.4174 22.5109 17.6052

FGB 74.16427 76.44885 104.8834

50%

STD 31.2021 46.1071 15.3445

One tip item

PR

T H TMPC

FGB 51.29057 64.53044 71.83983

100%

STD 20.4388 13.5494 11.6571

FGB 50.74943 58.5793 71.56415

80%

STD 19.4565 19.9839 12.5913

FGB 51.85883 56.73673 73.35483

50%

STD 21.7122 22.5624 11.434

Table 2 presents the buyers' gain at different PR levels in the simulation plans.

It can be observed that in the same circumstances, the model TMCP registers a higher profit than the competitive models, especially when transactions with two types of products are being made. We can say that in an electronic market in which all participants offer ratings, trust recommendation determines adequate sellers and makes good profits for the buyers. However, this scenario being idealistic, when 80% of the agents, respectively 50% of them offer a score to the transaction, it is observed that the profit of the buyer drops in the case of the traditional mode; and Hyunh' s model. TMCP is built to take into consideration the number of ratings and witnesses, fact which determined the absence of significant differences of profit in the case when only one part of the participants offer feedbacks to the transactions.

SOFA 2010· 4th International Workshop on Soft Computing Applications · 15-17 July, 2010 - Arad, Romania

V.CONCLUSION AND FUTURE WORK

The proposed model gains significantly more than competitive models in all tests both in the presence of noise and at levels of incomplete information. Buyers, after the consulting process, will gain more in the electronic market, are more satisfied and are stimulated to use it more frequently. As future directions, I will demonstrate that the proposed model always favors the sellers who are trustworthy and penalizes those who are not trustworthy. This is in concordance with the behavior proposed in the research. The trend of the model compared with the sellers' behavior will be studied and analyzed and the profit accomplished by them in the presence of abusive clauses which converge in the spirit of a realistic projection of the buyer will be highlighted.

Satisfying buyers and sellers, the proposed model for the consultant agent can aid the building and maintenance of a prosperous digital market in which the legislative terms tip the balance in favor of the honesty and loyalty for all its entrepreneur agents.

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[12]. http://ec.europa.eu/inforrnation_society/eyouguide/index_en.htrn