简介：Theaimofthispaperistodevelopamulti-periodeconomicmodeltointerprethowthepeoplebecomeoverconfidentbyabiasedlearningthatpeopletendtoattributethesuccesstotheirabilitiesandfailurestootherfactors.TheauthorssupposethattheinformedtraderdoesnotknowthedistributionoftheprecisionofhisprivatesignalandupdateshisbeliefonthedistributionoftheprecisionofhisknowledgebyBayer'srule.Theinformedtradercaneventuallyrecognizethevalueoftheprecisionofhisknowledgeafteranenoughlongtimebiasedlearning,butthevalueisoverestimatedwhichleadshimtobeoverconfident.Furthermore,basedonthedefinitionontheluckiertraderwhosucceedsthesametimesbuthasthelargervarianceoftheknowledge,theauthorsfindthattheluckiertheinformedtraderis,themoreoverconfidenthewillbe;thesmallerthebiasedlearningfactoris,themoreoverconfidenttheinformedtraderis.Theauthorsalsoobtainalinearequilibriumwhichcanexplainsomeanomaliesinfinancialmarkets,suchasthehighobservedtradingvolumeandexcessvolatility.

简介：这份报纸基于sum-of-processing-time与更一般的学习效果处理单个机器的安排问题。在这研究，一个工作的处理时间被减少定义的sum-of-processing-time-based学习效果工具处理在顺序先于它的工作的时间的全部的正常工作。甚至与sum-of-processing-time-based的介绍，到工作处理的学习效果预定的结果表演，单个机器的makespan最小化问题仍然保持polynomially可解决。一个全部的结束时间最小化问题的最佳的时间表的曲线关于处理时间的工作正常是塑造V的。

简介：Theslowconvergencerateofreinforcementlearningalgorithmslimitstheirwiderapplication.Inengineeringdomains,hierarchicalreinforcementlearningisdevelopedtoperformactionstemporallyaccordingtopriorknowledge.Thissystemcanconvergefastduetoreducedstatespace.Thereisatestofelevatorgroupcontroltoshowthepowerofthenewsystem.Twoconventionalgroupcontrolalgorithmsareadoptedaspriorknowledge.Performanceindicatesthathierarchicalreinforcementlearningcanreducethelearningtimedramatically.

简介：AninformationtheorymethodisproposedtotesttheGrangercausalityandcontemporaneousconditionalindependenceinGrangercausalitygraphmodels.Inthegraphs,thevertexsetdenotesthecomponentseriesofthemultivariatetimeseries,andthedirectededgesdenotecausaldependence,whiletheundirectededgesreflecttheinstantaneousdependence.Thepresenceoftheedgesismeasuredbyastatisticsbasedonconditionalmutualinformationandtestedbyapermutationprocedure.Furthermore,fortheexistedrelations,astatisticsbasedonthedifferencebetweengeneralconditionalmutualinformationandlinearconditionalmutualinformationisproposedtotestthenonlinearity.Thesignificanceofthenonlinearteststatisticsisdeterminedbyabootstrapmethodbasedonsurrogatedata.Weinvestigatethefinitesamplebehavioroftheprocedurethroughsimulationtimeserieswithdifferentdependencestructures,includinglinearandnonlinearrelations.

简介：Inthispaper,aKELM-basedensemblelearningapproach,integratingGrangercausalitytest,greyrelationalanalysisandKELM(KernelExtremeLearningMachine),isproposedfortheexchangerateforecasting.Thestudyusesasetofsixteenmacroeconomicvariablesincluding,import,export,foreignexchangereserves,etc.Furthermore,theselectedvariablesarerankedandthenthreeofthem,whichhavethehighestdegreesofrelevancewiththeexchangerate,arefilteredoutbyGrangercausalitytestandthegreyrelationalanalysis,torepresentthedomesticsituation.Then,basedonthedomesticsituation,KELMisutilizedformedium-termRMB/USDforecasting.TheempiricalresultsshowthattheproposedKELM-basedensemblelearningapproachoutperformsallotherbenchmarkmodelsindifferentforecastinghorizons,whichimpliesthattheKELM-basedensemblelearningapproachisapowerfullearningapproachforexchangeratesforecasting.

简介：Detectionandclarificationofcause-effectrelationshipsamongvariablesisanimportantproblemintimeseriesanalysis.Thispaperprovidesamethodthatemploysbothmutualinformationandconditionalmutualinformationtoidentifythecausalstructureofmultivariatetimeseriescausalgraphicalmodels.Athree-stepprocedureisdevelopedtolearnthecontemporaneousandthelaggedcausalrelationshipsoftimeseriescausalgraphs.Contrarytoconventionalconstraint-basedalgorithm,theproposedalgorithmdoesnotinvolveanyspecialkindsofdistributionandisnonparametric.Thesepropertiesareespeciallyappealingforinferenceoftimeseriescausalgraphswhenthepriorknowledgeaboutthedatamodelisnotavailable.Simulationsandcaseanalysisdemonstratetheeffectivenessofthemethod.

简介：Thisresearchexaminesthemicro-levelcorrelationbetweentraditionalmarketingactions(TVadsandpublicrelations)andpre-releaseconsumers’sociallearningaboutvideogameconsoles(WiiandPS3,launchedin2006).Weevaluateconsumers’learningprocessesviatheperusalofinformationinonlinecommunitiesusing“pageview”dataformultiplewebsitesfromaclickstreampanelasindicators.WeproposeabivariateBayesianlearningmodelcombinedwithcomplementarypurchasechoices.Theproposedmodelenablessimplerestimationofparametersandallowstoaccommodatedetailedinformationaboutinteractionsbetweensocialandpersonallearningprocesses.Fromtheresults,wefindempiricalevidencethatcompanies’traditionalmarketingactionshaveagreaterimpactonsociallearningthanonregularpersonallearningduringthepre-launchperiod.Whenconsumersmakepurchasedecisions,theirsocialbeliefsaboutproductqualityareweighedatleastthreetimesmoreheavilythantheirpersonalbeliefs.Counterfactualsimulationssuggestthatbyoptimizingmarketingactions,firmscanstimulateconsumers’learningandpromoteincreasedproductengagement.

简介：Abankhadbetterrationitscreditifitfirstentersagraysystemofcreditinformationwhereitcannotdistinguishbetweenthelow-andhigh-riskborrowers.Asthebankkeepsalong-termrelationshipwithitsborrowersthebanklearnsabouttheborrowersthroughtime.WiththehelpoflogitmodelandBayesrule,abankcanprocesscustomer＇screditinformationandbuildabettercredittermitgives.

简介：在最近的年里，电子learning系统在许多公司作为一个训练工具被采用了，当它能帮助雇员到在一短时间并且以低成本的完全的训练。在这上下文以内，自从2003，在台湾的经济事情(MOEA)的部的工业开发局(IDB)每年赞助了超过50家企业帮助他们实现设定的电子learning系统。关于电子learning的过去的研究主要集中了于一个标准并且采用了回归途径，例如检验在满足或继续的用法意愿和各种各样的先前的因素之间的关系。尽管回归方法能揭开电子learning采纳的重要因素，他们不能显示出每个因素的罐头(weighting)，并且因此，公司不能分配资源的最佳的层次。克服这个问题，这研究收集了与从文学和专家意见的评论实现设定的电子learning系统的公司有关的因素，然后构造了一张层次因素桌子。基于因素的适当和独立，并且是由IDB，电子learning方案供应商和IDB的工程领导人的工程提升办公室的专家估计了赞助企业，这篇论文然后设计了一个分析层次过程(AHP)格式问询表并且送了它给这些一样的专家收集他们的回答。因素weightings基于模糊分析层次过程(FAHP)方法被导出。最后，关键因素的分析被执行，建议提供了。基于这个工作的结果，有有限资源的服务工业公司能完成合适的资源分配并且更快速并且成功地采用设定的电子Learning系统。

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简介：在变量之中的原因效果关系的察觉和澄清及时是一个重要问题系列分析。传统的诱发性推理方法有模型一定是线性的突出的限制并且与Gaussian噪音。尽管添加剂模型回归能有效地推断添加剂的非线性的原因的关系非线性的时间系列，它受不了变量的同时期的原因的关系一定线性、不总是有效测试有条件的独立关系的限制。这份报纸提供采用相互的信息和有条件的相互的信息识别非线性的时间系列模型的一个班的原因的结构的一个nonparametric方法，它扩大到非线性的结构的向量autoregressive的非线性的时间系列建模的添加剂。一个算法被开发学习同时期并且变量的落后原因的关系。模拟表明建议方法的有效性。

简介：Whilelastdecadehaswitnessedarapidgrowthofdigitaleconomy,thereislimitedunderstandinginliteratureonwhethertheconventionalwisdomonpricingstrategystillholdsforinformationgoods.Ononehand,informationgoods,similartodurablegoods,aresubjecttovaluedepreciation;ontheother,theydifferfromtraditionalgoodsinnegligiblemarginalcostandthesensitivitytosocialinfluences.Thispaperdevelopsatwo-period,game-theoreticmodeltoinvestigateoptimalpricingstrategyofinformationgoods.Ononedimension,twodifferentdepreciationmechanisms(self-andtime-depreciation)areconsidered;ontheother,twoprevalentpricingschemes(perpetuallicensingandsubscription-feemodels)arestudied.Weobtainclosed-formsolutionsinallscenarios.Ourfindingssuggestthatvendorsoftime-depreciationinformationgoodsshouldadoptsubscription-feemodeltoattractearlyadoptersandexploitsocialinfluences,whilethevendorsofself-depreciationinformationgoodsshouldstrategicallybalancebetweendepreciationandsocialinfluences.Interestingly,associalinfluencesbecomestrongenough,thedifferencebetweenpricingschemesdiminishesandthetradeoffbetweencandidatestrategiesvanishes.Wealsoextendthemodeltostaticpricinginwhichthevendorcommitstofutureprice.Wediscoverthatthesuperiorityofsubscription-feemodelmightbeoverturnedunderstaticpricing.Ourresultsabovealsoimplythatbuildingconsumerfeedbackandinteractionsystemscouldbehelpfulforminimizingthepotentiallossofasuboptimalpricingscheme.

简介：High-frequencystocktrendpredictionusingmachinelearnershasraisedsubstantialinterestinliterature.Nevertheless,thereisnogoldstandardtoselecttheinputsforthelearners.Thispaperinvestigatestheapproachofadaptiveinputselection(AIS)forthetrendpredictionofhigh-frequencystockindexpriceandcomparesitwiththecommonlyuseddeterministicinputsetting(DIS)approach.TheDISapproachisimplementedthroughcomputationoftechnicalindicatorvaluesondeterministicperiodparameters.TheAISapproachselectsthemostsuitableindicatorsandtheirparametersforthetime-varyingdatasetusingfeatureselectionmethods.Twostate-of-the-artmachinelearners,supportvectormachine(SVM)andartificialneuralnetwork(ANN),areadoptedaslearningmodels.AccuracyandF-measureofSVMandANNmodelswithboththeapproachesarecomputedbasedonthehigh-frequencydataofCSI300index.TheresultssuggestthattheAISapproachusingt-statistics,informationgainandROCmethodscanachievebetterpredictionperformancethantheDISapproach.Also,theinvestmentperformanceevaluationshowsthattheAISapproachwiththesamethreefeatureselectionmethodsprovidessignificantlyhigherreturnsthantheDISapproach.

简介：ThearticleExploringthevalueofinstalledbase:PricinginformationgoodsundervaluedepreciationandconsumersociallearningpublishedinVolume22,Issue3,pages362-382byYifanDouandTianliangLiu,hasbeenretractedbyagreementbetweentheauthorsandthejournalEditor-in-Chief.Thecorecontributionsinthisarticlehavebeenusedwithoutsignificantacknowledgementandapprovalbytheprojectresearchteam.