Modeling and Simulation of Multi-Agent System of China's Real Estate Market Based on Bayesian Network Decision-Making

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1 Int. J. on Recent Trends n Engneerng and Technology, Vol. 11, No. 1, July 2014 Modelng and Smulaton of Mult-Agent System of Chna's Real Estate Market Based on Bayesan Network Decson-Makng Yang Shen, Shan L, Yongchen Guo and Yu Fu College of Economcs and Management, Nanjng Unversty of Aeronautcs and Astronautcs, Nanjng, Chna Emal:{shen.y, Abstract In recent years, agent-based modelng and smulaton (ABMS) methods have attracted the people s attenton n the feld of management scence and become an mportant tool solvng complex problems. In ths paper, the Bayesan network s ntroduced to ABMS methods, and a mult-agent system of Chna s real estate market s proposed based on the Bayesan network wth uncertan nformaton, whch has the abltes of onlne learnng and effect descrbng for group behavor. And Chna s real estate market ecology s smulated by the system. Smulaton results can accurately reproduce the operaton of Chna s real estate market, so prove the effectveness of the model. Through smulatng for related parameters, some valuable fndngs on operaton rule of estate market are obtaned. The model and method developed n the paper provde reference for studyng Chna s real estate market rules. Index Terms mult-agent systems, agent-based modelng and smulaton, Bayesan network, real estate market I. INTRODUCTION Agent technology, orgnated n the computer scence, has attracted more and more attenton n management scence, and the agent-based modelng and smulaton (ABMS) method has become an mportant tool especally n the related felds of complexty. In 1994, the famous Amerca scentst, John Holland, proposed the complex adaptve system (CAS) [1], whch advanced the research of mult-agent system n the feld of management to a new level. Then, some mult-agent smulaton platforms such as Swarm, Repast and NetLogo were establshed n successon, and they provded the basc envronment for modelng and smulaton of mult-agent system [2~4]. Enterng the 21st century, some management problems ncludng economc, socal and so on show a complex trend, and lead more and more scholars to use ABMS as an mportant tool supportng ther studes, these research covered producton management [5], emergency management [6], human resources management [7], ecommerce [8] and many other felds. On the other hand, n each perod and n all countres, the real estate s the very mportant property for people. Especally n Chna, people are affected by the tradtonal concept of " set up a home, then establsh busness ", and the real estate s partcularly mportant. At the same tme, the real estate s usually the bggest part of natonal wealth. Because of the characterstcs of ntensve materal and ntensve captal, the real estate ndustry has become the natonal pllar ndustry, and the economc fluctuatons of real estate have a huge mpact on the overall economc envronment. Therefore, an n-depth study on the operaton of the real DOI: 01.IJRTET Assocaton of Computer Electroncs and Electrcal Engneers, 2014

2 healthy and stable operaton of the real estate market. At present, there are two man research methods for the operaton rules of the real estate market: the normatve research based on the fnancal and economc theory; the emprcal research based on the analyss of hstorcal data [9,10]. Through these studes, people can understand the real estate market and ts effects, but these methods are only qualtatve analyss n the macroscopc vew, and lack the mcroscopc quanttatve studes on operaton rules of the real estate market. Recently, ABMS method has also been used to study the real estate market. Startng from the descrpton of nteracton rules among man agents n real estate market, Ettema (2011) adopts a dfferent prce formaton mechansm, where buyer and seller can update ther perceptons on tradng probablty and the reservaton prce s dscounted wth the tme [11], Ma(2012) develops an agent-based household resdental relocaton model (HRRM) to study the effect of resdental relocaton polcy [12], but n ther models, some mportant ssues, such as buyer s decson-makng process under uncertanty, the nfluence of group behavors, and so on, are not consdered. In ths paper, based on Bayesan network, a mult-agent model of real estate market s proposed, and some operaton rules of Chnese real estate market are analyzed. II. MULTI-AGENT MODEL OF REAL ESTATE MARKET BASED ON BAYESIAN NETWORK 1 The real estate market s a typcal complex adaptve system. Agents n the system make decsons n accordng wth ther dfferent purposes, and nteract wth the envronment or other agents under the gudance of the decson-makng results. Some repeated decson-makng type of agents contnue to learn and accumulate experence n the process of decson-nteracton to adjust ther own behavor, so that the follow-up decson-makng results are more n lne wth ther own nterests. In the real estate market system, the man agents should nclude buyers, developers, government and banks. In order to smplfy the model, combned wth Chna s specfc stuaton, the bank agent s not ntroduced to ths model, but the functons of the banks are regarded as one of the functons of government. In addton, market agent representng the envronment s ntroduced to the model, and the structure s shown n Fgure.1. The real estate market system Market Buyers Developers Government Normal buyers Speculators Fgure.1. Structure dagram of real estate market system As shown n Fgure.1, the buyers n ths model are dvded nto two types: Normal buyers and Speculatve buyers. The dfferences between them are manly reflected n two aspects: the normal buyers decsons are made ndependently, but speculatve buyers tend to form groups and cooperate wth each other to make decson; the normal buyers decsons are generally made once, and they leave the system after fnshng the purchase, whle the speculatve buyer s decson-makng s repettve. In ths model, the man responsblty of the market agent s mantanng hstorcal data to support decson-makng of other agents. A. Decson-makng process modelng of buyer agent Decson-makng model of buyer agent based on Bayesan network Based on Bayesan network[13], the decson nfluence dagram of the buyer agent n the mult-agent smulaton system of real estate market s shown n Fgure The relevant research done n ths paper s supported by the Foundaton for Humantes and Socal Scences of the Chnese Natonal Mnstry of Educaton (No. 10YJCZH073), the Natural Scence Foundaton of Jangsu Provnce (No. BK ), and the Fundamental Research Funds for the Central Unverstes (No.NJ and No.NR ). 20

3 Fgure.2 Decson nfluence dagram of buyer agent As shown n Fgure. 2, the decson nfluence dagram of buyer agent conssts of a decson node (Buy), two utlty nodes (ULve, UMoey) and a Bayesan network whch ncludes four chance nodes. We consder that buyers judge the future prce changes manly accordng to the polcy and the hstorcal prce movement, and the factors affectng the buyers expectaton utltes are the mprovement of lvng condtons and the ncomes from purchase. Therefore, Bayesan network can be regarded as a thought process of buyers n two aspects of mprovng ther lvng condtons and gettng ncomes from ther purchase decsons under the uncertan market condtons. The meanng and value of each Chance node n Bayesan network of ths model are shown n Table 1. TABLE 1. MEANINGS AND VALUES OF NODES IN BAYESIAN NETWORK Node name Meanng Range Polcy Government polces {None,Bottle, Encourage} Hstory Prces n Hstorcal Trend {Stable, Apprecate,Deprecate} Market Market trends {Stable, Better,Worse} Lve Lvng condtons {Improvement, No mprovement} In the table, {None, Bottle, Encourage} respectvely represent no polcy, regulaton and control polcy, and encouragement polcy; {Stable, Apprecate, Deprecate} respectvely represent the steady, rsng and fallng hstorcal trends ; {Stable, Better, Worse} respectvely represent the stable, better and worse market tends judged by buyers. In Fg. 2, expect that the probabltes of both Polcy node Po) and Hstory node H) are natural probabltes, the probabltes of remanng node values are condtonal probabltes of ther parent nodes, whch s the source of the causal relatonshp. Accordng to a seres of predefned CPT, the probabltes of the assocated nodes are calculated. The followng descrbes the purchase decson-makng process of buyers under the acton of the nfluence dagram. Let each natural probablty and CPT are known, based on the nfluence dagram n Fg.2, the purchase decson process s descrbed as the followng steps: (1) By the natural probabltes of both the government polces Po) and the hstorcal prce H), and the assocated CPT, solve the state probablty of the market. Accordng to the prncple of Bayesan probablty, we have MaPo,, H) Ma Po, H) Po, H) that s then t s concluded that P ma, po, h ) ma po, h ) po, h ) ( j k j k j k ma ) ma, po j, hk ) j k (2) By Ma) obtaned from step (1) and the purchase decson, solve the expected utltes of the lvng condtons and the ncomes from purchase E(U L ) and E(U M ), shown as 21

4 1 buy E ( U L) 0.5 not tobuy u ma ) buy E ( U ) M 0.5 not to buy where u s the ncomes from purchase of buyers when the ma event happens(.e., the -th market state happens). (3) By the relatve weght w of utltes of lvng condtons and ncomes from purchase, solve the expectaton value of the total utlty U, that s E( U L ) w E( U M ) E( U) 1 w (4) Set the purchase belef probablty value of buyers, shown as (0) (0) E( u ) E(u ) 0 wll (0) 0 E(u ) 0 (0) where E ( u ) s the expectaton value of the total utlty when buyers buy a house. (5)Select a random number r [0,1 ), f r wll, buyers choose to buy a house, otherwse not to buy. In the fve steps above, steps (1,2) can be automatcally calculated by the Bayesan network decson-makng model software package whch s developed by the author, so just only smple numercal calculatons of steps(3-5) need to be performed. Onlne learnng of speculatve buyer agent After completng a purchase, the speculatve buyer agent does not leave the market, but contnues to learn the nformaton obtaned from the envronment n order to make more accurate decson n the next cycle. On the bass of Bayesan network theory [14,15], the authors desgned the onlne learnng algorthm of mult-agent Bayesan network decson-makng, and developed a correspondng software package. Due to space lmtaton, the detals of software package wll be descrbed n another paper. Accordng to the decson nfluence dagram descrbed n Fg. 2, the hstorcal data avalable for learnng are the actual values of node Polcy, Hstory and Market n every sngle-step smulaton. From these hstorcal data, speculatve buyers can obtan the more accurate dependence relatonshps among the future prces, the polces and the hstorcal prce movements. Therefore, supported by the onlne learnng software package of mult-agent Bayesan network decson model, onlne learnng of speculatve buyers only requres two smple steps: obtanng hstorcal data from the actual values of node Polcy, Hstory and Market; performng the parameter learnng of Bayesan network by callng correspondng functons of software package. Influence of group behavor on purchase decson of normal buyer agent Purchase decsons of normal buyers are easly affected by decson-makng results of other ndvdual around. In some cases, the nfluence of group behavor on the decsons of normal buyers may become so severe that can change the state of the smulaton system. In order to descrbe the nfluence, the decson ntenton of agent s corrected n the paper based on the theory of herd behavor[16]. Buyers face two choces: buyng or not buyng, whose ncomes are shown as V B and V N. If the ncome from buyng s hgher,.e., V V 0, t means that event V happens. Conversely, f the ncome from not B N buyng s hgher, t means that event V happens. Before observng the surroundng communty nformaton, buyers get ther purchase ntenton wll through decson-makng, whch s a pror probablty p of choosng buyng. Let X represents buyers observaton nformaton on the decson-makng results of group around. X > 0.5 means that more than half of the buyers choose to purchase; X < 0.5 means that the number of buyers choosng buyng n the crowd around s less than half; X = 0.5 means that the number of buyers choosng buyng s just half. For the buyers, the condtonal probabltes of X n events V and X 0.5 V ) X 0.5 V X 0.5 V ) X 0.5 V ) 1 where s the belef accuracy, whch reflects the decson confdence of buyers from the group nformaton, In ths model, s correlated wth the type of buyer s decson-makng. If buyers are more lkely 22 ) V are

5 to beleve the group nformaton, the value of closes to 1; on the contrary, f the buyers are very confdent, that s to say, ther decsons are made manly based on the prvate nformaton, and the nfluence of group nformaton on ther decson-makng s small, the value of closes to 0.5. When the purchase ntenton of buyers s corrected, the frst step s to observe the decson-makng results of other buyers around, and then the posteror probablty s calculated based on the observaton. When observaton result s X > 0.5, the purchase ntenton of normal buyers s revsed as wll V X 0.5 V ) p X 0.5) X 0.5 V ) p X 0.5 V p p (1 ) (1 p) Smlarly, when observaton result s X < 0.5, we have wll V X 0.5 V ) p X 0.5) X 0.5 V ) p X 0.5 V (1 ) p (1 ) p (1 p) ) (1 p) ) (1 p) Obvously, when observaton result s X = 0.5, the posteror probablty equals the pror probablty, t means that group nformaton does not affect the ndvdual decsons. III. MULTI-AGENT SIMULATION OF CHINA S REAL ESTATE MARKET A. Expermental parameters and scene desgn In order to fully control the smulaton, and to study effects of varous parameters on the real estate, a seres of smulaton parameters are desgned n the paper, n whch the man parameters are shown n Table 2. TABLE 2. MEANINGS AND VALUES OF NODES IN BAYESIAN NETWORK Parameter Name Parameter meanng Value COUNT_OF_BUYERS Intal number of buyers 700 COUNT_OF_DEVELOPERS Number of developers 5 INPUT_SPEED Enterng rate of normal buyers 230 LAND_SUPPLY_PER Land supply n each perod 250 COUNT_OF_SPECULATOR_GROUP Number of speculatve groups 1 RATIO_OF_RIGID_DEMAND Proporton of buyers wth rgd demand RELATIVE_WEIGHT_OF_LIVE_MONEY Relatve weght of utlty {2,0.5} MIU Belef accuracy of normal buyers Most of the parameters n Table 2 are easy to be understood. The enterng rate of buyers s the number of new home buyers enterng the system n each perod of smulaton, whch wll drectly affects the purchase demands of the real estate market; Land supply n each perod determnes the market supply. In the smulaton, the buyers are frst dvded nto two categores: speculatve buyers and normal buyers. Speculatve buyers appear wth group form, and each speculatve group contans 10 speculatve buyers who make collaboratve decsons. Further, normal buyers are classfed accordng to affordablty and rgd purchase demand. The relatve weght descrbes the rato of mprovng lvng condton utlty and purchase ncome utlty n the total expected utlty. In Table 2, the values {2,0.5} represent that for the normal buyers wth rgd demand, the weght of mprovng lvng condton utlty s larger n the total expected utlty,and for normal buyers wth non-rgd demand, the weght of purchase ncome utlty s larger. B. Smulaton process Ths smulaton s carred out n Repast. In Repast, the runnng of the model s advanced by the smulaton tme (Tck). In each smulaton tme, the agent performs several autonomous or nteractve behavors, based on the cumulatve state of behavors before, so that the agent and the state of the system are changed. In the smulaton, frst create a grd as a smulaton space, and varous agents are randomly dstrbuted n the space, as shown n Fgure. 3. Dots and cross cons represent normal buyers and speculatve

6 buyers respectvely, lght colored dots represent developers, and pentagram and trangle represent the government and the market envronment. When smulatng group behavor, the buyer agents are affected by the decson of other buyers around accordng to ther locatons. Durng the smulaton process, the dynamc changes of system state can be observed, and the status value of any agent can be observed suspended n any tme. Perhaps ths s an mportant advantage of mult-agent smulaton methods compared wth analytcal methods, whch s benefcal to understand the operaton regularty of complex system. Fgure. 3 Smulaton grd space In each smulaton tme, frstly the market agent collects and updates the man nformaton of market envronment, and the new normal buyer agent enters the system. Then, government, developer and buyer agents (ncludng normal buyer agents and speculatve buyer agents) make ther decsons and perform nteractve actvtes at the same tme. Fnally, speculatve buyer agents also perform onlne learnng of Bayesan network to evolve ther decson models, and the normal buyers who fnsh purchase leave the system. C. Informaton collecton and processng In the Bayesan networks of buyer decson model, the CPT parameter wth three chance nodes, Polcy, Hstory and Market, s the key of how wll buyers make the evdence-based probablstc reasonng and further calculate the expected utlty of all optonal actons. Therefore, for testng whether ths model can descrbe the operaton rules of real estate market, the hstorcal operaton data of the real estate market under the double mpact of early trend and polcy need to be collected. Ths paper collects the average monthly prce data of Chna's real estate market and the government housng polcy nformaton from 2003 to 2010 [17].The nformaton s processed as the form of avalable nformaton for the model, as shown n Table 3. Durng ths perod, the Chnese government also promulgated a seres of real estate-related polces. Some representatve polces are selected as the bass for model learnng. In order to process the hstorcal prces and the government polcy data as the avalable nformaton on parameter learnng of Bayesan network decson model for buyer agents, let p be the -th monthly prce. When p p (1 f ) / p the -th monthly prces are stable. When p < p -1, the -th monthly prces are fallng. When p > p -1, the - th monthly prces are rsng. The f s the average monthly nflaton rate. Chna s long-term treasury nterest rate s 6%, so monthly one s 0.5%. Ths paper selects as the bass to judge whether prces s stable. Smlarly, when p 1 p 2 (1 f ) / p the -th monthly prce remans stable n the prevous perod. Thus, accordng to the annual nflaton rate, the data n Table 3 can be processed as the learnng nformaton of Bayesan network decson model combned wth the data of the typcal polcy mplcatons for the buyers. Substtute t nto the decson model for learnng parameters of Bayesan network, and then obtan the CPT of updated market node, as shown n Table 4. 24

7 TABLE 3. AVERAGE MONTHLY PRICE OF REAL ESTATE MARKET FROM 2003 TO 2010 Date Average prce ( yuan) Date Average prce ( yuan) TABLE 4. CPT OF MARKET NODE AFTER LEARNING Polcy None Hstory Stable Apprecate Deprecate Stable Better Worse Polcy Bottle Hstory Stable Apprecate Deprecate Stable Better Worse Polcy Encourage Hstory Stable Apprecate Deprecate Stable Better Worse

8 In the followng smulaton, CPT of market node after learnng s used to make probablstc reasonng n the buyers decson-makng process. D. Smulaton result analyss The model descrbed above s smulated n the Repast for 80 tcks, and the average prce per tck n the smulaton s obtaned, as shown n Table 5. TABLE 5. AVERAGE PRICE PER TICK Tck Average prce( Yuan) Tck Average prce( Yuan) Compared wth the real data n the same perod, set the average -th monthly prce as p and the smulaton data n the correspondng -th perod as p (s), and then calculate the average relatve error, shown as Standard devaton of relatve error s p p n 1 p p n 1 p ( p (s) n 1 / n p) Calculaton results show that the accuracy of the model s hgh, and the error s less dscrete. Fgure.4 depcts the comparson of the smulaton results and the real data of prce trend. Fgure.4 Note how the capton s centered n the column. 26

9 In Fgure.4, the sold lne represents the smulaton results, and the dotted lne represents the real data. As can be seen from Fg.4, the fttng degree of real data and smulaton results s satsfactory. It shows that the mult-agent smulaton model of real estate market n ths paper has good explanatory power, and t also proves that usng the Bayesan network decson model to descrbe the decson-makng process n multagent smulaton s reasonable and relable. After verfyng the valdty of the model, the effects of varous parameters on the operaton of the Chna s real estate market are studed. Because of the lmted space, here only two parameters, the amount of speculatve buyers and the belef accuracy of normal buyers, are brefly analyzed. In Chna's real estate market, speculatve buyer s a more senstve topc, but the quanttatve research on the effect of the amount of speculatve buyers on the operaton of the real estate market s seldom reported. In order to analyze the effect, based on the above model, the number of speculatve buyer groups are set to 0 and 5 (e, a total of 50 speculatve buyers) to conduct a smulaton experment, and the results are shown n Fgure.5. Fgure.5 Effect of number of speculatve buyers on mult-agent real estate market In Fgure.5, the sold lne s the smulaton results when the number of speculatve buyer groups s 0, and dotted lne s the smulaton results when the number of speculatve buyer groups s 5. As can be seen from Fg.5, n ths smulaton envronment, the effect of the number of speculatve buyers on average prce s not as great as expected. In some sectons, the ncrease n the number of speculatve buyers may lead to a declne n prces, whch may be understood that collaboratve decson-makng feature of speculatve buyers mproves barganng power of buyers wth real estate agency (n ths model, the drect barganng mechansm was not desgned, and the barganng power mentoned here refers that the buyers force the real estate agency to reduce the prce through collectve decsons of not purchasng). But at the same tme, t also should be noted that the ncrease n the number of speculatve buyers can ntensfy the market operaton volatlty. In order to descrbe the volatlty of average prce trends n real estate market, the smulaton tme s dvded nto a number of sectons wth equal length, and each secton represents 5 perods of smulaton. The range s calculated n each secton, and the results are shown n Table 6. In Table 6, r 1 s the average monthly prce range of each secton when the number of speculatve buyers groups s 0, and r 2 s the average monthly prce range of each secton when the number of speculatve buyers groups s 5. In most sectons, the values of r 2 are sgnfcantly greater than r 1. When the number of speculatve buyers groups s 0, the range of only one secton s greater than 500, but when the number of speculatve buyers groups s 5, there are nne such sectons, reachng 56% of the total number. The frequent short-term volatlty may cause buyers panc, whch wll affect the stable operaton of the entre real estate market. Compared wth other parameters, the effect of belef accuracy of normal buyers on the real estate market operaton s qute complex. In order to exclude the nterference of other relevant factors (manly ncludng prce volatlty caused by the mbalance of supply and demand), when carryng out the smulaton on the 27

10 belef accuracy, the parameters are adjusted, namely, the number of the ntal buyers s reduced to 400, and land supply n each perod s ncreased to 300. The large number of smulaton results show that the mpacton of belef accuracy of normal buyers on the real estate market s closely related to the number of speculatve buyers. More than 40 tmes of batch smulatons (= 0.5 and = 0.95, 21 tmes respectvely) are carred out when the number of speculatve buyer groups n = 1,2,,19,20, and each smulaton tme s set to 100. The hghest prces n each smulaton are recorded, as shown n Fgure. 6. TABLE 6. AVERAGE MONTHLY PRICE RANGE OF EACH SECTION Smulaton secton r 1 r 2 1~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Fgure.6 The hghest prces under dfferent numbers of speculatve buyers In Fgure.6, the sold lne s the case when =0.5, and the dotted lne s the case when =0.95. The fgure shows that when = 0.5 the curve s relatvely flat, whch s understood that when the majorty of decsonmakng processes of normal buyers are not affected by the surroundng group behavor, the mpacton of the ncrease n the number of speculatve buyers on the prce s not sgnfcant. The change of the hghest prce under dfferent numbers of speculatve buyers s more complex when =0.95, as descrbed by dashed lne. Compared wth the sold lne, t shows that when n 7 the ncrease n belef accuracy makes prces lower, and when n 8 the ncrease n belef accuracy makes prces hgher. Then when n contnues to ncrease, the effect of the belef accuracy on prces gradually becomes weaker. The authors argue that the reason that belef accuracy mpactng the real estate market operatons s that buyers are dvded nto two types: speculatve buyers and normal buyers. For normal buyers, they are affected by the surroundng group behavor, and at the same tme also affect other surroundng buyers. Overall, the mprovement of ther belef accuracy s conducve to the formaton of "jon forces", whch has nhbtory effect on housng prce. For speculatve buyers, they take concerted acton based on group decson, that s, they only undrectonally affect the surroundng normal buyers, and the herd behavor cased by the role of speculatve buyers leads more prces rsng. When speculatve buyers n the market s less, the 28

11 functon of the belef accuracy s manly reflected by the bdrectonal effects among normal buyers, then the ncrease n belef accuracy leads prces fallng. Wth the ncrease n the number of speculatve buyers, the role of belef accuracy s gradually replaced by the undrectonal effect of speculatve buyer groups on normal buyers, and then the ncrease n belef accuracy leads prces rsng. When speculatve buyers n the market account for a greater proporton, although herd behavor caused by speculatve buyers stll makes prces rsng, lttle proporton of normal buyers cannot lead to a larger-scale herd behavor, so belef accuracy wll also no longer play a sgnfcant role. IV. CONCLUSIONS AND OUTLOOK The authors ntroduce the ABMS method to the real estate market, develop the mult-agent decson model based on Bayesan network, and perform the smulaton by the model. Smulaton results show that the proposed model can accurately reproduce the operaton of the real estate market stuaton. The conclusons are drawn as follows: ABMS method s effectve and applcable for the real estate market. It not only provdes a new means for the study on the real estate market, but also makes t possble to deeply understand the real estate market operaton rules. Ideal smulaton results are obtaned by usng the buyer agent Bayesan decson model, and the model can be wdely appled to characterzaton of mult-agent decson-makng process wth uncertan nformaton. The effects of the number of speculatve buyers and the belef accuracy of normal buyers mpact on the real estate market are presented, whch provde new deas and bass for the polcymakng of government. Though the satsfactory results can be obtaned by usng the proposed model, t should be noted that the model s smplfed, whch makes some phenomena n the real estate market not to be descrbed. Future work s to refne the model, and to deeply study the effect of government regulaton and the emergence and evaporaton of the real estate bubble. REFERENCES [1] John H. Holland. Hdden order: How adaptaton bulds complexty. Zhou Xaomu, Han Hu, Translator. Shangha: Shangha Scentfc and Technologcal Educaton Publshng House, 2000.(n Chnese) [2] Mnar N, Burkhart R, Langton C, et al. The Swarm smulaton system: a toolkt for buldng mult-agent smulatons. Workng Paper , Santa Fe Insttute, [3] North M J, Coller N T, Vos R J. Experences creatng three mplementatons of the Repast agent modelng toolkt. ACM Transactons on Modelng and Computer Smulaton, 2006, 16(1):1-25. [4] Sklar E. Software Revew: NetLogo, a mult-agent smulaton envronment. Artfcal Lfe, 2007, 13(3): [5] Zolfpour-Arokhlo M, Selamat A, Hashm S Z M. Route plannng model of mult-agent system for a supply chan management. Expert Systems wth Applcatons, 2013, 40: [6] Uno K, Kashyama K. Development of smulaton system for the dsaster evacuaton based on mult-agent model usng. Tsnghua Scence and Technology, 2008(1): [7] Bondo A E, Pluchno A, Rapsarda A. Return mgraton after bran dran: A smulaton approach. Journal of Artfcal Socetes and Socal Smulaton, 2013, 16(2): 11. [8] Leta I A, Slavescu R R. Logc-Based Reputaton Model n E-Commerce Smulaton. Journal of Artfcal Socetes and Socal Smulaton, 2012, 15(3): 7. [9] Zhou Jngku. Monetary polcy, bank loan and housng prce---emprcal study on four muncpalty ctes n Chna. Fnance & Trade Economcs, 2005(5):22~23.(n Chnese) [10] Wheaton W C. Real estate Cycles : Some fundamentals. Real Estate Economcs, 1999, 27(2): 209~230. [11] Ettema D. A mult-agent model of urban processes: Modellng relocaton processes and prce settng n housng markets[j]. Computers, envronment and urban systems, 2011, 35(1): [12] Ma Y, Zhenjang S, Kawakam M. Agent-Based Smulaton of Resdental Promotng Polcy Effects on Downtown Revtalzaton[J]. Journal of Artfcal Socetes and Socal Smulaton, 2013, 16(2): 2. [13] Heckerman D, Geger D, Chckerng D M. Learnng Bayesan networks: The combnaton of knowledge and statstcal data. Machne Learnng, 1995, 20:197~243. [14] JI J Z, ZHANG H X, HU R B, et al. A Bayesan network learnng algorthm based on ndependence test and ant colony optmzaton[j]. Acta Automatca Snca, 2009, 35(3): [15] Wang S C, Leng C P, L X L. Learnng Bayesan network structure from small data set. Acta Automatca Snca, 2009, 35(8): [16] Cpran M, Guarno A. Estmatng a structural model of herd behavor n fnancal markets[j]. FRB of New York Staff Report, 2012 (561). [17] Natonal Bureau of Statstcs. Statstcal yearbook of Chna s real estate. Bejng: Chna Statstcs Press, 2011.(n Chnese) 29

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