Trust Formaton n a C2C Market: Effect of Reputaton Management System Htosh Yamamoto Unversty of Electro-Communcatons htosh@s.uec.ac.jp Kazunar Ishda Tokyo Unversty of Agrculture k-shda@noda.ac.jp Toshzum Ohta Unversty of Electro-Communcatons ohta@s.uec.ac.jp Abstract The formaton of trust among those partcpatng n an on-lne market s an mportant subject, especally n a C2C market that one can enter and leave easly and n whch one can easly change one s dentty. Whether partcpants can trust each other or not wll nfluence the contnuaton of a market. We therefore dscuss the formaton of trust n on-lne transactons. We beleve that a reputaton management system s the most effectve system for trust formaton system. We developed a computer smulaton model that descrbes onlne transactons wth a reputaton management system used to share nformaton concernng the reputatons of consumers. Its desgn was based on an agent-based approach used n prsoner s dlemma game theory. Smulaton results ndcate that a postve reputaton system can be more effectve than a negatve reputaton system. The results should provde mportant suggestons useful n desgnng a reputaton system for onlne transactons. 1. Introducton The e-commerce market s growng rapdly, thanks n part to the ease at whch partcpants can enter and ext, t s anonymty and ease of regstraton. However, these attractve features have led to a new problem, whch s ncreasng rsk of cheatng n onlne tradng, e.g., recevng goods wthout payment or recevng payment wthout sendng goods, as there are ncentves to get goods or payments wthout beng forced to make correspondng the contrbuton. A reputaton management system can promote trust n transactons n an onlne consumer-to-consumer (C2C) market. The reputaton management system should provde a motvaton for cooperaton to a partcpant despte the volatle nature of onlne denttes. The system also should be sutable for varous transacton forms. We study effcent reputaton management of the C2C market A reputaton can be classfed nto postve and negatve aspects concernng mutual reputaton nformaton (Kollock, 1999). The weght of nfluence assgned to postve and alternatvely negatve reputaton s an mportant determnant n the reputaton management system. The sutable weght seems to change wth the transacton form,.e., face-to-face or onlne. To desgn an effcent reputaton management system for a C2C market, t s mportant to analyze the factors affectng the choce of the weght. To do so, we developed a model that expresses whether a market s onlne or offlne by usng the market turnover rate. Reputaton formaton has been extensvely studed by many researchers. For example, n economcs, Shapro (1982) treated the propertes of reputaton as asymmetrc nformaton. To dscuss reputaton operatonally, we defne t based on the study of Wlson (1985) as a person s characterstc descrbed by others based on hs or her behavoral hstory.
In ths paper, we show that the prsoner's dlemma s a sutable model to deal wth ths problem. Before we descrbe the model though, we should brefly revew pertnent research on how to dentfy trustworthy partcpants and promote cooperatve behavor. Partcpants tend to enter and ext onlne C2C markets frequently. Employng reputaton to form trust among partcpants has been studed by many researchers. Dellarocas (2000) dscussed the robustness of reputaton management systems aganst unfar evaluatons by malcous partcpants. Axelrod (1984) used the noton of the shadow of the future to account for the evoluton of cooperatve behavor n the terated prsoner s dlemma. The shadow of the future can be expressed as a probablty for whch a transacton mght contnue n the future. The shadow of the future s often used as a mechansm for evoluton of cooperatve behavor n game theory. In our model, we can refer to turnover rate as the shadow of the future. For example, a large shadow of the future corresponds to an offlne transacton n whch t s dffcult to change one's dentty, and a small shadow of the future corresponds to an onlne transacton n whch t s easy to change one's dentty. Our model enables us to dscuss turnover rate as an essental element of a real-world market wthn the theoretcal framework of the game theory. We ntroduced the basc reputaton model n a prevous paper (Yamamoto et al., 2003); n the present paper, we descrbe a detaled model and analyze the characterstcs of a reputaton management system. 2. Trust on Onlne transacton Let us revew the types of onlne transacton on the Internet n order to dscuss the emergence of trust n C2C transactons. Based on ths revew, we wll dscuss the requrements of a reputaton management system for onlne transactons. There are two types of trust management system: the top-down type, e.g., one wth a trusted thrd party, and the bottom-up type, e.g., one where partcpants share reputaton nformaton. We wll dscuss these systems n 2.2.1 and 2.2.2 and show that the bottom-up type s more effectve than the top-down type for onlne transactons. 2.1 Onlne transactons In an onlne transacton, busness organzatons (B) and consumers (C) are the man partcpants. The most successful knd of onlne transacton s the busness organzaton to busness organzaton (B2B) one, e.g., a supply chan management (SCM) system. B2B transactons on the Internet are smlar to transactons made n other markets, except for the cost; B2B transactons tended to use on-lne systems before the era of the Internet. Another type of transacton s busness organzaton to consumer (B2C). Bank transactons and onlne tcket sales are popular examples because they are exchanges of nformaton nstead of physcal goods. Standardzed goods, e.g., a book and a musc compact dsk (CD), are also popular goods exchanged n onlne transactons. Amazon.com s one of the successful examples and t shows us that B2C transactons have evolved because of the Internet. A new type of Internet-powered retaler has appeared, called the Clck & Mortar retaler. Dstrbutors also have undergone large changes n the way they do busness. For example, Dell assembles a computer on demand from a consumer. It s an example of a drect transacton between a maker and a consumer, and t s also an example of a ntermedated transacton between supplers of computer parts and consumers. The new type of ntermedary s named the nfomedary, whch stands for an nternet powered ntermedary (Hagel and Snger, 1999). Consumer and consumer (C2C) s another type of onlne transacton that has only just begun to be seen. The
Internet has helped C2C transactons to grow, because the network has removed the constrants n terms of dstance and tme and has provded opportuntes for ndvduals to make deals wth lots of others. Examples of the new market nclude ebay and Yahoo aucton. We wll dscuss C2C onlne transactons because of the bg mpact of the Internet on ths knd of transacton. In onlne transactons, especally C2C, there s a larger rsk of cheatng, because t s easy for people to enter and ext the market and t s anonymous. The characterstcs of an onlne transacton lead to ncentves to get servces, goods or money wthout makng any correspondng contrbuton. Ths rsky stuaton s a knd of Prsoner s Dlemma and s the reason our model based on ths dlemma. We explan the dlemma n secton 3. 2.2 Classfcaton of Trust Formaton We have to pay more attenton to how to form trust between partcpants n the onlne C2C market because the rsk of cheatng s larger there than n other markets. To deal wth that rsk, we must fnd ways to form trust between the partcpants n a market. We frst classfy reputaton management systems nto top-down and bottom-up management systems and then show that the bottom-up systems are more effectve than the top-down systems. Top-down management systems can provde safety mechansm to protect partcpants from cheaters because a thrd party n the exchange of goods and money can evaluate how well the buyer and seller meet certan qualfcaton and can also provde transacton control procedures. Authorzaton for partcpaton s an example of a qualfcaton, and escrow servce (explaned n secton 2.2.1) s an example of a transacton control procedure. Bottom-up management system can also provde safety mechansms because partcpants can dentfy good and bad partcpants by consderng the ways the trustworthness of those partcpants s evaluated by other partcpants. The feedback mechansm on ebay, for example, whch s one of the famous and successful onlne aucton servces, s a bottom-up management system concernng trust. 2.2.1 Top-down trust management system The trusted thrd party (e.g., a gradng servce or an escrow servce) s a popular knd of top-down trust management system, but a gradng servce s not effectve n C2C transactons even though t s effectve n B2B transactons. Escrow, on the other hand, s effectve because t can elmnate any possblty of cheatng. Fgure 1 shows how escrow can complete transactons by ntermedatng between the buyer and seller to prevent any cheatng. 3: Notce of payment Seller Servce provder 6: Payment 4: Delvery 5: Notce of Approval 1: Makng decson to deal Fgure 1: Overvew of Escrow The procedures of Escrow servce are: 2: Payment Buyer 1. A seller and a buyer decde to deal for specfed goods. 2. The buyer transfers money to the account of the escrow servce company. 3. The escrow servce company notfes the seller that the money has been transferred. 4. The seller sends the goods to the buyer. 5. The buyer notfes the escrow company that the goods arrved. 6. The escrow company transfers the money for the goods to the account of the seller.
Even though escrow s effectve n C2C onlne transactons, there are three problems n ts use. The frst s ts hgh cost. The second s the complexty of ts procedure, whch reduces the convenence of usng the Internet. The thrd problem s ts lmted avalablty, whch lmts the areas n whch transactons can be made. Another example of top-down system s a legal system. It s the most trusted management system n many transactons, but we need a lot of money to mantan a legal system. Moreover, t s dffcult to apply legal systems among multple natons. Bakos and Dellarocas (2003) have shown that t costs more to mantan a legal system than t does to mantan reputatons. 2.2.2 Bottom-up management system A bottom-up management system lets partcpants crculate and share reputaton nformaton among themselves to promote cooperatve behavor. Many researchers have nvestgated the ways that the exchange of reputaton nformaton bulds trust among partcpants. Resnck et al. (2000) dscuss the ways that reputatons promote the formaton of trust among the partcpants n an onlne market and a communty. A bottom-up management system can also provde a safety mechansm n that partcpants can dstngush good offers from bad ones wth respect to trust. For example, the feedback mechansm n ebay, whch s one of the famous and successful onlne aucton servces, s a bottom-up management system wth respect to trust. Although many researchers understand the mportance of reputaton nformaton n an onlne market, there s no model takng nto account the characterstcs of onlne transacton that stablze cooperatve behavor. We wll therefore develop a model focused on reputaton nformaton as a key factor n the formaton of trust between the partcpants n an onlne market. We wll use the model to nvestgate how a reputaton management system can promote cooperatve behavor. Partcpants can easly enter and leave an onlne C2C market. Bottom-up management not only reduces the cost of nformaton management by elmnatng the need for cost due to central nformaton management, but can also deal wth the frequent change of partcpants over tme. In ebay s bottom-up management system, the partcpants evaluate each other. After a transacton between a buyer and a seller, they can evaluate each other n terms of good (1), so-so (0), and bad (-1). They can make deals wth trusted partcpants because the results of the estmatons are open to all partcpants. ebay s one of the successful examples of reputaton management systems that let partcpants evaluate wth each other and share the nformaton. Another example of a bottom-up system s one n whch unorganzed nformaton passes by word-of-mouth, n other words, by rumor. We often observe that one rumor bulds trusts n persons and organzatons and that another rumor destroys ths trust. Unorganzed nformaton exchanged by word-of-mouth, however, s not sutable for promotng effectve transactons n a market because t could destroy that market. 2.3 Summary In ths secton, we classfy systems for trust formaton nto top-down and bottom-up systems. We summarze the classfcaton n table 1. C2C market s a one of the examples of prsoners dlemma stuatons.
Table 1: Framework of trust formaton System Servce Strength Weakness Top down Escrow Flawless transacton of goods Cost per transacton and payment Calculaton of transacton cost mght be dffcult Bottom up Legal Strong enforcement The most trusted system Reputaton Low management cost Independent of outsde systems Word of mouth Anyone can partcpate. because of the characterstcs of goods Dffculty n applyng a legal system among multple natons Hgh management cost Entrance barrers for newcomers Conspracy of malcous partcpants Dffculty of usng management system for a market Informaton mght be dubous. 3. Modelng C2C onlne transactons To analyze and desgn a C2C onlne market, we developed our model based on an agent-based approach, because the analyss and desgn requre detaled and dynamc explanatons at the ndvdual partcpants level to exhbt socal phenomena. Axelrod (1997) concluded that the agent-based approach would be effectve for analyzng mechansms that can promote global phenomena from local nteractons between agents. By employng ths approach, we descrbe C2C onlne transactons wthn the framework of the Prsoner s Dlemma, to fnd the requste condtons and market mechansm for promotng the emergence of cooperatve behavor. 3.1 Prsoner s Dlemma n C2C onlne transactons A player who partcpates n a C2C onlne transacton always has an ncentve to cheat on others (non-cooperaton), because of the anonymty and ease of entry and ext from the transacton. On the one hand, a buyer may take goods from a seller wthout payng for them. On the other hand, a seller may get a payment from a buyer wthout sendng the goods to hm or her. The stuaton n C2C onlne transactons s representatve of the Prsoner s Dlemma. In ts smplest ncarnaton, there are two players,.e. player-1 and player-2, and they cannot communcate wth each other drectly because they are n soltary confnement n a prson. Each player has two strateges,.e. cooperaton (C) and defecton (D). We can consder a payoff matrx, as shown n Table 2. Table 2: Payoff matrx for prsoner s dlemma Acton of player-2 C D Acton of C S 1, S 2 W 1, B 2 player-1 D B 1, W 2 T 1, T 2 The necessary condtons for prsoner s dlemma are the followng three nequaltes (1). B > S > T > W 2S1 > B1 + W1 2S 2 > B2 + W2, = 1,2 (1) In the prsoner s dlemma of a C2C onlne transacton, a seller can have two actons,.e. cooperaton wth a buyer to gve goods for hs or her payments and defecton wth hm or her to get payments wthout sendng goods. A buyer also can cooperate or defect,.e. payng for goods or gettng goods wthout payng for them. Under these crcumstances, f there s no system to promote cooperaton, a partcpant who does not always cooperate could explot a partcpant who always cooperates wth everyone. To promote cooperaton, one can embed a reputaton nformaton management system
nto the C2C onlne transacton. An act of a seller / a buyer n C2C market and a payoff matrx of prsoner s dlemma correspond lke table 3. Table 3: correspondence payoff matrx and acton Seller C D of seller / buyer Buyer C (S,S) (delverng Goods, payng) (B,W) (NOT delverng Goods, payng) D (W,B) (delverng Goods, NOT payng) (T,T) (NOT delverng Goods, NOT payng) 3.2 A procedure of transacton on C2C market Our market model s for sellers and buyers dealng n goods through bds and awards. Transactons are performed by the followng procedure. 1. The seller puts the "goods" whch he has on the market. 2. The buyer chooses "goods" based on hs or her preference (whch s dentcal to demand, here). 3. The buyer performs matchng of "supply" and "demand." 4. The buyer chooses a transacton partner by checkng the seller's reputaton. 5. The seller chooses a transacton partner by checkng the buyer's reputaton. 6. If a transacton partner s chosen, they wll trade. 7. The profts of the seller and the buyer are found by consultng the prsoner's dlemma pay-off matrx. 8. A new partcpant enters the market every term. 9. The new partcpant copes the strategy of the partcpant who has the hghest current proft. Under these crcumstances, f there s no system to promote cooperaton, a partcpant who does not always cooperate could explot a partcpant who always cooperates wth everyone. To promote cooperaton, one can embed a reputaton management system nto the C2C onlne transacton. 3.3 Classfcaton of Reputaton To model reputaton operatonally, we defne t based on the study of Wlson (1985) as A person s characterstc descrbed by others based on hs or her behavoral hstory. Kollock (1999) provded a classfcaton of negatve and postve aspects of nformaton wth whch reputaton management systems deal. A negatve reputaton system s to prohbt bad behavor by dstrbutng the hstores of badly behavng partcpants to all partcpants. It s possble to exclude a member from a communty because of hs or her bad behavor. The negatve reputaton system s a sort of black lst system whose mechansm s one of excluson. It s effectve n real transactons; however, t seems to be not effectve n onlne transactons, because of ts anonymty and the ease by whch people can enter and ext from an onlne market. Moreover, there s the possblty to dstrbute ncorrect nformaton to downgrade another s reputaton. What s a sutable reputaton system for an onlne transacton? A postve reputaton system seems to be the one, because t provdes ncentve to behave cooperatvely. It also provdes an ncentve to stay n a market for a long tme, because the system promotes one s good reputaton, dstrbutng hs or her hstory concernng good behavor. However, there are two problems wth the postve reputaton system n an onlne transacton. The frst problem s that t s hard to dstngush the dfference between cooperatve and non-cooperatve partcpants. The second problem s the dffculty to establsh a good reputaton when partcpants frequently enter and ext from an onlne market. We wll analyze whch system s sutable for what type of market wth our agent-based
model and descrbe the advantages and dsadvantages of negatve and postve reputaton management systems. The reputaton of agent( ) s calculated based on focus of reputaton (α ) as descrbed n equaton (6). 3.4 Formulaton R t α T ( ) T D, t To model reputaton management system, we defne reputaton n terms of postve and negatve evaluaton of a partcpant based on Kollock (1999). For smplfcaton of the model, the reputaton we deal wth s the number of cooperatve and non-cooperatve actons n deals on a market. An acton of agent- durng a tme perod t ( ether cooperaton (C) or defecton (D). = { C D} (2) A t, A t ) can be A cooperatve agent always chooses C, whereas a non-cooperatve agent always chooses D. An agent wth a tt for tat strategy selects hs or her acton based on the prevous actons of the agent t s dealng wth. A random agent cooperates or defects wth others randomly. A transacton hstory (T t ) s recorded by the onlne transacton system. T { A k { 0,1, L t } = (3) t k, To make a deal, agents who want to buy bd on goods offered by other agents; the agent who has receved bds awards the goods to one of them. A bd or an award s decded by each agent based on the reputaton t calculates by usng the hstorcal records of the actons of others. Based on the hstorcal record, an agent can calculate the number of cooperatve and non-cooperatve actons n a certan tme span,.e., T respectvely. T T { k A = C, k { t Scope+ 1, t Scope+ 2, t } C, t k L, C, t, T D, t = (4) = { k A = D, k { t Scope + 1, t Scope + 2, t } (5) D, t k L, = C, t 1 α (6) Postve or negatve reputaton systems can be descrbed wth α equalng 1 or 0, respectvely. Based on the value calculated by (6), each agent makes hs or her bd or award. 3.5 Elements of Model In our model, the agent comprses the strateges of transacton, goods to sell, goods to buy, range of allowable dfference n goods between buyer and seller, focus on reputaton, and length of hstory taken nto account by the agent. The strateges of transacton are cooperatve, non-cooperatve, tt for tat, and random (Table 4). Table 4: Agent elements Propertes of an Types or meanng agent Strategy of agent Each agent has a choce of strategy:.e., cooperatve strategy, non-cooperatve strategy, tt for tat strategy or random strategy Goods to sell Property of goods to sell s descrbed by a strng of bts Goods to buy Preference of agent (n case of a buyer) concernng goods to buy s descrbed by a strng of bts Allowable Range of allowable dfference dfference n goods for an agent between the posted goods (the goods to sell) and A weght of choce between negatve and postve Length of hstory observed by agent the goods to buy A weght of choce between negatve reputaton and postve one when an agent evaluate a partner The length of hstory n transacton whch an agent takes nto account when the agent evaluates a partner
We can change the ntal number of agents wth cooperatve, non-cooperatve, tt for tat, and random strateges. We also change a number of characterstcs of goods, varetes of each characterstc, number of agents who enter and ext durng each tme perod. The entry/ext rules are randomly choosng whch agent exts and selectng the agent who has the best current strategy outsde of the onlne market as the entrant. In many cases the new partcpant enters a market after askng an acquantance who has already partcpated n a market about what the market s lke. If the acquantance has hgh profts from that market, the new agent begns to carry out actons n the market. In contrast, f the acquantance has low profts, the newcomer avods the market. Byrne (1965) showed that a person gets acquanted wth other persons who have smlar atttudes and characters. In our model, therefore, a new partcpant selects the best current strategy n the market. By repeatng such transactons, those partcpants who have a sutable strategy survve n the market as tme progresses. We vared the parameters of the envronment and reputaton management system n the smulaton. The smulaton experment explored the structure of the reputaton management system for whch cooperatve actons would be stable. We then formulzed the actons of partcpants and the reputaton management system. An agent s a seller and a buyer who has a strategy n the nsde of ndvdual and trades autonomously. 4. Smulaton Experment Market flexblty s one of the mportant factors dstngushng an onlne transacton from a transacton n the real world. In our model, t s descrbed as the number of agents enterng and extng wthn a certan tme perod. The markets of onlne transacton and real world can be descrbed by low and hgh values of the parameter, respectvely. The parameters concernng focus on reputaton and length of hstory are the characterstcs of the reputaton management system. Table 5 shows the parameters and ther values. Table 5: Expermental parameters Intal number of agents for each 25 strategy group Duraton 100 perods Number of characterstcs of goods 5 bts Varetes of each characterstc 5 bts Allowable dfference n goods 10 bts characterstcs Focus on reputaton Operatonal parameter [0,1] Length of hstory Operatonal parameter {0, 5, 10, 20} Number of entrances and exts Operatonal (turnover rate) parameter {10, 20, 30} To fnd an effectve strategy for each condton, we observed the populatons of each strategy. A large populaton ndcated the effectveness of the strategy for the gven condton. Frst, we smulated the stuaton where a reputaton management system does not exst. From the defnton of the prsoner's dlemma, the non-cooperatve strategy was expected to become domnant. Fgure 2 shows the trajectores of populaton for four groups when the entry and ext number s low and reputaton management system does not exst. Ths fgure llustrates that non-cooperatve strategy becomes domnant. A market collapses n the envronment where no reputaton management system exsts. Next, we ntroduced the reputaton management system descrbed n secton 3.2 and performed the smulaton over agan.
100 90 on-lne market. In such a stuaton, the negatve reputaton system could not elmnate non-cooperatve partcpants. 80 70 60 50 40 30 20 10 0 0 102030405060708090100110 Cooperatve Non-cooperateve Tt for tat Random That s, negatve reputaton systems lke the black lst of a tradtonal market do not functon effectvely n an on-lne market. Next, we checked f a postve reputaton system functoned effectvely n an on-lne market. We determned whether a cooperatve strategy s stable n a postve reputaton system. Fgure 2: Trajectores of populaton for a slow turnover rate and no reputaton system. The vertcal axs shows the populaton of agents. The horzontal axs shows smulaton tme. Fgure 3 shows the trajectores of populaton for four groups when the entry and ext number s low (=10) and the focus on reputaton s negatve (α =0). Ths fgure llustrates the effectveness of the cooperatve strategy n the negatve reputaton system. 100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Cooperatve Non-cooperatve Tt for tat Random Fgure 4: Trajectores of populaton for a hgh turnover rate and negatve reputaton system. The axes are the same as n Fg. 2. 100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 Fgure 3: Trajectores of populaton for a slow turnover rate and negatve reputaton system. The axes are the same as n Fgure 2. Cooperatve Non-cooperatve Tt for tat Random Fgure 4 shows the trajectores of populaton when the entry and ext number s hgh (=30) and the focus on reputaton s negatve (α =0). Ths fgure llustrates the effectveness of the non-cooperatve strategy. A hgh entry and ext number s ndcatve of an envronment of an Fgure 5 shows the trajectores when the entry and ext number s hgh (=30) and the focus on reputaton s both postve and negatve (α =0.5). In ths envronment, a partcpant can clearly dstngush cooperatve partcpants from non-cooperatve ones. Furthermore, a partcpant who accumulates a hgh reputaton s frequently selected as a transacton partner. He/She can get ncreasngly hgh profts. Ths system not only dstngushes and elmnates non-cooperatve partcpants, but can evaluate a cooperatve partcpant's postve reputaton. Ths envronment thus expresses a real C2C market.
100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Fgure 5: Trajectores of populaton for a hgh turnover rate and postve/negatve reputaton system. The axes are the same as n Fg. 2. Fgure 6 shows the trajectores when the entry and ext number s hgh (=30) and the focus on reputaton s only postve (α =1). In ths envronment, a partcpant can behave non-cooperatvely and change hs or her ID. Nonetheless, the cooperatve strategy becomes domnant. Ths ndcates the effectveness of a postve reputaton system n an on-lne market. 100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 Fgure 6: Trajectores of populaton for a hgh turnover rate and postve reputaton system. The axes 5. Dscusson are the same as n Fg. 2. Cooperatve Non-cooperatve Tt for tat Random Cooperatve Non-cooperatve Tt for tat Random In a negatve reputaton system, the cooperatve strategy s effectve when the turnover rate s low, as shown n Fgures 2, 3, 4, 5 and 6. Ths reflects the effectveness of the law punshng non-cooperatve partcpants n the real world. In a socety wth a low turnover rate, non-cooperatve actons lead to low reputatons for whch an affected partcpant would face dffculty n makng transactons. Hence, a negatve reputaton system n the real world makes non-cooperatve partcpants leave a market and lets cooperatve ones enter. However, a negatve reputaton system does not work when the turnover rate s hgh, because non-cooperatve partcpants frequently come and go from a market. If a partcpant has a low reputaton, he or she could re-enter as a new partcpant. Hence, cooperatve partcpants can be exploted and they wll dsappear from a hgh turnover rate market wth a negatve reputaton system. A postve reputaton system can overcome ths problem, because t counts cooperatve actons. Ths means that t s benefcal for a partcpant to cooperate wth others and to stay n the market for a long tme. Furthermore, the system makes non-cooperatve partcpants get out of t. Accordng to a study by McDonald (2002), a buyer who has a hgh reputaton can sell hs or her goods at a hgher prce compared wth others who have the same goods. 6. Concluson Usng an agent-based model for our logcal and vrtual experment, we showed the effectveness of sharng nformaton concernng the reputaton of partcpants n C2C onlne transactons to promote cooperatve actons. In such a hgh turnover rate market, a postve reputaton system can be more effectve than a negatve reputaton system. Ths means that we need a new framework to desgn nsttutons for the onlne transacton market, nstead of the tradtonal framework desgned to punsh crmnals. Moreover, t means that brandng strateges wll become more mportant n onlne markets than n
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