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Book ChapterDOI

A Bayesian Reputation System for Virtual Organizations

01 Jan 2008-pp 171-178

AbstractVirtual Organizations (VOs) are an emerging business model in today’s Internet economy. Increased specialization and focusing on an organization’s core competencies requires such novel models to address business opportunities. In a VO, a set of sovereign, geographically dispersed organizations temporarily pool their resources to jointly address a business opportunity. The decision making process determining which potential partners are invited to join the VO is crucial with respect to entire VO’s success. The possibility of a VO partner performing badly during the VO’s operational phase or announcing bankruptcy endangers the investment taken in integrating their processes and infrastructure for the purpose of the VO. A reputation system can provide additional decision support besides the a priori knowledge from quotations and bidding to avoid events such as VO partner replacement by helping to choose reliable partners in the first place. To achieve this, reputation, an objective trust measure, is optimally aggregated from multiple independent trust sources that inherently characterize an organization’s reliability. To allow for the desired predictions of an organization’s future performance, a stochastic modeling approach is chosen. The paper will present a taxonomy of TIs for VO environments, a stochastic model to maintain and aggregate trust sources, so called Trust Indicators, and the inclusion of other subjective measures such as feedback.

Topics: Reputation system (59%), Business model (54%), Decision support system (52%), Reputation (52%), Virtual organization (51%)

Summary (2 min read)

1 Introduction

  • In today’s business world, commercial relationships become increasingly flexible and are formed on demand whenever a business opportunity emerges.
  • Second, the VO manager negotiates with the potential partners, until the required set of VO members is selected, bringing the right set of specialized expertise in the VO.
  • In contrast to hard security measures that apply during regular operations between a buyer and supplier such as confidential communication channels or access control measures, reputation based decision support belongs to the class of soft security measures [3].
  • Following up on the above statement about the buyer’s expectation of a supplier, the authors define trust as the subjective probability by which the buyer expects a supplier to behave reliably [5].
  • A feedback mechanism will also be outlined.

2 Trust and Reputation Model

  • Many existing trust management approaches, especially in Peer-2-Peer (P2P) environments, root trust solely in feedback given by peers after having conducted a transaction [7, 8].
  • Feedback is a highly subjective, relative input that may vary between peers who participated in the same transaction.
  • Feedback is still an important input that improves the quality and accuracy of the reputation value aggregating the TI rooted trust values.

2.1 Taxonomy of TIs

  • This subsection provides an excerpt of an assembled classification/taxonomy of TIs and their individual modeling.
  • Figure 1 shows the top-level TI classes of a taxonomy currently encompassing 146 different TIs.
  • TIs modeling the financial trust aspects of an organization, an example is the cash flow quote indicator [9].
  • The distribution assumption, in case of delivery delay that it is exponentially distributed, determines the likelihood distribution P (X|θ) estimating the true parameter θ.
  • Each TI incorporates a monotonically increasing weighting function ω(t) > 0 that can implement forgetting of older observations and put emphasis on newer ones.

2.2 Stochastic Model

  • The TI’s posterior distributions periodically obtained for each TI after ∆tobs must then be aggregated to a reputation value for this organization.
  • The edges denote causal dependencies of the nodes and each node in the BN holds a Conditional Probability Table (CPT) with the probability values for its random variable(s) depending on the parent node’s random variables.
  • The bottom layer entails the TI information nodes periodically inputting newly observed data into the BN with the updated TI posterior distribution.
  • The same states are held by each middle layer node.
  • The information conveyed by an entire probability distribution typically overloads a human being while a computer system further using the reputation distribution for control decisions may benefit from the additional level of detail.

2.3 Feedback

  • As mentioned in the beginning, the reputation system relying on observable TIs may still benefit from feedback given by participants of a business transaction who received decision support from the reputation system.
  • It has to ensure, e.g. with an established protected session reference that this feedback uniquely relates to the correct transaction and stems from authenticated participants.
  • After several experiments with a prototypical implementation of the presented reputation system, that provides reputation values to human requestors, a feedback mechanism accepting feedback in the interval [0, 1] with an associated certainty value of the same interval was contrived, that can be interpreted as a density function as well.
  • The feedback hereby applies directly to the overall reputation which more closely aligns to human feedback strategies who typically do not break down feedback to the level of individual TIs but rather follow a gut-feeling approach rating the overall business transaction.

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A Bayesian Reputation System for Virtual
Organizations
Jochen Haller
SAP Research
Vincenz-Priessnitz-Str. 1,
76131 Karlsruhe, Germany
jochen.haller@sap.com
Abstract.
Virtual Organizations (VOs) are an emerging business model in to-
day’s Internet economy. Increased specialization and focusing on an organiza-
tion’s core competencies requires such novel models to address business oppor-
tunities. In a VO, a set of sovereign, geographically dispersed organizations tem-
porarily pool their resources to jointly address a business opportunity. The deci-
sion making process determining which potential partners are invited to join the
VO is crucial with respect to entire VO’s success. The possibility of a VO partner
performing badly during the VO’s operational phase or announcing bankruptcy
endangers the investment taken in integrating their processes and infrastructure
for the purpose of the VO. A reputation system can provide additional decision
support besides the a priori knowledge from quotations and bidding to avoid
events such as VO partner replacement by helping to choose reliable partners in
the first place. To achieve this, reputation, an objective trust measure, is optimally
aggregated from multiple independent trust sources that inherently characterize
an organization’s reliability. To allow for the desired predictions of an organiza-
tion’s future performance, a stochastic modeling approach is chosen. The paper
will present a taxonomy of TIs for VO environments, a stochastic model to main-
tain and aggregate trust sources, so called Trust Indicators, and the inclusion of
other subjective measures such as feedback.
1 Introduction
In today’s business world, commercial relationships become increasingly flexible and
are formed on demand whenever a business opportunity emerges. To cater for these
changing demands, business relationships integrate Information and Communication
Technologies (ICT) to (partially) automate certain decision processes, e.g. the swift dis-
covery and selection of business partners. Virtual Organizations (VOs) are a prominent
example of such emerging business models. A VO is defined as a temporary coalition
of otherwise independent organizations or individuals, collaborating to achieve a com-
mon business goal, one party alone could not master. A VO follows a phased life cycle,
first, a VO manager or system integrator observes a business opportunity and discovers
potential VO partners (identification phase). Second, the VO manager negotiates with
the potential partners, until the required set of VO members is selected, bringing the
right set of specialized expertise in the VO. During this phase, the ICT infrastructure
Dagstuhl Seminar Proceedings 06461
Negotiation and Market Engineering
http://drops.dagstuhl.de/opus/volltexte/2007/999

in each partner’s domain is set up and configured for collaboration in the VO and also
contracts are established (formation phase). Third, the VO executes, each VO member
runs his business processes contributing to the overall VO goal - to exploit the business
opportunity - until it is reached (operational phase). In case of abnormalities or excep-
tions like misperforming VO members, adaptations can be made by the VO manager
(evolution phase), but the VO stays within the operational phase. Finally, fourth, af-
ter having achieved the VO goal, common goods, VO results etc. are dispersed among
the VO members according to the contractual agreements. Final processes, for instance
billing, are performed (dissolution phase) [1, 2].
VO like structures can already be observed in different business domains. While
on the one hand established domains, such as automotive engineering still retain rather
static business relationships, a car manufacturer for instance tends to stay with well
established and confirmedly reliable car part suppliers, recently founded domains on
the other hand exhibit more agile buyer-supplier relationships. An example for the lat-
ter is the high-tech industry, such as chip manufacture, where chip prices change on
a daily basis. When a buyer selects a supplier, this essentially means taking a trusting
decision. If the buyer has no prior knowledge of a particular (set of) supplier(s), there
is no absolute certainty about the supplier’s future reliable behavior. In that case, an
online reputation system can help to minimize the risk of selecting a bad supplier who
frequently delivers late or not at all. In contrast to hard security measures that apply
during regular operations between a buyer and supplier such as confidential communi-
cation channels or access control measures, reputation based decision support belongs
to the class of soft security measures [3]. Soft security aims at complementing hard se-
curity rather than replacing it. In the context of this publication, we focus on centralized
reputation models that cater best for reputation and trust requirements in VOs [4]. The
reputation system is assumed to be owned or hosted by an explicitly trusted third party
(TTP). Following up on the above statement about the buyer’s expectation of a supplier,
we define trust as the subjective probability by which the buyer expects a supplier to
behave reliably [5]. While trust is a subjective probability, different buyers have differ-
ent trust perceptions or expectations of the same buyer, reputation strives at providing
an objective trust measure. It is defined as the business context specific aggregation of
(subjective) trust values from multiple independent sources to support a supplier’s deci-
sion making process with respect to an intended collaboration with prospective buyers
[6].
In the following, this short paper will outline a snapshot of work currently conducted
on a reputation system model that caters for the following, not necessarily only VO
specific, requirements:
1. integrate a stochastic trust management model that takes the specifics of business
relationships into account
2. in particular, tie trust to observable parameters (in the following termed as Trust
Indicators, short TI), inherently characterizing the abilities of a business partner
3. deal with uncertain, incomplete business partner information in dynamic VO envi-
ronments
4. take long running VOs into account that require all involved parties to query for
reputation, not only a buyer investigating about suppliers
2

5. integrate direct feedback to increase the quality of future reputation responses
Section 2 will introduce the reputation model, that integrates a stochastic trust man-
agement approach where trust is tied to a taxonomy of so-called trust indicators char-
acterizing business partners. A feedback mechanism will also be outlined. Section 3
concludes.
2 Trust and Reputation Model
Many existing trust management approaches, especially in Peer-2-Peer (P2P) environ-
ments, root trust solely in feedback given by peers after having conducted a transaction
[7, 8]. Feedback is a highly subjective, relative input that may vary between peers who
participated in the same transaction. In contrast to these approaches, we believe that
rooting trust in absolute, observable properties - the TIs - inherently characterizing an
organization’s reliability is a sounder approach for trust among VO members. Feedback
is still an important input that improves the quality and accuracy of the reputation value
aggregating the TI rooted trust values.
2.1 Taxonomy of TIs
This subsection provides an excerpt of an assembled classification/taxonomy of TIs
and their individual modeling. The full taxonomy with more extensive examples will
be published in a full paper soon. A TI models one aspect of trust in an organization,
participating in VO. Since VOs can emerge in different business domains, e.g. high-tech
or engineering industries, having a large set of members with specialized expertise, TIs
can have heterogeneous origins and meaning. Therefore, it makes sense to classify TIs
that share a similar origin and meaning. This approach has the benefit that the rele-
vance of a TI class for a particular business domain and even individual VO members
requesting reputation values can be easily determined.
Operational TIFinancial TI Organizational TI External TI Third Party TI
Trust IndicatorTrust Indicator
Fig. 1. TI Taxonomy
Figure 1 shows the top-level TI classes of a taxonomy currently encompassing 146
different TIs. TIs are classified into:
Financial TIs modeling the financial trust aspects of an organization, an example is
the cash flow quote indicator [9].
Organizational TIs modeling the organizational reliability and stability of an orga-
nization, e.g. with the employee fluctuation indicator [10].
Operational TIs model an organization’s operational reliability, for instance with
the delivery delay indicator [11, 12].
3

External TIs cater for trust relevant aspects external to an organization such as with
the country bond spread index indicator that aggregates country risk [13].
Third Party TIs allow for interfacing with other specialized, third party reputation
or expert systems providing trust relevant indicators in form of recommendation.
It already becomes obvious from the TI top-level class descriptions that the taxon-
omy exercise is an interdisciplinary effort drawing, among others, from the fields of
risk management, operations research and Key Performance Measurement. A related
approach was conducted by Tan [14] who assembled a Trust Matrix entailing trust as-
pects for Electronic Commerce but who remained with his work on a higher and abstract
level.
After the top-down description of the high level TI classes, we follow with the
detailed modeling of individual TIs. Re-iterating, a TI is an observable property char-
acterizing a trust aspect of an organization, therefore with an impact on its reputation.
Taking the operational TI ”delivery delay” as an example, the time difference passing
between an agreed upon delivery date by buyer and supplier and the actual delivery
date is observed. Most suppliers aim at minimizing delay, hence it can be expected that
suppliers more often deliver slightly late with the number of suppliers decreasing with
increased delivery delay.The goal of TI modeling is finally to predict future VO member
behavior based on previously collected (TI) data. The fact that the availability of cor-
rectly observed data can not be guaranteed in productive systems motivates a TI model
based on probability distributions. The described behavior of the ”delivery delay” TI
for instance suggests a model based on the Exponential distribution [6]. To cater for
missing or incorrect data, observed data does not directly determine the TI distribution.
Instead, a Bayes update, by applying the following Bayes theorem equation is used:
P (θ|X) =
P (X|θ)P (θ)
P (X)
=
P (X|θ)P (θ)
P
S
P (X|θ)P (θ)
Observed data X contributes to the prior or empirical distribution P (X). The dis-
tribution assumption, in case of delivery delay that it is exponentially distributed, deter-
mines the likelihood distribution P (X|θ) estimating the true parameter θ. Evaluating
the equation leads to the posterior distribution P (θ|X) with the best fit of the distribu-
tion parameterized by the observed data to the distribution assumption. The posterior
distribution is further employed for aggregating towards a reputation value. Assuming
discretionary density functions, allowing for more efficient numerical evaluation, the
right side of the equation is evaluated. The normalizing denominator’s sum index S
iterates over the equidistant intervals or states of the grid.
Figure 2 visualizes the described example distributions graphically, based on data
sampled from a Enterprise Resource Planning system.
Besides these mathematical properties, the TI model also entails the following at-
tributes:
Name N.
Every TI is uniquely identified by a name N.
Domain D.
A TI can be based on observations of a continuous or discrete variable x.
The possible values of x are the domain of the TI.
4

Fig. 2. TI Delivery Delay
Update time period ∆t
upd
.
Trust information is likely to arrive in different intervals.
The attribute ∆t
upd
defines a fixed time grid telling the reputation system, how
often to update a TI.
Observation time period ∆t
obs
.
The time period ∆t
obs
defines a maximal time win-
dow to look into the past. Beyond that, observations are regarded to carry no more
significance.
Time weighting function ω. Among n observations x
i
at times t
i
, i {1, ..., n} within
the time window, old ones are less likely to carry information about future values
than newer ones. Each TI incorporates a monotonically increasing weighting func-
tion ω(t) > 0 that can implement forgetting of older observations and put emphasis
on newer ones.
Trust preference mapping π.
In order to judge the level of trustworthiness displayed
by a TI, we define an ordinal scale 1 to p
max
, where 1 represents the lowest and
p
max
the highest level of trust indicated by the TI. To compare TIs, the scale is
the same for all TIs. p
max
= const. π defines a function π : S {1, ..., p
max
}
mapping the states S to the different levels of trust indicated by them. This mapping
enables an expert to incorporate his knowledge on the particular TI domain.
It has to be noted that this information rich TI model can express complex proper-
ties of business transactions and relationships. Other reputation systems in related work
already attempted stochastic models for business transactions or returned feedback, but
these attempts could only model binary events or transactions, that is if a transaction
ended positive or negative. A prominent example, the Beta Reputation System in [15]
uses the Beta distribution that is parametrized by the amount of positive and negative
previous outcomes of a particular transaction. Real business transactions involving de-
livery of chip components such as heatsinks, do not behave that atomically and may be
long-running, requiring a more sophisticated model of transaction indicators and their
attributes.
5

Citations
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Abstract: The level of readiness of an organisation to join a collaborative process depends on ‘hard’ factors such as competency fitness or technological compatibility, but also on several other factors of a ‘soft’ nature such as an organisation's character, willingness to collaborate, or affectivity/empathy relationships. Considering these aspects, a modelling approach to assess how prepared an enterprise is to join a collaborative network is proposed. The approach is based on the notion of ‘character’ of the organisation, addressing behavioural aspects regarding collaboration. The distinction between collaboration readiness and preparedness is established. In order to deal with the incompleteness of information and uncertainties associated to the readiness assessment process, it is proposed to use belief networks, which allow performing inference concerning behavioural characteristics of organisations. The approach is then extended in order to handle decision making under situations characterised by uncertainty. ...

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Abstract: Computational Trust and Reputation (CTR) systems are platforms capable of collecting trust information about candidate partners and of computing confidence scores for each one of these partners. These systems start to be viewed as vital elements in environments of electronic institutions, as they support fundamental decision making processes, such as the selection of business partners and the automatic and adaptive creation of contractual terms and associated enforcement methodologies. In this article, we propose a model for the aggregation of trust evidences that computes confidence scores taking into account dynamic properties of trust. We compare our model with a traditional statistical model that uses weighted means to compute trust, and show experimental results that show that in certain scenarios the consideration of the trust dynamics allows for a better estimation of confidence scores.

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Dissertation
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Abstract: It has been observed that users in a reputation system often hesitate in providing negative feedback due to the fear of retaliation. A solution to this issue is privacy preserving reputation systems, which compute reputation such that the individual feedback of any user is not revealed. In this thesis, we present privacy preserving reputation protocols, that are decentralized, do not require specialized platforms nor trusted third parties, protect privacy under a range of adversarial models (semi-honest, non-disruptive malicious, disruptive malicious), and are more efficient than comparable protocols (the most expensive protocol requires O(n) + O(log N) messages, where n and N are the number of feedback providers and the total number of users respectively). The techniques that we utilize include trust awareness, data perturbation, secret sharing, secure multi-party computation, additive homomorphic cryptosystems, and zero-knowledge proofs. We also address some issues related to trust recommendation and propagation. In particular, we present a solution to the problem of subjectivity in trust recommendation. Experimental results indicate the effectiveness of the proposed strategies.

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References
More filters

01 Jan 2000
Abstract: In this concluding essay I shall try to reconstruct what seem to me the central questions about trust that the individual contributions presented in this volume raise and partly answer. In the first section, I briefly qualify the claim that there is a degree of rational cooperation that should but does not exist, and I shall give a preliminary indication of the importance of the beliefs we hold about others, over and above the importance of the motives we may have for cooperation. In the second section, I define trust and the general conditions under which it becomes relevant for cooperation. In the third, I discuss the extent to which cooperation can come about independently of trust, and also whether trust can be seen as a result rather than a precondition of cooperation. In the final section, I address the question of whether there are rational reasons for people to trust and especially whether there are reasons to trust trust and, correspondingly, distrust distrust.

2,146 citations


Proceedings Article
01 Jan 2002
TL;DR: A new reputation system based on using beta probability density functions to combine feedback and derive reputation ratings is described which is flexibility and simplicity as well as its foundation on the theory of statistics.
Abstract: Reputation systems can be used to foster good behaviour and to encourage adherence to contracts in e-commerce. Several reputation systems have been deployed in practical applications or proposed in the literature. This paper describes a new system called the beta reputation system which is based on using beta probability density functions to combine feedback and derive reputation ratings. The advantage of the beta reputation system is flexibility and simplicity as well as its foundation on the theory of statistics.

1,586 citations


01 Jan 2001
TL;DR: A broad spectrum of issues related to graphical models (directed and undirected) are discussed, and how BNT was designed to cope with them all are described, at a high-level.
Abstract: The Bayes Net Toolbox (BNT) is an open-source Matlab package for directed graphical models. BNT supports many kinds of nodes (probability distributions), exact and approximate inference, parameter and structure learning, and static and dynamic models. BNT is widely used in teaching and research: the web page has received over 28,000 hits since May 2000. In this paper, we discuss a broad spectrum of issues related to graphical models (directed and undirected), and describe, at a high-level, how BNT was designed to cope with them all. We also compare BNT to other software packages for graphical models, and discuss the nascent OpenBayes e ort.

1,139 citations


Journal ArticleDOI
TL;DR: This work describes a graphical model for probabilistic relationships--an alternative to the Bayesian network--called a dependency network and identifies several basic properties of this representation and describes a computationally efficient procedure for learning the graph and probability components from data.
Abstract: We describe a graphical model for probabilistic relationships--an alternative to the Bayesian network--called a dependency network. The graph of a dependency network, unlike a Bayesian network, is potentially cyclic. The probability component of a dependency network, like a Bayesian network, is a set of conditional distributions, one for each node given its parents. We identify several basic properties of this representation and describe a computationally efficient procedure for learning the graph and probability components from data. We describe the application of this representation to probabilistic inference, collaborative filtering (the task of predicting preferences), and the visualization of acausal predictive relationships.

582 citations


Proceedings ArticleDOI
05 Jan 1999
TL;DR: This paper proposes two complementary reputation mechanisms that rely on collaborative ratings and personalized evaluation of the various ratings assigned to each user that have applicability in other types of electronic communities such as chatrooms, newsgroups, mailing lists, etc.
Abstract: The members of electronic communities are often unrelated to each other, they may have never met and have no information on each other's reputation. This kind of information is vital in electronic commerce interactions, where the potential counterpart's reputation can be a significant factor in the negotiation strategy. This paper proposes two complementary reputation mechanisms that rely on collaborative ratings and personalized evaluation of the various ratings assigned to each user. While these reputation mechanisms are developed in the context of electronic commerce, we believe that they may have applicability in other types of electronic communities such as chatrooms, newsgroups, mailing lists, etc.

373 citations


Frequently Asked Questions (2)
Q1. What have the authors contributed in "A bayesian reputation system for virtual organizations" ?

The paper will present a taxonomy of TIs for VO environments, a stochastic model to maintain and aggregate trust sources, so called Trust Indicators, and the inclusion of other subjective measures such as feedback. 

Currently ongoing and future work deals with a set of sound, business domain dependent, configuration sets for bootstrapping a reputation system instance without historic data. The bayesian trust management model itself will be improved by modeling dependencies among TIs themselves on the bottom BN layer. This may potentially lead to cycles in the BN that violates the claim that a directed BN has to be acyclic. Several approaches from BN and graph theory are currently explored, the most promising so far is the junction tree approach, transforming a BN ’ s graph with cycles into a acyclic junction tree [ 16, 17 ].