Brokering Private Reputation Information
In Online Transaction Communities
Hugo Liu, Judith Donath, Pattie Maes
MIT Media Laboratory
20 Ames St., Bldg E15
Cambridge, MA 02139 USA
{hugo, donath}@media.mit.edu
ABSTRACT
Online communities represent an exciting new forum for
user-to-user transactions, overcoming many of the physical
and social boundaries associated with traditional communities. To foster trust in transacting with virtual strangers,
online transaction communities have adopted user reputation systems, such as eBay’s Feedback Forum, and friendster.com’s testimonials. Because these reputation systems
are public, giving positive feedback to enhance someone’s
reputation becomes a form of social capital for the giver,
while social pressures often suppress publicly voicing
negative feedback. However there are high-cost domains
in which the dissemination of candid reputation information is particularly important, such as online dating or
equities trading.
In this paper, we explore how private reputation systems
may be better suited to the dissemination of negative or
candid reputation information in an online transaction
community. In particular, we are inspired by the activity of
gossip: the exchange of sensitive information about a third
party between two trusted parties. We adapt the idea to an
online transaction community, where a system called GossipMonger brokers a trusted exchange of private reputation
information between interested but not necessarily acquainted parties.
We implement and use a social agent simulation of an
online transaction community to more formally explore
how a system like GossipMonger affects information dissemination and decision-making.
Keywords
Gossip, reputation systems, social networks, ebay, game
theory, agent simulations.
THE NATURE OF GOSSIP
“If you don’t have anything nice to say, don’t say anything
at all!” In nearly all of the world’s cultures past and present, an offer of praise to a compatriot curries favor, is socially decorous, and often draws reciprocal praise. Praise is
so well-received that we are proud to give it in public, even
Copyright is held by the authors.
Draft of MIT Media Laboratory Technical Report.
Please do not cite.
in front of audiences. As a culture, we celebrate occasions
of public praise – benedictions, eulogies, celebrations, applause. It is even entrenched in language. In English, you
can give someone all your love, but it is ungrammatical to
give them your hate, or resentment, or anger, or disgust.
Whereas we are wanton to praise, we must always think
twice before expressing a negative opinion of someone
because it carries with it many serious ramifications. A
negative statement toward someone can be perceived as an
act of hostility and aggression. It can draw negative statements about us in retaliation. In our English vernacular,
every synonym of criticize, (e.g. belittle, nag, rebuke) carries with it negative connotation about the giver. Public
criticism is a social taboo too, and when given, is often
closely scrutinized for hints of slander or libel or defamation. Many Western cultures say it ok to talk down about a
person’s idea or behavior or action, but not ok to talk down
about the person directly. In Japan, the acceptable target of
criticism is even further restricted. The general acceptability of public praise, and close social scrutiny of public criticism are artifacts of our human culture and evolutionary
heritage, and our social designs should reflect these realities.
It is quite paradoxical then, that rumors about someone –
often negative or sensitive in nature – can spread through a
social community like wildfire, even outpacing the dissemination of public information. Perhaps it is precisely the
taboo of negative information that makes it such a social
commodity. Why do rumors spread so quickly and efficiently? The exchange of negative or sensitive information
about a third party between two friendly parties, or gossip,
as it is commonly known, strengthens the ties and trust between the two friendly parties. In fact, cultivating social
relationships may be the raison d’être of gossip. Ronald
Burt’s echo hypothesis (2001) suggests that selective disclosure of information in informal conversations often only
serves to reinforce predispositions and enhance shared feelings in a dyad. Evolutionary sociologist Robin Dunbar
argues that language and social gossip coevolved as a substitute for physical grooming that allowed social cohesion
to be maintained within larger-sized clans (1998). While
the individual motivation to gossip may be to cultivate dyadic social relationships, an important side effect of gossip
is that it facilitates tabooed information of a negative or
sensitive nature to be spread efficiently through a social
network.
Understanding the social dynamics of praise and criticism
in traditional communities help us to better understand and
design for online communities. The public nature of the
existing reputation systems for online transaction communities (e.g. eBay and online dating sites) encourage praise
and discourage criticism. We are exploring how private
reputation systems can be designed to facilitate the exchange of negative or candid reputation information, with
the goal of allowing users to make more informed decisions
based on balanced evidence. We adapt the concept of gossip to an online community where gossipers are not personally acquainted. In our proposal, a system called GossipMonger brokers a private and trusted exchange of gossip.
This paper is organized as follows. First, we compare public with private reputation systems. Second, we discuss a
design for a system-brokered gossip reputation system
called GossipMonger. Third, we describe a social agent
simulation and some theoretical experiments we performed
to test our proposed gossip model. We conclude with more
discussion on system-brokered gossip and a summary of
our findings.
transaction communities by creating a private reputation
system with many similar features. To establish privacy,
the identity of the gossiper is kept semi-anonymous, and
gossip is only shared with trusted and deserving parties.
Gossip is not accessible by the target of gossip so that there
is no retaliation. In traditional communities, the main motivation of gossip is improved social relationships; however, this reward model does not work with online communities, where users are more interested in maximizing the
information they collect to make informed decisions, than
making friends with and giving away information to other
users, who are often competing for the same transactions.
If a user’s goal is to maximize his/her own information,
perhaps users can be motivated to give gossip if doing so
enables them to hear gossip. The approach taken in this
paper is to look at gossip as a cooperative activity, in which
giving gossip is motivated by reciprocation.
In the following section, we sketch a design for a systembrokered gossip reputation system called GossipMonger.
SYSTEM-BROKERED GOSSIP
In this paper, we focus on online communities in which
users participate in transactions, such as eBay, friendster.com, or dating sites. In these communities, users have
a particular interest in being able to accurately assess the
reputation of other users, as this factors into the decision to
transact or not to transact. The more prominent of the user
reputation systems adopted by these communities, such as
eBay’s Feedback Forum, and friendster.com’s testimonials,
are ingeniously designed to have a very public nature. Reputation feedback is visible to the target of the feedback, as
well as to the entire community.
One scheme for cooperative gossip is to establish direct
reciprocity between two users. User X has gossip on User
A and needs information on User B. User Y has gossip on
User B and needs information on User A. However, requiring this exact precondition severely limits the possible
bandwidth of information available to Users X and Y, because many more Users may have gossip on Users A and
B. Instead of direct reciprocity, we design our gossip system for indirect reciprocity. Recent work in game theory
(cf. Nowak & Sigmund, 1998) has yielded mathematical
models which demonstrate that cooperation can exist even
if recipients have no chance to return the help to their helper. Instead, recipients return the help to someone else.
This resembles the dynamics of anonymous markets, which
are known for their efficiency.
As expected, these public reputation systems encourage
positive feedback while suppressing negative feedback.
Resnick and Zeckhauser’s empirical analysis of eBay’s
Feedback Forum (2002) found that feedback was almost
always positive and that a large portion of negative feedback was likely suppressed. They also found a strong correlation between buyer and seller feedback, providing evidence for reciprocation, which encourages positive feedback, and retaliation, which discourages negative feedback.
We say that these public reputations are ingenious because
they are designed to foster trust in transactions (Resnick et
al., 2000). They may be in the best interest of promoting
trust in the community, but are not necessarily in the best
interest of individual users trying to make informed decisions. Promoting praise and discouraging criticism denies
users a balanced picture of reputation that would help them
make more informed decisions.
In our GossipMonger design, the system brokers the cooperation within a gossip community, pairing information
needed with information available. The system tries to
qualify and quantify the contribution of each user to this
cooperative community. Quantity measures the ratio of
gossip given versus gossip received and assures that users
are only getting out what they put in. The quality of a user’s gossiping is compiled from feedback given by the consumers of that user’s gossip. To assure that gossip quality
is reported fairly, how a user rates a gossiper will also affect their direct trust relationship. If User X favors the gossip of User Y, then User Y’s reputation will be improved
and User Y’s gossip will be presented above the gossip of
others to User X in future searches. If User X rejects the
gossip of User Y, then User Y’s reputation will be hurt, but
User X forfeits the opportunity to get gossip from User Y
in the future.
In traditional communities, privacy assurances and the social rewards of gossiping facilitates the spread of negative
reputation information. We would like to create an infrastructure for negative reputation information flow in online
Users who give more candid and thorough gossip are likely
to have higher reputations. However, their candidness and
specificity might make them more vulnerable to retaliation
and identification if for example the target of the gossip
PUBLIC VS. PRIVATE REPUTATION SYSTEMS
hacks into the gossip community and discovers this gossip.
In our design, users can specify to only trust dealings with
other users of similarly high reputation, thus making better
confidentiality assurances. What we might expect to form
is a core user base of a high reputation that is difficult for
casual hackers to infiltrate, because building up a sufficiently high reputation has a high initiation cost.
Although the system does not require the content of gossip
to be negative, we hypothesize that it is primarily negative,
sensitive, and unique information that will thrive and
spread best in this private reputation system. Information
that is candid and publicly scarce will naturally be more
valuable commodities. Negative information tends to be
more scarce because there is a greater risk taken in sharing
it, so it is perceived as being more valuable. Negative information more so than positive information, can help a
user to avert the high cost of a bad transaction. Opportunity cost is not as much a responsibility of the gossip system
because users will generally advertise their virtues to attract
transaction business.
We hypothesize that system-brokered gossip will generate
a higher bandwidth of information than traditional gossip,
because users can access gossip from more people than in a
traditional social network, and also because there may be
less echo or redundancy in gossip. Previously we said that
in traditional gossip, people selectively disclose based on
what they think the recipient wants to hear. In systembrokered gossip, we speculate that users will want to create
gossip to maximize marketability, not necessarily to reinforce the predispositions of any user.
In the next section, we describe a social agent simulation
that we implemented to investigate the dynamics of systembrokered gossip.
SIMULATING GOSSIP IN AN ONLINE TRANSACTION
COMMUNITY
Real online transaction communities are fraught with
enormous complexity. It is a game of decision-making in
an environment of imperfect information, competing participants, and deception. Participants in this game must model the behaviors of other participants from spotty information, and manage the risk of their decisions.
We do not assume to be able to model the complexity of
reality into our simulation, but rather, we tried to create a
simple formal model whose system dynamics resemble
aspects of online transaction communities.
Simulation Design
Agent Qualities
In eBay, transaction decisions takes into account at least
these two factors: the value of the goods or services being
offered, and the characteristics of the seller. In online dating, the goods being offered and the characteristics of the
seller are one and the same. In our simulation we assume
that all sellers are offering the same goods and so only the
characteristics of the seller matter. A seller’s actualqualities are represented by a vector of five characteristics,
each of which is valued from 0 to 10. A seller also has pro-
jected-qualities, which is also a five-item vector. We can
think of the projected-qualities as either a willful or unwillful distortion of the actual-qualities, depending on whether
or not deception is involved. We experiment with both
types of distortion. The projected-qualities are what the
seller markets himself/herself as publicly. On the buyer’s
side, each buyer has a five-item vector of desired-qualities
that represents the ideal seller being sought out.
Combining all available information known to the buyer,
the buyer maintains, at any given point in time, interpretation-of-qualities for each seller, a five-item vector reflecting the buyer’s most current understanding of a seller.
Without extra information, interpretation-of-qualities will
just be the seller’s projected-qualities. However, if gossip
or public reputation information is available, the added
information will be used to revise the interpretation-ofqualities.
Representing Gossip
When an agent creates gossip about a seller, the gossip is a
five-item vector. Ideal-gossip is equal to the seller’s actual-qualities. To make gossip more plausible, we model two
types of distortion: insightfulness, and reporting bias. Insightfulness is an attribute of the agent, taking values from
0.0 (not insightful) to 1.0 (very insightful). The less insightful the agent is, the more distortion is added to the
ideal-gossip. We model this distortion with Gaussian noise,
a frequently used tool in modeling theory for adding variation or noise to a system. The second type of distortion is
reporting bias. To account for the fact that gossip is often
reported with a slant, or agenda, or emphasis, a reporting
bias is either a five-item vector added to the ideal-gossip
(slant, e.g. [+4,+4, 0, 0, 0]), or multiplied with the idealgossip (emphasis, e.g. [0%, 0%, 50%, 100%, 100%]).
Transaction Decision Process
The simulation operates on rounds. Within a round, each
agent plays the role of perspective buyer for one turn. Except the agent whose turn it is to play the role of perspective buyer, every other agent plays the role of seller. The
goal of the perspective buyer agent is to buy from the seller
whose actual-qualities are closest to the buyer’s own desired-qualities. Of course, the buyer does not know what
each seller’s actual-qualities are until there is an actual
transaction, because this is a game of imperfect and unreliable knowledge! Q(t) is a measure of how good a match
the buyer considers the seller to be, at current time t, based
on currently known information. As shown in Eq. (1), Q is
calculated as the numerical deviation of the buyer’s current
interpretation-of-qualities (I) from the buyer’s desiredqualities (D), divided through by the buyer’s decision confidence (C). Note that a smaller Q(t) maps to better buyerseller compatibility.
5
Q(t ) 
 D  I (t )
i 1
i
C (t )
i
(1)
If we set the buyer to have ideal decision confidence, so
that C(t) = 1, then we are saying that the agent trusts his/her
current interpretation-of-qualities the same, regardless of
the quality and quantity of information going into that decision. A more sophisticated model considers how the quality of information (i.e. gossip from bad sources, gossip from
good sources, from public reputation systems) and the
quantity of information (i.e. how much gossip, how much
public reputation information) affect the agent’s confidence
in the interpretation-of-qualities.
During an agent’s turn, the agent calculates the Q(t) for
every qualifying seller, factoring in all the buyer’s current
knowledge into the valuations. Because in this game we
are interested in learning about how various types of information can affect transaction decisions, a buyer can only
transact with a seller once. This means that qualifying
sellers are all agents, minus the buyer, minus the sellers the
buyer has previously transacted with. The output of this
process is a list of qualifying sellers rank-ordered by the
Q(t) scores (ascending).
At this point, an agent may do research on the top n candidates, by discovering either public reputation information
on each candidate, or gossip, or neither, or both, depending
on what information is available. After research is done, the
agent is given one chance to update the I(t) interpretationof-qualities for all the sellers, incorporating the new research. Modeling how the three sources of information –
gossip, public reputation information, and the seller’s projected-qualities – come together into a single interpretationof-qualities I(t) is perhaps the single most complex decision, requiring much more elaborate models of an agent’s
qualities, and of gossip, and perhaps better cognitive models of decision-making. Admittedly, we resort to a simple
and naïve formula for combining multiple sources of information. Eq. (2) shows that the interpretation-of-qualities
is given as the weighted sum of the three sources of information, where a, b, and c are preset constants. Eq. (3)
shows that the interpretation given by just gossip information is the weighted sum of each gossip G heard about
the seller, weighted by the gossip-reputation R of the gossiper giving the gossip G.
I (t ) 
aI from projected (t )  bI from gossip (t )  cI from publicreputation(t ) (2)
abc
gossipers
I from gossip(t ) 
 R( gossiper , t )  G
n
n 1
gossipern
( seller )
(3)
gossipers
 R( gossiper , t )
n 1
n
After I(t) is updated for each seller, the Q(t)’s are recalculated and sellers are once-again rank-ordered. At this point,
the buyer will decide whether or not to transact with the top
candidate seller. This decision can be based on a fixed
probability or made inversely proportional to the top candidate seller’s Q(t).
Creating Gossip and Giving Feedback on Gossip Quality
Consider the case where there exists an infrastructure for
system-brokered gossip. If a transaction is made, the buyer
may create gossip on the seller. For simplicity, we do not
presently consider gossip about the buyer. Depending on
the configuration of the simulation, the gossip may be ideal, or it may take into account insightfulness and reportingbias (as aforementioned).
Having encountered the seller’s actual-qualities, the buyer
agent now reviews any gossip he/she received about the
seller, and gives feedback on the gossip quality. Feedback
has five values, with the following meanings:
1. Very low quality. Don’t hear from this gossiper again.
2. Low quality. Hear less from this gossiper.
3. Indifferent.
4. Good quality. Hear more from this gossiper.
5. Great quality. Definitely hear from this gossiper again.
Feedback 1 and 2 will lower the gossip-reputation of the
gossiper, and deduct 1 or 2 points from the gossiper’s participation score. Recall that the gossiper earns a participation point every time the gossip is consumed. Gossipreputation is a score from 0 to 1.0 which tracks whether or
not an agent’s gossiping is well-received or ill-received by
the gossip community. Feedback 4 and 5 will raise the
gossiper’s gossip-reputation and increase the gossiper’s
participation score. Why would agents be motivated to
give accurate feedback? Because feedback improves the
type of gossip that will be shown to the agent in the future.
At the extremes, feedback 1 will suppress future gossip
from that gossiper, and feedback 5 will definitely show
future gossip from that gossiper, whenever available.
Simulation Experiments
Experiment one: effects of gossip on transaction quality
In this basic experiment, we generate a homogenously random population of agents and see how the presence or absence of system-brokered gossip affects the average quality
of transactions in the community.
The population of 100 agents is generated using Gaussian
noise distributions. Each agent’s actual-qualities and desired-qualities are generated using a Gaussian function
whose mean is 5, sigma is 2.5, min-value is 0, and maxvalue is 10. From here on, we will abbreviate this as:
gauss(5, 2.5, [0,10]). Each agent has an insightfulness variable and a truthfulness variable whose values are gauss(0.5,
0.25, [0,1]). The truthfulness variable is used to generate a
project-qualities for each agent only once. The less the
truthfulness, the greater the Gaussian distortion of the actual-qualities. The insightfulness variable is used to add
noise to gossip creation. Reporting bias is not used.
At this point, we are conservative and do not want to presume that the user trusts the gossip system enough for gossip information to improve decision confidence, so we assume ideal decision confidence, i.e., C(t) = 1.
When an agent creates a rank-order of qualifying sellers,
the agent will do no research if gossip is off, and will do
gossip research on the top three candidates. The decision
to transact for both the gossip-on and gossip-off scenarios
is fixed at 20% probability, so that transactions occur at the
same rate in both cases. When obtaining gossip, the agent
will get all available gossip on the top three candidates,
(setting no threshold criteria for the reputation of the gossiper) or will run out of gossip participation credits trying
to do so.
If a transaction is completed, agents will create one gossip
with 100% probability. And if applicable, will give feedback on gossip with 100% probability. Feedback is decided based on the deviation of the gossip from the actualqualities observed.
The population is closed, with no emigration or immigration, and the simulation is run for 100 rounds. At the end
of each round, the average quality of transactions from that
round is calculated. We chose the least implementationspecific metric for transaction quality possible.
A single transaction’s quality is the actual seller’s rank in
the rank-order of the actual compatibility of each potential
seller with the buyer. The deviations between the actualqualities of each potential seller and the desired-qualities
specified by the buyer are compiled and rank-ordered (ascending). The top of the list represents the potential seller
who has the best actual compatibility with the buyer. The
actual seller is located in this list, and his/her rank in this
list is the measure of transaction quality. For instance, if
the rank of the actual seller was 2, then that seller was the
second most ideal decision that the buyer could have possibly made. Figure 1 summarizes the results of the simulation experiment. Note that a transaction quality of 1 represents ideal transaction quality, while higher scores represent increasingly less ideal transaction decisions.
Fig. 1. Experiment one simulation results.
Without a gossip reputation system, agents only have the
projected-qualities of potential sellers to go on. In our
Gaussian population, some sellers project themselves honestly while equally many do not. Decisions based on unreliable projections cause the without-gossip curve to hover
with zero slope, at an average transaction quality of 12, and
to fluctuate dramatically. This is as expected. The reality
of most online dating sites, for example, are not far from
this. There are no public reputation systems, personals
profiles are often deceptive, and there is no way for com-
petitors to share gained knowledge. The same population
run in the scenario with gossip-on yields a much better
curve. Whereas the transaction quality starts out the same
way as in the gossip-off scenario, it takes about 7 rounds
for gossip to idealize buyer’s decisions. This corresponds
to the forming of a critical mass of gossip. Given that the
Pr(transaction) = 20%, and that the Pr(gossip|transaction) =
100%, the expected number of transactions after 7 rounds is
140. Considering that gauss(5, 2.5, [0, 10]) dictates the
likely histogram of which agents were transacted with, 140
transactions marks the milestone that at least one gossip is
probably available for each possible seller. We suggest that
this milestone is responsible for the minimization of fluctuations in the curve starting at round 8. Considering that the
system is performing quite well at transaction qualities of
under 5, the thing that could easily cause a fluctuation is a
couple of very bad transactions in that round. We argue
that having one gossip on each seller averts these bad transactions, and thus, averts fluctuations in the curve.
Let us illustrate this with an example. Suppose that 25% of
the population of sellers are rogues, and suppose that hearing just one gossip about a rogue is enough to knock that
seller out of the top ranks. Suppose the buyer always
chooses the top rank to transact with. If the buyer does no
research, he/she has a ¼ chance of transacting with a rogue
and incurring very high cost. If the buyer researches the
top 1 candidate, the chance of a rogue transaction falls to
1/16. Researching the top 2 candidates reduces the rogue
threat to 1/64, and so on… the rogue threat decreases exponentially as the number of sellers researched increases in
integer steps!
If we assume that n (as in the top n candidates to be researched) is a small fraction of the total candidate seller
population, then gossip’s main steady-state benefit is to
help agents avoid the high costs of bad transactions.
As the rounds progress, the transaction quality of the gossip-on curve gets steadily worse, and fluctuations begin to
return more dramatically. Whereas initially the saturation
of gossip helped agents to avoid rogue transactions, over
the rounds, there became fewer and fewer clear-cut rogue
transactions that could be avoided. And those early rogue
sellers who were “averted” earlier by moving them down
the list, have worked their ways back up the list. And this
time, there cannot be avoided any longer. The steady-state
benefits of gossip are diminishing, ushering in the return of
fluctuations (albeit, more moderate ones).
Experiment two: using gossip to identify and ostracize
rogues
In this experiment, we examine the notion of rogues and
how gossip can help buyers to identify and avert transactions with these rogues. For the purposes of this experiment, we define a rogue as an agent whose actual-qualities
are undesirable to the norm. The rogue is deceptive and
projects projected-qualities that seemingly blend in with the
norm. Unaware buyers with transact with rogue sellers will
face a high cost. There are many good examples of rogues
in online transaction communities, like a fraudulent merchant on eBay, or a predator on a dating site.
We implement rogues as having actual-qualities of
gauss(1.5, 0.5 [0, 3]) and projected-qualities of gauss(7.5,
1, [5, 10]). Non-rogue “normal” people have actualqualities of gauss(7.5, 1, [5, 10]). They project personalities with Gaussian noise based on a Gaussian truthfulness
value, just as in experiment one.
All other agent qualities are the same as in experiment one.
We generate a population of 400 agents, 20% of which are
rogues. The simulation is run with the gossip system running, and also without. After each round, we tabulate the
percentage of rogues that just sold in a transaction, and
compare this to the percentage of normal people who just
sold in a transaction. The results are shown in Figure 2.
Fig. 1. Experiment two simulation results.
These results show that without gossip, these rogues continue to sell as often as normal people. But with gossip,
buyers stop transacting with rogues after just 6 rounds.
What effectively happens is that gossip exposes rogues,
causing them to be ostracized from the community. This is
because the power of negative reputation lies in the principle of exclusion (cf. (Yamagichi & Matsuda, 2002)).
FURTHER DISCUSSION
In traditional communities, gossip is often the perfect quick
conduit for information that is not publicly known, either
because it is negative, of a sensitive nature, or discloses
secrets. It spreads because the act of telling gossip to one
of our friends is a social grooming behavior, helping to
strength a dyadic tie between the giver and recipient.
When we gossip, we feel freer to pass certain information
along that is otherwise taboo to spread publicly. We are
willing to gossip because there is some level of privacy and
trust that we assume will shield us from retaliation if the
target of the gossip should ever trace the gossip back to its
origins. Publicly spread information filters out information
that is not socially decorous to spread, so it is skewed positive, and less informative.
In the online transaction communities like eBay and online
dating sites that are of interest to us, there exists public
reputation systems to spread skewed positive information,
but there are no conduits for negative, sensitive, secretive
information. In this paper, we have proposed a design for a
private reputation system, based on the idea of gossip. Like
gossip, an environment of privacy and trust is created for
information exchange. Gossipers are kept anonymous, yet
the system remembers and tracks familiar gossipers and
helps to implicitly manage the trust relationship with familiar gossipers. Trust can be controlled by the user, who may
specify that he/she only wants to make his/her gossip viewable to other users of sufficiently high reputation or those
with established rapport. The motivation for gossiping is
different from the traditional motivation of social relationship grooming, because users do not want to befriend
strangers who are in competition with them. The motivation has to be acquiring information, because it is to a user’s advantage to command a broad range of information to
support decision-making. With this in mind, we designed a
system-brokered gossip system that establishes a market for
gossip. Participation level and reputation for gossip quality
are things that users can strive for. The indirect reciprocity
model rewards people who create helpful gossip with the
privilege to obtain the gossip of others.
This paper implements a system-brokered gossip system
design in an agent simulation of an online transaction
community. The multi-criteria decision-making aspects of
the problem are highly complex, so we tried to stay away
from complicated cognitive models of decision; but rather,
we design a simpler formal model for the simulation that
we hope to see many parallels in. The game of decisionmaking in an environment requires the interpretation of
imperfect information, coping with competing participants,
and coping with deception, and the behaviors of other participants. The game theory behind the various decision
processes is also quite complicated.
Despite these challenges, we were able to do some theoretical experiments illustrating the system dynamic with and
without gossiping activity. It is clear from our results that
gossiping’s main steady-state benefit is not to lower opportunity cost, but rather, it is to help agents avoid the high
costs of bad transactions. Looking at a population with a
rogue minority, we also show that system-brokered gossip
can quickly and efficiently ostracize rogue agents.
Our contributory summary. We have motivated the need
for a private reputation system that can conduit negative,
sensitive, and secretive information quickly and in a trusted
and indirectly cooperative way through an online transaction community. GossipMonger is a design we propose,
which realizes a private reputation system as a systembrokered gossip system that is semi-anonymous, trustworthy, and a valuable source of information. We created an
agent simulation of our proposed approach and used game
theory to interpret the results of our simulation experiments. We concluded from the simulation behavior that
gossiping’s main benefit is to help agents avoid the high
costs of bad transactions and to enable the quick identification and ostracization of rogue agents. Gossiping by itself
does less to address the opportunity cost problem where
great transactions are missed. Social collaborative filtering
may be better suited to solving the opportunity cost prob-
lem because it is widely used as a predictive recommender
system (Shardanand & Maes, 1995). In general, public
reputation systems, gossip systems, and collaborative filtering systems all solve non-overlapping problems in online
transaction communities. A great challenge is to create
more elegant ways to synthesize the three solutions.
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