(Im)possibility of Safe Exchange Mechanism Design

Size: px
Start display at page:

Download "(Im)possibility of Safe Exchange Mechanism Design"

Transcription

1 (Im)possbly of Safe Exchange Mechansm Desgn Tuomas Sandholm Compuer Scence Deparmen Carnege Mellon Unversy 5 Forbes Avenue Psburgh, PA sandholm@cs.cmu.edu XaoFeng Wang Deparmen of Elecrcal and Compuer Engneerng Carnege Mellon Unversy 5 Forbes Avenue Psburgh, PA xaofeng@andrew.cmu.edu Absrac Safe exchange s a key ssue n mulagen sysem especally n elecronc ransacons where nondelvery s a maor problem. In hs paper we presen a unfed framework for modelng safe exchange mechansms. I capures he dsparae earler approaches as well as new safe exchange mechansms (e.g., repuaon lockng). Beng an overarchng framework, also allows us o sudy wha s nherenly possble and mpossble n safe exchange. We sudy hs under dfferen game-heorec soluon concep wh and whou a rused hrd pary, and wh an offlne hrd pary ha only ges nvolved f he exchange fals. The resuls vary based on he generaly of he exchange seng, he exsence (or creave consrucon) of specal ypes of ems o be exchanged, and he magnude of ransfer cos defecon cos and escrow fees. Fnally, we presen an ncenve-compable negoaon proocol for selecng he bes safe exchange mechansm when he agens do no know each ohers coss for he dfferen alernaves. 1. Inroducon Safe exchange s a key ssue n mulagen sysem especally n elecronc ransacons. The rapd growh of Inerne commerce has nensfed hs due o anonymous exchange pare cheap pseudonym globaly (dfferen laws n dfferen counres), ec. A recen sudy showed ha 6% of people wh onlne buyng experence have repored nondelvery (NCL 1999). Sofware agens furher exacerbae he problem due o he ably o vansh by kllng her own processes. Whou effecve soluon he safe exchange problem s one of he greaes obsacles o he furher developmen of elecronc commerce. AI research has suded he problem usng gameheorec mechansm desgn. A safe exchange mechansm proposed n (Sandholm and Lesser 1995, Sandholm 1996) spls he exchange no small porons so ha each agen benefs more by connung he exchange han by vanshng. Ths dea has been operaonalzed n a safe exchange planner (Sandholm and Ferrandon 2). The problem of cheap pseudonyms has been ackled by forcng new raders o pay an enry fee (Fredman and Resnck 1998), and schemes have been proposed for mnmzng he needed enry fee (Masubara and Yokoo 2). Safe exchange was recenly modeled as a dynamc game (Buyan and Hubaux 21). Copyrgh 22, Amercan Assocaon for Arfcal Inellgence ( All rghs reserved. Safe exchange has also been suded n compuer secury. These echnques nclude he con rppng proocol (Jakobsson 1995) and zero knowledge proofs for exchangng sgnaures (Bao e al. 1998), whch we wll dscuss laer n hs paper. Some of he echnques rely on a rused hrd pary (Deng e al. 1996). Ths paper presens a unfed exchange model ha capures he prevous dsparae approaches (Secon 2). We also presen new safe exchange proocols. Mos mporanly, our model allows us o sudy wha s nherenly possble and mpossble o acheve n safe exchange. We sudy hs n he conex of no rused hrd pary (Secon 3), wh a rused hrd pary (Secon 4), and wh an offlne rused hrd pary ha only ges nvolved f he exchange fals (Secon 5). We ask wheher safe exchange s possble under dfferen game-heorec soluon conceps. We sudy hs n he general case, wh specal ypes of ems o be exchanged, and wh varous ransfer cos defecon cos and escrow fees. Fnally, we presen a negoaon mechansm for selecng among mulple safe exchange mechansms (Secon 6). To our knowledge, hs s he frs work o sysemacally nvesgae general exchange problems from he perspecve of mechansm desgn. 2. Our exchange model Our exchange seng has hree pares: wo sraegc agen N={1,2}, ha exchange ems (for example, goods and paymen), and a rused hrd pary (TTP) whch faclaes he exchange. The TTP s no a sraegc agen, and fahfully follows he exchange proocol. A any sage of he exchange, each pary s n some sae. The space of saes of pary s denoed by S.S=S 1 S 2 S TTP s he space of exchange saes. The sae s = P, A, α, c S of agen ncludes he followng componens: A possesson se P whch s he se of he ems ha he agen possesses. These ems coun oward he agen s uly. Inuvely, he agen can allocae hese ems o ohers. 1 However, we make he followng key generalzaon whch allows us o capure safe exchange mechansms 1 The model also capures duplcable ems such as sofware. The duplcaon s consdered o occur before he exchange, so a he sar all copes are n he possesson ses.

2 from he leraure and new ones ha would no f a model based solely on possesson ses. Specfcally, somemes a pary may have he rgh o allocae an em even f does no possess he em. For example, one can rp a $1 bll no wo halves and gve one of he halves o anoher pary. Hence, neher pary owns he money bu can gve o he oher. In order o descrbe an agen s conrol of such ems ha do no conrbue o he agen s uly, we nroduce he noon of an allocaon se. An allocaon se A whch s he se of ems ha an agen does no posses bu can allocae o ohers (no o self). An acvy flag α {ACTIVE, INACTIVE} whch defnes wheher he pary s sll acve. Ths echncaly s needed n order o have a well-defned seng (whch requres ha an oucome sae s defned). An oucome sae s an exchange sae where no pary s acve anymore ( does no necessarly mean ha he exchange s complee). An acve pary can ake acons whle an nacve one canno. For example, an agen ha vanshes n he mddle of an exchange becomes nacve. Once an agen becomes nacve, canno become acve agan. Acosc whch s he cumulave cos ha he agen has ncurred n he exchange so far (cos of sendng em defecon penale ec). Because he TTP can conrol some ems durng he exchange, has an allocaon se, bu because s no a sraegc player and hus does no have a uly funcon, has no possesson se. The TTP self does no have a co bu agan, n order o have well-defned oucome he TTP does have an acvy flag. Pu ogeher, he sae of he TTP s s = A, α S TTP. The se of oucome saes s O. We say ha n any oucome sae, all allocaon ses become empy because he pares are nacve and canno allocae ems anymore. An exchange sars from an nal sae s, where he possesson ses P conan he ems ha are o be exchanged, and he allocaon ses A are empy. In any complee sae s complee O, he exchange s successfully compleed: each agen possesses he ems was supposed o receve and los only he ems was supposed o lose. Each pary can ake acons. M=M 1 M 2 M TTP s he acon space of he exchange. All hree exchange pares have he followng ypes of acons: 1. Wa: A pary can wa for ohers o ake acons. 2. Transfer: A pary can ransfer ems from a source se (possesson se or allocaon se) o a se of desnaon ses. Each pary has a Boolean funcon T (src, em, DES) o deermne wheher he agen can ransfer em from se src o all ses n DES (hs does no mply he possbly of ransferrng o a subse of ses n DES). For example, f em P 1, hen T 1 (P 1, em, {P 1 })=1. Some ems (e.g., $1 bll) can be moved no allocaon ses. 3. Ex: Agen can deacvae self by exng a any sae. Boh agens and he TTP auomacally ex f any complee sae s complee s reached. 2 Each acon aken by an agen may ncur a cos for ha agen. The TTP also has an exra acon ype: can punsh an agen by addng o he agen s cumulave cos. We assume ha each agen N has quas-lnear preferences over sae so s uly funcon can be wren as u ( s ) = v ( λ, λ,..., λ ) c, where v 1 2 k s agen s valuaon, λ s he quany of em he possesse and c s he cumulave cos defned above. The uple E = N, TTP, S, M, u 1, u 2 s called an exchange envronmen. An nsance of he envronmen s an exchange. The mechansm desgner operaes n E o desgn an exchange mechansm EM= S, ρ, M, F, o, where S S and M M. The player funcon ρ:s \O N {TTP} deermnes whch pary akes acons n a sae. The space of sraegy profles s F=F 1 F 2 F TTP. Agen s sraegy f :S \O M specfes he acon agen wll ake n a sae. The oucome funcon o( f 1, f 2, f TTP ) O denoes he resulng oucome f pares follow sraeges f 1, f 2, f TTP sarng from sae s. Snce he TTP s no a sraegc player, we om s sac sraegy from hs funcon. We denoe an agen by, and he oher agen by. An exchange mechansm EM has a domnan sraegy equlbrum (DSE) f and only f each agen N has a sraegy f ha s s bes sraegy no maer wha sraegy he oher agen chooses. Formally, f, f )) f, f )) for all f F, f - F - (he oher agen s sraegy) and s S. The mechansm has a subgame perfec Nash equlbrum (SPNE) f and only f f, f )) f, f )) for all f F and s S. In eher equlbrum concep, f he nequaly s src, he equlbrum s src. Oherwse,sweak. An exchange can be represened as an exchange graph (Sandholm and Ferrandon 2), where saes are represened as verces and acons as dreced edges beween saes. Each edge has a wegh ndcang he cos of he move. An exchange mechansm s presened as a subgraph (see all fgures 3 ). A pah from s o any s complee s called a compleon pah. An exchange mechansm s a safe exchange mechansm (SEM) for an exchange n envronmen E f he mechansm has a leas one equlbrum ( f, f ) n whch he pah of play s a 1 2 complee compleon pah, ha o ( s, f, f ) = s.suchapah 1 2 s called a safe exchange pah. If he equlbrum s a (src/weak) DSE, we say he safe exchange s mplemened n (src/weak) DSE. If he equlbrum s a (src/weak) SPNE, we say he safe exchange s mplemened n (src/weak) SPNE. We make he followng assumpons: 2 One concern s ha an agen could pospone (ndefnely) whou declarng ex. However, snce we sudy exchanges for whch we desgn well-defned exchange proocol he pares can rea ohers ou-of-proocol acons as ex acons. 3 When we llusrae mechansms n hs paper, for smplcy of drawng, we draw one verex o represen all he saes ha have he same em allocaon (bu whch may have dfferen cumulaed coss).

3 1. Sequenaly. For any gven sae of he exchange, he SEM specfes exacly one agen ha s supposed o make ransfers. We make hs sequenaly assumpon for convenence only. Allowng for parallel acons does no affec safey because f an agen s safe n a parallel acon, would have o be safe even f he oher pary dd no complee s poron of he parallel acon. 2. Possessons close. If an agen possesses an em, he oher agen has no possesson of ha em. There s no exogenous subsdy o he exchange. Formally, em P a sae s only f ( em P P ) ( em P ). 3. Ex rules. Boh agens can ex a any sae. Exng s cosless n any complee sae s complee. Exng n any oher sae may subec he exng agen o a cos (repuaon los some chance of geng caugh and fnancally penalzed, ec.). 4. Nondecreasng uly. An agen s uly wll no decrease from possessng addonal ems. Formally, f P ' P, ' u ( s ) u ( s ) gven he same coss. An mmedae resul, whch we wll use n several place s ha durng any exchange, no agen can ake acon o mprove s own mmedae uly: Lemma 2.1. I s mpossble o have wo saes s, s +1 of he exchange such ha ( N) ((s s +1 ) M ) (u (s )<u (s +1 )). 3. SEM desgn whou a TTP In hs secon, we sudy he possbly of SEM desgn n an exchange envronmen wh no TTP Resuls for unresrced ems and coss Here we derve general resuls for safe exchange whou a TTP. 4 The resuls are general n ha he exchange may conan any ypes of ems and ex coss can be arbrary. Proposon 3.1. Whou a TTP, here exs exchanges ha canno be mplemened safely n (even weak) DSE. Proof. Consder an exchange of an em k. Whou loss of generaly, le agen make he frs move from he nal sae s. Denoe he resulng sae by s 1. Le (1) v ( λ ) < v ( λ ) f λ < λ, and (2) say em k canno be k k k k ransferred o any allocaon se. The only possble move s o ransfer some amoun of em k o he oher agen s possesson se. Thu from (1) above and Assumpon 2, we ge u (s )>u (s 1 ). Le f be any sraegy conanng he above frs move. Le here be free ex a he nal sae s,andlef and f - be sraeges ha prescrbe ex a s. Whou a TTP, agen becomes he only player once he oher agen exs. Accordng o Lemma 2.1, 1 s, f, f )) u ( s ) < u ( s ) = s, f, f )). Thu f s no a domnan sraegy. Snce any safe exchange pah should conan he frs move, we have ha s mpossble o mplemen he safe exchange n DSE. In fac, we can prove a sronger clam whch would mply Proposon 3.1 (we neverheless presened he proof of 3.1 because s based on a dfferen prncple): 4 The mpossbly of a smlar noon, far exchange, has been suded n a non-gameheorec framework (Pagna and Gaerner 1999). Proposon 3.2. Whou a TTP, here exs exchanges ha canno be mplemened safely n (even weak) SPNE. Proof. Consder an exchange where properes (1) and (2) from he prevous proof hold (a leas for he las em o be delvered), and ex coss are zero. Consder any parcular sae s ha precedes a complee sae s complee. Whou loss of generaly, le N make he las acon o ge o s complee. By properes (1) and (2), u (s )>u (s complee ). Le f F, f F be any sraegy profle whch forms a pah from s o s and ncludes he las move. Le f be a sraegy dencal o f excep ha ex s played a s. Snce here s no defecon co complee s, f, f )) = u ( s ) < u ( s ) s, f, f )). Thus any sraegy profle conanng he las move s no a SPNE. However, a compleon pah has o nclude he las move. Therefore, s mpossble o mplemen he safe exchange n SPNE Resuls for exchanges ncludng one-way ems The resuls above show ha here are exchange sengs where safe exchange s mpossble whou a TTP. In hs secon we sudy n more deal he condons under whch he mpossbly holds. I urns ou ha he exsence of one-way ems o be exchanged plays a key role. A oneway em s an em he can be moved no allocaon se(s). Recall ha an agen ha has an em n s allocaon se canno ransfer he em o s own possesson se. The followng resuls hghlgh he mporance of our nroducon of allocaon ses no he exchange model. Defnon 3.1. An em k s a one-way em f here exss an agen such ha (, N) (A Y) (T (P, λ k, Y)=1). 5 An em s a one-way em also f s worhless o some agen (hs s because he possesson of such an em does no brng s owner any value whle allocang o ohers may ncrease her uly). The nex wo proocols enable safe exchange whou a TTP by consrucng one-way ems n dfferen ways. Proocol 3.1. Con rppng (Jakobsson 1995). Ths proocol uses a crypographc dgal con whch can be rpped no wo halves. A sngle half has no value and once a half con has been spen, canno be spen agan. 6 The exchange proceeds as follows: 1) agen 1 rps a con p and gves he frs half con o agen 2; 2) agen 2 delveres he good g o agen 1; 3) agen 1 gves he oher half of he con o agen 2. In hs proocol, he con serves as a oneway em. In he fgure, denoes an nacve agen. P 1:{p} A 1:{} P 2:{g} A 2:{} P 1:{} A 1:{p} P 2:{g} A 2:{p} Ex Ex Ex P 1:{g} A 1:{p} P 2:{} A 2:{p} P 1:{g} A 1:{} P 2:{p} A 2:{} 5 An em may be n several agens allocaons ses smulaneously, and n some oher agen s possesson se. 6 (Jakobsson 1995) proposed a scheme whch allows a buyer o gve a seller he hash value of a dgal con verfable o a bank. The orgnal con canno be spen (agan) afer he seller has gven he hash value o he bank.

4 A weakness here s ha agen 1 s ndfferen beween delverng he second half of he con and no. Hence, he safe exchange s only a weak SPNE. The con rppng proocol requres a specal crypographc dgal con. Here, we nroduce a new proocol whch s applcable more broadly because enables safe exchange even f money s no one of he ems o be exchanged. Proocol 3.2. Repuaon lockng. Ths proocol uses repuaon as a one-way em! Suppose here s a publc onlne repuaon daabase. In our proocol, an agen s repuaon record can be encryped by oher agens wh he agen s permsson, and only he agens ha encryped can read/decryp. We call hs repuaon lockng because he agen does no have an observable repuaon whle s encryped. The proocol proceeds as follows: 1) agen 2 perms agen 1 o lock s repuaon R; 2) agen 1 gves agen 2 paymen p v 2 (R); 3) agen 2 sends good g o agen 1; 4) agen 1 unlocks he repuaon. The repuaon R s a one-way em whch can be moved no agen 1 s allocaon se. Ths proocol mplemens he safe exchange n SPNE. However, as n con rppng, s only a weak mplemenaon because agen 1 s ndfferen beween cooperaon and ex a sep 4. P 1:{p} A 1:{} P 2:{g,R} A 2:{} P 1:{p} A 1:{R} P 2:{g} A 2:{} P 1:{} A 1:{R} P 2:{p,g} A 2:{} P 1:{g} A 1:{R} P 2:{p} A 2:{} Ex Ex Ex Ex P 1:{g} A 1:{} P 2:{p,R} A 2:{} The wo proocols above show ha one can enable safe exchange by creavely consrucng one-way ems. (Ths s no always he case, for example, f he one-way ems are oo mnor compared o he oher ems). Ineresngly, creaon of one-way ems s he only approach ha works n anonymous commerce where here s no rused hrd pary and no coss o premaure ex from he exchange! Proposon 3.3. Wh zero ex coss and no TTP, an exchange can be mplemened n weak SPNE only f here exss a one-way em. Proof. Suppose here exss an SEM for an exchange ncludng no one-way ems. By he defnon of a one-way em, f an em s no a one-way em, sasfes properes (1) and (2) from he proof of Proposon 3.1. Therefore, he proof of Proposon 3.2 apples. I urns ou ha he weakness of he con rppng and repuaon lockng proocols s nevable: Proposon 3.4. Wh zero ex coss and no TTP, no exchange can be mplemened n src SPNE (even wh one-way ems). Proof. If agen exs a sae s, (any parcular sae before a complee sae) whou a TTP, he oher agen (say ) becomes he only player. Accordng o Lemma 2.1, canno mprove s own uly, so exng becomes one of s bes response acons. In ha case, agen obans s fnal uly already a s. Therefore, exng a s becomes one of s bes response acons. Therefore, here exss a connuaon equlbrum a s where boh ex, and he exchange does no complee Defecon cos (cos of premaure ex) Proposon 3.4 showed ha wh free ex and no TTP, weak SPNE s he bes one can acheve. Proposon 3.3 showed ha even weak mplemenaon requres he exsence of one-way ems. However, f here are coss o premaure ex (defecon cos) such as loss of repuaon, chance of geng caugh and punshed, loss of fuure busnes ec. hen safe exchange can be acheved more broadly (Sandholm and Lesser 1995) (Sandholm 1996) (Sandholm and Ferrandon 2). We model hs by an ex cos d ( s ) ha may depend on he agen and he exchange sae s. We allow for he possbly ha he ex cos s zero n some saes (for example n he nal sae n cases where parcpaon n he exchange s volunary). Proposon 3.5. Whou a TTP, an exchange can be mplemened safely n SPNE f and only f here exss a pah s s s T (where s T s a complee sae) such ha T u( s ) u( s ) d( s ) for all [,]. The exchange can be mplemened safely n DSE f and only f q u ( s ) mn u ( s ) d ( s ) for all [,]onsucha q=, + 1,..., T pah. In eher case, f each nequaly s src, he equlbrum s src. Oherwse he equlbrum s weak. Proof. If par for SPNE: Consruc an exchange mechansm as follows. The players should follow he compleon pah. If eher agen devaes from he pah, boh agens ex, and he devaor has o pay he ex cos d ( s ). For any agen, le f be a sraegy ha follows he pah and f be a sraegy ha defecs a s. So, complee s, f, f )) = u ( s ) u ( s ) d ( s ) s, f, f )). = Thus ( f, f ) s a SPNE. Only f par for SPNE: Le s s 1 s s complee be any parcular safe exchange pah for an SPNE. Thu complee u ( s ) = s, f, f )) s, f, f )) = u ( s ) d ( s ) for all saes on he pah and for boh agens. q If par for DSE: If u ( s ) mn u ( s ) d ( s ) for q=, + 1,..., T all [,], hen agen s beer off by followng he exchange no maer wha he oher agen does. Only f par for DSE: If here exss a sae s where he nequaly does no hold for agen, hen f he oher agen s sraegy s o defec a sae s m sasfyng m u ( s ) d ( s ) > u ( s ), agen s beer off by exng a s. Thus followng he compleon pah s no a domnan sraegy for. 4. SEM desgn wh an onlne TTP A smple way of achevng safe exchange s o use a TTP. A TTP faclaes exchange by helpng agens allocae ems and by punshng a defecor. We assume ha any em from eher agen s possesson se can be moved o he TTP s allocaon se and vce versa. We also assume ha

5 he TTP can observe he sae of he exchange. TTP-based safe exchange mechansms have been explored n compuer secury (Buyan and Hubaux 21). Two ypes of TTPs have been proposed: onlne TTPs (Deng e al. 1996) and offlne TTPs (Ba e al. 2) (Bao e al. 1998) (Asokan e al. 1997). An onlne TTP s always nvolved n he exchange whle an offlne TTP only ges nvolved f a defecon has occurred. We dscuss onlne TTPs frs. 7 The exsence of onlne TTP makes he safe exchange mplemenable n DSE: Proocol 4.1. Onlne TTP-based SE. Each agen gves s ems o be exchanged o he TTP. If boh agens do h he TTP swaps he ems. Else he TTP reurns he ems. P 1:{1} P 1:{} P 1:{} P 1:{2} A TTP:{} A TTP:{1} A TTP:{1,2} A TTP:{} P 2:{2} P 2:{2} P 2:{} P 2:{1} Ex Ex P 1:{1} A TTP:{} P 2:{2} If he onlne TTP requres an escrow fee (as mos of he curren ones do), we say ha he escrow fee s pad before he exchange begns. Wh hs undersandng we have: Proposon 4.1. Wh an onlne TTP, f (1) each agen s uly of he complee sae s greaer han ha of he nal sae, and (2) for each sae on he exchange pah and for each agen, he agen s sum of acon coss (for ransfer acons and wa acons) from ha sae o he complee sae s less han he agen s ex cos a ha sae, hen Proocol 4.1 mplemens he exchange safely n src DSE. Theproofsnohard,andweomdueo lmed space. 5. SEM desgn wh an offlne TTP Wh no TTP, he safey of he exchange can usually be assured only n weak SPNE. Wh an onlne TTP, domnan sraegy mplemenaon s achevable, bu he TTP s closely nvolved, ncurs operang expense and hus usually charges an escrow fee even f he exchange complees whou problems. A radeoff beween hese wo exremes s o use an offlne TTP whch does no parcpae n he exchange as long as execues correcly, bu ges nvolved f eher agen exs premaurely. Offlne TTPs have been praccally mplemened (such as ebay s feedback sysem) and heorecally nvesgaed (Masubara and Yokoo 2) (Asokan e al. 1997) General resuls Here we nvesgae wha can be acheved wh an offlne TTP when here are no lms on em ypes and ex coss. If he TTP does no have (and canno oban) allocaon rghs on he defecor s ems afer defecon, he TTP can do no more han punsh he defecor. Ths s equvalen o mposng an ex co so Proposon 3.5 suffces o characerze wha s (m)possble n hs case. 7 (Kechpel and Garca-Molna 1996) suded, n a non-game-heorec way, how dfferen pars of an exchange should be sequenced when here are several onlne TTP bu each TTP s only rused by some subse of he pares. So, wha can be acheved wh an offlne TTP depends on how much penaly he TTP can mpose on a defecor. Punshng under dfferen forms of nformaon asymmery s dffcul (Fredman and Resnck 1998) (Masubara and Yokoo 2), for example due o cheap pseudonyms on he Inerne, dfferen laws n dfferen counre ec. Therefore, s mporan o sudy wha can be acheved when he TTP has oo lle power o punsh defecors. Tha s wha we address n he res of hs secon Revocable and relnqushable ems When here s no relable penaly for premaure ex (defecon cos s dffcul o esmae or he TTP has nadequae power o punsh), a TTP ha has he ably o reallocae he defecors possessons could faclae safe exchange. Unforunaely, an offlne TTP only ges nvolved afer he defecon a whch me has no conrol on any ems (s allocaon se s empy). In hs case, he acve agen (he defecor s nacve) s he only one ha can gve he TTP such reallocaon rghs on (some of) he defecor s ems. Ths furher requres ha he acve agen have conrol of he ems. In he language of our exchange model, such ems are n one agen s allocaon se and he oher agen s possesson/allocaon se a he same me. We now analyze such specal ems ha an offlne TTP can use o faclae safe exchange. We call an em revocable f s possessor can ransfer o he oher agen s allocaon or possesson se whle ransferrng no s own allocaon se (hus keepng he rgh o ransfer he em from he oher agen o he TTP). We call an em relnqushable f s possessor can keep n he possesson se whle ransferrng no he oher agen s allocaon se (hus gvng he oher agen he rgh o ransfer he em from he former agen o he TTP). 8 Smlar conceps have been dscussed n he conex of a parcular exchange proocol for exchangng wo ems (Asokan e al. 1997). Defnon 5.1. Denoe by x he possesson se or allocaon se of agen, and denoe he oher agen by. An em k s revocable o agen f T ( P, λ,{ A, x }) = 1. 9 k (To handle he rval case where an em s no of srcly posve value o s orgnal possessor, we also call such ems revocable.) An em k s relnqushable f here exss an agen such ha T ( P, λ,{ x, A }) = 1. k The followng proocols use hese ypes of specal ems. Proocol 5.1. Cred card paymen. A cred card paymen can be vewed as a revocable em. Agen 1 pays agen 2 a paymen p for good g wh a cred card. A ha pon, p A 1 P 2. If agen 2 does no delver g, agen 1 sends a reques o he offlne TTP (cred card company). Ths corresponds o ransferrng p from A 1 o A TTP. The company hen revokes he paymen (ransfers p from A TTP 8 Recall ha by he defnon of allocaon se, he oher agen can ransfer he em o he TTP s allocaon se or he former agen s allocaon se, bu no no s own possesson se. 9 Recall ha agen canno gve ems ha are n s allocaon se no s possesson se, and ha agen can gve ems n s allocaon se o oher pare parcularly he TTP s allocaon se. Then he TTP can ransfer he em o agen s possesson se.

6 and from P 2 o P 1 ). Wh zero acon cos he safe exchange pah s followed n DSE. P 1:{p} A 1:{} P 2:{g} Ex P 1:{p} A 1:{} A TTP:{} P 2:{g} P 1:{} A 1:{p} P 2:{g, p} Ex P 1:{} A 1:{} A TTP:{p} P 2:{g, p} P 1:{g} A 1:{} P 2:{p} Proocol 5.2. Escrowed sgnaure (Bao e al. 1998). The proocol s for exchangng sgnaures on a conrac. A dgal sgnaure can be convered no a relnqushable em. The proocol proceeds as follows: 1) agen 1 encryps s dgal sgnaure (σ 1 ) wh he publc key of an offlne TTP and hen sends along wh a zero knowledge proof (Bao e al. 1998) o agen 2; 2) agen 2 checks ha he daa s an encryped verson of agen 1 s sgnaure, and gves s sgnaure (σ 2 ) o agen 1; 3) agen 1 sends agen 2 σ 1. If agen 1 nsead exs a sep 3, agen 2 sends he daa fromsep2ohettpfordecrypon,andhettpwll gve he decryped sgnaure of agen 1 o agen 2. Wh zero acon cos he safe exchange pah s followed n DSE. 1 P 1:{σ 1} P 2:{σ 2}A 2:{} Ex P 1:{σ 1} P 2:{σ 2}A 2:{} P 1:{σ 1} P 2:{σ 2}A 2:{σ 1} Ex P 1:{σ 1, σ 2} P 2:{} A 2:{σ 1} Ex P 1:{σ 1, σ 2} A TTP:{σ 1} P 2:{} A 2:{} P 1:{σ 2} P 2:{σ 1} A 2:{} I urns ou ha revocable or relnqushable ems are n a sense necessary for safe exchange! Proposon 5.1. Le here be only an offlne TTP and zero acon coss (for ransfer, wa, and ex acons). Le he ems o be exchanged nclude no revocable or relnqushable ems. Now, he exchange canno be mplemened n src SPNE or even n weak DSE. 11 Proof. Src SPNE: Afer a defecon, he defecor s nacve, and he offlne TTP s allocaon se s empy. So, he only way he TTP can affec a defecor s possesson se s f he acve agen can pu ems ha are n he defecor s possesson se no he TTP s allocaon se (hs requres he ems o be n he acve agen s allocaon se). 12 By he defnons of revocable/relnqushable em such a sae can be reached only f revocable or relnqushable ems exs. If, on he oher hand, he TTP canno affec he defecor s possesson se, hen Proposon 3.4 apples. Weak DSE: Suppose here exss a compleon pah mplemened n weak DSE. By he assumpon of 1 Recall we assume ha he TTP can observe saes so ha an agen canno ge he sgnaure decryped whou gvng s own sgnaure o he oher. The proocol works even f he TTP does no observe saes: n hs case, each agen needs o gve s own sgnaure o he TTP (whch wll pass o he oher agen) o ge he oher s sgnaure decryped. 11 As shown earler n he paper, a weak SPNE can exs f here exss a one-way em. 12 Recall ha possessons close, so he ems n he defecor s possesson se canno be n he acve agen s possesson se. Also, by he defnon of an allocaon se, he acve agen canno move ems from s allocaon se o s own possesson se. sequenal acons and he fac ha evenually all ems are exchanged, here has o be some sae where one agen (say A) has ransferred an em I no he oher agen s (say B) possesson se before recevng any ems no s own possesson se. A ha sae, because here are no revocable or relnqushable em I canno be n anyone s allocaon se. If B now defec A wll have receved nohng, and wll have los I whch s of value o A. Therefore, A would have been srcly beer off exng n he nal sae. Thus A s sraegy of followng he safe exchange s no a weak domnan sraegy. Conradcon Transfer coss and offlne TTP s escrow fee In many seng especally when exchangng physcal good here s a cos assocaed wh each ransfer acon. Anoher ype of cos ha s assocaed wh a ransfer acon s he fee ha an agen has o pay an offlne TTP when he agen asks he offlne TTP o carry ou a ransfer acon agans a defecor. (In he case of onlne TTP he escrow fee had no sraegc effecs because had o be pad anyway, bu n he offlne TTP case has sraegc effecs because has o be pad only f he TTP s help s used). Proposon 5.2. Wh an offlne TTP and no relnqushable em no exchange can be safely mplemened n weak DSE f he compleon pah conans any posve ransfer cos. Proof. Suppose here exss a compleon pah mplemened n weak DSE. By he assumpon of sequenal acons and he fac ha evenually all ems are exchanged, here has o be some sae where one agen (say A) has ransferred an em no he oher agen s (say B) possesson se before recevng any ems no s own possesson se. A ha sae (say s), because here are no relnqushable em A canno conrol any of B s orgnal em bu may be able o ake back some of he ems gave o B. However, because here was a posve ransfer co A would have been srcly beer off exng n he nal sae. Thus A s sraegy of followng he safe exchange s no a weak domnan sraegy. Conradcon. Proposon 5.3. Wh a posve offlne TTP fee and no relnqushable em no exchange can be safely mplemened n weak DSE. Proof. The proof s analogous o ha of Proposon 5.2. Proposon 5.4. Wh no revocable em an exchange can be safely mplemened n weak DSE only f for each agen, he offlne TTP s escrow fee plus s sum of ransfer coss on he compleon pah s a mos complee u ( s ) u ( s ). The proof s no hard, and we om due o lmed space. 6. Selecng a safe exchange mechansm In he real world, dfferen ypes of SEMs co-exs. For example, on he Inerne, onlne TTPs such as TradeSafe ex offlne TTPs such as he Beer Busness Bureau ex and obvously drec exchange s possble (and safe exchange planners for ha exs (Sandholm and Ferrandon 2)). Now, whch SEM should he agens selec? For a gven exchange, dfferen SEMs have dfferen coss. Onlne TTPs have an escrow fee. Drec exchange and

7 offlne TTPs may have varous coss: some requre agens o expose her fxed enes (e.g., cred based exchange) hus ncurrng prvacy cos; some need nensve compuaon (e.g., escrowed sgnaure); almos all of hem expose he agens o rsks (rraonal play by he oher pary, accden ec.). Furhermore, agens may have dfferen coss for a gven SEM, and he agens coss are generally only prvaely known by he agen. We presen a mechansm ha wll selec he bes SEM and movaes he agens o ruhfully repor her coss. We presen as choosng beween an onlne TTP based SEM (TSEM) and anoher SEM (ASEM). We assume ha 1) he onlne TTP s escrow fee c s commonly known and he agens have an agreemen o share n proporons d 1 and d 2 (where d 1 +d 2 =1), and 2) agens prefer exchange hrough eher SEM o no exchange a all. Proocol 6.1. SEM selecon. Each agen reveals o he oher whch SEM prefers. If boh agens prefer he same SEM, ha SEM s chosen. Oherwse, he agens resolve he conflc as follows: 1) each agen ransfers a paymen c (he oal amoun of he escrow fee) and reveals s ASEM cos ĉ o he onlne TTP. (If he oher agen does no subm s paymen and cos nformaon, he TTP reurns he former agen s paymen.); 2a) If c ˆ 1 + cˆ 2 < c,asems chosen, he TTP reurns a paymen c- c ˆ + d c o he agen who preferred ASEM, and reurns he enre amoun c o he oher agen (who preferred TSEM). So, he TTP ends up keepng a nonnegave amoun, whch we consder s fee for resolvng he SEM selecon conflc. 2b) If cˆ 1 + cˆ 2 c, TSEM s chosen, he TTP reurns a paymen cˆ o he agen ha preferred TSEM, and reurns d c o he oher agen -. A hs pon, he TTP has goen pad he escrow fee plus a nonnegave conflc resoluon fee. Proposon 6.1. Proocol 6.1 s ex pos ndvdually raonal, weak DSE ncenve compable, and effcen (ha he cheapes SEM s chosen). Proof. Skech. The mechansm s an applcaon of he Clarke ax vong scheme (Clark 1971), whch has hese properes. 7. Conclusons and fuure research Safe exchange s a key problem n mulagen sysem especally n elecronc ransacons. A large number of dfferen approaches have been proposed for safe exchange. In hs paper we presened a unfed framework for modelng safe exchange mechansms. Our framework capures he dsparae earler approache as well as new SEMs (e.g., repuaon lockng). Beng an overarchng framework, also allowed us o sudy wha s nherenly possble and mpossble n safe exchange. We showed wha role specal ypes of ems play, and derved quanave condons on defecon coss. The followng able summarzes he qualave resuls a a hgh level. No TTP Offlne TTP General resuls No weak SPNE. Suffcen punshmen weak/src SPNE/DSE. Specal ems No src SPNE. One-way em weak SPNE (Revocable or relnqushable em) src SPNE. Wh coss Suffcenexcoss weak/src SPNE/DSE. No relnqushable em: (ransfer cos or escrow fee) no weak DSE No revocable em: weak DSE (low escrow fee & low ransfer cos) Onlne (Suffcen ex coss & low ransfer coss) src DSE TTP Fnally, we presened an ncenve-compable mechansm for selecng he bes SEM when he agens do no know each ohers coss for he dfferen SEMs. Fuure work ncludes exendng he resuls o exchanges wh more han 2 agen and o sengs where he agens and/or he TTP are unceran abou he exchange sae. Acknowledgemens We hank Kark Hosanagar for llumnang dscussons a he early sage of hs work. We also hank Ramayya Krshnan and Pradeep Khosla for her encouragemen. Sandholm s suppored by NSF CAREER Award IRI , and NSF grans IIS-98994, ITR IIS-81246, and ITR IIS Wang s suppored by NSF gran IIS , he DARPA OASIS program, and he PASIS proec a CMU. References Asokan, N; Schuner, M; and Wadner, M Opmsc proocols for far exchange. ACM Compuer & Communcaon Secury Conerence. p Ba, S; Whnson, A. B.; Zhang, H. 2. The dynamcs of he elecronc marke: an evoluonary game approach. Informaon Sysem Froners 2:1, Clarke, E Mul-par prcng of publc goods. Publc Choce, 11: Bao, F; Deng, R; and Mao, W; Effcen and praccal far exchange proocols wh off-lne TTP. IEEE symposum S&P. p Buyan, L; Hubaux, J.P. 2. Toward a formal model of far exchange-a game heorec approach. Inernaonal workshop on ecommerce. Deng, R; Gong, L; Lazar, A; and Wang, W Praccal proocols for cerfed elecronc mal. Journal of Nework & Sysems Managemen 4(3), Fredman, E; Resnck, P The socal cos of cheap pseudonyms. Journal of Economcs and Managemen Sraegy 1(2): Jakobsson, M Rppng cons for a far exchange. EUROCRYPT, p Kechpel, S. P; Garca-Molna, H Makng Trus Explc n Dsrbued Commerce Transacons. Inernaonal Conference on Dsrbued Compung Sysem p Masubara, S; Yokoo, M. 2. Defecon-free exchange mechansm for nformaon goods. ICMAS, p Naonal Consumers League New NCL survey shows consumers are boh exced and confused abou shoppng onlne, Pagna, H; Gaerner. F On he mpossbly of far exchange whou a rused hrd pary. Darmsad Unversy of Technology, Deparmen of Compuer Scence echncal repor TUD Sandholm, T Negoaon among Self-Ineresed Compuaonally Lmed Agens. PhD Thess. UMass Amher Compuer Scence Dep. Sandholm, T; Lesser, V Equlbrum analyss of he possbles of uneforced exchange n mulagen sysems. IJCAI, p Sandholm,T; Ferrandon. V. 2. Safe exchange planner. ICMAS. p

MORE ON TVM, "SIX FUNCTIONS OF A DOLLAR", FINANCIAL MECHANICS. Copyright 2004, S. Malpezzi

MORE ON TVM, SIX FUNCTIONS OF A DOLLAR, FINANCIAL MECHANICS. Copyright 2004, S. Malpezzi MORE ON VM, "SIX FUNCIONS OF A DOLLAR", FINANCIAL MECHANICS Copyrgh 2004, S. Malpezz I wan everyone o be very clear on boh he "rees" (our basc fnancal funcons) and he "fores" (he dea of he cash flow model).

More information

Capacity Planning. Operations Planning

Capacity Planning. Operations Planning Operaons Plannng Capacy Plannng Sales and Operaons Plannng Forecasng Capacy plannng Invenory opmzaon How much capacy assgned o each producon un? Realsc capacy esmaes Sraegc level Moderaely long me horzon

More information

How To Calculate Backup From A Backup From An Oal To A Daa

How To Calculate Backup From A Backup From An Oal To A Daa 6 IJCSNS Inernaonal Journal of Compuer Scence and Nework Secury, VOL.4 No.7, July 04 Mahemacal Model of Daa Backup and Recovery Karel Burda The Faculy of Elecrcal Engneerng and Communcaon Brno Unversy

More information

Methodology of the CBOE S&P 500 PutWrite Index (PUT SM ) (with supplemental information regarding the CBOE S&P 500 PutWrite T-W Index (PWT SM ))

Methodology of the CBOE S&P 500 PutWrite Index (PUT SM ) (with supplemental information regarding the CBOE S&P 500 PutWrite T-W Index (PWT SM )) ehodology of he CBOE S&P 500 PuWre Index (PUT S ) (wh supplemenal nformaon regardng he CBOE S&P 500 PuWre T-W Index (PWT S )) The CBOE S&P 500 PuWre Index (cker symbol PUT ) racks he value of a passve

More information

The Rules of the Settlement Guarantee Fund. 1. These Rules, hereinafter referred to as "the Rules", define the procedures for the formation

The Rules of the Settlement Guarantee Fund. 1. These Rules, hereinafter referred to as the Rules, define the procedures for the formation Vald as of May 31, 2010 The Rules of he Selemen Guaranee Fund 1 1. These Rules, herenafer referred o as "he Rules", defne he procedures for he formaon and use of he Selemen Guaranee Fund, as defned n Arcle

More information

Network Effects on Standard Software Markets: A Simulation Model to examine Pricing Strategies

Network Effects on Standard Software Markets: A Simulation Model to examine Pricing Strategies Nework Effecs on Sandard Sofware Markes Page Nework Effecs on Sandard Sofware Markes: A Smulaon Model o examne Prcng Sraeges Peer Buxmann Absrac Ths paper examnes sraeges of sandard sofware vendors, n

More information

Lecture 40 Induction. Review Inductors Self-induction RL circuits Energy stored in a Magnetic Field

Lecture 40 Induction. Review Inductors Self-induction RL circuits Energy stored in a Magnetic Field ecure 4 nducon evew nducors Self-nducon crcus nergy sored n a Magnec Feld 1 evew nducon end nergy Transfers mf Bv Mechancal energy ransform n elecrc and hen n hermal energy P Fv B v evew eformulaon of

More information

Y2K* Stephanie Schmitt-Grohé. Rutgers Uni ersity, 75 Hamilton Street, New Brunswick, New Jersey 08901 E-mail: grohe@econ.rutgers.edu.

Y2K* Stephanie Schmitt-Grohé. Rutgers Uni ersity, 75 Hamilton Street, New Brunswick, New Jersey 08901 E-mail: grohe@econ.rutgers.edu. Revew of Economc Dynamcs 2, 850856 Ž 1999. Arcle ID redy.1999.0065, avalable onlne a hp:www.dealbrary.com on Y2K* Sephane Schm-Grohé Rugers Unersy, 75 Hamlon Sree, New Brunswc, New Jersey 08901 E-mal:

More information

An Architecture to Support Distributed Data Mining Services in E-Commerce Environments

An Architecture to Support Distributed Data Mining Services in E-Commerce Environments An Archecure o Suppor Dsrbued Daa Mnng Servces n E-Commerce Envronmens S. Krshnaswamy 1, A. Zaslavsky 1, S.W. Loke 2 School of Compuer Scence & Sofware Engneerng, Monash Unversy 1 900 Dandenong Road, Caulfeld

More information

Spline. Computer Graphics. B-splines. B-Splines (for basis splines) Generating a curve. Basis Functions. Lecture 14 Curves and Surfaces II

Spline. Computer Graphics. B-splines. B-Splines (for basis splines) Generating a curve. Basis Functions. Lecture 14 Curves and Surfaces II Lecure 4 Curves and Surfaces II Splne A long flexble srps of meal used by drafspersons o lay ou he surfaces of arplanes, cars and shps Ducks weghs aached o he splnes were used o pull he splne n dfferen

More information

The Virtual Machine Resource Allocation based on Service Features in Cloud Computing Environment

The Virtual Machine Resource Allocation based on Service Features in Cloud Computing Environment Send Orders for Reprns o reprns@benhamscence.ae The Open Cybernecs & Sysemcs Journal, 2015, 9, 639-647 639 Open Access The Vrual Machne Resource Allocaon based on Servce Feaures n Cloud Compung Envronmen

More information

INTERNATIONAL JOURNAL OF STRATEGIC MANAGEMENT

INTERNATIONAL JOURNAL OF STRATEGIC MANAGEMENT IJSM, Volume, Number, 0 ISSN: 555-4 INTERNATIONAL JOURNAL OF STRATEGIC MANAGEMENT SPONSORED BY: Angelo Sae Unversy San Angelo, Texas, USA www.angelo.edu Managng Edors: Professor Alan S. Khade, Ph.D. Calforna

More information

MODEL-BASED APPROACH TO CHARACTERIZATION OF DIFFUSION PROCESSES VIA DISTRIBUTED CONTROL OF ACTUATED SENSOR NETWORKS

MODEL-BASED APPROACH TO CHARACTERIZATION OF DIFFUSION PROCESSES VIA DISTRIBUTED CONTROL OF ACTUATED SENSOR NETWORKS MODEL-BASED APPROACH TO CHARACTERIZATION OF DIFFUSION PROCESSES IA DISTRIBUTED CONTROL OF ACTUATED SENSOR NETWORKS Kevn L. Moore and YangQuan Chen Cener for Self-Organzng and Inellgen Sysems Uah Sae Unversy

More information

Levy-Grant-Schemes in Vocational Education

Levy-Grant-Schemes in Vocational Education Levy-Gran-Schemes n Vocaonal Educaon Sefan Bornemann Munch Graduae School of Economcs Inernaonal Educaonal Economcs Conference Taru, Augus 26h, 2005 Sefan Bornemann / MGSE Srucure Movaon and Objecve Leraure

More information

Pedro M. Castro Iiro Harjunkoski Ignacio E. Grossmann. Lisbon, Portugal Ladenburg, Germany Pittsburgh, USA

Pedro M. Castro Iiro Harjunkoski Ignacio E. Grossmann. Lisbon, Portugal Ladenburg, Germany Pittsburgh, USA Pedro M. Casro Iro Harjunkosk Ignaco E. Grossmann Lsbon Porugal Ladenburg Germany Psburgh USA 1 Process operaons are ofen subjec o energy consrans Heang and coolng ules elecrcal power Avalably Prce Challengng

More information

Case Study on Web Service Composition Based on Multi-Agent System

Case Study on Web Service Composition Based on Multi-Agent System 900 JOURNAL OF SOFTWARE, VOL. 8, NO. 4, APRIL 2013 Case Sudy on Web Servce Composon Based on Mul-Agen Sysem Shanlang Pan Deparmen of Compuer Scence and Technology, Nngbo Unversy, Chna PanShanLang@gmal.com

More information

Linear Extension Cube Attack on Stream Ciphers Abstract: Keywords: 1. Introduction

Linear Extension Cube Attack on Stream Ciphers Abstract: Keywords: 1. Introduction Lnear Exenson Cube Aack on Sream Cphers Lren Dng Yongjuan Wang Zhufeng L (Language Engneerng Deparmen, Luo yang Unversy for Foregn Language, Luo yang cy, He nan Provnce, 47003, P. R. Chna) Absrac: Basng

More information

CLoud computing has recently emerged as a new

CLoud computing has recently emerged as a new 1 A Framework of Prce Bddng Confguraons for Resource Usage n Cloud Compung Kenl L, Member, IEEE, Chubo Lu, Keqn L, Fellow, IEEE, and Alber Y. Zomaya, Fellow, IEEE Absrac In hs paper, we focus on prce bddng

More information

Insurance. By Mark Dorfman, Alexander Kling, and Jochen Russ. Abstract

Insurance. By Mark Dorfman, Alexander Kling, and Jochen Russ. Abstract he Impac Of Deflaon On Insurance Companes Offerng Parcpang fe Insurance y Mar Dorfman, lexander Klng, and Jochen Russ bsrac We presen a smple model n whch he mpac of a deflaonary economy on lfe nsurers

More information

Revision: June 12, 2010 215 E Main Suite D Pullman, WA 99163 (509) 334 6306 Voice and Fax

Revision: June 12, 2010 215 E Main Suite D Pullman, WA 99163 (509) 334 6306 Voice and Fax .3: Inucors Reson: June, 5 E Man Sue D Pullman, WA 9963 59 334 636 Voce an Fax Oerew We connue our suy of energy sorage elemens wh a scusson of nucors. Inucors, lke ressors an capacors, are passe wo-ermnal

More information

International Journal of Mathematical Archive-7(5), 2016, 193-198 Available online through www.ijma.info ISSN 2229 5046

International Journal of Mathematical Archive-7(5), 2016, 193-198 Available online through www.ijma.info ISSN 2229 5046 Inernaonal Journal of Mahemacal rchve-75), 06, 9-98 valable onlne hrough wwwjmanfo ISSN 9 506 NOTE ON FUZZY WEKLY OMPLETELY PRIME - IDELS IN TERNRY SEMIGROUPS U NGI REDDY *, Dr G SHOBHLTH Research scholar,

More information

PerfCenter: A Methodology and Tool for Performance Analysis of Application Hosting Centers

PerfCenter: A Methodology and Tool for Performance Analysis of Application Hosting Centers PerfCener: A Mehodology and Tool for Performance Analyss of Applcaon Hosng Ceners Rukma P. Verlekar, Varsha Ape, Prakhar Goyal, Bhavsh Aggarwal Dep. of Compuer Scence and Engneerng Indan Insue of Technology

More information

Genetic Algorithm with Range Selection Mechanism for Dynamic Multiservice Load Balancing in Cloud-Based Multimedia System

Genetic Algorithm with Range Selection Mechanism for Dynamic Multiservice Load Balancing in Cloud-Based Multimedia System ISSN : 2347-8446 (Onlne) Inernaonal Journal of Advanced Research n Genec Algorhm wh Range Selecon Mechansm for Dynamc Mulservce Load Balancng n Cloud-Based Mulmeda Sysem I Mchael Sadgun Rao Kona, II K.Purushoama

More information

HEURISTIC ALGORITHM FOR SINGLE RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM BASED ON THE DYNAMIC PROGRAMMING

HEURISTIC ALGORITHM FOR SINGLE RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM BASED ON THE DYNAMIC PROGRAMMING Yugoslav Journal o Operaons Research Volume 19 (2009) Number 2, 281-298 DOI:10.2298/YUJOR0902281S HEURISTIC ALGORITHM FOR SINGLE RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM BASED ON THE DYNAMIC PROGRAMMING

More information

Ground rules. Guide to the calculation methods of the FTSE Actuaries UK Gilts Index Series v1.9

Ground rules. Guide to the calculation methods of the FTSE Actuaries UK Gilts Index Series v1.9 Ground rules Gude o he calculaon mehods of he FTSE Acuares UK Gls Index Seres v1.9 fserussell.com Ocober 2015 Conens 1.0 Inroducon... 4 1.1 Scope... 4 1.2 FTSE Russell... 5 1.3 Overvew of he calculaons...

More information

Proceedings of the 2008 Winter Simulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds.

Proceedings of the 2008 Winter Simulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. Proceedngs of he 008 Wner Smulaon Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. DEMAND FORECAST OF SEMICONDUCTOR PRODUCTS BASED ON TECHNOLOGY DIFFUSION Chen-Fu Chen,

More information

Boosting for Learning Multiple Classes with Imbalanced Class Distribution

Boosting for Learning Multiple Classes with Imbalanced Class Distribution Boosng for Learnng Mulple Classes wh Imbalanced Class Dsrbuon Yanmn Sun Deparmen of Elecrcal and Compuer Engneerng Unversy of Waerloo Waerloo, Onaro, Canada y8sun@engmal.uwaerloo.ca Mohamed S. Kamel Deparmen

More information

Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C.

Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Fnance and Economcs Dscusson Seres Dvsons of Research & Sascs and Moneary Affars Federal Reserve Board, Washngon, D.C. Prcng Counerpary Rs a he Trade Level and CVA Allocaons Mchael Pyhn and Dan Rosen 200-0

More information

Cooperative Distributed Scheduling for Storage Devices in Microgrids using Dynamic KKT Multipliers and Consensus Networks

Cooperative Distributed Scheduling for Storage Devices in Microgrids using Dynamic KKT Multipliers and Consensus Networks Cooperave Dsrbued Schedulng for Sorage Devces n Mcrogrds usng Dynamc KK Mulplers and Consensus Newors Navd Rahbar-Asr Yuan Zhang Mo-Yuen Chow Deparmen of Elecrcal and Compuer Engneerng Norh Carolna Sae

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

Event Based Project Scheduling Using Optimized Ant Colony Algorithm Vidya Sagar Ponnam #1, Dr.N.Geethanjali #2

Event Based Project Scheduling Using Optimized Ant Colony Algorithm Vidya Sagar Ponnam #1, Dr.N.Geethanjali #2 Inernaonal Journal of Compuer Trends and Technology (IJCTT) Volume 18 Number 6 Dec 2014 Even Based Projec Schedulng Usng Opmzed An Colony Algorhm Vdya Sagar Ponnam #1, Dr.N.Geehanjal #2 1 Research Scholar,

More information

Attribution Strategies and Return on Keyword Investment in Paid Search Advertising

Attribution Strategies and Return on Keyword Investment in Paid Search Advertising Arbuon Sraeges and Reurn on Keyword Invesmen n Pad Search Adversng by Hongshuang (Alce) L, P. K. Kannan, Sva Vswanahan and Abhshek Pan * December 15, 2015 * Honshuang (Alce) L s Asssan Professor of Markeng,

More information

Index Mathematics Methodology

Index Mathematics Methodology Index Mahemacs Mehodology S&P Dow Jones Indces: Index Mehodology Ocober 2015 Table of Conens Inroducon 4 Dfferen Varees of Indces 4 The Index Dvsor 5 Capalzaon Weghed Indces 6 Defnon 6 Adjusmens o Share

More information

DEPARTMENT OF ECONOMETRICS AND BUSINESS STATISTICS. Exponential Smoothing for Inventory Control: Means and Variances of Lead-Time Demand

DEPARTMENT OF ECONOMETRICS AND BUSINESS STATISTICS. Exponential Smoothing for Inventory Control: Means and Variances of Lead-Time Demand ISSN 440-77X ISBN 0 736 094 X AUSTRALIA DEPARTMENT OF ECONOMETRICS AND BUSINESS STATISTICS Exponenal Smoohng for Invenory Conrol: Means and Varances of Lead-Tme Demand Ralph D. Snyder, Anne B. Koehler,

More information

Selected Financial Formulae. Basic Time Value Formulae PV A FV A. FV Ad

Selected Financial Formulae. Basic Time Value Formulae PV A FV A. FV Ad Basc Tme Value e Fuure Value of a Sngle Sum PV( + Presen Value of a Sngle Sum PV ------------------ ( + Solve for for a Sngle Sum ln ------ PV -------------------- ln( + Solve for for a Sngle Sum ------

More information

RESOLUTION OF THE LINEAR FRACTIONAL GOAL PROGRAMMING PROBLEM

RESOLUTION OF THE LINEAR FRACTIONAL GOAL PROGRAMMING PROBLEM Revsa Elecrónca de Comuncacones y Trabajos de ASEPUMA. Rec@ Volumen Págnas 7 a 40. RESOLUTION OF THE LINEAR FRACTIONAL GOAL PROGRAMMING PROBLEM RAFAEL CABALLERO rafael.caballero@uma.es Unversdad de Málaga

More information

HAND: Highly Available Dynamic Deployment Infrastructure for Globus Toolkit 4

HAND: Highly Available Dynamic Deployment Infrastructure for Globus Toolkit 4 HAND: Hghly Avalable Dynamc Deploymen Infrasrucure for Globus Toolk 4 L Q 1, Ha Jn 1, Ian Foser,3, Jarek Gawor 1 Huazhong Unversy of Scence and Technology, Wuhan, 430074, Chna quck@chnagrd.edu.cn; hjn@hus.edu.cn

More information

Estimating intrinsic currency values

Estimating intrinsic currency values Cung edge Foregn exchange Esmang nrnsc currency values Forex marke praconers consanly alk abou he srenghenng or weakenng of ndvdual currences. In hs arcle, Jan Chen and Paul Dous presen a new mehodology

More information

A robust optimisation approach to project scheduling and resource allocation. Elodie Adida* and Pradnya Joshi

A robust optimisation approach to project scheduling and resource allocation. Elodie Adida* and Pradnya Joshi In. J. Servces Operaons and Informacs, Vol. 4, No. 2, 2009 169 A robus opmsaon approach o projec schedulng and resource allocaon Elode Adda* and Pradnya Josh Deparmen of Mechancal and Indusral Engneerng,

More information

How Much Life Insurance is Enough?

How Much Life Insurance is Enough? How Much Lfe Insurance s Enough? Uly-Based pproach By LJ Rossouw BSTRCT The paper ams o nvesgae how much lfe nsurance proecon cover a uly maxmsng ndvdual should buy. Ths queson s relevan n he nsurance

More information

MULTI-WORKDAY ERGONOMIC WORKFORCE SCHEDULING WITH DAYS OFF

MULTI-WORKDAY ERGONOMIC WORKFORCE SCHEDULING WITH DAYS OFF Proceedngs of he 4h Inernaonal Conference on Engneerng, Projec, and Producon Managemen (EPPM 203) MULTI-WORKDAY ERGONOMIC WORKFORCE SCHEDULING WITH DAYS OFF Tar Raanamanee and Suebsak Nanhavanj School

More information

Anomaly Detection in Network Traffic Using Selected Methods of Time Series Analysis

Anomaly Detection in Network Traffic Using Selected Methods of Time Series Analysis I. J. Compuer Nework and Informaon Secury, 2015, 9, 10-18 Publshed Onlne Augus 2015 n MECS (hp://www.mecs-press.org/) DOI: 10.5815/jcns.2015.09.02 Anomaly Deecon n Nework Traffc Usng Seleced Mehods of

More information

Load Balancing in Internet Using Adaptive Packet Scheduling and Bursty Traffic Splitting

Load Balancing in Internet Using Adaptive Packet Scheduling and Bursty Traffic Splitting 152 IJCSNS Inernaonal Journal of Compuer Scence and Nework Secury, VOL.8 No.1, Ocober 28 Load Balancng n Inerne Usng Adapve Packe Schedulng and Bursy Traffc Splng M. Azah Research Scholar, Anna Unversy,

More information

COASTAL CAROLINA COMMUNITY COLLEGE

COASTAL CAROLINA COMMUNITY COLLEGE 20152016 BULLETI N Foradd onal nf or ma onpl easev s :h p: / / www. coas al car ol na. edu/ academ cs/ webs es/ f r e/ COASTAL CAROLINA COMMUNITY COLLEGE Equal Educaon Opporuny and Equal Employmen Opporuny

More information

The Feedback from Stock Prices to Credit Spreads

The Feedback from Stock Prices to Credit Spreads Appled Fnance Projec Ka Fa Law (Keh) The Feedback from Sock Prces o Cred Spreads Maser n Fnancal Engneerng Program BA 3N Appled Fnance Projec Ka Fa Law (Keh) Appled Fnance Projec Ka Fa Law (Keh). Inroducon

More information

GUIDANCE STATEMENT ON CALCULATION METHODOLOGY

GUIDANCE STATEMENT ON CALCULATION METHODOLOGY GUIDANCE STATEMENT ON CALCULATION METHODOLOGY Adopon Dae: 9/28/0 Effecve Dae: //20 Reroacve Applcaon: No Requred www.gpssandards.org 204 CFA Insue Gudance Saemen on Calculaon Mehodology GIPS GUIDANCE STATEMENT

More information

Optimal Taxation. 1 Warm-Up: The Neoclassical Growth Model with Endogenous Labour Supply. β t u (c t, L t ) max. t=0

Optimal Taxation. 1 Warm-Up: The Neoclassical Growth Model with Endogenous Labour Supply. β t u (c t, L t ) max. t=0 Opmal Taxaon Reference: L&S 3rd edon chaper 16 1 Warm-Up: The Neoclasscal Growh Model wh Endogenous Labour Supply You looked a lle b a hs for Problem Se 3. Sudy planner s problem: max {c,l,,k +1 } =0 β

More information

An Anti-spam Filter Combination Framework for Text-and-Image Emails through Incremental Learning

An Anti-spam Filter Combination Framework for Text-and-Image Emails through Incremental Learning An An-spam Fler Combnaon Framework for Tex-and-Image Emals hrough Incremenal Learnng 1 Byungk Byun, 1 Chn-Hu Lee, 2 Seve Webb, 2 Danesh Iran, and 2 Calon Pu 1 School of Elecrcal & Compuer Engr. Georga

More information

Guidelines and Specification for the Construction and Maintenance of the. NASDAQ OMX Credit SEK Indexes

Guidelines and Specification for the Construction and Maintenance of the. NASDAQ OMX Credit SEK Indexes Gudelnes and Specfcaon for he Consrucon and Manenance of he NASDAQ OMX Cred SEK Indexes Verson as of Aprl 7h 2014 Conens Rules for he Consrucon and Manenance of he NASDAQ OMX Cred SEK Index seres... 3

More information

Social security, education, retirement and growth*

Social security, education, retirement and growth* Hacenda P úblca Espa ñola / Revsa de Econom ía P úblca, 198-(3/2011): 9-36 2011, Insuo de Esudos Fscales Socal secury, educaon, reremen and growh* CRUZ A. ECHEVARR ÍA AMAIA IZA** Unversdad del Pa ís Vasco

More information

12/7/2011. Procedures to be Covered. Time Series Analysis Using Statgraphics Centurion. Time Series Analysis. Example #1 U.S.

12/7/2011. Procedures to be Covered. Time Series Analysis Using Statgraphics Centurion. Time Series Analysis. Example #1 U.S. Tme Seres Analyss Usng Sagraphcs Cenuron Nel W. Polhemus, CTO, SaPon Technologes, Inc. Procedures o be Covered Descrpve Mehods (me sequence plos, auocorrelaon funcons, perodograms) Smoohng Seasonal Decomposon

More information

Monopolistic Competition and Macroeconomic Dynamics

Monopolistic Competition and Macroeconomic Dynamics Monopolsc Compeon and Macroeconomc Dynamcs Pasquale Commendaore, Unversà d Napol Federco II Ingrd Kubn, Venna Unversy of Economcs and Busness Admnsraon Absrac Modern macroeconomc models wh a Keynesan flavor

More information

SPC-based Inventory Control Policy to Improve Supply Chain Dynamics

SPC-based Inventory Control Policy to Improve Supply Chain Dynamics Francesco Cosanno e al. / Inernaonal Journal of Engneerng and Technology (IJET) SPC-based Invenory Conrol Polcy o Improve Supply Chan ynamcs Francesco Cosanno #, Gulo Gravo #, Ahmed Shaban #3,*, Massmo

More information

Using Cellular Automata for Improving KNN Based Spam Filtering

Using Cellular Automata for Improving KNN Based Spam Filtering The Inernaonal Arab Journal of Informaon Technology, Vol. 11, No. 4, July 2014 345 Usng Cellular Auomaa for Improvng NN Based Spam Flerng Faha Bargou, Bouzane Beldjlal, and Baghdad Aman Compuer Scence

More information

Market-Clearing Electricity Prices and Energy Uplift

Market-Clearing Electricity Prices and Energy Uplift Marke-Clearng Elecrcy Prces and Energy Uplf Paul R. Grbk, Wllam W. Hogan, and Susan L. Pope December 31, 2007 Elecrcy marke models requre energy prces for balancng, spo and shor-erm forward ransacons.

More information

A binary powering Schur algorithm for computing primary matrix roots

A binary powering Schur algorithm for computing primary matrix roots Numercal Algorhms manuscr No. (wll be nsered by he edor) A bnary owerng Schur algorhm for comung rmary marx roos Federco Greco Bruno Iannazzo Receved: dae / Acceed: dae Absrac An algorhm for comung rmary

More information

Distributed Load Balancing in a Multiple Server System by Shift-Invariant Protocol Sequences

Distributed Load Balancing in a Multiple Server System by Shift-Invariant Protocol Sequences 03 IEEE Wreess Communcaons and Neorkng Conference (WCNC): NETWORS Dsrbued Load Baancng n a Mupe Server Sysem by Shf-Invaran rooco Sequences Yupeng Zhang and Wng Shng Wong Deparmen of Informaon Engneerng

More information

Chapter 8: Regression with Lagged Explanatory Variables

Chapter 8: Regression with Lagged Explanatory Variables Chaper 8: Regression wih Lagged Explanaory Variables Time series daa: Y for =1,..,T End goal: Regression model relaing a dependen variable o explanaory variables. Wih ime series new issues arise: 1. One

More information

Return Persistence, Risk Dynamics and Momentum Exposures of Equity and Bond Mutual Funds

Return Persistence, Risk Dynamics and Momentum Exposures of Equity and Bond Mutual Funds Reurn Perssence, Rsk Dynamcs and Momenum Exposures of Equy and Bond Muual Funds Joop Hu, Marn Marens, and Therry Pos Ths Verson: 22-2-2008 Absrac To analyze perssence n muual fund performance, s common

More information

Distribution Channel Strategy and Efficiency Performance of the Life insurance. Industry in Taiwan. Abstract

Distribution Channel Strategy and Efficiency Performance of the Life insurance. Industry in Taiwan. Abstract Dsrbuon Channel Sraegy and Effcency Performance of he Lfe nsurance Indusry n Tawan Absrac Changes n regulaons and laws he pas few decades have afeced Tawan s lfe nsurance ndusry and caused many nsurers

More information

COASTAL CAROLINA COMMUNITY COLLEGE

COASTAL CAROLINA COMMUNITY COLLEGE Eme r ge nc ymanage me n BULLETI N20152016 Foradd onal nf or ma on,pl eas ev s h p: c oas al c ar ol na. edu ac adem c s webs es ep COASTAL CAROLINA COMMUNITY COLLEGE Equal Educaon Opporuny and Equal Employmen

More information

OUTPUT, OUTCOME, AND QUALITY ADJUSTMENT IN MEASURING HEALTH AND EDUCATION SERVICES

OUTPUT, OUTCOME, AND QUALITY ADJUSTMENT IN MEASURING HEALTH AND EDUCATION SERVICES bs_bs_banner row_504 257..278 Revew of Income and Wealh Seres 58, Number 2, June 2012 DOI: 10.1111/j.1475-4991.2012.00504.x OUTPUT, OUTCOME, AND QUALITY ADJUSTMENT IN MEASURING HEALTH AND EDUCATION SERVICES

More information

A Background Layer Model for Object Tracking through Occlusion

A Background Layer Model for Object Tracking through Occlusion A Background Layer Model for Obec Trackng hrough Occluson Yue Zhou and Ha Tao Deparmen of Compuer Engneerng Unversy of Calforna, Sana Cruz, CA 95064 {zhou,ao}@soe.ucsc.edu Absrac Moon layer esmaon has

More information

TAX COMPETITION AND BRAIN DRAIN IN THE EUROPEAN UNION MEMBERS

TAX COMPETITION AND BRAIN DRAIN IN THE EUROPEAN UNION MEMBERS Year V, No.7/2008 133 AX COMPEON AND BRAN DRAN N HE EUROPEAN UNON MEMBERS Lec. Raluca DRĂCEA, PhD Lec. Crsan SANCU, PhD Unversy of Craova 1. nroducon he presen paper ams o sudy he correlaon beween he bran

More information

The Incentive Effects of Organizational Forms: Evidence from Florida s Non-Emergency Medicaid Transportation Programs

The Incentive Effects of Organizational Forms: Evidence from Florida s Non-Emergency Medicaid Transportation Programs The Incenve Effecs of Organzaonal Forms: Evdence from Florda s Non-Emergency Medcad Transporaon Programs Chfeng Da* Deparmen of Economcs Souhern Illnos Unversy Carbondale, IL 62901 Davd Denslow Deparmen

More information

The Multi-shift Vehicle Routing Problem with Overtime

The Multi-shift Vehicle Routing Problem with Overtime The Mul-shf Vehcle Roung Problem wh Overme Yngao Ren, Maged Dessouy, and Fernando Ordóñez Danel J. Epsen Deparmen of Indusral and Sysems Engneerng Unversy of Souhern Calforna 3715 McClnoc Ave, Los Angeles,

More information

Currency Exchange Rate Forecasting from News Headlines

Currency Exchange Rate Forecasting from News Headlines Currency Exchange Rae Forecasng from News Headlnes Desh Peramunelleke Raymond K. Wong School of Compuer Scence & Engneerng Unversy of New Souh Wales Sydney, NSW 2052, Ausrala deshp@cse.unsw.edu.au wong@cse.unsw.edu.au

More information

Pricing Rainbow Options

Pricing Rainbow Options Prcng Ranbow Opons Peer Ouwehand, Deparmen of Mahemacs and Appled Mahemacs, Unversy of Cape Town, Souh Afrca E-mal address: peer@mahs.uc.ac.za Graeme Wes, School of Compuaonal & Appled Mahemacs, Unversy

More information

Contract design and insurance fraud: an experimental investigation *

Contract design and insurance fraud: an experimental investigation * Conrac desgn and nsurance fraud: an expermenal nvesgaon * Frauke Lammers and Jörg Schller Absrac Ths paper nvesgaes he mpac of nsurance conrac desgn on he behavor of flng fraudulen clams n an expermenal

More information

Expiration-day effects, settlement mechanism, and market structure: an empirical examination of Taiwan futures exchange

Expiration-day effects, settlement mechanism, and market structure: an empirical examination of Taiwan futures exchange Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 1, 2011 Cha-Cheng Chen (Tawan), Su-Wen Kuo (Tawan), Chn-Sheng Huang (Tawan) Expraon-day effecs, selemen mechansm, and marke srucure: an emprcal

More information

The Sarbanes-Oxley Act and Small Public Companies

The Sarbanes-Oxley Act and Small Public Companies The Sarbanes-Oxley Ac and Small Publc Companes Smry Prakash Randhawa * June 5 h 2009 ABSTRACT Ths sudy consrucs measures of coss as well as benefs of mplemenng Secon 404 for small publc companes. In hs

More information

Performance Center Overview. Performance Center Overview 1

Performance Center Overview. Performance Center Overview 1 Performance Cener Overview Performance Cener Overview 1 ODJFS Performance Cener ce Cener New Performance Cener Model Performance Cener Projec Meeings Performance Cener Execuive Meeings Performance Cener

More information

Fixed Income Attribution. Remco van Eeuwijk, Managing Director Wilshire Associates Incorporated 15 February 2006

Fixed Income Attribution. Remco van Eeuwijk, Managing Director Wilshire Associates Incorporated 15 February 2006 Fxed Incoe Arbuon eco van Eeuwk Managng Drecor Wlshre Assocaes Incorporaed 5 February 2006 Agenda Inroducon Goal of Perforance Arbuon Invesen Processes and Arbuon Mehodologes Facor-based Perforance Arbuon

More information

Preface. Frederick D. Wolf Director, Accounting and Financial Management Division

Preface. Frederick D. Wolf Director, Accounting and Financial Management Division f Preface The hallmark of a professonal audor s a commmen o connued self-developmen. As new and challengng problems and ssues confron us, we mus respond by sharpenng and expandng our echncal experse, analycal

More information

A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting*

A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting* journal of compuer and sysem scences 55, 119139 (1997) arcle no. SS971504 A Decson-heorec Generalzaon of On-Lne Learnng and an Applcaon o Boosng* Yoav Freund and Rober E. Schapre - A6 Labs, 180 Park Avenue,

More information

A Heuristic Solution Method to a Stochastic Vehicle Routing Problem

A Heuristic Solution Method to a Stochastic Vehicle Routing Problem A Heursc Soluon Mehod o a Sochasc Vehcle Roung Problem Lars M. Hvaum Unversy of Bergen, Bergen, Norway. larsmh@.ub.no Arne Løkkeangen Molde Unversy College, 6411 Molde, Norway. Arne.Lokkeangen@hmolde.no

More information

Random Walk in 1-D. 3 possible paths x vs n. -5 For our random walk, we assume the probabilities p,q do not depend on time (n) - stationary

Random Walk in 1-D. 3 possible paths x vs n. -5 For our random walk, we assume the probabilities p,q do not depend on time (n) - stationary Random Walk in -D Random walks appear in many cones: diffusion is a random walk process undersanding buffering, waiing imes, queuing more generally he heory of sochasic processes gambling choosing he bes

More information

Towards a Trustworthy and Controllable Peer- Server-Peer Media Streaming: An Analytical Study and An Industrial Perspective

Towards a Trustworthy and Controllable Peer- Server-Peer Media Streaming: An Analytical Study and An Industrial Perspective Towards a Trusworhy and Conrollable Peer- Server-Peer Meda Sreamn: An Analycal Sudy and An Indusral Perspecve Zhja Chen, Hao Yn, Chuan n, Xuenn u, Yan Chen* Deparmen of Compuer Scence & Technoloy, *Deparmen

More information

A Theory of the Risks of Venture Capital Financing

A Theory of the Risks of Venture Capital Financing Amercan Journal of Economcs and Busness Admnsraon 1 (): 194-05, 009 ISSN 1945-5488 009 Scence Publcaons A heory of he Rsks of Venure Capal Fnancng Edmund H. Manell Deparmen of Fnance, Lubn School of Busness,

More information

THE IMPACT OF QUICK RESPONSE IN INVENTORY-BASED COMPETITION

THE IMPACT OF QUICK RESPONSE IN INVENTORY-BASED COMPETITION Workng Paper WP no 722 November, 2007 THE IMPACT OF QUICK RESPONSE IN INVENTORY-BASED COMPETITION Felpe Caro Vícor Marínez de Albénz 2 Professor, UCLA Anderson School of Managemen 2 Professor, Operaons

More information

SHIPPING ECONOMIC ANALYSIS FOR ULTRA LARGE CONTAINERSHIP

SHIPPING ECONOMIC ANALYSIS FOR ULTRA LARGE CONTAINERSHIP Journal of he Easern Asa Socey for Transporaon Sudes, Vol. 6, pp. 936-951, 2005 SHIPPING ECONOMIC ANALYSIS FOR ULTRA LARGE CONTAINERSHIP Chaug-Ing HSU Professor Deparen of Transporaon Technology and Manageen

More information

CONTROLLER PERFORMANCE MONITORING AND DIAGNOSIS. INDUSTRIAL PERSPECTIVE

CONTROLLER PERFORMANCE MONITORING AND DIAGNOSIS. INDUSTRIAL PERSPECTIVE Copyrgh IFAC 5h Trennal World Congress, Barcelona, Span CONTROLLER PERFORMANCE MONITORING AND DIAGNOSIS. INDUSTRIAL PERSPECTIVE Derrck J. Kozub Shell Global Soluons USA Inc. Weshollow Technology Cener,

More information

National Public Debt and Fiscal Insurance in. a Monetary Union with Ramsey Taxes

National Public Debt and Fiscal Insurance in. a Monetary Union with Ramsey Taxes Naonal Publc Deb and Fscal Insurance n a Moneary Unon wh Ramsey Taxes Kenneh Klezer Deparmen of Economcs Unversy of Calforna Sana Cruz, CA 95064 July 2013 Absrac Opmal fscal polcy s suded n an nerdependen

More information

Prices of Credit Default Swaps and the Term Structure of Credit Risk

Prices of Credit Default Swaps and the Term Structure of Credit Risk Prces of Cred Defaul Swaps and he Term Srucure of Cred Rsk by Mary Elzabeh Desrosers A Professonal Maser s Projec Submed o he Faculy of he WORCESTER POLYTECHNIC INSTITUTE n paral fulfllmen of he requremens

More information

II. IMPACTS OF WIND POWER ON GRID OPERATIONS

II. IMPACTS OF WIND POWER ON GRID OPERATIONS IEEE Energy2030 Alana, Georga, USA 17-18 November 2008 Couplng Wnd Generaors wh eferrable Loads A. Papavaslou, and S. S. Oren UC Berkeley, eparmen of Indusral Engneerng and Operaons esearch, 4141 Echeverry

More information

Tax Deductions, Consumption Distortions, and the Marginal Excess Burden of Taxation

Tax Deductions, Consumption Distortions, and the Marginal Excess Burden of Taxation a Deducons, Consumpon Dsorons, and he argnal Ecess Burden of aaon Ian W. H. Parry Dscusson Paper 99-48 Augus 999 66 P Sree, NW Washngon, DC 20036 elephone 202-328-5000 Fa 202-939-3460 Inerne: hp://www.rff.org

More information

Optimization of Nurse Scheduling Problem with a Two-Stage Mathematical Programming Model

Optimization of Nurse Scheduling Problem with a Two-Stage Mathematical Programming Model Asa Pacfc Managemen Revew 15(4) (2010) 503-516 Opmzaon of Nurse Schedulng Problem wh a Two-Sage Mahemacal Programmng Model Chang-Chun Tsa a,*, Cheng-Jung Lee b a Deparmen of Busness Admnsraon, Trans World

More information

Kalman filtering as a performance monitoring technique for a propensity scorecard

Kalman filtering as a performance monitoring technique for a propensity scorecard Kalman flerng as a performance monorng echnque for a propensy scorecard Kaarzyna Bjak * Unversy of Souhampon, Souhampon, UK, and Buro Informacj Kredyowej S.A., Warsaw, Poland Absrac Propensy scorecards

More information

What Explains Superior Retail Performance?

What Explains Superior Retail Performance? Wha Explans Superor Real Performance? Vshal Gaur, Marshall Fsher, Ananh Raman The Wharon School, Unversy of Pennsylvana vshal@grace.wharon.upenn.edu fsher@wharon.upenn.edu Harvard Busness School araman@hbs.edu

More information

Both human traders and algorithmic

Both human traders and algorithmic Shuhao Chen s a Ph.D. canddae n sascs a Rugers Unversy n Pscaaway, NJ. bhmchen@sa.rugers.edu Rong Chen s a professor of Rugers Unversy n Pscaaway, NJ and Peng Unversy, n Bejng, Chna. rongchen@sa.rugers.edu

More information

Modelling Operational Risk in Financial Institutions using Hybrid Dynamic Bayesian Networks. Authors:

Modelling Operational Risk in Financial Institutions using Hybrid Dynamic Bayesian Networks. Authors: Modellng Operaonal Rsk n Fnancal Insuons usng Hybrd Dynamc Bayesan Neworks Auhors: Professor Marn Nel Deparmen of Compuer Scence, Queen Mary Unversy of London, Mle nd Road, London, 1 4NS, Uned Kngdom Phone:

More information

Structural jump-diffusion model for pricing collateralized debt obligations tranches

Structural jump-diffusion model for pricing collateralized debt obligations tranches Appl. Mah. J. Chnese Unv. 010, 54): 40-48 Srucural jump-dffuson model for prcng collaeralzed deb oblgaons ranches YANG Ru-cheng Absrac. Ths paper consders he prcng problem of collaeralzed deb oblgaons

More information

Applying the Theta Model to Short-Term Forecasts in Monthly Time Series

Applying the Theta Model to Short-Term Forecasts in Monthly Time Series Applyng he Thea Model o Shor-Term Forecass n Monhly Tme Seres Glson Adamczuk Olvera *, Marcelo Gonçalves Trenn +, Anselmo Chaves Neo ** * Deparmen of Mechancal Engneerng, Federal Technologcal Unversy of

More information

Statistical Analysis with Little s Law. Supplementary Material: More on the Call Center Data. by Song-Hee Kim and Ward Whitt

Statistical Analysis with Little s Law. Supplementary Material: More on the Call Center Data. by Song-Hee Kim and Ward Whitt Saisical Analysis wih Lile s Law Supplemenary Maerial: More on he Call Cener Daa by Song-Hee Kim and Ward Whi Deparmen of Indusrial Engineering and Operaions Research Columbia Universiy, New York, NY 17-99

More information

An Ensemble Data Mining and FLANN Combining Short-term Load Forecasting System for Abnormal Days

An Ensemble Data Mining and FLANN Combining Short-term Load Forecasting System for Abnormal Days JOURNAL OF SOFTWARE, VOL. 6, NO. 6, JUNE 0 96 An Ensemble Daa Mnng and FLANN Combnng Shor-erm Load Forecasng Sysem for Abnormal Days Mng L College of Auomaon, Guangdong Unversy of Technology, Guangzhou,

More information

THE HEALTH BENEFITS OF CONTROLLING CARBON EMISSIONS IN CHINA 1. by Richard F. GARBACCIO; Mun S. HO; and Dale W. JORGENSON

THE HEALTH BENEFITS OF CONTROLLING CARBON EMISSIONS IN CHINA 1. by Richard F. GARBACCIO; Mun S. HO; and Dale W. JORGENSON THE HEALTH BENEFITS OF CONTROLLING CARBON EMISSIONS IN CHINA 1 by Rchard F. GARBACCIO; Mun S. HO; and Dale W. JORGENSON 1. Inroducon Ar polluon from rapd ndusralzaon and he use of energy has been recognzed

More information

Prot sharing: a stochastic control approach.

Prot sharing: a stochastic control approach. Pro sharng: a sochasc conrol approach. Donaen Hanau Aprl 2, 2009 ESC Rennes. 35065 Rennes, France. Absrac A majory of lfe nsurance conracs encompass a guaraneed neres rae and a parcpaon o earnngs of he

More information

Auxiliary Module for Unbalanced Three Phase Loads with a Neutral Connection

Auxiliary Module for Unbalanced Three Phase Loads with a Neutral Connection CODEN:LUTEDX/TEIE-514/1-141/6 Indusral Elecrcal Engneerng and Auomaon Auxlary Module for Unbalanced Three Phase Loads wh a Neural Connecon Nls Lundsröm Rkard Sröman Dep. of Indusral Elecrcal Engneerng

More information

Cost- and Energy-Aware Load Distribution Across Data Centers

Cost- and Energy-Aware Load Distribution Across Data Centers - and Energy-Aware Load Dsrbuon Across Daa Ceners Ken Le, Rcardo Banchn, Margare Maronos, and Thu D. Nguyen Rugers Unversy Prnceon Unversy Inroducon Today, many large organzaons operae mulple daa ceners.

More information

Scientific Ontology Construction Based on Interval Valued Fuzzy Theory under Web 2.0

Scientific Ontology Construction Based on Interval Valued Fuzzy Theory under Web 2.0 JOUNAL OF SOFTWAE, VOL. 8, NO. 8, AUGUST 2013 1835 Scenfc Onology Consrucon Based on Inerval Valued Fuzzy Theory under Web 2.0 Na Xue, Sulng Ja, Jnxng Hao and Qang Wang School of Economcs and Managemen,

More information