Assessment of Risk of Misinforming: Dynamic Measures

Transcription

1 nerdisciplinry Journl of nformion, Knowledge, nd Mngemen Volume 6, 20 Assessmen of is of Misinforming: Dynmic Mesures Dimir Chrisozov Americn Universiy in Bulgri, Blgoevgrd, Bulgri Sefn Chuov Vicori Universiy of Wellingon, Wellingon, New Zelnd Plmen Meev Sofi Universiy S. Klimen Ohridsi, Sofi, Bulgri Absrc This pper ddresses he impc of wrrny of mlfuncioning nd wrrny of misinforming on ming informed purchse decisions over he dopion period. We propose mhemicl models for qunificion of he ris of misinforming by exploring cusomers lerning evoluion. These mesures, dynmic in heir nure, llow evluion of cusomer s sisfcion or dissisfcion s funcion of he misinforming wrrny coverge over lerning ime. Keywords. is, Misinforming, Lerning, Wrrny nroducion This pper dvnces he reserch on wrrny of misinforming by exending he models developed by Chrisozov, Chuov, nd Meev (2009b. ddresses he evoluion of he ris of wrong purchse decision over he period of dopion of new produc nd he role of wrrny in his process. Le us ssume h cusomer purchses new produc, e.g., new personl compuer (PC. The purchse in mde in order o ddress some priculr needs nd solve priculr se of cusomer s ss, e.g., o develop ex documens, o me simple or complex clculions, o send nd receive e-mils, for nerne browsing, o ply gmes, or o lisen music nd wch movies. The PC hs priculr chrcerisics (specificion, such s CPU speed, memory cpciy, ec., which llow he cusomer o Meril published s pr of his publicion, eiher on-line or in prin, is copyrighed by he nforming Science nsiue. Permission o me digil or pper copy of pr or ll of hese wors for personl or clssroom use is grned wihou fee provided h he copies re no mde or disribued for profi or commercil dvnge AND h copies ber his noice in full nd 2 give he full ciion on he firs pge. is permissible o bsrc hese wors so long s credi is given. To copy in ll oher cses or o republish or o pos on server or o redisribue o liss requires specific permission nd pymen of fee. Conc Publisher@nformingScience.org o reques redisribuion permission. solve her ss. There re wo sges in he dopion (ccepnce of new produc. The firs one is ming he purchse decision. The second one is reled o he iniil period of produc usge when he buyer discovers wheher he produc mees her needs. The produc wrrny, offered by he producer, could provide coverge for wo issues h he buyer my encouner. The firs Edior: Eli Cohen

2 Assessmen of is of Misinforming one is reled o he mlfuncioning of he produc, i.e., he produc does no funcion ccording o is specificions. n his cse, he produc is repired or replced wih no chrge o he cusomer. The second issue is reled o buyer s sisfcion, i.e., o wh exend he produc mees he buyer s needs o solve for her ss. n oher words, o wh exen, he ime of he purchse, he cusomer hs been misinformed regrding he produc s biliy o solve her ss nd o sisfy her needs. The wrrny h provides coverge gins he second issue we cll wrrny of misinforming. f he cusomer is no fully sisfied, he wrrny of misinforming llows for he reurn of he produc. The wrrny of misinforming provides n opporuniy for he clien o explore nd lern bou he properies nd feures of he produc wihou incurring ny ris. Over he period of lerning, he ris h he produc will no sisfy he cusomer s needs evolves s well. On one hnd, he cusomer lerns how he produc properies rele o her needs, bu on he oher hnd, he clien lerns wh he produc cn offer, nd bsed on his new informion, exends/modifies he se of her ss nd reled needs. Modelling he dynmics of his ris is he obecive of our pper. The role of wrrny s he produc s mlfuncioning ris shring mechnism is well sudied. The role of wrrny s promoionl ool is lso well explored. Models o evlue he ris of misinforming were inroduced by Chrisozov, Chuov, nd Meev (2007 nd furher developed in series of ppers (2008, 2009, 2009b, 2009c, 200. n hese ppers his ris nd is wrrny coverge were sudied. The role of wrrny o suppor lerning ws discussed s well. Here we invesige how his lerning process influences he ris of misinforming. A brief lierure review you cn find in Chriozov, Chuov, nd Meev (2009b. The noions nd definiions used so fr re given in he Appendix. n he nex secion we briefly inroduce he bsic erms nd noions used in he models, composiion of wrrny policy, nd he sic mesures of he ris of misinforming. The hird secion ddresses he dynmic mesures of he ris of misinforming by inroducing nd exploring he buyer s lerning evoluion. The Model An Overview Bsic Terms nd Noions The group of buyers is denoed by B = {b }, =, 2,, n, where b represens he h buyer. Ech b hs se of ss h s/he needs o solve for. Le A = { i }, i =, 2,,, be he se of he ss of he b h s/he needs o solve for by using produc D. Assume h for every s i, he b hs degree of ccepnce of he produc wih respec o his s, sy q i. The degree of ccepnce of he produc q i is mesure of he buyer s udgmen regrding he miniml quliy he produc D hs o hve in order o be suible o solve for his s. is described in erms of quliy of he produc nd is vlue is wihin [0, ]. f ny produc is ccepble hen q i = 0, wheres, if q i =, he buyer hs very high expecions on he quliy of he produc regrding i. Abou esimion of q i from empiricl d see Chrisozov, Chuov, nd Meev (2008. n his sudy we ssume h q i is nown nd normlized. The se of ss A n = U A cn be srucured ccording o he ss h re common for mny, = no necessrily ll, buyers. Ech common s specifies priculr cegory wihin he se of ss A (e.g., nerne surfing. Of course, differen buyers hve differen crieri of wh is ccepble performnce of he produc in using ss in given cegory. For compleeness n ssumpion is mde h ll buyers hve ll ss, bu for some of hese ss he buyer s need o solve for is equl o zero. Le A * i, i =,2,..., denoe he i h cegory of ss for he se of buyers 64

3 Chrisozov, Chuov, & Meev B. Noe h some of he ss he buyer needs o solve using he produc my no be mong he ss he produc is design for. Therefore, he probbiliy h he produc is suible o solve for such ss is ssumed o be equl o zero. Then, = U A * is he se of ll differen ss nd, of * course, A = A. Se A describes he se of ss s union of buyers ss, wheres A * is he sme se of s from cegory poin of view. The need of he b o solve for s from cegory A * i is n i, where 0 n i. f n i = 0, hen he b does no need o solve for ny s from A * i, wheres, if n i =, b definiely needs o solve for s from A * i. n cse of n i = 0, hen q i =0, i.e., if b does no need o solve s from A * i, hen ny produc is ccepble for him. The vlue of n i is compleely subecive. The b is described by he riple b ={( i, q i, n i i A * i,, 2,,, 0 n. For ech buyer ll i A * i, qi re presened in his descripion, bu for some of hese ss he corresponding need my be zero. The seller sells he produc D, which is cpble of solving ss from A * i. Le p i = p(a * i be he probbiliy h D is suible o solve for s from A * i. f p(a * i =0, hen D is no suible ll for solving for his cegory of ss, wheres, if p(a * i =, D is n excellen choice for solving for ss from A * i, i.e., D mees ny level of buyer s degree of ccepnce, reled o ss in A * i. Furher, s pr of his mreing policy, he seller sends messge o group of buyers describing he properies/quliies/feures of D. The conen of his messge is bsed on he seller s evluion of p(a * i nd i does no e ino ccoun he vlue of {q i }, =,2,,n. n his communicion, he informion symmery is due o he difference beween he experise of he seller nd he buyer regrding he produc D. The usge of specific erminology in he messge my increse he level of informion symmery. Bsed on his messge nd his/her bcground, he b ssesses he probbiliy pˆi = p ˆ( i h he produc D is suible o solve for his/her s i. f ni = 0 nd/or q i = 0, hen here is no need o esime p ˆ(. b mes his/her purchse decision bsed on he comprison beween { p ˆ i } nd {q i } over ll ss from his/her se A. Due o he informion symmery, he vlues of p i nd p ˆi my differ significnly. Also hese vlues my differ becuse p i is evlued by he seller for he cegory of ss A * i nd no for he priculr i A * i, which is of ineres o b. Pure nd Mixed Wrrny Policies The wo ypes of pure wrrnies, he wrrny of mlfuncioning W ( nd wrrny of misinforming W (, hve one min prmeer of ineres heir wrrny period, nd, re- specively. During his wrrny period, wrrny clims gins fuly produc re legiime. A mixed wrrny policy Wp = {, } ccouns for boh ypes of wrrnies nd i is idenified by wo ordered ime periods, nd. During ime period of lengh fer he produc sle ll wrrny clims gins he produc performnce re legiime nd during ll wrrny clims gins he produc suibiliy re legiime. The mixed wrrny combines he wrrny of ml- A * i i Here, by ime we men ny mesure of he wrrny coverge, no necessrily he clendr ime. For exmple, in uomobile indusry, he wrrny coverge is idenified no only by he vehicle s ge, bu ccouns lso for he ccumuled milege, i.e., ime could by he ge of he vehicle, or i could be he ccumuled milege. 65

4 Assessmen of is of Misinforming funcioning nd wrrny of misinforming nd provides uniform mechnism for ris shring for boh ypes of uncerinies uncerinies reled o mlfuncioning s well s uncerinies reled o misinforming. We cll wrrny policy, which covers only one cegory of riss, pure wrrny policy, nd policy h covers boh riss mixed. Wih µ i, 0 µ i, we denoe he impornce of he wrrny of misinforming for clien b wih respec o s i. The vlue BW ( i = μi + ( μi represens he blnced vlue of he wrrny prmeer h provides he suppor for he b purchse decision wih respec o s i. From clien s viewpoin, he blnced vlue BW ( i inegres he impornce of he wo wrrny specs mlfuncioning nd misinforming wih respec o s i. ncresing he vlue of he blnced wrrny prmeer B ( W i increses he quliy of he wrrny policy for he clien b wih respec 2 2 o s i. The sndrd deviion QW ( i = ( BW ( i μi. + ( BW ( i.( μi mesures he error from he bes blnced vlue of he wrrny prmeer nd represens he unceriny llowed by he wrrny policy in supporing clien s b correc purchse decision wih respec o s i. Decresing he vlue of his unceriny increses he quliy of he wrrny policy. Therefore, he clien b is described by he uple b ={( i, q i, n i, µ i i A * i,, 2,,, 0 n i, q i, µ i. n he proposed dynmic mesures for he ris of misinforming we incorpore he mesure of impornce µ i nd he ides of he noion of quliy of wrrny policy. From producer s poin of view, mixed wrrny policy W p is of high (opiml quliy, if i minimizes he expeced wrrny servicing cos nd i mximizes he level of produc ccepnce by he cliens. From clien s poin of view, mixed wrrny policy W p is of high (opiml quliy, if he blnced vlue of he wrrny prmeer is mximl nd he level of unceriny i llows in supporing clien s correc purchse decision is miniml. μ i By normlizing μ i over ll possible cegories of ss by using μ i = nd over ll pos- μ sible cegories of ss nd ll cliens μ i = J μ i = i we obin he relive mesure of impor- μ nce of misinforming wrrny for s i for clien b. Bsed on he quliy of he wrrny policy for clien b wih respec o s i, he overll quliy of he wrrny policy for clien b cn be define s B( W = μ B( W. Similrly, he quliy of he wrrny policy (for his group of buyers is evlued s follows J B( W μ B( = i = W. We ssess he quliy of wrrny policies by compring heir blnced wrrny prmeers. From clien s viewpoin, he wrrny sregy wih he lrges blnced wrrny prmeer is he one wih he highes quliy. For furher deils see Chrisozov, Chuov, nd Meev (200. i i i i 66

5 Chrisozov, Chuov, & Meev is of Misinforming: Sic Mesures Mesuring he ris of ming wrong purchse decision he ime of he purchse is ddressed by Chrisozov, Chuov, nd Meev (2008, Here we will cll his ris nd is mesures sic o disinguish from he ris of ming wrong decision, nd is mesures during he dopion process we will cll dynmic. There re wo possible oucomes for he buyer s purchse decision regrding ny produc D. could be YES (posiive, i.e., go hed nd purchse he produc or NO (negive, do no purchse he produc. The clien b will me posiive decision if for ll i A, such h n i >0 nd q i > 0, he inequliy pˆi q i holds nd he decision will be negive if he opposie inequliy pˆi < q i is vlid for les one s. n ddiion, he correcness of his decision depends lso on he relionship beween p i, he cul biliy of D o solve for i nd p ˆi - he esimed biliy of D o solve for i. Ech of he six possible orderings beween p i, p ˆi nd q i idenify wheher buyer s decision o buy he produc is wrong or correc wih respec o s i. The following noion is used o indice he correcness/incorrecness of he buyer s purchse decision wih respec o s i : r i = 0 he decision is wrong he decision is correc The following six cses re considered:. p ˆ i < pi < qi - he produc is no suible o solve for s i, he buyer s esimion of he suibiliy of he produc is opimisic, i.e., p i < p ˆi, nd below he degree of ccepnce, hus he decision is negive nd correc nd r i =0; 2. p i < q i < pˆi - he produc is no suible o solve for s i, he buyer s esimion of he suibiliy of he produc is opimisic nd bove he hreshold of ccepnce, hus he decision is posiive nd wrong, nd r i =; 3. q ˆ i < pi < pi - he produc is suible o solve for s i, he buyer s esimion of he suibiliy of he produc is opimisic nd bove he hreshold of ccepnce, hus he decision is posiive nd correc, nd r i =0; 4. pˆi < p i < q i - he produc is no suible o solve for s i, he buyer s esimion of he suibiliy of he produc is pessimisic, i.e., p ˆi is less hn p i, nd below he hreshold of ccepnce, hus he decision is gin negive nd correc, nd r i =0; 5. pˆi < q i < p i - he produc is suible o solve for s i, he buyer s esimion of he suibiliy of he produc is pessimisic nd below he hreshold of ccepnce, hus he decision is negive nd wrong, nd r i =; 6. q ˆ i < pi < pi - he produc is suible o solve for s i, he buyer s esimion of he suibiliy of he produc is pessimisic nd bove he hreshold of ccepnce, hus he decision is posiive nd correc, nd r i =0. The disnce beween p i nd p ˆi llows o define hree cegories of cusomers opimiss, pessimiss nd reliss, ccording o he degree nd direcion of messge misinerpreion. The 67

6 Assessmen of is of Misinforming moun of misinforming is mesured by he disnce beween p i nd p ˆi, i.e., he degree of informion symmery of s i, nd i is denoed by i = bs( p pˆ. is of Misinforming: Dynmic Mesures The Nure of Dynmic Mesures Here we propose dynmic mesures of he ris of misinforming over he produc wrrny periods nd, in priculr, is dependence on he coverge of he misinforming ris. This pe- riod de fco provides ris-free lerning opporuniies in respec o boh: produc s properies pˆ i nd cusomer s level of ccepnce q i. We ssume h he degree of relive impornce of he wrrny of misinforming µ i for clien b wih respec o s i is nown. Dynmic mesures re usified by he fc h while using he produc he clien ccumules nowledge on he feures nd suibiliy of he produc o solve for her ss, i.e., pˆ = pˆ ( p. i i i n Chrisozov, Chuov, nd Meev (200 we focus on he nowledge ccumulion process by inroducing he mesure of he quliy of wrrny policy, which depends on nd. Here, we refine he ime dependency of he ris of misinforming by looing he clien s lerning process reled o her needs. By using he produc, he clien lerns no only wheher he produc is suible for her iniil needs, bu is ble o idenify new needs h could be ddressed by his cegory of producs. Obviously, his ime dependen lerning could ffec he levels of produc ccepnce q i, i.e., q = q ( s well. i i The hird prmeer h chrcerizes he clien b is her se of ss nd he needs o solve hem. We ssume h his prmeer will no chnge during he ril period nd herefore will no influence he dynmics of he misinforming ris n ( =. i i n i Figures nd 2 illusre he relionship of he wo lerning curves: lerning bou he produc s propery how pˆ i ( chnges over ime nd lerning regrding qi ( for he wo cegories of cliens opimiss nd pessimiss. The opimiss overesime he produc quliy; h is why he lerning curve pˆ i ( decreses in ime. The pessimiss iniilly underesime he produc quliy; herefore for hem he curve pˆ i ( is incresing. For pessimiss he wo curves my inersec if he lerning regrding pˆ i ( decreses slower in ime hn he lerning regrding q. Here, we consider only he cses when he level of ccepnce q ( increses in ime. n ( i cse he buyer is pessimis nd he level of ccepnce decreses wih lerning, he wo lerning curves will no inersec nd he misinforming wrrny coverge doesn ply ny role. n he opimis cse nd decresing level of ccepnce he siuion is similr, bu symmericl, wih he cse of pessimiss nd incresing level of ccepnce he wo curves my inersec if he lerning regrding pˆ ( decreses fser hn he lerning regrding q (. i i i i i n i 68

7 Chrisozov, Chuov, & Meev lerning Cse: Opimiss Produc's biliy Needs Figure. Dynmics of lerning in cse of opimis cusomers lerning Pessimiss Needs Produc's biliy Figure 2. Dynmics of lerning in cse of pessimis cusomers f he wo lerning curves inersec, which mens h he produc s quliy becomes under he level of ccepnce, he cusomer will relize h she hs mde wrong purchse decision regrding he produc. Therefore, cusomer s sisfcion depends on he coverge provided by he wrrny of misinforming nd he wrrny periods ply significn role in qunifying he ris of misinforming r ( = r ( p, pˆ (, q (. i i i i i ( i To illusre he bove ides nd he proposed mesures, we provide n exmple on how o evlue he riss r. Le us ssume h he produc is personl compuer, nd he clien b evlues is performnce using he response ime, i.e., he PC response ime is he min prmeer of ineres. This prmeer depends on severl fcors such s he speed of he processor, he PC rchiecure nd he speed of he bus, he ccess ime of he hrd dis, ec. Also, he response ime depends on he priculr pplicion in use. For exmple, he response ime of compuionl s is mosly influenced by he CPU speed, nd no much by he hrd dis ccess ime. A d- 69

8 Assessmen of is of Misinforming bse reled s is us he opposie he hrd dis ccess ime is wh influences mosly he PC performnce/response ime. Therefore, he cul response ime of PC is user specific prmeer. Usully, he seller provides informion on he echnicl prmeers of he produc, bu no he subecive, user reled performnce prmeers. is well nown h in he process of using he produc, he user lerns wheher he PC performnce mees her specific needs. Also, during his lerning period, he user migh fce he need o solve lrger scle problems hn iniilly inended. This ddiionl requiremen will ffec (increse he level of needed performnce compred o h iniilly expeced. Dynmic Generlizion of Mesures of he is Furher we will presen how he dynmic mesures will influence he overll riss ssocied wih he informing process by following he hree levels in modelling his ris s presened in Chrisozov, Chuov, nd Meev (2009 nd generlized for he group of cliens. One-o-one informing process: The ris of clien Following Chrisozov, Chuov, nd Meev (2009b, we will define severl models o evlue he overll ris regrding ll ss i clien b is exposed o by ming decision bsed on received informion regrding he feures of he produc D nd ccording o her undersnding of his informion. We ssume h he difference in hese models is ssocied wih differen informion componens vilble in evluion of he ris: he needs n i, he ris of wrong decision regrding given s - r i, nd he degree of informion symmery - i i. ncluding differen componens, ccording o wh d is vilble, we my consruc he following hree mesures: A simple model only d regrding he probbiliy of wrong decisions r i is used. is no difficul o collec such d in commercil civiies; one hs o coun only he clims of he unsisfied cliens: r ( s i = r ( ( μ μ i i + ( μ + ( μ i i. ( f ll decisions re wrong nd corresponding degrees of misinforming impornce µ i re s equl o, hen, s expeced, r =. Model ccouning for he clien s needs. We ssume h if clien b doesn need o solve ss from given cegory * i i A hen n = 0 she doesn need o now nd inerpre he messge regrding hese ss. n generl, i is esy o see h here is simple relionship beween he level of needs nd he ris of misinforming, i.e., he higher he need of clien o solve for given s is, he higher corresponding ris of misinforming is: r n ( i = n ( r ( ( μ i i i i n ( ( μ + ( μ + ( μ i i. (2 70

9 Chrisozov, Chuov, & Meev Model ccouning for he needs nd for he degree of informion symmery. n his model, we collec nd use feedbc d on he hird componen, he degree of error in undersnding he messge. The mesure of he ris in his cse is he mos complex, bu he mos precise: r ( i = n ( r ( i ( μ + ( μ i i i n ( ( μ i i + ( μ i i (3 One-o-Mny informing process n defining hese riss from he seller s viewpoin we generlize he bove formul for whole group of cliens. This ris is mesure of he informing quliy of he messge h is sen ou o cliens. mesures he quliy of he messge, i.e., how he conen or mening he sender inends o convey o he cliens is described nd presened in he messge. n his cse, we lso consider hree models ccouning for he hree informion componens: A simple model: s ( J i = = J = r ( ( μ ( μ i i + ( μ + ( μ i i (4 A model ccouning for he cliens needs: n ( = J i = J = r ( n (( μ + ( μ i i n (( μ + ( μ i i i i (5 A model ccouning for he needs nd degree of informion symmery:. ( = J r ( n ( i ( μ + ( μ i i i = J = i n (( μ + ( μ i i i i (6 An llusrive Exmple Nex, in Tble, we illusre he dynmics of he ris of misinforming on PC cse wih fixed period of = yer of wrrny of mlfuncioning nd four differen misinforming wrrny periods, such s = {0 monhs, 3 monhs, 6 monhs, 9 monhs}. Le q pˆ ( min( pˆ ( is he expeced produc performnce for ll ss for clien b nd = i ( = mx( q ( is he hreshold of he level of ccepnce for he sme clien. i 7

10 Assessmen of is of Misinforming niilly, he ime of he purchse, = 0 = 0, ˆ ( 0 =. 0. This is so, becuse h ime, he clien believes h he produc is suible for ll of his ss, i.e., pˆ ( =, for,.,, nd p 0 = i 0 pˆ ( min( pˆ ( =. Le us ssume h for he se of ss of clien b, he cul performnce of his PC is p = 0.7 < p ( 0, or b is n opimis. Also, ssume h for clien q ( = 0.75 b he level of ccepnce for her iniil need for he PC performnce is nd 5 monhs re needed o fully undersnd he cpbiliy of he produc. Le us ssume h he lerning curve for he cully needed performnce follows n incresing funcion. The dynmics of hese d is presened in Tble. Tble. Dissisfcion riss in lerning process Time (monhs pˆ ( ( q is when is when = 0 = 3 i 0 = 6 0 is when is when * 0* 0* * 0* * 0* * 0* * * * = 2 * The dissisfcion ris is zero, becuse he clien is proeced by he wrrny of misinforming nd cn reurn he produc for full reimbursemen. To illusre he behviour of riss of misinforming over he ime, we will me n ddiionl ssumpion h for b he rio of impornce of he wo wrrnies is se o μ = Also, for simpliciy, we ssume h he group of ss is composed by only one s. Under hese seings, he riss for b re shown in Tble 2. 72

11 Chrisozov, Chuov, & Meev Tble 2. Dynmics of misinforming ris s funcion of ime nd wrrny coverge. Time r i r ( s i = r ( ( μ + ( μ i i μ + ( μ The vlues of he oher wo ris mesures re obined by muliplying he vlues in Tble 2 by he needs or by he needs nd coefficien of informion symmery. Conclusion n his pper we proposed n pproch o qunify he dynmics of ris of misinforming over he lerning period during he dopion of new produc. The proposed mesures my help in beer undersnding he process of dopion wih respec of buyer s sisfcion nd he riss ssocied wih dissisfcion. The misinforming wrrny coverge is considered s ool o encourge lerning nd o fcilie dopion by reducing he ris of unceriny nd poenil dissisfcion. This pper is our firs emp o ddress he dynmics in he purchse nd dopion processes. Furher incorporion of hese ides ino mesuring he quliy of wrrny policies will provide beer insigh on he seller/buyer relionship nd on insrumens used o fcilie nd suppor successful communicion beween hem. Anoher open issue is how o collec d regrding he lerning evoluion in he dopion process of new produc. Our nex s will be o design n experimen for collecing pproprie d, which will llow for beer illusrion of our new models. Also, we will focus on modelling he dynmic mesures of he ris of misinforming in he conex of compeing producs. i i 73

12 Assessmen of is of Misinforming eferences Chrisozov, D., Chuov, S., & Meev, P. (2007. On he relionship beween wrrny nd he ris of informion symmery. Journl of ssues in nforming Science nd nformion Technology, 4, erieved from hp://proceedings.informingscience.org/nste2007/stv4p chri295.pdf Chrisozov, D., Chuov, S., & Meev, P. (2008. Wrrny nd he ris of misinforming: Evluion of he degree of ccepnce. Journl of ssues in nforming Science nd nformion Technology, 5, erieved from hp://proceedings.informingscience.org/nste2008/stv5p chris444.pdf Chrisozov, D., Chuov, S., & Meev, P. (2009. On wo ypes of wrrnies: Wrrny of mlfuncioning nd wrrny of misinforming. Asi-Pcific Journl on Operion eserch, 26(3, Chrisozov, D., Chuov, S., & Meev, P. (2009b. Chper. nforming processes, riss, evluion of he ris of misinforming. n T. G. Gill & E. Cohen (Eds., Foundions of informing science: (pp Sn os, CA: nforming Science Press. Chrisozov, D., Chuov, S., & Meev, P. (2009c. The ris of misinforming for compeing messges. Journl of ssues in nforming Science nd nformion Technology, 6, erieved from hp://iisi.org/vol6/stv6p35-364chrisozov627.pdf Chrisozov, D., Chuov, S., & Meev, P. (200. Assessmen of quliy of wrrny policy. nerdisciplinry Journl of nformion, Knowledge, nd Mngemen, 5, erieved from hp://iim.org/volume5/jkmv5p06-072chrisozov447.pdf D Appendix. Noions nd Definiions Noion B = {b }, =, 2,, n A = { i }, i =, 2,, A he produc he se of buyers n se of ss of ll buyers = U A = A *, i =,2,..., cegories of ss i Definiion ss, which he b needs o solve by using he produc n i he need of b o solve her s i. 0 n q i p i = p(a i * degree of ccepnce. The miniml quliy ( hreshold, which he produc mus possess in order o mee he cusomer b expecions regrding her s i. probbiliy h he produc will solve problems from cegory A i *. Or he level o which he produc D my sisfy he buyers needs regrding he ss from his cegory pˆi = p ˆ( i subecive ssessmen of he buyer b regrding he probbiliy (level of sisfcion h he produc will be suible for solving her s i i 74

13 Chrisozov, Chuov, & Meev r i Noion Definiion indicor of he decision correcness r i =0 if he decison is correc; r i = mens wrong decision i = bs( p pˆ mesure of informion symmery i i i W p ( wrrny policy. ime of he coverge W ( wrrny policy regrding ris of mlfuncioning (ris of low relibiliy W ( wrrny policy regrding he ris of misinforming W = {, } mixed wrrny policy, if p ( W,0 or W (0, pure wrrny policies p p 0 nd 0 µ i, 0 µ i subecive ssessmen of impornce of he misinforming wrrny policy for ming purchse decision by b in respec o s i. BW ( i = μi + ( μi blnced vlue represens he effecive coverge of wrrny policy QW BW BW 2 2 ( i ( ( i μi. ( ( i.( μi = + sndrd deviion - represens he unceriny ssocied wih he wrrny policy s r ( r i simple mesure of he ris in purchse decision for b, depends only on wheher he decision is correc or no n r ( ri, ni mesure of he ris in purchse decision for b, depends on wheher he decision is correc or no; nd he needs r ( ri, ni, ii mesure of he ris in purchse decision for b, which incorpores he indicor for correcness of he decision, he needs nd he mesure of informion symmery s ( r i simple mesure of he ris in purchse decision for group B, depends only on wheher he decision is correc or no n ( ri, ni Mesure of he ris in purchse decision for group B, depends on wheher he decision is correc or no; nd he needs ( ri, ni, ii mesure of he ris in purchse decision for group B, which incorpores he indicor for correcness of he decision, he needs nd he mesure of informion symmery r ( nd ( dynmic mesures of he ris for b nd for group B 75

14 Assessmen of is of Misinforming Biogrphies Dimir Chrisozov is Professor of Compuer Science he Americn Universiy in Bulgri, Blgoevgrd 2700, Bulgri since 993, e- mil: He hs more hn 30 yers of experience in res s compuer science, quliy mngemen nd informion sysems. He grdued Mhemics from Sofi Universiy S. Klimen Ohridsi in 979. He compleed his PhD hesis Compuer Aided Evluion of Mchine elibiliy in 986. n 2009 he complees his D.Sc. hesis on Quniive Mesures of nforming Quliy. n CTT nform ( Dr. Chrisozov ws involved in esblishing he nionl informion newor for echnology rnsfer nd reserch in he res of echnologies ssessmen, inegrl quliy mesures nd informion sysems for quliy mngemen. n hese res he ws recognized s one of he leding expers in Bulgri. Professor Chrisozov hs more hn 70 publicions s sepre volume, ournl ppers nd ppers in refereed proceedings. He is founding member of nforming Science nsiue nd chir of Bulgrin nforming Science Sociey; nd member of he Bulgrin Sisicl Sociey. Dr. Sefn Chuov is eder in Operions eserch he School of Mhemics, Sisics nd Operion eserch, Vicori Universiy of Wellingon, Prive Bg 600, Wellingon, New Zelnd, e-mil: schuov@msor.vuw.c.nz. She hs PhD nd MSc in Mhemics (concenrion in Probbiliy nd Sisics nd BSc in Mhemics from Universiy of Sofi, Sofi, Bulgri. Her reserch ineress re in pplied sochsic models, wrrny nlysis, relibiliy nd queuing. She hs more hn 50 publicions nd hs presened ppers nionl nd inernionl conferences. Dr. Plmen S. Meev is Associe professor in Fculy of Mhemics nd nformics, Sofi Universiy S.Klimen Ohridsi, Deprmen Probbiliy, Operion eserch, Sisics, Bulgri, 64 Sofi, 5, J.Boucher sr., e-mil: pm@fmi.uni-sofi.bg. His MSc in Mhemicl Sisics is from Sofi Universiy nd his PhD is from Moscow Se Universiy. The reserch ineress re in communicion heory, pplied sisics, sisicl sofwre nd pplicions. More hn 70 ppers re published in scienific ournls nd proceedings of scienific conferences. He ws he Chir of Bulgrin Sisicl Sociey nd member of ENBS nd Bulgrin nforming Science Sociey. 76