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 Please do not cite.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 publicreputation(t ) (2) abc 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. 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