A MODEL TO RATE TRUST IN COMMUNITIES OF PRACTICE

Javier Portillo-Rodriguez, Juan Pablo Soto

Alarcos Research Group, University of Castilla-La Mancha, Paseo de la Universidad, 4 , Ciudad Real, Spain

Aurora Vizcaino, Mario Piattini

Alarcos Research Group, University of Castilla-La Mancha, Paseo de la Universidad, 4 , Ciudad Real, Spain

Keywords: Communities of Practice, Knowledge Management, Trust, Reputation.

Abstract: Communities of Practice are an important centre of knowledge exchange in which feelings such as

membership or trust play a significant role since both is the basis for a suitable sharing of knowledge.

However, current Communities of Practice are often “virtual” as their members may be geographically

distributed. This makes it more difficult for a feeling of trust to take place. In this paper we describe a trust

model designed to help software agents, which represent communities of practice members, to rate how

trustworthy a knowledge source is. It is important to clarify that we also consider members as knowledge

sources since, in fact, they are the most important knowledge providers.

1 INTRODUCTION

In recent years Knowledge Management (KM) has

become an important success factor for companies.

The purpose of knowledge management is to help

companies to create, share and use knowledge more

effectively (Davenport, 1997). Information

technologies play a key role in achieving these goals

but are only a small component in an overall system

that must integrate the supporting technology with

people-based business processes. Nowadays,

organizations must operate in a climate of rapid

market change and high information volume, and

this increases the necessity to create knowledge

management systems which support the knowledge

process. KM is not a technological solution but is

rather, primarily, a people oriented process which

takes into account such factors as leadership, culture,

expertise and learning, with technology playing a

supporting role. Using this idea as a base, we have

studied how people obtain and increase their

knowledge in their daily work. This study led us to

the conclusion that employees frequently exchange

knowledge with people who work on similar topics,

and consequently communities are either formally or

informally created. These communities can be called

“communities of practice”, by which we mean

groups of people with a common interest where each

member contributes knowledge about a common

domain (Wenger, 1998).

Communities of practice (CoPs) enable their

members to benefit from each other’s knowledge.

This knowledge resides not only in people’s minds

but also in the interaction between people and

documents. CoPs share values, beliefs, languages,

and ways of doing things. Many companies report

that such communities help reduce problems caused

by a lack of communication, and save time by

“working smarter” (Wenger, 2002). An interesting

fact is that members of a community are frequently

more likely to use knowledge built by their

community team members than those created by

members outside their group (Desouza, 2006). This

factor occurs because people trust more in the

information offered by a member of their

community than in that supplied by a person who

does not belongs to that community. Of course, the

fact of belonging to the same community of practice

already implies that these people have similar

interests and perhaps the same level of knowledge

about a topic. Consequently, the level of trust within

a community is often higher than that which exists

outside the community. As a result of this, as is

claimed in (Desouza, 2006), knowledge reuse tends

to be restricted within groups. Therefore, people, in

real life in general and in companies in particular,

193

Portillo-Rodriguez J., Pablo Soto J., Vizcaino A. and Piattini M. (2008).

A MODEL TO RATE TRUST IN COMMUNITIES OF PRACTICE.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - AIDSS, pages 193-198

DOI: 10.5220/0001685701930198

 SciTePress

prefer to exchange knowledge with “trustworthy

people” by which we mean people they trust. For

these reasons we consider the implementation of a

mechanism in charge of measuring and controlling

the confidence level in a community in which the

members share information to be of great

importance.

Bearing in mind that people exchange

information with “trustworthy knowledge sources”

we have designed a trust model to help CoPs

members to decide whether a knowledge source (for

instance a person) is trustworthy or not. In the

following section we describe various definitions of

two related concepts: trust and reputation. In Section

3 we then explain a trust model which can be used in

CoPs. Section 4 describes how the trust model can

be used and how it works. In Section 5 we compare

our proposal with other related works and finally, in

Section 6, we present some conclusions and future

work.

2 TRUST AND REPUTATION

Trust is a complex notion whose study is usually

narrowly scoped. This has given rise to an evident

lack of coherence among researchers in the

definition of trust. For instance in (Barber, 2004)

the authors define trust as confidence in the ability

and intention of an information source to deliver

correct information. In (Wang, 2003), Wang and

Vassileva define trust as a peer’s belief in another

peer’s capabilities, honesty and reliability based on

his/her own direct experiences. In (Mui, 2001) trust

is defined as a subjective expectation that one agent

has about another’s future behavior based on the

history of their encounters.

Social scientists have collectively identified

three types of trust, which are:

- Interpersonal trust which is the trust one

agent directly has in another agent

(McKnight, 1996).

- System trust or impersonal trust refers to

trust that is not based on any property or

state of trustee but rather on the perceived

properties or reliance on the system or

institution within which that trust exists.

For instance, inherited experiences of an

organization.

- Dispositional trust, or Basic trust, describes

the general trusting attitude of the truster.

This is “a sense of basic trust, which is a

pervasive attitude toward oneself and the

world” (McKnight, 1996).

Experiences and knowledge form the basis for

trust in future familiar situations (Luhmann, 1979).

For this reason, the frequency and intensity of

interactions between people provide an increased

level of habituation which reinforces trust between

the parties.

Another important concept related to trust is

reputation. Several definitions of reputation can be

found in literature, such as that of Mui et al in (Mui,

2001) who define reputation as a perception that one

agent has of another’s intentions and norms. Barber

and Kim define this concept as the amount of trust

that an agent has in an information source, created

through interactions with information sources

(Barber, 2004) and Wang and Vassileva in (Wang,

2003) define reputation as a peer’s belief in another

peer’s capabilities, honesty and realibility based on

recommendations received from other peers.

In our work we intend to follow the definition

given by Wang and Vassileva which considers that

the difference between both concepts depends on

who has the previous experience, so if a person has

direct experiences of, for instance, a knowledge

source we can say that this person has a trust value

in this knowledge. However if another person has

had the previous experience and recommends a

knowledge source to us, then we can say that this

source has a reputation value.

3 TRUST MODEL IN CoPs

Our aim is to provide a trust model based on real

world social properties of trust in Communities of

Practice (CoPs) by which we mean groups of people

with a common interest where each member

contributes knowledge about a common domain

(Wenger, 1998). An interesting fact is that members

of a community are frequently more likely to use

knowledge built by their community team members

than those created by members outside their group

(Desouza, 2006). This factor occurs because people

trust more in the information offered by a member of

their community than in that supplied by a person

who does not belong to that community. Of course,

the fact of belonging to the same community of

practice already implies that these people have

similar interests and perhaps the same level of

knowledge about a topic. Consequently, the level of

trust within a community is often higher than that

which exists outside the community. As a result of

this, as is claimed in (Desouza, 2006), knowledge

reuse tends to be restricted within groups. Therefore,

people, in real life in general and in companies in

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194

particular, prefer to exchange knowledge with

“trustworthy people” by which we mean people they

trust. For these reasons we consider the

implementation of a mechanism in charge of

measuring and controlling the confidence level in a

community in which the members share information

to be of great importance.

Most previous trust models calculate trust by

using the users’ previous experience with other users

but when there is no previous experience, for

instance, when a new user arrives, these models

cannot calculate a reliable trust value. We propose

calculating trust by using four factors that can be

stressed depending on the circumstances. These

factors are:

• Position: employees often consider information

that comes from a boss as being more reliable

than that which comes from another employee

in the same (or a lower) position as him/her

(Wasserman, 1994). However, this is not a

universal truth and depends on the situation. For

instance in a collaborative learning setting

collaboration is more likely to occur between

people of a similar status than between a boss

and his/her employee or between a teacher and

pupils (Dillenbourg, 1999). Such different

positions inevitably influence the way in which

knowledge is acquired, diffused and eventually

transformed within the local area. Because of

this, as will later be explained, this factor will

be calculated in our research by taking into

account a weight that can strengthen this factor

to a greater or to a lesser degree.

• Expertise: This term can be briefly defined as

the skill or knowledge that a person who knows

a great deal about a specific thing has. This is an

important factor since people often trust experts

more than novice employees. In addition,

“individual” level knowledge is embedded in

the skills and competencies of the researchers,

experts, and professionals working in the

organization (Nonaka, 1995). The level of

expertise that a person has in a company or in a

CoP could be calculated from his/her CV or by

considering the amount of time that a person has

been working on a topic. This is data that most

companies are presumed to have.

• Previous experience: This is a critical factor in

rating a trust value since, as was mentioned in

the definitions of trust and reputation, previous

experience is the key value through which to

obtain a precise trust value. However, when

previous experience is scarce or it does not exist

humans use other factors to decide whether or

not to trust in a person or a knowledge source.

One of these factors is intuition.

• Intuition: This is a subjective factor which,

according to our study of the state of the art, has

not been considered in previous trust models.

However, this concept is very important

because when people do not have any previous

experience they often use their “intuition” to

decide whether or not they are going to trust

something. Other authors have called this issue

“indirect reputation or prior-derived reputation”

(Mui, 2002). In human societies, each of us

probably has different prior beliefs about the

trustworthiness of strangers we meet. Sexual or

racial discrimination might be a consequence of

such prior belief (Mui, 2002). We have tried to

model intuition according to the similarity

between personal profiles: the greater the

similarity between one person and another, the

greater the level of trust in this person as a result

of intuition.

Figure 1: Trust Model.

By taking all these factors into account, we have

defined our own model with which to rate trust in

CoPs, and this is summarized in Figure 1.

4 USING OUR TRUST MODEL

The main goal of this model is to rate the level of

confidence in an information source or in a provider

of knowledge in a CoP.

As the model will be used in virtual communities

where people are usually distributed in different

locations we have implemented a multi-agent

architecture in which each software agent acts on

behalf of a person and each agent uses this trust

A MODEL TO RATE TRUST IN COMMUNITIES OF PRACTICE

195

model to analyze which person or piece of

knowledge is more trustworthy.

We have chosen the agent paradigm because it

constitutes a natural metaphor for systems with

purposeful interacting agents, and this abstraction is

close to the human way of thinking about their own

activities (Wooldridge, 2001). This foundation has

led to an increasing interest in social aspects such as

motivation, leadership, culture or trust (Fuentes,

2004).

In our case, the model is going to be used in

CoPs and this fact implies several considerations.

The number of interactions that an agent will

have with other agents in the community will be low

in comparison with other scenarios such as auctions.

This is very important because we cannot use trust

models which need a lot of interactions to obtain a

reliable trust value; it is more important to obtain a

reliable initial trust value and it is for this reason that

we use position, expertise and intuition.

As we observed in the previous section in Figure

1, we use four factors to obtain a trust value, but

how do we use these factors? We have classified

these four factors into two groups: objective factors

(position and expertise) and subjective factors

(intuition and previous experience). The former is

given by the company or community and the latter

depends on the agent itself and the agent’s

experience in time. There are four different ways of

using these factors, which depend upon the agent’s

situation (see Figure 2):

Figure 2: Using the Trust Model.

• If the agent has no previous experience, for

instance because it is a new user in the

community, then the agent uses position,

expertise and intuition to obtain an initial

trust value and this value is used to

discover which other agents it can trust.

• When the agent has previous experience

obtained through interactions with other

agents but this previous experience is low

(low number of interactions), the agent

calculates the trust value by considering

the intuition value and the experience

value. For instance, if an agent A has a

high experience value for agent B but

agent A has a low intuition value for agent

B (profiles are not very similar), then agent

A reduces the value obtained through

experience. In this case the agent does not

use position and expertise factors

(objective factors) because the agent has its

own experience and this experience is

adjusted with its intuition which is

subjective and more personalized.

• When the agent has enough previous

experience to consider that the trust value it

has obtained is reliable, then the agent only

considers this value.

In order to test our model we have developed a

prototype system into which CoPs members can

introduce documents and where these documents

can also be consulted by other people. The goal of

this prototype is to allow software agents to help

users to discover the information that may be useful

to them, thus decreasing the overload of information

that employees often have and strengthening the use

of knowledge bases in enterprises. In addition, we

try to avoid the situation of employees storing

valueless information in a knowledge base.

The main feature of this system is that when a

person searches for knowledge in a community

his/her software agent has to evaluate that

knowledge in order to indicate whether:

 The knowledge obtained was useful.

 How it was related to the topic of the search (for

instance a lot, not too much, not at all).

With this information, and by using our trust

model, the agent calculates the most trustworthy

knowledge sources and sorts these documents by

using the trust model and considering the most

reliable documents according to his/her user profile

and preferences (Soto et al., 2007).

5 RELATED WORKS

This research can be compared with other trust

models. In models such as eBay(ebay, 2007) and

Amazon (Amazon.com, 2007), which were

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proposed to resolve specific situations in online

commerce, the ratings are stored centrally and the

reputation value is computed as the sum of those

ratings over six months. Thus, reputation in these

models is a global single value. However, these

models are too simple (in terms of their trust values

and the way they are aggregated) to be applied in

open multi-agent systems. For instance, in (Zacharia,

1999) the authors present the Sporas model, a

reputation mechanism for loosely connected online

communities where, among other features, new

users start with a minimum reputation value, the

reputation value of a user never falls below the

reputation of a new user and users with very high

reputation values experience much smaller rating

changes after each update. The problem in this

approach is that when somebody has a high

reputation value it is difficult to change this

reputation or the system needs a high amount of

interactions. A further approach of the Sporas

authors is Histos which is a more personalized

system than Sporas and is orientated towards highly

connected online communities. In (Sabater, 2002)

the authors present another reputation model called

REGRET in which the reputation values depend on

time: the most recent rates are more important than

previous rates. (Carbó, 2003) presents the AFRAS

model, which is based on Sporas but uses fuzzy

logic. The authors presents a complex computing

reputation mechanism that handles reputation as a

fuzzy set while decision making is inspired in a

cognitive human-like approach. In (Abdul-Rahman,

2000) the authors propose a model which allows

agents to decide which agents’ opinions they trust

more and to propose a protocol based on

recommendations. This model is based on a

reputation or word-of-mouth mechanism. The main

problem with this approach is that every agent must

keep rather complex data structures which represent

a kind of global knowledge about the whole

network.

Barber and Kim present a multi-agent belief

revision algorithm based on belief networks (Barber,

2004). In their model the agent is able to evaluate

incoming information, to generate a consistent

knowledge base, and to avoid fraudulent information

from unreliable or deceptive information sources or

agents. This work has a similar goal to ours.

However, the means of attaining it are different. In

Barber and Kim’s case they define reputation as a

probability measure, since the information source is

assigned a reputation value of between 0 and 1.

Moreover, every time a source sends knowledge that

source should indicate the certainty factor that the

source has of that knowledge. In our case, the focus

is very different since it is the receiver who

evaluates the relevance of a piece of knowledge

rather than the provider as in Barber and Kim’s

proposal.

In (Huynh, 2004) the authors present a trust and

reputation model which integrates a number of

information sources in order to produce a

comprehensive assessment of an agent’s likely

performance. In this case the model uses four

parameters to calculate trust values: interaction trust,

role-based trust, witness reputation and certified

reputation. We use a certified reputation when an

agent wants to join a new community and uses a

trust value obtained in other communities but in our

case this certified reputation is composed of the four

previously explained factors and is not only a single

factor.

The main differences between these reputation

models and our approach are that these models need

an initial number of interactions to obtain a good

reputation value and it is not possible to use them

discover whether or not a new user can be trusted. A

further difference is that our approach is orientated

towards collaboration between users in CoPs. Other

approaches are more orientated towards competition,

and most of them are tested in auctions.

6 CONCLUSIONS AND FUTURE

WORK

This paper describes a trust model which can be

used in CoPs. The goal of this model is to help

members to estimate how trustworthy a person or a

knowledge source is since when a community is

spread geographically, the advantages of face-to-

face communication often disappear and therefore

other techniques, such as our trust model, should be

used to obtain information about other members.

One contribution of our model is that it takes

into account objective and subjective parameters

since the degree of trust that one person has in

another is frequently influenced by both types of

parameters. We therefore try to emulate social

behaviour in CoPs.

We are testing our model in a prototype into

which CoPs members can introduce documents, and

software agents should decide how trustworthy these

documents are for the user that they represent.

As future work, we are planning to add new

functions to the prototype such as for instance,

expert detection and recognition of fraudulent

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197

members who contribute with no useful knowledge.

We would like to stress that we are working on

depurating our trust model in order for it to be used

in knowledge management systems with the goal of

fostering the usage of this kind of tools since

employees who frequently complain about them

claim that these systems often store a lot of

knowledge but it is difficult to know how

trustworthy it is and which is more relevant for each

user.

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