Discussion Papers in Economics

Size: px
Start display at page:

Download "Discussion Papers in Economics"

Transcription

1 scusson Ppers n Economcs No. 10/0 Alloctve Effcency nd n Incentve Scheme for Reserch By Anndy Bhttchry, Unversty of York; Herbert Newhouse, Unversty of Clforn eprtment of Economcs nd Relted Studes Unversty of York Heslngton York, YO10 5

2

3 Alloctve Effcency nd n Incentve Scheme for Reserch 1 Anndy Bhttchry eprtment of Economcs nd Relted Studes, The Unversty of York, UK Herbert Newhouse eprtment of Economcs Unversty of Clforn, Sn ego Frst Verson: October 005 Ths Verson: Jnury 010 Abstrct In ths pper we exmne whether n ncentve scheme for mprovng reserch cn hve dverse effect on reserch tself. Ths work s mnly motvted by the Reserch Assessment Exercse (RAE) nd the Reserch Excellence Frmework (REF) n UK. In gme theoretc frmework we show tht scheme lke RAE/REF cn ctully result n deterorton of the over-ll reserch n country though t my crete few solted centres of excellence. The centrl ssumpton behnd ths result s tht hgh blty reserchers produce postve externltes to ther collegues. We ssume these externltes hve declnng mrgnl beneft s the number of hgh blty reserchers n deprtment ncreses. Becuse of ths declnng mrgnl beneft n ncentve scheme lke the RAE or REF my led to over concentrton of the hgh blty reserchers n few deprtments. JEL Clssfcton No: I, I8, C71, C7. Keywords: reserch, RAE, REF, coltons, strong nsh equlbrum. 1 We re ndebted to Alessndr Cnep, Vncent Crwford, Kren Mumford, Arunv Sen nd semnr udences t Cen, Lncster nd York for useful comments nd dscussons. Of course, the errors nd shortcomngs re ours.

4 1 Introducton In ths pper we exmne whether ncentve schemes for reserch cn hve n dverse effect on reserch tself. Ths work s mnly motvted by the Reserch Assessment Exercse (RAE) nd the Reserch Excellence Frmework (REF) n UK. In gme theoretc frmework we show tht scheme lke RAE/REF cn ctully result n deterorton of the over-ll reserch n country though t my crete few solted centres of excellence. To cpture the pyment from such ncentve schemes, better deprtment my hve tendency to replce persons wth reltvely lower blty by persons wth reltvely hgher blty. A worse deprtment my be unble to keep ts persons wth reltvely hgher blty nd forced to rely on persons wth reltvely lower blty. The ntuton behnd ths s s follows. We ssume tht resercher gns from the externl effects of her cdemc surroundngs. Furthermore, we ssume tht for n cdemc professonl ths effect declnes s her reserch blty ncreses. Therefore, overll reserch output my ncrese when hgher blty reserchers re dstrbuted more eqully mong reserch nsttutons. However, n ncentve scheme lke RAE/REF wrds n entre deprtment some lump-sum pyment on the bss of some over-ll reserch output of the deprtment nd every member n the deprtment hs ccess to ths rewrd. So, to cpture ths pyment, deprtment wll hve tendency to replce persons wth reltvely lower blty by persons wth reltvely hgher blty. Thus, strct herrchy of deprtments would emerge few very good deprtments followed by strng of bd deprtments. For RAE 001 such phenomenon hs been observed n the deprtments of UK (see Hre (003)). ue to the declnng returns of externltes n blty, overll reserch my ncrese f we move hgh blty resercher from the top deprtment n ths herrchy to lower-rnked deprtment. Ths pper s smply forml modellng of ths de to hghlght ths possble phenomenon n precse mnner. Our nterest s on the peer effects between the reserchers rther thn the qulty of the mtch between prtculr resercher nd her nsttuton. For ths reson we dopt the model of strtegc colton formton of Hrt nd Kurz (1983) rther thn the more stndrd mtchng model (for exmple Bulow nd

5 Levn (006)). We nlyze the equlbr of deprtment-formton gme nd show tht t s possble to obtn the somewht perverse result tht overll reserch my decrese for socety f deprtments re rewrded for ther ndvdul reserch outputs. Our purpose s smply to show tht n ncentve scheme such s the RAE/REF my hve undesrble consequences on the llocton of reserchers cross deprtments. We mke wht we feel re resonble ssumptons nd show tht such n ncentve scheme for reserch lowers the overll reserch output for one prtculr exmple. The vldty of our ssumptons nd whether or not our exmple s relevnt re both emprcl questons nd re beyond the scope of ths pper. In Secton we provde smple numercl exmple tht llustrtes the pont mde n ths pper. Our model s explned n detl n Secton 3. The results re collected n Secton 4. Secton 5 concludes. An Introductory Exmple To llustrte the mn pont of our pper we strt wth smplfed exmple. The full model s formulted n Secton 3. Suppose there re twelve reserchers n n economy, sx wth hgh blty nd sx wth low blty. Reserch must be performed n deprtment nd ech deprtment requres exctly four reserchers. An ndvdul s reserch output s bsed on her ntrnsc blty nd on the bltes of her collegues. Specfclly n ndvdul s reserch output ncreses ddtvely by fxed mount f exctly one of her collegues s of hgh blty. Her output ncreses by lrger mount f two of her collegues re of hgh blty but there s no ddtonl ncrese f ll three of her collegues re of hgh blty. Arrnge the twelve reserchers nto three deprtments. efne perfectly stble deprtment s one where ech resercher s producng her mxmum possble reserch output. An unstble deprtment s one where t lest one resercher s producng below Our frmework could be dpted to nclude number of reserchers nd deprtment tht my form colton. However, for smplcty, here we model deprtment solely s collecton of ts cdemc members. 3

6 her mxmum possble reserch output. Assumng ech resercher s pd strctly ncresng functon of her own reserch output, no resercher t perfectly stble deprtment cn strctly gn by formng new deprtment wth other reserchers. The reserchers re dvded nto the followng deprtments: Confgurton A (HHHH), (HLLL), (HLLL). The frst deprtment gven here s perfectly stble; ech resercher n ths deprtment hs t lest two collegues of hgh blty. The other two deprtments re not; ech resercher t these deprtments hs zero or one collegues of hgh blty. From these two deprtments, two hgh blty reserchers nd two low blty reserchers would ll strctly gn f they formed new deprtment. Recll tht strong Nsh equlbrum s coltonl equlbrum concept where no colton of gents cn ontly devte so tht ech member of ths colton becomes strctly better off. For our exmple strong Nsh equlbrum wll consst of perfectly stble deprtment, followed by deprtment wth the remnng hgh blty reserchers, followed by deprtment consstng entrely of low blty reserchers. (The generl cse s proved n Proposton below.) Two strong Nsh equlbrum confgurtons re Confgurton B (HHHH), (HHLL), (LLLL) nd Confgurton C (HHHL), (HHHL), (LLLL). Now suppose n ncentve scheme rewrds members of deprtment bsed on the verge level of reserch tht occurs t tht deprtment. The ncentve scheme pyment s strctly ncresng wth regrds to the verge reserch level of deprtment. Confgurton C s no longer n equlbrum. The three hgh blty reserchers from the frst deprtment nd one of the hgh blty reserchers from the second deprtment could form new deprtment nd ll of them would strctly gn. The only strong Nsh equlbrum gven ths ncentve scheme s Confgurton B. Totl reserch output s lower n Confgurton B thn t s n Confgurton C. The fourth hgh blty resercher n the frst deprtment does not dd to the externlty wheres thrd hgh blty resercher n the second deprtment would dd to the externlty. In the remnder of ths pper we nlyze more generl model. We show, frst, tht under some ssumptons, such n ncentve scheme wll result n strct herrchy s the unque equlbrum outcome. Next we show tht wth such n ncentve scheme, the totl equlbrum reserch output my fll. 4

7 3 The Model The Agents: The fnte set of n plyers (ech n cdemc) s denoted by N. Ech n N hs n ntrnsc blty for reserch lyng n the ntervl [0, 1]. Wthout loss of generlty, we order the plyers ccordng to ther blty for reserch,.e., > f nd only f <. We model the producton of reserch s two-stge gme. In the frst stge cdemcs form deprtments s descrbed below. In the second stge ech chooses level of effort, 0, e e to produce reserch. The cost of effort for ech s gven by c, wth c c 0, 0 c c. If > then, for ech e,,,. The Envronment for Reserch: Frst, we represent the dfferent spects of reserch (volume, qulty etc.) s composte sclr vrble. Next we ssume tht reserch cn be conducted only n n nsttutonl settng sy, n n cdemc deprtment. A deprtment s non-empty subset of N. We ssume tht ech fesble deprtment must hve exctly k ( n) members 3. As we hve mentoned bove, plyer gets some postve externlty n reserch from the presence of other members n deprtment 4. We ssume tht ths externlty for plyer n deprtment s gven by: q () : 1. 3 In the next subsecton we expln the precse menng of fesble deprtment. We cn generlze ths ssumpton bt n the followng wy. Suppose the mnml sze of deprtment must be k (ths s ntutve s deprtment consstng of only one member (sy) s rdculous!) nd there s congeston cost f the deprtment sze exceeds k. Then we cn show tht n equlbrum every deprtment wll hve exctly k members. However, lttle of mportnce s gned by ths ddtonl complcton. 4 Ths externlty s emprclly observed n U.S. unverstes n Km, Morse nd Zngles (006) lthough they fnd the effect hs dmnshed over tme. 5

8 The totl reserch output for person n deprtment s gven by: x e,, q where e s person s level of effort, s her ntrnsc blty nd q () s the level of the x externlty obtned by n deprtment. For ll, 0 wth x 0 x e nd 0. For ll, x 0. We sometmes express the output of by x wth no possblty of confuson. A person s reserch output s contnuous nd lso strctly ncresng n q, the level of externlty, untl fxed q <1. In [ q,1], m m x s x constnt wth respect to q. Addtonlly we ssume 0 s ncresng n q untl In [ q,1], m x s constnt wth respect to q. We ssume tht f > then m q. q m q m, tht s, the less the ntrnsc blty of resercher, the further she s helped by externlty from her collegues. We denote the mxmum vlue of m q (cross ll s) by q. Ths ssumpton expresses the de tht the effect of externlty ceses t some pont. Ths s crtcl ssumpton for our result. Next we ssume tht f plyer s more ble thn plyer then the mrgnl reserch output of s more thn tht of t ny gven effort level gven the sme coworkers. We stte ths ssumpton s Condton A. Condton A: If > x x e q e q. then for ll q, e,,,,, Next we ssume tht f plyer s more ble thn plyer then the mrgnl reserch output of s more thn tht of t ny gven effort level f they re n the sme deprtment. 6

9 The eprtment Formton Gme: Insted of modellng the recrutng of fculty members s mtchng process (see, e.g., Roth nd Sotomyor (1990)) we model the process of the formton of deprtment b nto. We use the model of strtegc colton formton ntroduced n Hrt nd Kurz (1983). Ths s gme n norml form where the outcome resultng from strtegy profle s confgurton of deprtments formed endogenously s result of the strtegc choce of the members. Gven the well-known flux of the cdemcs pror to the RAEs, such modelng should be cceptble! We ssume perfect nformton. Ths s ustfble s the reserch blty of person s observed qute precsely by publctons, prtcptons n conferences etc. The set of plyers s N. The strtegy of plyer s to nnounce the deprtment she wnts to be n. Therefore, formlly, the strtegy set for plyer, The outcome of strtegy profle { S N S}. ( S ) s prtton of N, C=( 1,..., ). Ech N member of ths prtton s deprtment. If deprtment s of crdnlty k then t s fesble; otherwse t s nfesble. Suppose for n N, C() s the unque element of C tht contns. Then, C( ) { } { N S S }. So, complete greement mong the potentl members concernng who re to be ncluded n the deprtment s necessry for fesble deprtment to be formed 5. If deprtment s nfesble (contnng more members thn k or less) then ech plyer n such deprtment receves py-off of 0. If deprtment s fesble, then we dstngush two regmes. In the orgnl regme, clled O-regme, person n deprtment receves py-off equl to her reserch output mnus her cost of effort: O u e x e,, q c e, s descrbed bove. The totl reserch output of deprtment s the sum of the reserch outputs of ts members. 5 Any prtton tht contns the mxmum number of fesble deprtments s Nsh equlbrum for ths stge of the gme. We chose the strong Nsh equlbrum soluton concept to elmnte the equlbr we feel re vcuous. 7

10 Now suppose there s n ncentve scheme so tht the Government pys lump-sum pyment to the entre deprtment on the bss of ts verge reserch output 6. We model the py-off of the plyers n such regme (we cll t R-regme) s follows. Suppose for deprtment the verge reserch output s x. Then gets n ddtonl pyment nd every n gets n ddtonl lump-sum pyment x x 0 where s strctly ncresng, concve functon. 7 Then the py-off to plyer n deprtment s gven by: R u e x e,, q c e, x Ths completes the descrpton of the gme. The soluton concept we use s hybrd one n the sprt of bckwrd nducton. We ssume tht n the effort subgme ech n ech deprtment plys Nsh equlbrum n pure strteges gven the effort choce of other members. Below we show tht, fortuntely, gven our ssumptons, such Nsh equlbrum s unque. Then, gven the Nsh equlbrum effort choces, we look t the Strong Nsh Equlbr n pure strteges (SNE) (see, e.g., Bernhem et l. (1987)) of the reduced gme of deprtment formton under the two regmes. Recll tht strtegy profle exst ( S ) s n SNE f there does not N such tht the plyers n cn ontly devte (whle those n N\ stck to the equlbrum strteges) nd ech plyer n cn strctly gn by such devton 8. As we hve lredy noted bove, gven the structure of our gme, the power of n ndvdul plyer s mnml. So, SNE, rther thn non-coopertve soluton concept, s n pproprte soluton concept for the deprtment formton subgme. N 6 Although the exct rnkng ccordng to specfc ncentve scheme lke the RAE s much more complcted, the verge reserch performnce of deprtment s good summry ndctor of such rnkngs becuse, for exmple, RAE tkes nto ccount both the totl output of the deprtment s well s the percentge of the cdemc stff ncluded n the RAE submssons. 7 We cn use other mesures of centrl tendency lke the medn or some quntle of the dstrbuton of reserch outputs of the deprtment members s the bss for the Governmentl lump-sum pyment. Then lso, the ntuton of our result would be vld. However, of course, the precse condtons for the results would chnge. 8 The noton of SNE s smlr to the noton of stblty n the mtchng lterture. See, for nstnce, Kelso nd Crwford (198). 8

11 4 The Results We proceed s follows. Frst (n Lemm 1) we show tht for both the regmes, there exsts unque Nsh equlbrum level of effort choce for ech plyer n ech deprtment. Gven ths, we cn menngfully nlyze the reduced gme of deprtment formton n the frst stge s ech plyer s the sure bout wht py-off she wll get t the ply n the second stge. Next we show n Proposton 1 tht under Condton A nd ssumng tht s lner, the unque equlbrum confgurton of deprtments tht wll be obtned n stge 1 under the R-regme s strctly herrchcl,.e., the k hghest blty persons would be n one deprtment, the set of next k hghest persons n nother deprtment, etc. Then we show n Proposton tht n generl, more thn one equlbrum confgurtons of deprtments my emerge n the O-regme. Fnlly, n Proposton 3 we provde n exmple where the totl reserch output n one of the equlbrum confgurtons of deprtments for the O-regme s more thn tht n the unque equlbrum confgurton of deprtments n the R-regme. Frst we look t the level of effort chosen n equlbrum by plyer wthn deprtment t the second stge of the gme. Lemm 1 There exsts unque Nsh equlbrum n pure strteges for the effort choce subgme n both regmes. Proof: Note tht for both regmes the py-off functon of ech s contnuous n the profle of effort choces nd strctly concve n e. Snce the strtegy set s compct, Nsh equlbrum n pure strteges exsts. Next, for ether regme, for ny deprtment,, consder the mtrx J such tht the -th entry of J s gven by u. 9

12 Gven tht c 0 e, J cn be esly shown to be negtve defnte. To see ths, for the R-regme, note tht the -th entry of J s gven by: x ( 1 ') c x ' ' x x nd the -th entry (wth dfferent from ) s gven by ''. x Therefore, J cn be wrtten s the sum of two mtrces J nd dgonl mtrx whose -th entry s gven by: x (1 ') e c ' ' J where J s wheres, the -th entry of J s gven by x x. x For ny -dmensonl vector, the product ' ' J s ' '( ) whch, gven the concvty of s non-postve. And gven our ssumptons on the functons x nd c, the mtrx J s negtve defnte. Therefore, the mtrx J s lso negtve defnte. Then by Rosen (1965), there exsts unque pure strtegy Nsh equlbrum n the effort subgme. The effort level e chosen by plyer n equlbrum of the effort-subgme n deprtment n the orgnl (O) regme stsfes the followng frst order condton: x c,, q, [1] Smlrly, the effort level e chosen by plyer n equlbrum of the effort-subgme n deprtment n the ncentve (R) regme stsfes the followng frst order condton: 10

13 x c 1 ' x e,, q e, [] Now we look t the problem of the exstence of n SNE n the reduced gme of deprtment formton. We strt wth nother lemm. We show tht totl reserch output s hgher n better deprtment. Frst we defne better deprtment. efnton 1 eprtment B s better deprtment thn W B W f 1,, k wth B for t lest one 1,, kwhen B nd W re both ordered from the W best member to the worst member (here deprtment ). stnds for the blty of the -th plyer n Lemm Suppose B s better deprtment thn W. Then the totl reserch output n B n equlbrum s more thn tht n W. Moreover, let be lner. Let B be the -th rnked plyer (ccordng to blty) n B nd be the -th rnked plyer n W. Then e * e * where e * W B W s the equlbrum effort choce of the -th rnked plyer n deprtment. Proof: Suppose otherwse. Then W x W x B s.t.. (Here, by x() we denote the totl reserch output n deprtment n equlbrum.) For ths condton to hold, t lest one resercher n W must exert more effort thn the dentclly rnked resercher n B W B. Tht s, nd such tht the equlbrum level of effort by plyer s more thn tht of plyer, whch we wrte smply s e > e. Recll tht resercher s frst order condton for the effort subgme n the R-regme (Equton ): x c 1 ' x e,, q e, 11

14 1 ' 1 ' x B Now, x W x by concvty of. Also, by Condton A, x,, W B e q,, q B W snce e > e, nd q q. c However,,, c due to our ssumptons bout c,. Then the frst order condton cnnot hold for both nd. Therefore totl output must be hgher n the better deprtment. Ths rgument, clerly, lso works for the O- regme (there, smply, the condton x W 1 ' 1 ' x B s left out). Now, ssume tht s lner, tht s, ts dervtve s constnt. Then, replctng the rgument bove we fnd tht e * e * where e * B W s the equlbrum effort choce of the -th rnked plyer n deprtment s requred n the lemm. 9 Lemm mples tht the equlbrum totl reserch output n better deprtment (s defned bove) s more thn n ny worse deprtment. Next we show tht under Condtons A nd the ssumpton of lnerty of n the R- regme, the unque SNE outcome would be such tht strct herrchy of deprtments would form. The k hghest blty persons would be n one deprtment, the set of next k hghest persons n nother deprtment, etc. Formlly: Proposton 1 Suppose Condton A holds nd let be lner. Then, n the R-regme, the unque SNE outcome tht, k,..., 1, m n k m n s s follows. For every, k N & m, n 1,...,. such 9 The lnerty of s smple suffcent condton, but not necessry for the second prt of Lemm to be vld. 1

15 Proof: Let 1 be the deprtment consstng of the k best reserchers. Let be ny other deprtment wth, '. The deprtment 1 1 s better deprtment thn s specfed n efnton 1. Rnk the reserchers n 1 nd ccordng to blty. Lemm 1 ' shows tht e e for ech person of rnk n ether deprtment. By Lemm we lso get tht x x. 1 ' R ' R 1 Suppose u * ' u * 1, tht s, let plyer s py-off gven her equlbrum choce of effort n deprtment be more thn her py-off gven her equlbrum choce of effort n deprtment 1. R ' R 1 Then u * 1 u * 1. 1 But then e * cnnot be Nsh equlbrum choce for plyer n the effort choce subgme n deprtment 1. Remrk 1: Proposton 1 gves the unque equlbrum for the R-regme. Specfclly ths equlbrum hs the k best reserchers n the frst deprtment, the remnng k best reserchers n the second deprtment nd so on 10. Next we wll demonstrte the possble exstence of other equlbr n the O-regme. Proposton There exsts t lest one SNE for the deprtment formton gme n the O- regme. Proof: Our proof s constructve. Frst we sy tht deprtment s perfectly stble (gven regme) f for every n O O ', u * u * ' for every other deprtment. Gven the SNE soluton concept no plyer would devte out of perfectly stble deprtment. Now we descrbe the constructon. 10 In pper on ths theme L Mnn (008) explns tht ths type of herrchy wll result becuse the better deprtments get more fundng from the RAE whch leds them to hre better reserchers. He then uses relblty theory to determne when such herrchy s desrble. 13

16 Step 1: Form perfectly stble deprtment 1. Then, from N \ 1, form nother perfectly stble deprtment. Contnue ths process s long s possble. Ths process would termnte owng to the fnteness of N. Let ths collecton be ( 1,..., ). (Note tht the set of such deprtments my be empty.) Step : Form 1 by tkng k ``best plyers (n terms of ndvdul ntrnsc blty) from the remnng N {... } nd so on untl no more fesble deprtments cn \ 1 be formed. Note tht plyer from deprtment 1 my gn by formng new deprtment wth plyers from ( 1,..., ). However, by Lemm, none of the plyers n (,..., 1 ) would gn strctly by formng such deprtment. Furthermore, plyer from deprtment 1 cnnot gn by formng new deprtment wth plyers from,..., 1 n k Therefore tkng ( 1,..., ) s gven, no plyer from deprtment 1 wll devte. Smlr logc demonstrtes tht no plyer wll leve the remnng deprtments formed n Step. The resultng set of deprtments s n SNE outcome.. Remrk : Note tht the strct herrchy found s the unque SNE n the R-regme s lso n SNE n the O-regme. Proposton 3 below gves our desred result. It shows tht t s possble to hve strctly hgher totl reserch output n n equlbrum outcome n the O-regme compred to the unque equlbrum outcome n the R-regme. Proposton 3 Gven the bove ssumptons t s possble to hve strctly hgher totl reserch output n n equlbrum outcome n the O-regme compred to the unque equlbrum outcome n the R-regme. Proof: We demonstrte ths wth n exmple. 14

17 Suppose n = 4 nd k =. 0.7, q 0. m , q 0.3 m 0.4, q 0.6 m , q 0.7 m 4 4, c e e e The followng results hold for ny sensble output functon. Consder two structures of deprtments: Structure A: 1 = (1, ), = (3, 4). Structure B: 1 = (, 3), = (1, 4). Note tht by Proposton 1, Structure A s the unque equlbrum for the R-regme nd by Proposton, s lso n equlbrum for the O-regme. Structure B s n equlbrum for the O-regme; both deprtments re perfectly stble snce ech plyer n ech deprtment receves her mxmum externlty. Consder the two structures under the O-regme. Plyer 1 nd plyer ech receve her mxmum externlty. Therefore ech of them wll produce the sme mount n ether structure. Recll tht by Equton 1, plyer 3 s frst order condton for equlbrum choce of effort for structure A, A e 3, requres tht: x c A A 3 *, 3,0. 3 *, Smlrly, plyer 3 s frst order condton for equlbrum choce of effort for structure B, B e 3, requres tht: x B c B ( e3, 3,0.6) ( e3, 3)

18 Also note tht: B A 3 3 x x B A 3 *, 3,0.6 3 *, 3, e * e * by our ssumpton on the functon c,. B A Smlrly, e4 * e4 *. Therefore the totl output under the O-regme t Structure B s greter thn tht for Structure A. Then, f ' s smll enough, then by the contnuty of the py-off functons of the plyers, the totl output under the O-regme t Structure B s greter thn tht for the unque equlbrum under the R-regme. Remrk 3: Of course, ths result holds for mny other exmples; we ust provde one frly smple cse. 5 Concluson The bove results re not ment s condemnton of ny specfc ncentve scheme such s the RAE or the REF. Rther, we merely demonstrte tht t s possble tht such ncentve schemes my lower totl reserch output gven ndvdully optmzng reserchers. Snce our results re dependent on the vlues of the prmeters, ny specfc ncentve scheme for reserch my or my not lower the overll level of reserch n n economy. One re of future reserch s how probble t s tht such perverse scenro exsts. 16

19 References: 1. Bernhem., B. Peleg nd M. Whnston (1987): Colton-Proof Nsh Equlbr I: Concepts, Journl of Economc Theory, 4, Bulow, J. nd J. Levn (006): Mtchng nd Prce Competton, Amercn Economc Revew, 96, Epple,. nd R. E. Romno (1998): Competton Between Prvte nd Publc Schools, Vouchers, nd Peer-Group Effects, Amercn Economc Revew, 6, Hre, P. (003): The UK's Reserch Assessment Exercse: Impct on Insttutons, eprtments, Indvduls, Hgher Educton Mngement nd Polcy, 15, Hrt, S. nd M. Kurz (1983): Endogenous Formton of Coltons, Econometrc, 51, Kelso, A. nd V. Crwford (198): Job Mtchng, Colton Formton, nd Gross Substtutes. Econometrc, 50, Km, E. H., A. Morse nd L. Zngles (006): Are Elte Unverstes Losng Ther Compettve Edge?, NBER Workng Pper L Mnn, M. (008): Assessng the Assessment. Or the RAE nd the Optml Orgnzton of Unversty Reserch. Scottsh Journl of Poltcl Economy, 55, Rosen, J. B. (1965): Exstence nd Unqueness of Equlbrum Ponts for Concve N-Person Gmes, Econometrc, 33,

20 10. Roth, A. nd M. Sotomyor (1990): Two-sded mtchng: study n gmetheoretc modelng nd nlyss. Econometrc Socety Monogrphs, vol. 18. Cmbrdge Unversty Press, Cmbrdge. 11. Wnston, G. C. nd. J. Zmmermn (003): Peer Effects n Hgher Educton, n College ecsons: How Students Actully Mke Them nd How they Could, C. Hoxby, ed., Unversty of Chcgo Press. 18

Optimal Pricing Scheme for Information Services

Optimal Pricing Scheme for Information Services Optml rcng Scheme for Informton Servces Shn-y Wu Opertons nd Informton Mngement The Whrton School Unversty of ennsylvn E-ml: shnwu@whrton.upenn.edu e-yu (Shron) Chen Grdute School of Industrl Admnstrton

More information

Newton-Raphson Method of Solving a Nonlinear Equation Autar Kaw

Newton-Raphson Method of Solving a Nonlinear Equation Autar Kaw Newton-Rphson Method o Solvng Nonlner Equton Autr Kw Ater redng ths chpter, you should be ble to:. derve the Newton-Rphson method ormul,. develop the lgorthm o the Newton-Rphson method,. use the Newton-Rphson

More information

Resistive Network Analysis. The Node Voltage Method - 1

Resistive Network Analysis. The Node Voltage Method - 1 esste Network Anlyss he nlyss of n electrcl network conssts of determnng ech of the unknown rnch currents nd node oltges. A numer of methods for network nlyss he een deeloped, sed on Ohm s Lw nd Krchoff

More information

WiMAX DBA Algorithm Using a 2-Tier Max-Min Fair Sharing Policy

WiMAX DBA Algorithm Using a 2-Tier Max-Min Fair Sharing Policy WMAX DBA Algorthm Usng 2-Ter Mx-Mn Fr Shrng Polcy Pe-Chen Tseng 1, J-Yn Ts 2, nd Wen-Shyng Hwng 2,* 1 Deprtment of Informton Engneerng nd Informtcs, Tzu Ch College of Technology, Hulen, Twn pechen@tccn.edu.tw

More information

Use Geometry Expressions to create a more complex locus of points. Find evidence for equivalence using Geometry Expressions.

Use Geometry Expressions to create a more complex locus of points. Find evidence for equivalence using Geometry Expressions. Lerning Objectives Loci nd Conics Lesson 3: The Ellipse Level: Preclculus Time required: 120 minutes In this lesson, students will generlize their knowledge of the circle to the ellipse. The prmetric nd

More information

Lecture 3 Gaussian Probability Distribution

Lecture 3 Gaussian Probability Distribution Lecture 3 Gussin Probbility Distribution Introduction l Gussin probbility distribution is perhps the most used distribution in ll of science. u lso clled bell shped curve or norml distribution l Unlike

More information

Polynomial Functions. Polynomial functions in one variable can be written in expanded form as ( )

Polynomial Functions. Polynomial functions in one variable can be written in expanded form as ( ) Polynomil Functions Polynomil functions in one vrible cn be written in expnded form s n n 1 n 2 2 f x = x + x + x + + x + x+ n n 1 n 2 2 1 0 Exmples of polynomils in expnded form re nd 3 8 7 4 = 5 4 +

More information

Math 135 Circles and Completing the Square Examples

Math 135 Circles and Completing the Square Examples Mth 135 Circles nd Completing the Squre Exmples A perfect squre is number such tht = b 2 for some rel number b. Some exmples of perfect squres re 4 = 2 2, 16 = 4 2, 169 = 13 2. We wish to hve method for

More information

Joint Opaque booking systems for online travel agencies

Joint Opaque booking systems for online travel agencies Jont Opque bookng systems for onlne trvel gences Mlgorzt OGOOWSKA nd Domnque TORRE Mrch 2010 Abstrct Ths pper nlyzes the propertes of the dvnced Opque bookng systems used by the onlne trvel gences n conjuncton

More information

Positive Integral Operators With Analytic Kernels

Positive Integral Operators With Analytic Kernels Çnky Ünverte Fen-Edeyt Fkülte, Journl of Art nd Scence Sy : 6 / Arl k 006 Potve ntegrl Opertor Wth Anlytc Kernel Cn Murt D KMEN Atrct n th pper we contruct exmple of potve defnte ntegrl kernel whch re

More information

1 Example 1: Axis-aligned rectangles

1 Example 1: Axis-aligned rectangles COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton

More information

Multi-Market Trading and Liquidity: Theory and Evidence

Multi-Market Trading and Liquidity: Theory and Evidence Mult-Mrket Trdng nd Lqudty: Theory nd Evdence Shmuel Bruch, G. Andrew Kroly, b* Mchel L. Lemmon Eccles School of Busness, Unversty of Uth, Slt Lke Cty, UT 84, USA b Fsher College of Busness, Oho Stte Unversty,

More information

WHAT HAPPENS WHEN YOU MIX COMPLEX NUMBERS WITH PRIME NUMBERS?

WHAT HAPPENS WHEN YOU MIX COMPLEX NUMBERS WITH PRIME NUMBERS? WHAT HAPPES WHE YOU MIX COMPLEX UMBERS WITH PRIME UMBERS? There s n ol syng, you n t pples n ornges. Mthemtns hte n t; they love to throw pples n ornges nto foo proessor n see wht hppens. Sometmes they

More information

Small Business Cloud Services

Small Business Cloud Services Smll Business Cloud Services Summry. We re thick in the midst of historic se-chnge in computing. Like the emergence of personl computers, grphicl user interfces, nd mobile devices, the cloud is lredy profoundly

More information

DlNBVRGH + Sickness Absence Monitoring Report. Executive of the Council. Purpose of report

DlNBVRGH + Sickness Absence Monitoring Report. Executive of the Council. Purpose of report DlNBVRGH + + THE CITY OF EDINBURGH COUNCIL Sickness Absence Monitoring Report Executive of the Council 8fh My 4 I.I...3 Purpose of report This report quntifies the mount of working time lost s result of

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

UNIVERSITY OF NOTTINGHAM. Discussion Papers in Economics STRATEGIC SECOND SOURCING IN A VERTICAL STRUCTURE

UNIVERSITY OF NOTTINGHAM. Discussion Papers in Economics STRATEGIC SECOND SOURCING IN A VERTICAL STRUCTURE UNVERSTY OF NOTTNGHAM Discussion Ppers in Economics Discussion Pper No. 04/15 STRATEGC SECOND SOURCNG N A VERTCAL STRUCTURE By Arijit Mukherjee September 004 DP 04/15 SSN 10-438 UNVERSTY OF NOTTNGHAM Discussion

More information

A Hadoop Job Scheduling Model Based on Uncategorized Slot

A Hadoop Job Scheduling Model Based on Uncategorized Slot Journl of Communctons Vol. 10, No. 10, October 2015 A Hdoop Job Schedulng Model Bsed on Unctegored Slot To Xue nd Tng-tng L Deprtment of Computer Scence, X n Polytechnc Unversty, X n 710048, Chn Eml: xt73@163.com;

More information

EQUATIONS OF LINES AND PLANES

EQUATIONS OF LINES AND PLANES EQUATIONS OF LINES AND PLANES MATH 195, SECTION 59 (VIPUL NAIK) Corresponding mteril in the ook: Section 12.5. Wht students should definitely get: Prmetric eqution of line given in point-direction nd twopoint

More information

Incorporating Negative Values in AHP Using Rule- Based Scoring Methodology for Ranking of Sustainable Chemical Process Design Options

Incorporating Negative Values in AHP Using Rule- Based Scoring Methodology for Ranking of Sustainable Chemical Process Design Options 20 th Europen ymposum on Computer Aded Process Engneerng ECAPE20. Perucc nd G. Buzz Ferrrs (Edtors) 2010 Elsever B.V. All rghts reserved. Incorportng Negtve Vlues n AHP Usng Rule- Bsed corng Methodology

More information

Cardiff Economics Working Papers

Cardiff Economics Working Papers Crdff Economcs Workng Ppers Workng Pper No. E204/4 Reforms, Incentves nd Bnkng Sector Productvty: A Cse of Nepl Kul B Luntel, Shekh Selm nd Pushkr Bjrchry August 204 Crdff Busness School Aberconwy Buldng

More information

Problem Set 3. a) We are asked how people will react, if the interest rate i on bonds is negative.

Problem Set 3. a) We are asked how people will react, if the interest rate i on bonds is negative. Queston roblem Set 3 a) We are asked how people wll react, f the nterest rate on bonds s negatve. When

More information

Extending Probabilistic Dynamic Epistemic Logic

Extending Probabilistic Dynamic Epistemic Logic Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σ-algebra: a set

More information

Vector Geometry for Computer Graphics

Vector Geometry for Computer Graphics Vector Geometry for Computer Grphcs Bo Getz Jnury, 7 Contents Prt I: Bsc Defntons Coordnte Systems... Ponts nd Vectors Mtrces nd Determnnts.. 4 Prt II: Opertons Vector ddton nd sclr multplcton... 5 The

More information

Irregular Repeat Accumulate Codes 1

Irregular Repeat Accumulate Codes 1 Irregulr epet Accumulte Codes 1 Hu Jn, Amod Khndekr, nd obert McElece Deprtment of Electrcl Engneerng, Clforn Insttute of Technology Psden, CA 9115 USA E-ml: {hu, mod, rjm}@systems.cltech.edu Abstrct:

More information

Example A rectangular box without lid is to be made from a square cardboard of sides 18 cm by cutting equal squares from each corner and then folding

Example A rectangular box without lid is to be made from a square cardboard of sides 18 cm by cutting equal squares from each corner and then folding 1 Exmple A rectngulr box without lid is to be mde from squre crdbord of sides 18 cm by cutting equl squres from ech corner nd then folding up the sides. 1 Exmple A rectngulr box without lid is to be mde

More information

Experiment 6: Friction

Experiment 6: Friction Experiment 6: Friction In previous lbs we studied Newton s lws in n idel setting, tht is, one where friction nd ir resistnce were ignored. However, from our everydy experience with motion, we know tht

More information

Example 27.1 Draw a Venn diagram to show the relationship between counting numbers, whole numbers, integers, and rational numbers.

Example 27.1 Draw a Venn diagram to show the relationship between counting numbers, whole numbers, integers, and rational numbers. 2 Rtionl Numbers Integers such s 5 were importnt when solving the eqution x+5 = 0. In similr wy, frctions re importnt for solving equtions like 2x = 1. Wht bout equtions like 2x + 1 = 0? Equtions of this

More information

9 CONTINUOUS DISTRIBUTIONS

9 CONTINUOUS DISTRIBUTIONS 9 CONTINUOUS DISTIBUTIONS A rndom vrible whose vlue my fll nywhere in rnge of vlues is continuous rndom vrible nd will be ssocited with some continuous distribution. Continuous distributions re to discrete

More information

What is Candidate Sampling

What is Candidate Sampling What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble

More information

v a 1 b 1 i, a 2 b 2 i,..., a n b n i.

v a 1 b 1 i, a 2 b 2 i,..., a n b n i. SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are

More information

All pay auctions with certain and uncertain prizes a comment

All pay auctions with certain and uncertain prizes a comment CENTER FOR RESEARC IN ECONOMICS AND MANAGEMENT CREAM Publiction No. 1-2015 All py uctions with certin nd uncertin prizes comment Christin Riis All py uctions with certin nd uncertin prizes comment Christin

More information

PROF. BOYAN KOSTADINOV NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY

PROF. BOYAN KOSTADINOV NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY MAT 0630 INTERNET RESOURCES, REVIEW OF CONCEPTS AND COMMON MISTAKES PROF. BOYAN KOSTADINOV NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY Contents 1. ACT Compss Prctice Tests 1 2. Common Mistkes 2 3. Distributive

More information

MATH 150 HOMEWORK 4 SOLUTIONS

MATH 150 HOMEWORK 4 SOLUTIONS MATH 150 HOMEWORK 4 SOLUTIONS Section 1.8 Show tht the product of two of the numbers 65 1000 8 2001 + 3 177, 79 1212 9 2399 + 2 2001, nd 24 4493 5 8192 + 7 1777 is nonnegtive. Is your proof constructive

More information

Graphs on Logarithmic and Semilogarithmic Paper

Graphs on Logarithmic and Semilogarithmic Paper 0CH_PHClter_TMSETE_ 3//00 :3 PM Pge Grphs on Logrithmic nd Semilogrithmic Pper OBJECTIVES When ou hve completed this chpter, ou should be ble to: Mke grphs on logrithmic nd semilogrithmic pper. Grph empiricl

More information

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2)

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2) MATH 16T Exam 1 : Part I (In-Class) Solutons 1. (0 pts) A pggy bank contans 4 cons, all of whch are nckels (5 ), dmes (10 ) or quarters (5 ). The pggy bank also contans a con of each denomnaton. The total

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

Fuzzy Clustering for TV Program Classification

Fuzzy Clustering for TV Program Classification Fuzzy Clusterng for TV rogrm Clssfcton Yu Zhwen Northwestern olytechncl Unversty X n,.r.chn, 7007 yuzhwen77@yhoo.com.cn Gu Jnhu Northwestern olytechncl Unversty X n,.r.chn, 7007 guh@nwpu.edu.cn Zhou Xngshe

More information

Pricing Strategy of Platform: An Investigation to the Internet Service Provider (ISP) Industry

Pricing Strategy of Platform: An Investigation to the Internet Service Provider (ISP) Industry Prng trtegy of Pltform: n Investgton to the Internet erve Provder (IP Industry by WDEH KUMR MT, HUI P Correspondng ddress: Dept. of Computng nd Eletron ystems, Unversty of Essex, Wvenhoe Prk, Colhester,

More information

4.11 Inner Product Spaces

4.11 Inner Product Spaces 314 CHAPTER 4 Vector Spces 9. A mtrix of the form 0 0 b c 0 d 0 0 e 0 f g 0 h 0 cnnot be invertible. 10. A mtrix of the form bc d e f ghi such tht e bd = 0 cnnot be invertible. 4.11 Inner Product Spces

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

Luby s Alg. for Maximal Independent Sets using Pairwise Independence Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent

More information

The Velocity Factor of an Insulated Two-Wire Transmission Line

The Velocity Factor of an Insulated Two-Wire Transmission Line The Velocity Fctor of n Insulted Two-Wire Trnsmission Line Problem Kirk T. McDonld Joseph Henry Lbortories, Princeton University, Princeton, NJ 08544 Mrch 7, 008 Estimte the velocity fctor F = v/c nd the

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

Treatment Spring Late Summer Fall 0.10 5.56 3.85 0.61 6.97 3.01 1.91 3.01 2.13 2.99 5.33 2.50 1.06 3.53 6.10 Mean = 1.33 Mean = 4.88 Mean = 3.

Treatment Spring Late Summer Fall 0.10 5.56 3.85 0.61 6.97 3.01 1.91 3.01 2.13 2.99 5.33 2.50 1.06 3.53 6.10 Mean = 1.33 Mean = 4.88 Mean = 3. The nlysis of vrince (ANOVA) Although the t-test is one of the most commonly used sttisticl hypothesis tests, it hs limittions. The mjor limittion is tht the t-test cn be used to compre the mens of only

More information

Recurrence. 1 Definitions and main statements

Recurrence. 1 Definitions and main statements Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.

More information

Rate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Priority-based scheduling. States of a process

Rate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Priority-based scheduling. States of a process Dsadvantages of cyclc TDDB47 Real Tme Systems Manual scheduler constructon Cannot deal wth any runtme changes What happens f we add a task to the set? Real-Tme Systems Laboratory Department of Computer

More information

Binary Representation of Numbers Autar Kaw

Binary Representation of Numbers Autar Kaw Binry Representtion of Numbers Autr Kw After reding this chpter, you should be ble to: 1. convert bse- rel number to its binry representtion,. convert binry number to n equivlent bse- number. In everydy

More information

Small Business Networking

Small Business Networking Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology

More information

Basic Analysis of Autarky and Free Trade Models

Basic Analysis of Autarky and Free Trade Models Bsic Anlysis of Autrky nd Free Trde Models AUTARKY Autrky condition in prticulr commodity mrket refers to sitution in which country does not engge in ny trde in tht commodity with other countries. Consequently

More information

and thus, they are similar. If k = 3 then the Jordan form of both matrices is

and thus, they are similar. If k = 3 then the Jordan form of both matrices is Homework ssignment 11 Section 7. pp. 249-25 Exercise 1. Let N 1 nd N 2 be nilpotent mtrices over the field F. Prove tht N 1 nd N 2 re similr if nd only if they hve the sme miniml polynomil. Solution: If

More information

Boolean Algebra. ECE 152A Winter 2012

Boolean Algebra. ECE 152A Winter 2012 Boolen Algebr ECE 52A Wnter 22 Redng Assgnent Brown nd Vrnesc 2 Introducton to Logc Crcuts 2.5 Boolen Algebr 2.5. The Venn Dgr 2.5.2 Notton nd Ternology 2.5.3 Precedence of Opertons 2.6 Synthess Usng AND,

More information

Bayesian Updating with Continuous Priors Class 13, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom

Bayesian Updating with Continuous Priors Class 13, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom Byesin Updting with Continuous Priors Clss 3, 8.05, Spring 04 Jeremy Orloff nd Jonthn Bloom Lerning Gols. Understnd prmeterized fmily of distriutions s representing continuous rnge of hypotheses for the

More information

Models and Software for Urban and Regional Transportation Planning : The Contributions of the Center for Research on Transportation

Models and Software for Urban and Regional Transportation Planning : The Contributions of the Center for Research on Transportation Models nd Softwre for Urbn nd Regonl Plnnng : The Contrbutons of the Center for Reserch on Mchel Florn Aprl 2008 CIRRELT-2008-11 Models nd Softwre for Urbn Regonl Plnnng: The Contrbutons of the Center

More information

Small Business Networking

Small Business Networking Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology

More information

Econ 4721 Money and Banking Problem Set 2 Answer Key

Econ 4721 Money and Banking Problem Set 2 Answer Key Econ 472 Money nd Bnking Problem Set 2 Answer Key Problem (35 points) Consider n overlpping genertions model in which consumers live for two periods. The number of people born in ech genertion grows in

More information

Health insurance marketplace What to expect in 2014

Health insurance marketplace What to expect in 2014 Helth insurnce mrketplce Wht to expect in 2014 33096VAEENBVA 06/13 The bsics of the mrketplce As prt of the Affordble Cre Act (ACA or helth cre reform lw), strting in 2014 ALL Americns must hve minimum

More information

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

ALABAMA ASSOCIATION of EMERGENCY MANAGERS

ALABAMA ASSOCIATION of EMERGENCY MANAGERS LBM SSOCTON of EMERGENCY MNGERS ON O PCE C BELLO MER E T R O CD NCY M N G L R PROFESSONL CERTFCTON PROGRM .. E. M. CERTFCTON PROGRM 2014 RULES ND REGULTONS 1. THERE WLL BE FOUR LEVELS OF CERTFCTON. BSC,

More information

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by 6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng

More information

How To Network A Smll Business

How To Network A Smll Business Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology

More information

7.5. Present Value of an Annuity. Investigate

7.5. Present Value of an Annuity. Investigate 7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on

More information

ORIGIN DESTINATION DISAGGREGATION USING FRATAR BIPROPORTIONAL LEAST SQUARES ESTIMATION FOR TRUCK FORECASTING

ORIGIN DESTINATION DISAGGREGATION USING FRATAR BIPROPORTIONAL LEAST SQUARES ESTIMATION FOR TRUCK FORECASTING ORIGIN DESTINATION DISAGGREGATION USING FRATAR BIPROPORTIONAL LEAST SQUARES ESTIMATION FOR TRUCK FORECASTING Unversty of Wsconsn Mlwukee Pper No. 09-1 Ntonl Center for Freght & Infrstructure Reserch &

More information

LINEAR TRANSFORMATIONS AND THEIR REPRESENTING MATRICES

LINEAR TRANSFORMATIONS AND THEIR REPRESENTING MATRICES LINEAR TRANSFORMATIONS AND THEIR REPRESENTING MATRICES DAVID WEBB CONTENTS Liner trnsformtions 2 The representing mtrix of liner trnsformtion 3 3 An ppliction: reflections in the plne 6 4 The lgebr of

More information

Lecture 3: Force of Interest, Real Interest Rate, Annuity

Lecture 3: Force of Interest, Real Interest Rate, Annuity Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annuty-mmedate, and ts present value Study annuty-due, and

More information

Small Business Networking

Small Business Networking Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd business. Introducing technology

More information

We are now ready to answer the question: What are the possible cardinalities for finite fields?

We are now ready to answer the question: What are the possible cardinalities for finite fields? Chapter 3 Fnte felds We have seen, n the prevous chapters, some examples of fnte felds. For example, the resdue class rng Z/pZ (when p s a prme) forms a feld wth p elements whch may be dentfed wth the

More information

Physics 43 Homework Set 9 Chapter 40 Key

Physics 43 Homework Set 9 Chapter 40 Key Physics 43 Homework Set 9 Chpter 4 Key. The wve function for n electron tht is confined to x nm is. Find the normliztion constnt. b. Wht is the probbility of finding the electron in. nm-wide region t x

More information

Texas Instruments 30X IIS Calculator

Texas Instruments 30X IIS Calculator Texas Instruments 30X IIS Calculator Keystrokes for the TI-30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the

More information

Small Business Networking

Small Business Networking Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd business. Introducing technology

More information

Integration by Substitution

Integration by Substitution Integrtion by Substitution Dr. Philippe B. Lvl Kennesw Stte University August, 8 Abstrct This hndout contins mteril on very importnt integrtion method clled integrtion by substitution. Substitution is

More information

Health insurance exchanges What to expect in 2014

Health insurance exchanges What to expect in 2014 Helth insurnce exchnges Wht to expect in 2014 33096CAEENABC 02/13 The bsics of exchnges As prt of the Affordble Cre Act (ACA or helth cre reform lw), strting in 2014 ALL Americns must hve minimum mount

More information

Section 5.4 Annuities, Present Value, and Amortization

Section 5.4 Annuities, Present Value, and Amortization Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today

More information

Integration. 148 Chapter 7 Integration

Integration. 148 Chapter 7 Integration 48 Chpter 7 Integrtion 7 Integrtion t ech, by supposing tht during ech tenth of second the object is going t constnt speed Since the object initilly hs speed, we gin suppose it mintins this speed, but

More information

5.2. LINE INTEGRALS 265. Let us quickly review the kind of integrals we have studied so far before we introduce a new one.

5.2. LINE INTEGRALS 265. Let us quickly review the kind of integrals we have studied so far before we introduce a new one. 5.2. LINE INTEGRALS 265 5.2 Line Integrls 5.2.1 Introduction Let us quickly review the kind of integrls we hve studied so fr before we introduce new one. 1. Definite integrl. Given continuous rel-vlued

More information

19. The Fermat-Euler Prime Number Theorem

19. The Fermat-Euler Prime Number Theorem 19. The Fermt-Euler Prime Number Theorem Every prime number of the form 4n 1 cn be written s sum of two squres in only one wy (side from the order of the summnds). This fmous theorem ws discovered bout

More information

Generalizing the degree sequence problem

Generalizing the degree sequence problem Mddlebury College March 2009 Arzona State Unversty Dscrete Mathematcs Semnar The degree sequence problem Problem: Gven an nteger sequence d = (d 1,...,d n ) determne f there exsts a graph G wth d as ts

More information

COMPARISON OF SOME METHODS TO FIT A MULTIPLICATIVE TARIFF STRUCTURE TO OBSERVED RISK DATA BY B. AJNE. Skandza, Stockholm ABSTRACT

COMPARISON OF SOME METHODS TO FIT A MULTIPLICATIVE TARIFF STRUCTURE TO OBSERVED RISK DATA BY B. AJNE. Skandza, Stockholm ABSTRACT COMPARISON OF SOME METHODS TO FIT A MULTIPLICATIVE TARIFF STRUCTURE TO OBSERVED RISK DATA BY B. AJNE Skndz, Stockholm ABSTRACT Three methods for fitting multiplictive models to observed, cross-clssified

More information

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia To appear n Journal o Appled Probablty June 2007 O-COSTAT SUM RED-AD-BLACK GAMES WITH BET-DEPEDET WI PROBABILITY FUCTIO LAURA POTIGGIA, Unversty o the Scences n Phladelpha Abstract In ths paper we nvestgate

More information

Rotating DC Motors Part II

Rotating DC Motors Part II Rotting Motors rt II II.1 Motor Equivlent Circuit The next step in our consiertion of motors is to evelop n equivlent circuit which cn be use to better unerstn motor opertion. The rmtures in rel motors

More information

Factoring Polynomials

Factoring Polynomials Fctoring Polynomils Some definitions (not necessrily ll for secondry school mthemtics): A polynomil is the sum of one or more terms, in which ech term consists of product of constnt nd one or more vribles

More information

Lecture 5. Inner Product

Lecture 5. Inner Product Lecture 5 Inner Product Let us strt with the following problem. Given point P R nd line L R, how cn we find the point on the line closest to P? Answer: Drw line segment from P meeting the line in right

More information

How To Set Up A Network For Your Business

How To Set Up A Network For Your Business Why Network is n Essentil Productivity Tool for Any Smll Business TechAdvisory.org SME Reports sponsored by Effective technology is essentil for smll businesses looking to increse their productivity. Computer

More information

Helicopter Theme and Variations

Helicopter Theme and Variations Helicopter Theme nd Vritions Or, Some Experimentl Designs Employing Pper Helicopters Some possible explntory vribles re: Who drops the helicopter The length of the rotor bldes The height from which the

More information

Simple Interest Loans (Section 5.1) :

Simple Interest Loans (Section 5.1) : Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part

More information

Revenue Management Games: Horizontal and Vertical Competition

Revenue Management Games: Horizontal and Vertical Competition Revenue Mngement Gmes: Horzontl nd Vertl Competton Sergue Netessne he Whrton Shool Unversty of Pennsylvn Phldelph, PA 19103-6340 netessne@whrton.upenn.edu Robert A. Shumsky Smon Shool of usness Admnstrton

More information

SPECIAL PRODUCTS AND FACTORIZATION

SPECIAL PRODUCTS AND FACTORIZATION MODULE - Specil Products nd Fctoriztion 4 SPECIAL PRODUCTS AND FACTORIZATION In n erlier lesson you hve lernt multipliction of lgebric epressions, prticulrly polynomils. In the study of lgebr, we come

More information

Operations with Polynomials

Operations with Polynomials 38 Chpter P Prerequisites P.4 Opertions with Polynomils Wht you should lern: Write polynomils in stndrd form nd identify the leding coefficients nd degrees of polynomils Add nd subtrct polynomils Multiply

More information

ASP On-Demand versus MOTS In-House Software Solutions *

ASP On-Demand versus MOTS In-House Software Solutions * ASP On-Demn versus In-House Softwre Solutons Dn M School of Informton Systems Sngpore Mngement Unversty Abrhm Semnn Xerox Professor of Computer n Informton Systems n Opertons Mngement W. E. Smon Grute

More information

More equal but less mobile? Education financing and intergenerational mobility in Italy and in the US

More equal but less mobile? Education financing and intergenerational mobility in Italy and in the US Journl of Publc Economcs 74 (1999) 351 393 www.elsever.nl/ locte/ econbse More equl but less moble? Educton fnncng nd ntergenertonl moblty n Itly nd n the US *, Aldo Rustchn b, c Dnele Checch, Andre Ichno

More information

Regular Sets and Expressions

Regular Sets and Expressions Regulr Sets nd Expressions Finite utomt re importnt in science, mthemtics, nd engineering. Engineers like them ecuse they re super models for circuits (And, since the dvent of VLSI systems sometimes finite

More information

FAULT TREES AND RELIABILITY BLOCK DIAGRAMS. Harry G. Kwatny. Department of Mechanical Engineering & Mechanics Drexel University

FAULT TREES AND RELIABILITY BLOCK DIAGRAMS. Harry G. Kwatny. Department of Mechanical Engineering & Mechanics Drexel University SYSTEM FAULT AND Hrry G. Kwtny Deprtment of Mechnicl Engineering & Mechnics Drexel University OUTLINE SYSTEM RBD Definition RBDs nd Fult Trees System Structure Structure Functions Pths nd Cutsets Reliility

More information

Mathematics. Vectors. hsn.uk.net. Higher. Contents. Vectors 128 HSN23100

Mathematics. Vectors. hsn.uk.net. Higher. Contents. Vectors 128 HSN23100 hsn.uk.net Higher Mthemtics UNIT 3 OUTCOME 1 Vectors Contents Vectors 18 1 Vectors nd Sclrs 18 Components 18 3 Mgnitude 130 4 Equl Vectors 131 5 Addition nd Subtrction of Vectors 13 6 Multipliction by

More information

Production. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem.

Production. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem. Producer Theory Producton ASSUMPTION 2.1 Propertes of the Producton Set The producton set Y satsfes the followng propertes 1. Y s non-empty If Y s empty, we have nothng to talk about 2. Y s closed A set

More information

Chapter 7: Answers to Questions and Problems

Chapter 7: Answers to Questions and Problems 19. Based on the nformaton contaned n Table 7-3 of the text, the food and apparel ndustres are most compettve and therefore probably represent the best match for the expertse of these managers. Chapter

More information

Calculation of Sampling Weights

Calculation of Sampling Weights Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

More information

Reasoning to Solve Equations and Inequalities

Reasoning to Solve Equations and Inequalities Lesson4 Resoning to Solve Equtions nd Inequlities In erlier work in this unit, you modeled situtions with severl vriles nd equtions. For exmple, suppose you were given usiness plns for concert showing

More information

Appendix D: Completing the Square and the Quadratic Formula. In Appendix A, two special cases of expanding brackets were considered:

Appendix D: Completing the Square and the Quadratic Formula. In Appendix A, two special cases of expanding brackets were considered: Appendi D: Completing the Squre nd the Qudrtic Formul Fctoring qudrtic epressions such s: + 6 + 8 ws one of the topics introduced in Appendi C. Fctoring qudrtic epressions is useful skill tht cn help you

More information

Strategic Labor Supply

Strategic Labor Supply Prelmnry drft My 1999 Do not quote wthout permsson of uthor Comments re welcome Strtegc Lor Supply A dynmc rgnng model nd ts econometrc mplementton Mrm Belo Free nversty of Berln Astrct In ths pper dynmc

More information

Using Series to Analyze Financial Situations: Present Value

Using Series to Analyze Financial Situations: Present Value 2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated

More information