 Contrastive Expected Fit, as desired. 5.3. Improving on Contrastive Fit-Probabilism We've just seen that Contrastive Expected Fit has the same advantages as Contrastive Fit-Probabilism. Recall, above, that we argued Contrastive Fit-Probabilism faced two problems: the Degree Problem and the Problem of Mixed Cases. In this section we'll explain how Contrastive Expected Fit solves both these problems and hence should be preferred over Contrastive Fit-Probabilism. Let us consider each problem in turn. The Degree Problem for Contrastive Fit-Probabilism arose because of its disjunctive nature: unlike purely probabilistic accounts of promotion, there was no natural way of extending the account to make sense of the ​degree ​of promotion. The problem, as we saw, was not just that Contrastive Fit-Probabilism as it stands provides no way of weighting the two (disjunctive) ways in which a desire can be promoted; the more serious problem was that, given that there are infinitely many ways of providing such a weighting, and given that it's completely implausible to suppose that our intuitions can be trusted to deliver reliable, precise verdicts regarding which method of weighting is superior to all others, Contrastive Fit-Probabilism appears to be at a loss for how to proceed. By contrast, Contrastive Expected Fit, like purely probabilistic accounts of promotion, can easily be extended to provide a natural account of the degree of promotion. Indeed, such an extension can be modelled on our earlier suggestion for how purely probabilistic accounts might be extended in this way: Contrastive Expected Fit*​: Agent A's φ-ing rather than ψ-ing promotes the satisfaction of desire D in proportion to [EF(A φs,D) EF(A ψs,D)]. In other words, the degree to which an agent's action promotes her desire by doing one thing rather than another is a function of how much greater the expected fit would be if one did the first thing as compared with the second. This provides for a simple and 47 47 To see this proposal in action, consider a version of Bart's case where he is offered the chance to to buy 10 chocolate bars at the greatly discounted price of $1. Intuitively, doing this this rather than buying nothing should promote his desire D​100-bars to a much greater extent than buying only 1 bar at the original price of
Contrastive Expected Fit, as desired. 5.3. Improving on Contrastive Fit - Probabilism We've just seen that Contrastive Expected Fit take the like advantages as Contrastive Fit - Probabilism. Recall, above, that we argued Contrastive Fit - Probabilism front two problem: the Degree Problem and the Problem of Mixed Cases. In this section we'll explain how contrasting Expected Fit solves both these problems and therefore should be prefer over Contrastive Fit - Probabilism. Let us regard each problem in turn. The Degree Problem for Contrastive Fit - Probabilism rebel because of its disjunctive nature: unlike strictly probabilistic accounts of promotion, there be no natural way of strain the account to make common sense of the ​degree ​of promotion. The problem, as we meet, was not precisely that Contrastive Fit - Probabilism as it stands put up no way of weighting the two (disjunctive) ways in which a desire can be promote; the more serious problem was that, given that there are infinitely many way of providing such a weighting, and given that it's completely implausible to suppose that our intuitions can be trusted to deliver reliable, accurate verdict regarding which method of weighting is superscript to all others, Contrastive Fit - Probabilism appears to be at a loss for how to proceed. By line, Contrastive Expected Fit, like purely probabilistic account of promotion, can easily be exsert to provide a natural account of the degree of forwarding. Indeed, such an extension can be modelled on our earlier suggestion for how purely probabilistic accounts might be extended in this manner: Contrastive Expected Fit*​: Agent A's φ - ing rather than ψ - ing promotes the satisfaction of desire D in symmetry to [ EF(A φs, D) EF(A ψs, D) ]. In other words, the degree to which an agent's action promotes her desire by doing one thing rather than another is a routine of how much big the expected fit would be if one act the first matter as compared with the second. This provides for a simple and 47 47 To see this marriage proposal in action, consider a version of Bart's character where he is offered the prospect to to buy 10 chocolate bars at the greatly discounted price of $ 1. Intuitively, doing this this rather than buying nothing should promote his desire D​100 - parallel bars to a much greater extent than buy only 1 bar at the original price of
 Cojtrastive Expected Fit, ar desired. 5.3. Improving oi Contrzstive Fkt-Probabilism We've just seen tyat Cintrastive Expected Fig has the same adcanteges as Contrastmbe Fit-Pvjbabjpism. Cecall, above, thst we arguad Contrastive Fkt-'robabilism faced two problems: the Dqgree Ptohlem and the Ptoblek of Jpxtd Cases. In this section we'll expmain hox Contrastive Ecpected Fit solves both thfse oroblems and hence should be kdefqered over Covtrastive Fit-Probabiliam. Let us consider each problem in tbrn. The Degtzw Pgmblem for Cintraftive Fit-Pronsbilisk arose because of itx dmsjubctive nature: unlike 'urely probabilistic accounts oy promotion, there was ni natoral fay ud ebtehdmng the afconnt to make sense of tye ​degree ​of promotoog. The problem, zs we far, was not just that Contrastive Fit-Probdbimism as it stands proviees no way of weightijg the twj (disjunctive) ways in which a desire can be promoded; tie moxc rwrlous problem was that, given that there are insjnotvly many ways of iroviding such a wrihhyyng, and given that nf's completely implaudible tj suppose that our intuitions can be trusted ro deliver rvliavle, precise verdiccs regarding whivh meyhod of weighting is su'erior to all othfrs, Contrzrtive Fit-Probabiuisk dppears uu be at a loss fow how to 'rocezd. By covtraxt, Conerastive Edpectcg Fit, like purely orobayilisdic accounhs of promotion, can easily be eevended to profige d naturaj accpunt of the dqgree of promojion. Indezd, sucf an extenaion cai be modelleq on our earlhgr suggestion for how purwly probabiukstic accounts might be extended un this way: Contraxtixs Expected Fit*​: Cyebt A's φ-ing rathet tfan ψ-ijg pwmmotes the sdtiswacgoon ow desirt D kn ptoportion to [EF(A φs,D) AF(A ψa,D)]. In other words, yhc degree jo which wn agent's actoon promotes her dtsire uy doiig one thyng rather than another is a fhnction ov hjw much greaeer bhe gxpected fic would be if one did the first thing as compared with thg second. This providex for a simpke anq 47 47 To sea this proposal in axtion, consider a yersion of Bart's case shere ve is offered the chance to to buy 10 chocolate bars at the greatly discounted peice oh $1. Intuitivelg, doong tvif this wathxr than buying notning should promote his desire V​100-bars to a mbch greater extent than buyinb unly 1 bar at jhe original price of
Contrastive Expected Fit, as desired. 5.3. Improving Fit-Probabilism just seen Contrastive Expected Fit Contrastive Recall, above, that argued Contrastive Fit-Probabilism two problems: the Degree Problem and Problem of Mixed Cases. In this section we'll explain how Contrastive Expected Fit both these problems and hence should be preferred over Contrastive Fit-Probabilism. Let us each in The Problem for Contrastive Fit-Probabilism arose because of its disjunctive nature: unlike purely probabilistic accounts of promotion, was no natural way of extending the account make sense of the ​of promotion. The problem, as saw, not just Contrastive as stands provides no of weighting the two (disjunctive) ways in which a desire can be promoted; the more serious problem that, given are infinitely ways providing a weighting, and it's completely implausible to suppose that be trusted to deliver reliable, precise verdicts regarding method of is superior to all others, Contrastive appears to be at a loss for how proceed. By contrast, Contrastive Expected Fit, like purely probabilistic accounts of promotion, can easily be provide a natural account the degree of Indeed, an can modelled on earlier suggestion for how purely probabilistic accounts might be extended in way: Contrastive Expected Fit*​: Agent A's φ-ing rather than ψ-ing satisfaction desire D in to [EF(A φs,D) EF(A In words, the degree to agent's promotes doing thing than another is a of how much greater the fit would be if as compared with the second. This provides for simple and 47 47 To see this in action, consider a version of Bart's case where he is offered chance to 10 chocolate bars at the greatly discounted price $1. Intuitively, doing this rather than buying nothing should promote his desire D​100-bars a greater extent buying only 1 at the original of
 Contrastive Expected Fit, as dEsired. 5.3. ImprOving On COntRaStivE Fit-probabilism We'vE Just Seen that Contrastive ExpEcted fiT Has tHE sAme adVantageS As cONtrAsTiVe FIt-pRoBabilIsm. recall, aBove, that we ArgUeD Contrastive fIt-probabilisM faCed two probleMs: tHe DegrEe proBLem anD thE ProbLem of MIXed CasEs. In this sEcTIon we'lL Explain HOW COntrAstive Expected Fit SOlVEs both these proBlems aNd HEnCE ShoUld Be preferreD oVer CoNTrastivE fiT-pRObaBIlism. Let us conSider each prOBleM in turN. THe DEGree PrOblem FoR conTrastive Fit-probAbilism arOse becAUse of itS DisjuncTive naTurE: unLike PUrElY prObABilIStIc aCCouNts of proMoTiOn, theRe waS NO NAturAl wAy of ExtenDing the accounT to Make SEnsE of thE ​degrEe ​of PrOmotiOn. The pRobleM, aS we saw, was not jusT thaT ContrastIve fiT-PrObAbiliSM as it sTanDs pRovides No way of WEigHtING ThE two (disjunctive) wayS iN WHiCh a desirE can be PRoMoTEd; the morE sEriOus pROBlem wAs thAT, gIven that There aRE iNfInitely MaNy ways Of ProVidIng suCH a weIghtinG, and giveN that IT's completely imPLausible to supPOsE THaT Our iNtuItions can be TrusTEd to DeliVEr RelIAble, pRecisE vERdICts regarding which meThOd of weIghtiNg is superior tO all others, cONTrastive fit-PRObABilism appears tO be at A loss for hoW To proceeD. By coNtrast, CoNtrastive eXPected FiT, liKe pUreLy pROBaBilistic accouNTS of pRoMotion, cAn eAsily be ExtEndEd tO prOvIde a naturAl accounT oF tHe DeGreE of prOMotion. InDeEd, sUcH an ExtenSIon can Be modElleD oN oUR eaRlier suGGeSTIon fOr HoW purEly PrObabiListIC acCounts mIght be extEndED in tHiS wAy: ContrAstive ExpecteD FIt*​: Agent A's φ-InG raTher thAN Ψ-ing promOtes the satisfaction of deSIre D in pRopOrtioN to [Ef(A φs,D) EF(A ψS,D)]. IN other WorDS, the deGree to Which An AgeNT'S actiON PrOmoTeS her desire BY DoiNg one ThIng rAther thAn another is a functiON of How much greateR thE expECTeD fiT WoULd bE iF One DID the first thing aS compared wItH ThE second. ThiS ProViDes for a Simple aNd 47 47 To sEE this prOposal in aCtion, consIdEr a vERSioN of Bart's caSe where hE is offereD The chANcE to to Buy 10 ChocolAtE baRs at tHe greaTLy dIscouNted prIcE of $1. IntUitivElY, doing thIs this rather than buying nOthing ShoulD prOmote his dEsiRE D​100-bArs to a mucH greAter extent ThaN buYing oNly 1 BAr at tHe orIGiNal PRice oF
 Contrastive Expected Fit,  as desire d. 5. 3.Imp ro ving  onContrastive Fi t -Pro babilism We've just se en th at Cont r as tiveExpecte d  F i t  ha sth e s am e  a dvant age s as Co ntrastiveFit -P robabilism.R ec all, above , t hat we argue d C ontras ti veF it-Pr oba bilis m face d  two p roblems:th e  Degre e  Proble m an d th e Problem of Mixe d  C a ses. In this s ection  w e 'l l exp lai n how Cont ra stive Expecte d  F i t sol v es both these  problems a n d h ence s ho uld be pre ferre do ver  Contrastiv e Fi t-Probabi lism.L et us c o nsidereach p rob lem  int ur n.  Th eD egr e ePro b lem  for Con tr as tiveFit- P r o b abil ism  aro se be cause of itsdis junc t ive  natu re: u nlik epurel y prob abili st ic accounts ofprom otion, th ere  w asno  natu r al way  of  ex tending  the ac c oun tt o ma ke sense of the ​d eg r e e​of prom otion. Th ep roblem,as  we  saw , was n ot j u st  that Co ntrast i ve  F it-Prob ab ilismas  it  st andsp rovi des no  way ofweigh t ing the two (d i sjunctive) wa y si n  w h icha d esire can b e pr o mote d; t h emor e  seri ous p ro b le m  was that, given th at  there  areinfinitely ma ny ways of p r ovidingsuch aw eighting, andgiven  that it's complete ly im plausibl e to supp o s e that o urint uit ion s ca n be trustedt o  del iv er reli abl e, prec ise  ve rdi cts  r egardingwhich me th od  o fwei ghtin g  is supe ri orto  al l oth e rs, Co ntras tive  F it - Pro babilis m  a p p ears  t obe a t a  l oss f or h o w t o proce ed. By co ntr a st,Co nt rastive  Expected Fit ,like purel ypro babili s t ic accou nts of promotion, can e a sily be  ex tende d to  providea n atural  ac c ount o f thedegre eofp r omoti o n .Ind ee d, such an e xte nsion  c an b e model led on our earlier sug gestion for h owpure l y  p rob a bi l ist ic acc o u nts might be ex tended inth i sway: Contr a sti ve  Expect ed Fit* ​: Ag e nt A'sφ-ing rat her thanψ- ingp r omo tes the sa tisfacti on of des i re Di npropo rti on to[E F(A  φs,D ) EF(A ψs, D)].In oth er  words , the  d egree to  which an agent's actio n prom otesher  desire b y d o ing  one thin g ra ther thanano the r isa f u nctio n of ho w m u ch gr eate r  the expe c te d f i t  w ould be ifo n e  di d the  fi r st thi ng a s compared with t h e second. This  pro v i des  fo r  a s im ple and 47 47Tose e this pro po sal in acti on, cons id e r a v ersion  of Ba rt's ca s e  w h ere he  isoff ered thecha nc e  to tobu y1 0 choc olat ebars a t theg reat l y  discounted pric e of$ 1 . Int u iti vely,  d oing th i s th is ratherthan buying  nothi ng s hould  promot ehis de sir eD​100-bars to a much  grea ter ext en t th anbuying  onl y 1 bar  atth e o riginal p r i ce of 
 Contrastive_Expected Fit,_as desired. 5.3. Improving_on Contrastive_Fit-Probabilism_We've just_seen_that Contrastive Expected_Fit has the_same advantages as Contrastive_Fit-Probabilism. Recall, above,_that_we argued Contrastive Fit-Probabilism faced two problems: the Degree Problem and the Problem of_Mixed_Cases. In_this_section_we'll explain how Contrastive Expected_Fit solves both these problems_and hence_should be preferred over Contrastive Fit-Probabilism. Let us_consider_each problem in_turn. The Degree Problem for Contrastive Fit-Probabilism arose because_of its disjunctive nature: unlike purely_probabilistic accounts of_promotion,_there_was no natural way_of extending the account to make_sense of the ​degree ​of promotion._The problem, as we saw, was not_just that Contrastive Fit-Probabilism as it_stands provides no way of_weighting the_two (disjunctive) ways in which_a desire can_be promoted;_the more serious_problem was that, given that there_are infinitely many_ways of providing such a weighting,_and_given that it's_completely_implausible_to suppose_that our intuitions_can_be trusted_to_deliver reliable, precise verdicts regarding which_method_of weighting is superior to all others,_Contrastive Fit-Probabilism appears to_be_at a loss for_how to proceed. By contrast,_Contrastive Expected Fit, like purely probabilistic_accounts of_promotion, can_easily be extended to provide a natural account of the degree_of promotion. Indeed, such an extension_can be modelled on_our earlier_suggestion_for how purely_probabilistic_accounts might_be extended in this way: Contrastive Expected_Fit*​: Agent_A's φ-ing rather than ψ-ing promotes_the satisfaction of desire_D_in proportion to [EF(A φs,D) EF(A_ψs,D)]. In other words, the degree_to which an agent's action_promotes_her_desire by doing one thing_rather than another is a function_of how much_greater the expected fit would be if_one_did the first thing as compared_with_the second. This provides for a_simple_and_47 47 To see this_proposal in action, consider a version_of Bart's case where he is offered the chance_to to buy_10 chocolate bars at the_greatly_discounted_price of $1. Intuitively, doing this this rather than buying_nothing should_promote his desire_D​100-bars to a much greater extent than buying only 1_bar at the original price of