The economics of a stage-structured wildlife population model

Size: px
Start display at page:

Download "The economics of a stage-structured wildlife population model"

Transcription

1 The econoics o a stage-structured wildlie population odel Anders Skonhot and Jon Ola Olaussen Departent o Econoics Norwegian University o Science and Technology N-7491 Dragvoll-Trondhei, Norway Abstract A siple three-stage odel o the Scandinavian oose (Alces alces) (young, adult eale and adult ale) is orulated. Fecundity is density dependent while ortality is density independent. Two dierent harvesting regies are explored; hunting or eat and trophy hunting. The paper gives an econoic explanation o the biological notion o eales as valuable and ales as non-valuable. The paper also deonstrates how this notion ay change under shiting econoic and ecological conditions. 1. Introduction The ai o this paper is two-old; irstly, to deonstrate the econoic content o a structured wildlie population odel, and secondly, to show how this econoic content ay change under dierent anageent scenarios. Analysing structured wildlie harvesting odels, i.e., odels where the species is grouped in dierent classes according to age and sex, has a long tradition within biology and Caswell (21) gives a recent in-depth overview. No econoic considerations are, however, present here. This is also so in e.g., Milner-Gulland et al. (24). Econoic analysis is introduced in Cooper (1993) who orulates a siulation odel that inds the econoically optial level o deer tags or hunting zones and where the deer population is structured in bucks and does. Skonhot et al. (22) analyses various anageent strategies or a ountain ungulate living in a protected area and a hunting area. Four stages are included here; eales and ales within and outside the protected area. Due to the coplexity o these odels, however, it is diicult to understand the various econoic echaniss inluencing harvesting and abundance. The present paper analyses such econoic echanis ore explicitly where a siple three stages odel, including young, adult eale an adult ale, is orulated. The present analysis is ost siilar to that o Clark and Tait (1982) who studied the optial harvest value in a sex-selective harvesting odel and where the population hence was grouped in two stages. As in Clark and Tait, we are analysing biological equilibriu where natural growth is balanced by harvesting. However, in contrast 1

2 to Clark and Tait, trophy hunting, in addition to eat value axiisation, is analysed. We also calculate the shadow values o the adult ales and eales. We are thus giving an econoic explanation o the biological notion o eales as valuable and ales as nonvaluable. The odel is applied or a oose population (Alces alces), and is studied within a Scandinavian ecological and institutional context. Moose is by ar the ost iportant gae species in Scandinavia, and in Norway and Sweden abut 4, and 1, anials, respectively, are shot every year. Moose hunting has traditionally been a local activity, and the landowners receive the hunting value. The hunters have basically been the local people; the landowners and their ailies and riends, and where the anageent goal has been to axiise the eat value or stable populations (ore details are provided in Skonhot and Olaussen 25). During the last years, however, a ore coercialised hunting and wildlie industry has eerged, and the Scandinavian oose hunting is gradually shiting ro a aily and riend activity to a gae hunting arket. The trophy value o old ales plays an iportant role here. Both the traditional exploitation schee and the new coercialised schee are studied, and the consequences or harvesting and the population coposition are analysed. The paper is organised as ollows. In the next section, the three stage oose population odel is orulated. Section three deonstrates what happens when the hunting is steered by the traditional landowner goal o axiising eat value. Next, in section our we study the sex and age coposition under the new exploitation regie o trophy hunting. A downward sloping inverse deand curve or hunting old ales is introduced. In addition, it is also assued that the deand ay shit due to a quality eect captured by the density o old ales. Section ive illustrates the odels by soe nuerical siulations, while section six suarises the indings. 2. The population odel The Alces alces is a large ungulate with ean slaughter body weight (about 55% o live weight) or adult oose in Scandinavia o about 17 kg or ales and 15 kg or eales. The non-harvest ortality rates are generally low due to lack o predators, and there is no evidence o density dependent survival. On the other hand, ecundity has proven to be 2

3 aected by the eale density while the nuber o ales, within the range o oose densities in Scandinavia, sees to be o negligible iportance (see, e.g., Nilsen et al. 25 or ore details). Usually, the oose population is structured in our stages; calves, yearlings, adult ales and adult eales. In what ollows, however, to obtain a traceable analytical odel, while still catching the ain ecological content, just three stages are considered as yearlings are let out. The population at tie (year) t is hence structured as calves and adult ales ( 1 yr) X t so that the total population is X t, adult eales ( 1 yr) X = X + X + X. Yearlings t t t t are thereore included aong the adult ale and eale. These three stages are henceorth called young, eale and ale. The population is easured in spring ater calving. All stages are generally harvested, and the hunting takes place in Septeber-October. All natural ortality is assued to take place during the winter, ater the hunting season as the natural ortality throughout suer and all is sall and negligible. The sae natural ortality rate is iposed or ales and eales. As indicated, natural ortality is ixed and density independent, while reproduction is density dependent. It is urther assued the sae sex ratio or the young when they enter the old stages (again, see Nilsen et al. 25). X t Neglecting any stochastic variations in biology and environent, and neglecting any dispersal in and out o the considered area, the nuber o young at tie ( t + 1) is irst governed by: (1) X t+ 1 = rx t t with r t > as the ertility rate (nuber o young per eale). The ertility rate is density dependent in the nuber o eales: (2) r = r( X t ) t with dr / dx = r ' < (when oitting the tie subscript) and where r () > is ixed. Cobing (1) and (2) gives the recruitent unction ( X = r X ) X and dierentiating yields dx / dx = ( r ' X + r). The recruitent unction is assued to be concave. For obvious reasons dx / dx should hold in an optial harvesting prograe. 3

4 The abundance o (old) eale ollows next as: (3) X h X h X t+ 1 =.5(1 )(1 t) t + (1 )(1 t) t where > and > are the density independent ortality ractions o young and eale (and ale), respectively, while h t and h t are the harvesting ractions. Hal o the young population enters the eale, ater harvesting and natural ortality. The nuber o (old) ales is inally given by: (4) X h X h X t+ 1 =.5(1 )(1 t) t + (1 )(1 t) t where h t is the ale harvesting raction. When cobining equations (1) - (3), the eale population dynaic reads X =.5(1 )(1 h ) r( X ) X + (1 )(1 h ) X. This is a second order non-linear t+ 1 t t t 1 t t dierence equation, and nuerical analyses deonstrate that the equilibriu is stable or ixed harvesting ractions (see, e.g., Gandolo 21 or a theoretical exposition). Oitting the tie subscript, the equilibriu reads: (5) X =.5(1 )(1 h ) r( X ) X + (1 )(1 h ) X. There are two equilibria; the trivial one o X = and X > given by 1.5(1 )(1 ) ( h r X ) (1 )(1 h ) = +. Because r ' <, the non-trivial equilibriu will be unique and ay be written as: (5 ) X = F( h, h ) where F(..) represents a unctional or. We ind F / h = F < and F <. The isopopulation eale lines slope thereore downwards in the closer to the origin yield a higher stock. (, ) h h plane, and where lines 4

5 By cobining equations (1), (2) and (4), the ale population growth reads X =.5(1 )(1 h ) r( X ) X + (1 )(1 h ) X. The dynaic o the ales is t+ 1 t t t 1 t t thereore contingent upon the eale growth (but not the vice versa as only eale abundance regulates ertility), and again nuerical analyses deonstrate that the equilibriu is stable. The equilibriu is: (6) X =.5(1 )(1 h ) r( X ) X + (1 )(1 h ) X. There are two equilibria or the ale population as well; the trivial one when X =, and X > when X >. Equation (6) ay also be written as: (6 ) X G h h r X X = (, ) ( ) where Gh (, h ) =.5(1 )(1 h) /[1 (1 )(1 h )]. Again, it ay be conired that higher harvesting rates ean ewer anials, G < and G <. The ale iso-population lines hence slope downwards in the (, ) h h plane, and lines closer to the origin yield a higher stock. On the other hand, a higher eale sub-population shits these iso-population lines away ro the origin (suggested that the slope o the recruitent unction is positive, see above) eaning that there is roo or ore ale harvesting or a given young sub-population harvesting, and the vice versa. Equation (6 ) also indicates that the equilibriu ale eale proportion decreases with ore eales. This holds, but the ale-eale proportion ay ore easily be recognized when cobing (5) and (6) which yields X / X = [1 (1 )(1 h )]/[1 (1 )(1 h )]. We thereore siply have X / X = 1 i h = h, as the ortality o the ale and eale are equal, and the sae raction o young enters the eale and ale sub-populations. 3. Exploitation. The traditional regie; hunting or eat As indicated, the traditional exploitation o the Scandinavian oose has been directed by axiising the eat value in ecological equilibriu. Because natural ortality takes place 5

6 ater the hunting season, the equilibriu nubers o anials reoved are siply H = hrx, H = h X and H = h X so that the total harvest equals H = H + H + H. The anageent goal o the landowner(s) in the given area is accordingly: (7) ax U = p( w H + w H + w H ) = p[ w h r( X ) X + w h X + w h X ] X, X, h, h, h subject to the ecological constraints (5 ) and (6 ). w < w < w are the (average) body slaughter weights (kg per anial) o the three stages while p is the eat price (NOK per kg). However, or obvious reasons, the eat price will not aect the optiization except or scaling the shadow price values (see below). The Lagrangian o this proble writes L = p w h r X X + w h X + w h X X F h h X G h h r X X with λ > and µ > as the shadow prices o the eale and ale population, respectively 1. [ ( ) )] λ [ (, )] µ [ (, ) ( ) ] The irst order conditions o this axiising proble are (the second order conditions are ulilled due to the concavity o the recruitent unction): (8) L/ X = p[ w h ( r' X + r) + w h ] λ + µ G( r' X + r) =, (9) L / X = pw h µ =, (1) L h = pw rx + F + G rx ; / λ µ h 1 <, (11) L/ h = pw X + λf ; h < 1 and 1 The interpretation o λ and µ as shadow prices are not obvious as the population sizes are deterined within the odel. However, when adding X, interpreted as an exogenous nuber o introduced eales, to the stock constraint (5 ) it can be shown that * * U / X = λ, and where U denotes the axiu value o U. In the sae anner, adding X as an exogenous nuber o introduced ales to (6 ), gives * U / X = µ. 6

7 (12) / L h = pw X + µ G rx ; < h 1. Conditions (8) and (9) steer the shadow price values, and (9) says that the ale shadow price should just be equal its arginal harvesting value. Equation (8) is soewhat ore coplex, but says basically that the eale shadow price should be equal the su o the arginal harvesting value o the eale and the young sub-populations, plus the indirect ale arginal harvesting value, evaluated at its shadow price. Rewriting equation (8) when using condition (9) yields λ = p( wh + w h G)( r' X + r) + pw h. As the slope o the recruitent unction is non-negative, ( r' X + r) (see above), λ pw h holds. While the shadow value o the ale population is exactly equal its arginal harvesting value, we thereore ind that the shadow value o the eale population is above its arginal harvesting value. In this sense, eales ay be considered as ore valuable than ales and is line with the biological notion o eales as valuable and ale as non-valuable. Conditions (1) (12) are the control conditions with the actual copleentary slackness conditions stated. Fro the ale condition (12), harvesting down the whole population is hence considered as a possibility as this is the biological end product. On the other hand, keeping the eale and young sub-populations unexploited are options as these stages represent the reproductive and potentially reproductive biological capital. Condition (1) indicates that the harvesting o the young should take place up to the point where the harvesting beneit is equal, or below, the cost in ters o reduced population o ales and eales evaluated at their respective shadow prices. When (1) holds as an inequality, we have that the arginal harvesting beneit is below its arginal cost and harvesting is thus not proitable, h =. The interpretation o the eale harvesting condition (11) is soewhat sipler. Due to the ecundity density eect, eaning that one ore eale on the argin yields a saller recruitent when the eale population is high than when being low, h = sees less likely. The ale harvesting condition (12) is parallel to that o the eale harvesting condition (11), but the cost-beneit ratio works generally in the opposite direction. It can be shown that this condition always will hold as an inequality. This is revealed when irst cobining conditions (9), (12) and (6 ) which yields ( G+ h G ). When next inserting or G (and G ) ro equation (6) this condition reads 7

8 {.5(1 )(1 h ) /[1 (1 )(1 h )]}{1 (1 ) h /[1 (1 )(1 h )]}. Ater soe sall rearrangeents, this condition boils down to. Accordingly, because >, we ind h = 1and the whole ale population should be harvested. Notice that this result holds irrespective o the values o ales and eales (as given by the body weights). The reason or harvesting down the whole biological end product as the best option is the lack o any trade-os when the eat value is axiised; there is neither any biological eedback eects ro the other stages nor any price deand response. The ale-eale proportion becoes accordingly X / X = [1 (1 )(1 h )] (section two above) in the optial progra while one ore ale (c. also ootnote 1) yields a beneit o µ = pw (NOK per anial). I the optial policy at the sae tie yields h =, the eale shadow price reads λ = pw GrX ( ' + r) + pwh. As G =.5(1 ) when h 1 = and h = (equation 6 ), and w < w, the eale shadow price ay be lower than the ale shadow price irrespective o the above notion o eales as ore valuable than ales. In addition, ro condition (8), it ay also be shown that i 1 Gr ( ' X + r) > wh then µ > λ. This shows ore directly that a low eale slaughter weight ay pull in the sae direction. 4. Exploitation. Modern ties; trophy hunting The oose harvesting regie in Scandinavia (like wildlie hunting other places) is gradually changing, and a hunting and wildlie industry is eerging (see e.g., Skogeierorbundet 24). Modern ties is odelled by introducing a arket or trophy hunting or ales while still having eat value hunting o the other two stages. The arket or trophy hunting is probably soething between a copetitive arket and onopoly. One o these extrees is chosen, and we assue that trophy hunting licences are supplied under onopolistic conditions. Following the practice in Norway, one licence allows the buyer to kill one anial, which is paid only i the anial is killed. In addition to price, the deand or trophy hunting licences ay also be contingent upon quality, expressed by the abundance o ales (Skonhot and Olaussen 25). The inverse arket deand or ale hunting licences is hence given as: (13) q= q( h X, X ). 8

9 The licence price q (NOK per anial) decreases with a higher otake, q = q/ ( h X ) <, while it shits up with ore anials available, q >. Supplying H trophy hunting licences is also costly and depends on the nuber o anials shot: X (14) C = C( h X ) with ixed cost C () >, and variable cost C ' > and C ''. The ixed coponent includes the cost o preparing and arketing the hunting, whereas the variable coponent includes the cost o organising the perit sale, the costs o guiding and various transportation services, and so orth. The landowner anageent goal is now accordingly to ind a harvesting policy that axiises the su o the eat value and trophy hunting proit, or (15) ax π = p[ w h r( X ) X + w h X ] + [ q( h X, X ) h X C( h X )], X, X, h, h, h again subject to the constraints (5 ) and (6 ). The irst order conditions o this proble are (where L again is reerring to the Lagrange unction): (16) L X = q h X + qh C h + q h X µ = 2 / H( ) ' X and (17) L h = q X h + qx C X + G rx ; < h 1, 2 / H( ) ' µ in addition to conditions (8), (1) and (11). The ale harvesting beneit is now expressed by a arginal proit ter plus a arginal stock eect through the deand quality eect and the interpretation o (16) and (17) is straightorward (see also above). Cobing these conditions and (6 ) yields ( ')( ) qhx h + q C G+ h G + qxh X G, and where the ter ( G+ h G ) still is strictly positive because > (see section three above). When irst disregarding the quality 9

10 eect, q X =, we thereore ind that h < 1and hence not harvesting all ales, will represent the optial solution i the arginal harvesting proit equalises zero, ( q X h + q C') =. Fro condition (16) (as well as ro equation 17), it is seen that this iplies a zero value ale shadow price. A zero value shadow value while not harvesting down the whole stage is a counterintuitive result, but hinges on the biological end product nature o the adult ales; the ertility is not contingent upon the nuber o ales. The zero arginal harvesting proit condition ay be et i the arginal cost is high and/or the inverse deand schedule is steep (or in-elastic). On the other hand, i the arginal revenue exceeds the arginal cost or h = 1, we obtain the sae solution type as above and where µ is positive. H When taking the deand quality eect into account, q X >, h < 1 ay still hold as an optial solution when the arginal revenue exceeds the arginal cost ( q X h + q C') >, as G ' < and ( G+ h G' ) > (see above). The econoic reason or this result is siple as constraining the harvest and keeping a high stock size works in the direction o a higher trophy hunting licence price. Fro equation (16) it is seen that this situation iplies µ >. The corner solution o h = 1 is also now a possibility, but the arginal harvesting proit ust then exceed a certain iniu, equal the shadow price. H While the irst order conditions or harvesting eale and young are the sae as in the traditional harvesting regie, the new conditions or ale harvesting will obviously spill over to these stages. With h < 1, we ay typically ind that the ale-eale proportion X / X increases copared to the traditional regie which ay be reinorced i h shits up at the sae tie. Moreover, while the eat price p had no eect on the optial harvesting policy in the traditional regie, it ay now inluence the optial harvesting policy o all three stages. This will generally occur when the quality eect is included and we have µ >. In line with the standard harvesting theory, one ay expect that ore harvest and ewer anials o ale and young then will go hand in hand with a higher price. On the other hand, with no quality eect and with a zero shadow price value o the ales it will have no eect as the equations (5), (8), (1) and (11) then alone deterine h, h, X and λ. 1

11 5. Nuerical illustrations Data and speciic unctional ors The two exploitation schees will now be illustrated nuerically. The reproduction rate, decreasing in the nuber o eales, is speciied as a sigoidal unction with an increasing degree o density dependence at high densities (Nilsen et al. 25): r (2 ) r = r( X ) = 1 + ( X / K) b with r > as the intrinsic growth rate (axiu nuber o young per eale) and K > as the eale stock level or which density dependent ertility equalises density independent ertility. Thus, or a stock level above K, density dependent actors doinate. The copensation paraeter b > indicates to what extent density independent eects copensate or changes in the stock size. (2 ) iplies a recruitent unction ( ) /[1 ( / ) ] which is o the so-called Sheperd-type. b X = r X X = rx + X K The trophy deand unction is speciied linear. In addition, it is assued that the quality eect as given by the nuber o ales, through the paraeterγ, shits the deand uniorly up: (13 ) X γ q= αe βh X. Accordingly, the choke priceα > gives the axiu willingness to pay with a zero quality eect, γ =, whereas β > relects the arket price response in a standard anner. The trophy cost unction is given linear as well: (14 ) = + C c ch X so that c is the ixed cost and c > is the constant arginal cost. Table 1 gives the baseline paraeter values. For these deand and cost unctions we ind that the optial nuber o hunted ales will be h X = ( α c) / 2β without deand quality eect and when µ = at the sae tie (equation 16). 11

12 Table 1 about here Results Table 2 reports the results with baseline paraeter values. As a benchark, a no-hunting scenario is also included (irst row). Since the young enters the (adult) ale and eale stages at the sae sex ratio, the nuber o ales and eales are here the sae. In the traditional regie with eat value axiisation, the eale harvest rate becoes.26 while no harvest o young represents an optial policy. The arginal harvesting beneit o young is hence below the arginal cost in ter o losses ro reduced harvesting o ales and eales. Notice that the nuber o young is lower in the no-hunting scenario than in the traditional regie. This is due to the act that the nuber o eales is above the value representing the peak value o the recruitent unction and we have dx / dx = ( r ' X + r) < without harvesting. The ale shadow value is about our ties above that o the eale shadow value. As deonstrated in section three, the ale shadow value is exactly equal its arginal h harvesting value µ = pw while the eale shadow value should be above its arginal harvesting value. However, due to the low eale harvesting raction and an optial harvesting policy close to the peak o the recruitent unction; that is ( r' X + r) is sall positive (see above), the eale shadow value becoes low. Table 2 about here The odern ties exploitation schee is irst studied when the quality eect is disregarded; that isγ = and the inverse deand unction (13 ) reads q = α βh X. Harvesting down all the ales is no longer an optial policy, and we now ind a substantial lower harvesting raction, h =.24. As expected, the ale-eale proportion increases, and at the sae tie the eale harvesting raction shits soewhat up copared to the traditional regie. However, it is still beneicial to keep the young population unexploited. When the quality eect is added, the ale harvesting rate, as expected, is urther reduced accopanied by a positive shadow value indicating that the arginal harvesting incoe exceeds the arginal cost in optiu. The dierence between the ale and eale shadow values is now quite sall. The eale harvesting rate decreases soewhat as well. As a 12

13 consequence, the total stock size is higher when the quality eect is included and substantially higher than that o the traditional harvesting schee o eat value axiisation. The table also deonstrates that the proit increases copared to the traditional regie, and it urther increases when the deand quality eect is added. However, or obvious reasons, the speciication o the deand unction and paraeterisation play a critical role here. Shiting up the eat price p, just scales up the shadow price values in the traditional regie. In the trophy hunting regie with no quality eect and with a zero shadow price value o the ales, the harvesting activity and stock sizes will be unaected as well. As indicated, the logic behind this result is that the equations (5), (8), (1) and (11) alone deterine h, X and λ when µ =.On the other hand, with the quality included and µ >, the ale harvesting activity interacts with the other stages and hence p has an allocation eect. However, sensitivity analyses show that the eale harvest rate increase only odestly even or a quite draatic price increase. The reason or this hinges on the act that eale stock is close to the peak o the recruitent unction. h, 6. Concluding rearks The paper has analysed a three stages odel o the Scandinavian oose with ertility as the only density dependent actor. Two exploitation schees have been studied and it is deonstrated that harvesting down the whole biological end product, i.e., the (adult) ale population in this odel, always represents the best option when eat value axiisation is the goal. In the nuerical exaples, this option is accopanied by zero harvesting o the young and odest eale harvesting. Within this regie the biological notion o eales as valuable and ales as non-valuable is easily recognized even i the shadow value o the ales ight be higher than that o the eales. The odern ties exploitation schee with a arket or trophy hunting, changes the optial harvesting condition o the ales. Hunting down the whole population will no longer be the best option suggested that a well developed arket or trophy hunting is present. In addition, the allocation in the trophy hunting arket also spills over to the conditions o eat value axiisation o young and eales. The aleeale population ratio will increase, and the nuerical exaples show that ore eale harvesting ay take place. 13

14 Although the odel is siple and thereore soewhat unrealistic, it encopasses soe general results that will survive in ore coplex stage-structured odels. Most iportant, we have highlighted the econoic orces inluencing harvest in three dierent stages that, in various degrees, are present in any structured population odels. In our odel there are two recruiting stages which recruit in dierent ways. The young represents a value through recruitent to the (old) ale and eale stages. As long as density dependent growth actors are weak, or non-existing (as here), harvesting young does not pay o. For the eales, on the other hand, a traditional trade-o between recruitent and harvest is present through the density dependent ertility echanis. This echanis will also be present in ore coplex odels. Finally, the (old) ale stage is considered as the biological end product, and thus inluences not the recruitent. It is thereore tacitly assued that there are always enough ales or reproduction. However, irrespective o this, our odel deonstrates that the ale optial harvest policy depends critically on econoic circustances. Literature Caswell, H. 21: Matrix population odels: Construction, analysis and interpretation (2th. Ed.). Sinauer, Boston Clark, C. and D. Tait 1982: Sex-selective harvesting o wildlie populations. Ecological Modelling 14, Cooper, J. 1993: A bioeconoic odel or estiating the optial level o deer and tag sales. Environental and Resource Econoics 3, Gandolo, G. 1996: Econoic dynaics. Springer, Berlin. Milner-Gulland, E.J., T. Coulson and T. Clutton-Brock 24: Sex dierences and data quality as deterinants o incoe ro hunting red deer Cervus elaphus. Wildlie Biology 1, Nilsen, E., T. Pettersen, H. Gundersen, A. Mysterud, J. Milner, E. Solberg, H. Andreassen and N.C. Stenseth 25: Moose harvesting strategies in the presence o wolves. spatially structured populations. Journal o Applied Ecology 42,

15 Skonhot, A., N. Yoccoz and N.C. Stenseth 24: Manageent o a chaois (Rupicapra rupicapra) oving between a protected area and a hunting area. Ecological Applications 12, Skonhot, A. and J. O. Olaussen 25: Managing a igratory species that is both a value and pest. Land Econoics 81, 34-5 Storaas, T., H.Gundersen, H. Henriksen and H. Andreassen 21: The econoic value o oose in Norway. Alces 37, Skogeierorbundet (Norwegian Forest Owners Association) 24: Utvikling av utarksbaserte reislivsbedriter (in Norwegian), Oslo 15

16 Table 1: Biological and econoic paraeter values Paraeters Description Baseline value Reerence/source r> ax. speciic growth rate 1.15 Nielsen et al (25) K eale stock level where density 1, Nielsen et al (25) dependent actors doinates density independent actors b density copensation paraeter 2 Nielsen et al (25) w average weight young 6 Kg SSB 24 w average weight eales 15 Kg SSB 24 w average weight ale 17 Kg SSB 24 natural ortality young.5 Nielsen et al (25) natural ortality eale and ale.5 Nielsen et al (25) p eat price 5 NOK Storaas og Henriksen (22) α choke price 3, NOK Calibrated γ quality paraeter deand.1 Calibrated β slope paraeter deand 6 NOK Calibrated c ixed harvest cost 5, NOK Calibrated c arginal harvest cost 2, NOK Calibrated 16

17 Table 2: Ecological and econoic equilibriu, dierent anageent regies. h harvest raction young, h harvest raction eale, h harvest raction ale,h total harvest raction, X nuber o young (in 1 anials),x nuber o eales (in 1 anials), X nuber o ales (in 1 anials), X total stock ( in 1, anials), λ eale shadow price (in 1, NOK per anial), µ ale shadow price (in 1, NOKper anial) and, π proit (in 1, NOK) Hunting regies h h h h X X X X λ µ π No harvest Traditional regie, hunting or eat Modern ties; trophy hunting. No quality eect Modern ties; trophy hunting. With quality eect , , ,594 (-- indicates value not calculated) 17

The economics of a stage structured wildlife population model

The economics of a stage structured wildlife population model Stageodel115 The econoics o a stage structured wildlie population odel Anders Skonhot(*) and Jon Ola Olaussen Departent o Econoics Norwegian University o Science and Technology N-7491 Dragvoll-Trondhei,

More information

A method to determine relative stroke detection efficiencies from multiplicity distributions

A method to determine relative stroke detection efficiencies from multiplicity distributions A ethod to deterine relative stroke detection eiciencies ro ultiplicity distributions Schulz W. and Cuins K. 2. Austrian Lightning Detection and Inoration Syste (ALDIS), Kahlenberger Str.2A, 90 Vienna,

More information

Sexually Transmitted Diseases VMED 5180 September 27, 2016

Sexually Transmitted Diseases VMED 5180 September 27, 2016 Sexually Transitted Diseases VMED 518 Septeber 27, 216 Introduction Two sexually-transitted disease (STD) odels are presented below. The irst is a susceptibleinectious-susceptible (SIS) odel (Figure 1)

More information

Optimization of Flywheel Weight using Genetic Algorithm

Optimization of Flywheel Weight using Genetic Algorithm IN: 78 7798 International Journal o cience, Engineering and Technology esearch (IJET) Volue, Issue, March 0 Optiization o Flywheel Weight using Genetic Algorith A. G. Appala Naidu*, B. T.N. Charyulu, C..C.V.aanaurthy

More information

Deflation of the I-O Series Some Technical Aspects. Giorgio Rampa University of Genoa April 2007

Deflation of the I-O Series Some Technical Aspects. Giorgio Rampa University of Genoa April 2007 Deflation of the I-O Series 1959-2. Soe Technical Aspects Giorgio Rapa University of Genoa g.rapa@unige.it April 27 1. Introduction The nuber of sectors is 42 for the period 1965-2 and 38 for the initial

More information

A Simplified Analytical Approach for Efficiency Evaluation of the Weaving Machines with Automatic Filling Repair

A Simplified Analytical Approach for Efficiency Evaluation of the Weaving Machines with Automatic Filling Repair Proceedings of the 6th SEAS International Conference on Siulation, Modelling and Optiization, Lisbon, Portugal, Septeber -4, 006 0 A Siplified Analytical Approach for Efficiency Evaluation of the eaving

More information

I. Understand get a conceptual grasp of the problem

I. Understand get a conceptual grasp of the problem MASSACHUSETTS INSTITUTE OF TECHNOLOGY Departent o Physics Physics 81T Fall Ter 4 Class Proble 1: Solution Proble 1 A car is driving at a constant but unknown velocity,, on a straightaway A otorcycle is

More information

Chapter 4 FORCES AND NEWTON S LAWS OF MOTION PREVIEW QUICK REFERENCE. Important Terms

Chapter 4 FORCES AND NEWTON S LAWS OF MOTION PREVIEW QUICK REFERENCE. Important Terms Chapter 4 FORCES AND NEWTON S LAWS OF MOTION PREVIEW Dynaics is the study o the causes o otion, in particular, orces. A orce is a push or a pull. We arrange our knowledge o orces into three laws orulated

More information

A Simple Model of Reliability, Warranties, and Price-Capping

A Simple Model of Reliability, Warranties, and Price-Capping International Journal of Business and Econoics, 2006, Vol. 5, No. 1, 1-16 A Siple Model of Reliability, Warranties, and Price-Capping Donald A. R. George Manageent School & Econoics, University of Edinburgh,

More information

Physics 11 HW #6 Solutions

Physics 11 HW #6 Solutions Physics HW #6 Solutions Chapter 6: Focus On Concepts:,,, Probles: 8, 4, 4, 43, 5, 54, 66, 8, 85 Focus On Concepts 6- (b) Work is positive when the orce has a coponent in the direction o the displaceent.

More information

Equilibria on the Day-Ahead Electricity Market

Equilibria on the Day-Ahead Electricity Market Equilibria on the Day-Ahead Electricity Market Margarida Carvalho INESC Porto, Portugal Faculdade de Ciências, Universidade do Porto, Portugal argarida.carvalho@dcc.fc.up.pt João Pedro Pedroso INESC Porto,

More information

EVOLUTION INTERNATIONAL JOURNAL OF ORGANIC EVOLUTION

EVOLUTION INTERNATIONAL JOURNAL OF ORGANIC EVOLUTION EVOLUTION INTERNATIONAL JOURNAL OF ORGANIC EVOLUTION PUBLISHED BY THE SOCIETY FOR THE STUDY OF EVOLUTION Vol. 57 Noveber 003 No. 11 Evolution, 57(11), 003, pp. 433 449 SEXUAL DIMORPHISM AND ADAPTIVE SPECIATION:

More information

A Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine. (1900 words)

A Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine. (1900 words) 1 A Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine (1900 words) Contact: Jerry Farlow Dept of Matheatics Univeristy of Maine Orono, ME 04469 Tel (07) 866-3540 Eail: farlow@ath.uaine.edu

More information

9. Sex Linkage and Other Sex Differences. Extensions to the HWC model to allow for differences between the sexes

9. Sex Linkage and Other Sex Differences. Extensions to the HWC model to allow for differences between the sexes 9. Sex Linkage and Other Sex Dierences Extensions to the HWC odel to allow or dierences between the sexes. Unequal allele requences or eales & ales: I, in the current generation, the two sexes have dierent

More information

Optimal Pigouvian Taxation when Externalities Affect Demand

Optimal Pigouvian Taxation when Externalities Affect Demand Optial Pigouvian Taxation when Externalities Affect Deand Enda Patrick Hargaden Departent of Econoics University of Michigan enda@uich.edu Version of August 2, 2015 Abstract Purchasing a network good such

More information

COS 424: Interacting with Data. Written Exercises

COS 424: Interacting with Data. Written Exercises COS 424: Interacting with Data Hoework #4 Spring 2007 Regression Due: Wednesday, April 18 Written Exercises See the course website for iportant inforation about collaboration and late policies, as well

More information

Support Vector Machines MIT Course Notes Cynthia Rudin

Support Vector Machines MIT Course Notes Cynthia Rudin Support Vector Machines MIT 5.097 Course Notes Cynthia Rudin Credit: Ng, Hastie, Tibshirani, Friedan Thanks: Şeyda Ertekin Let s start with soe intuition about argins. The argin of an exaple x i = distance

More information

Topic 5a Introduction to Curve Fitting & Linear Regression

Topic 5a Introduction to Curve Fitting & Linear Regression /7/08 Course Instructor Dr. Rayond C. Rup Oice: A 337 Phone: (95) 747 6958 E ail: rcrup@utep.edu opic 5a Introduction to Curve Fitting & Linear Regression EE 4386/530 Coputational ethods in EE Outline

More information

ma x = -bv x + F rod.

ma x = -bv x + F rod. Notes on Dynaical Systes Dynaics is the study of change. The priary ingredients of a dynaical syste are its state and its rule of change (also soeties called the dynaic). Dynaical systes can be continuous

More information

Support Vector Machine Classification of Uncertain and Imbalanced data using Robust Optimization

Support Vector Machine Classification of Uncertain and Imbalanced data using Robust Optimization Recent Researches in Coputer Science Support Vector Machine Classification of Uncertain and Ibalanced data using Robust Optiization RAGHAV PAT, THEODORE B. TRAFALIS, KASH BARKER School of Industrial Engineering

More information

A NEW APPROACH TO DYNAMIC BUCKLING LOAD ESTIMATION FOR PLATE STRUCTURES

A NEW APPROACH TO DYNAMIC BUCKLING LOAD ESTIMATION FOR PLATE STRUCTURES Stability o Structures XIII-th Syposiu Zakopane 202 A NW APPROACH TO DYNAMIC BUCKLIN LOAD STIMATION FOR PLAT STRUCTURS T. KUBIAK, K. KOWAL-MICHALSKA Departent o Strength o Materials, Lodz University o

More information

Functions: Review of Algebra and Trigonometry

Functions: Review of Algebra and Trigonometry Sec. and. Functions: Review o Algebra and Trigonoetry A. Functions and Relations DEFN Relation: A set o ordered pairs. (,y) (doain, range) DEFN Function: A correspondence ro one set (the doain) to anther

More information

ASSIGNMENT BOOKLET Bachelor s Degree Programme (B.Sc./B.A./B.Com.) MATHEMATICAL MODELLING

ASSIGNMENT BOOKLET Bachelor s Degree Programme (B.Sc./B.A./B.Com.) MATHEMATICAL MODELLING ASSIGNMENT BOOKLET Bachelor s Degree Prograe (B.Sc./B.A./B.Co.) MTE-14 MATHEMATICAL MODELLING Valid fro 1 st January, 18 to 1 st Deceber, 18 It is copulsory to subit the Assignent before filling in the

More information

Journal of Solid Mechanics and Materials Engineering

Journal of Solid Mechanics and Materials Engineering Journal o Solid Mechanics and Materials Engineering First Order Perturbation-based Stochastic oogeniation Analysis or Short Fiber Reinorced Coposite Materials* Sei-ichiro SAKATA**, Fuihiro ASIDA** and

More information

Measuring Temperature with a Silicon Diode

Measuring Temperature with a Silicon Diode Measuring Teperature with a Silicon Diode Due to the high sensitivity, nearly linear response, and easy availability, we will use a 1N4148 diode for the teperature transducer in our easureents 10 Analysis

More information

A Note on Scheduling Tall/Small Multiprocessor Tasks with Unit Processing Time to Minimize Maximum Tardiness

A Note on Scheduling Tall/Small Multiprocessor Tasks with Unit Processing Time to Minimize Maximum Tardiness A Note on Scheduling Tall/Sall Multiprocessor Tasks with Unit Processing Tie to Miniize Maxiu Tardiness Philippe Baptiste and Baruch Schieber IBM T.J. Watson Research Center P.O. Box 218, Yorktown Heights,

More information

Physics 4A Solutions to Chapter 15 Homework

Physics 4A Solutions to Chapter 15 Homework Physics 4A Solutions to Chapter 15 Hoework Chapter 15 Questions:, 8, 1 Exercises & Probles 6, 5, 31, 41, 59, 7, 73, 88, 90 Answers to Questions: Q 15- (a) toward -x (b) toward +x (c) between -x and 0 (d)

More information

Block failure in connections - including effets of eccentric loads

Block failure in connections - including effets of eccentric loads Downloaded ro orbit.dtu.dk on: Apr 04, 209 Block ailure in connections - including eets o eccentric loads Jönsson, Jeppe Published in: Proceedings o the 7th European conerence on steel and coposite structures

More information

Curious Bounds for Floor Function Sums

Curious Bounds for Floor Function Sums 1 47 6 11 Journal of Integer Sequences, Vol. 1 (018), Article 18.1.8 Curious Bounds for Floor Function Sus Thotsaporn Thanatipanonda and Elaine Wong 1 Science Division Mahidol University International

More information

Economic Resource Balancing in Plant Design, Plant Expansion, or Improvement Projects

Economic Resource Balancing in Plant Design, Plant Expansion, or Improvement Projects Econoic Resource Balancing in lant Design, lant Expansion, or Iproveent rojects Dan Trietsch MSIS Departent University of Auckland New Zealand --------------------------------------------------------------------------------------------------------

More information

Interactive Markov Models of Evolutionary Algorithms

Interactive Markov Models of Evolutionary Algorithms Cleveland State University EngagedScholarship@CSU Electrical Engineering & Coputer Science Faculty Publications Electrical Engineering & Coputer Science Departent 2015 Interactive Markov Models of Evolutionary

More information

e-companion ONLY AVAILABLE IN ELECTRONIC FORM

e-companion ONLY AVAILABLE IN ELECTRONIC FORM OPERATIONS RESEARCH doi 10.1287/opre.1070.0427ec pp. ec1 ec5 e-copanion ONLY AVAILABLE IN ELECTRONIC FORM infors 07 INFORMS Electronic Copanion A Learning Approach for Interactive Marketing to a Custoer

More information

Q5 We know that a mass at the end of a spring when displaced will perform simple m harmonic oscillations with a period given by T = 2!

Q5 We know that a mass at the end of a spring when displaced will perform simple m harmonic oscillations with a period given by T = 2! Chapter 4.1 Q1 n oscillation is any otion in which the displaceent of a particle fro a fixed point keeps changing direction and there is a periodicity in the otion i.e. the otion repeats in soe way. In

More information

Example A1: Preparation of a Calibration Standard

Example A1: Preparation of a Calibration Standard Suary Goal A calibration standard is prepared fro a high purity etal (cadiu) with a concentration of ca.1000 g l -1. Measureent procedure The surface of the high purity etal is cleaned to reove any etal-oxide

More information

Model Fitting. CURM Background Material, Fall 2014 Dr. Doreen De Leon

Model Fitting. CURM Background Material, Fall 2014 Dr. Doreen De Leon Model Fitting CURM Background Material, Fall 014 Dr. Doreen De Leon 1 Introduction Given a set of data points, we often want to fit a selected odel or type to the data (e.g., we suspect an exponential

More information

BALLISTIC PENDULUM. EXPERIMENT: Measuring the Projectile Speed Consider a steel ball of mass

BALLISTIC PENDULUM. EXPERIMENT: Measuring the Projectile Speed Consider a steel ball of mass BALLISTIC PENDULUM INTRODUCTION: In this experient you will use the principles of conservation of oentu and energy to deterine the speed of a horizontally projected ball and use this speed to predict the

More information

Lost-Sales Problems with Stochastic Lead Times: Convexity Results for Base-Stock Policies

Lost-Sales Problems with Stochastic Lead Times: Convexity Results for Base-Stock Policies OPERATIONS RESEARCH Vol. 52, No. 5, Septeber October 2004, pp. 795 803 issn 0030-364X eissn 1526-5463 04 5205 0795 infors doi 10.1287/opre.1040.0130 2004 INFORMS TECHNICAL NOTE Lost-Sales Probles with

More information

A Better Algorithm For an Ancient Scheduling Problem. David R. Karger Steven J. Phillips Eric Torng. Department of Computer Science

A Better Algorithm For an Ancient Scheduling Problem. David R. Karger Steven J. Phillips Eric Torng. Department of Computer Science A Better Algorith For an Ancient Scheduling Proble David R. Karger Steven J. Phillips Eric Torng Departent of Coputer Science Stanford University Stanford, CA 9435-4 Abstract One of the oldest and siplest

More information

lecture 36: Linear Multistep Mehods: Zero Stability

lecture 36: Linear Multistep Mehods: Zero Stability 95 lecture 36: Linear Multistep Mehods: Zero Stability 5.6 Linear ultistep ethods: zero stability Does consistency iply convergence for linear ultistep ethods? This is always the case for one-step ethods,

More information

Non-Parametric Non-Line-of-Sight Identification 1

Non-Parametric Non-Line-of-Sight Identification 1 Non-Paraetric Non-Line-of-Sight Identification Sinan Gezici, Hisashi Kobayashi and H. Vincent Poor Departent of Electrical Engineering School of Engineering and Applied Science Princeton University, Princeton,

More information

Page 1. Physics 131: Lecture 22. Today s Agenda. SHM and Circles. Position

Page 1. Physics 131: Lecture 22. Today s Agenda. SHM and Circles. Position Physics 3: ecture Today s genda Siple haronic otion Deinition Period and requency Position, velocity, and acceleration Period o a ass on a spring Vertical spring Energy and siple haronic otion Energy o

More information

Qualitative Modelling of Time Series Using Self-Organizing Maps: Application to Animal Science

Qualitative Modelling of Time Series Using Self-Organizing Maps: Application to Animal Science Proceedings of the 6th WSEAS International Conference on Applied Coputer Science, Tenerife, Canary Islands, Spain, Deceber 16-18, 2006 183 Qualitative Modelling of Tie Series Using Self-Organizing Maps:

More information

Intelligent Systems: Reasoning and Recognition. Perceptrons and Support Vector Machines

Intelligent Systems: Reasoning and Recognition. Perceptrons and Support Vector Machines Intelligent Systes: Reasoning and Recognition Jaes L. Crowley osig 1 Winter Seester 2018 Lesson 6 27 February 2018 Outline Perceptrons and Support Vector achines Notation...2 Linear odels...3 Lines, Planes

More information

When Short Runs Beat Long Runs

When Short Runs Beat Long Runs When Short Runs Beat Long Runs Sean Luke George Mason University http://www.cs.gu.edu/ sean/ Abstract What will yield the best results: doing one run n generations long or doing runs n/ generations long

More information

List Scheduling and LPT Oliver Braun (09/05/2017)

List Scheduling and LPT Oliver Braun (09/05/2017) List Scheduling and LPT Oliver Braun (09/05/207) We investigate the classical scheduling proble P ax where a set of n independent jobs has to be processed on 2 parallel and identical processors (achines)

More information

Monetary Policy E ectiveness in a Dynamic AS/AD Model with Sticky Wages

Monetary Policy E ectiveness in a Dynamic AS/AD Model with Sticky Wages Monetary Policy E ectiveness in a Dynaic AS/AD Model with Sticky Wages Henrik Jensen Departent of Econoics University of Copenhagen y Version.0, April 2, 202 Teaching note for M.Sc. course on "Monetary

More information

A DESIGN GUIDE OF DOUBLE-LAYER CELLULAR CLADDINGS FOR BLAST ALLEVIATION

A DESIGN GUIDE OF DOUBLE-LAYER CELLULAR CLADDINGS FOR BLAST ALLEVIATION International Journal of Aerospace and Lightweight Structures Vol. 3, No. 1 (2013) 109 133 c Research Publishing Services DOI: 10.3850/S201042862013000550 A DESIGN GUIDE OF DOUBLE-LAYER CELLULAR CLADDINGS

More information

Supplementary Information for Design of Bending Multi-Layer Electroactive Polymer Actuators

Supplementary Information for Design of Bending Multi-Layer Electroactive Polymer Actuators Suppleentary Inforation for Design of Bending Multi-Layer Electroactive Polyer Actuators Bavani Balakrisnan, Alek Nacev, and Elisabeth Sela University of Maryland, College Park, Maryland 074 1 Analytical

More information

NUMERICAL MODELLING OF THE TYRE/ROAD CONTACT

NUMERICAL MODELLING OF THE TYRE/ROAD CONTACT NUMERICAL MODELLING OF THE TYRE/ROAD CONTACT PACS REFERENCE: 43.5.LJ Krister Larsson Departent of Applied Acoustics Chalers University of Technology SE-412 96 Sweden Tel: +46 ()31 772 22 Fax: +46 ()31

More information

Monetary Policy Effectiveness in a Dynamic AS/AD Model with Sticky Wages

Monetary Policy Effectiveness in a Dynamic AS/AD Model with Sticky Wages Monetary Policy Effectiveness in a Dynaic AS/AD Model with Sticky Wages H J Departent of Econoics University of Copenhagen Version 1.0, April 12, 2012 Teaching note for M.Sc. course on "Monetary Econoics:

More information

Fairness via priority scheduling

Fairness via priority scheduling Fairness via priority scheduling Veeraruna Kavitha, N Heachandra and Debayan Das IEOR, IIT Bobay, Mubai, 400076, India vavitha,nh,debayan}@iitbacin Abstract In the context of ulti-agent resource allocation

More information

C na (1) a=l. c = CO + Clm + CZ TWO-STAGE SAMPLE DESIGN WITH SMALL CLUSTERS. 1. Introduction

C na (1) a=l. c = CO + Clm + CZ TWO-STAGE SAMPLE DESIGN WITH SMALL CLUSTERS. 1. Introduction TWO-STGE SMPLE DESIGN WITH SMLL CLUSTERS Robert G. Clark and David G. Steel School of Matheatics and pplied Statistics, University of Wollongong, NSW 5 ustralia. (robert.clark@abs.gov.au) Key Words: saple

More information

Cooperative spectrum sensing with secondary user selection for cognitive radio networks over Nakagami-m fading channels

Cooperative spectrum sensing with secondary user selection for cognitive radio networks over Nakagami-m fading channels IET Counications Research Article Cooperative spectru sensing with secondary user selection or cognitive radio networks over Nakagai- ading channels ISSN 1751-8628 Received on 14th May 2015 Revised on

More information

Block designs and statistics

Block designs and statistics Bloc designs and statistics Notes for Math 447 May 3, 2011 The ain paraeters of a bloc design are nuber of varieties v, bloc size, nuber of blocs b. A design is built on a set of v eleents. Each eleent

More information

Kernel Methods and Support Vector Machines

Kernel Methods and Support Vector Machines Intelligent Systes: Reasoning and Recognition Jaes L. Crowley ENSIAG 2 / osig 1 Second Seester 2012/2013 Lesson 20 2 ay 2013 Kernel ethods and Support Vector achines Contents Kernel Functions...2 Quadratic

More information

Lecture #8-3 Oscillations, Simple Harmonic Motion

Lecture #8-3 Oscillations, Simple Harmonic Motion Lecture #8-3 Oscillations Siple Haronic Motion So far we have considered two basic types of otion: translation and rotation. But these are not the only two types of otion we can observe in every day life.

More information

A Simple Regression Problem

A Simple Regression Problem A Siple Regression Proble R. M. Castro March 23, 2 In this brief note a siple regression proble will be introduced, illustrating clearly the bias-variance tradeoff. Let Y i f(x i ) + W i, i,..., n, where

More information

Enzyme kinetics: A note on negative reaction constants in Lineweaver-Burk plots

Enzyme kinetics: A note on negative reaction constants in Lineweaver-Burk plots Enzye kinetics: A note on negative reaction constants in Lineweaver-Burk plots Sharistha Dhatt# and Kaal Bhattacharyya* Departent of Cheistry, University of Calcutta, Kolkata 700 009, India #pcsdhatt@gail.co

More information

1 Bounding the Margin

1 Bounding the Margin COS 511: Theoretical Machine Learning Lecturer: Rob Schapire Lecture #12 Scribe: Jian Min Si March 14, 2013 1 Bounding the Margin We are continuing the proof of a bound on the generalization error of AdaBoost

More information

Nonmonotonic Networks. a. IRST, I Povo (Trento) Italy, b. Univ. of Trento, Physics Dept., I Povo (Trento) Italy

Nonmonotonic Networks. a. IRST, I Povo (Trento) Italy, b. Univ. of Trento, Physics Dept., I Povo (Trento) Italy Storage Capacity and Dynaics of Nononotonic Networks Bruno Crespi a and Ignazio Lazzizzera b a. IRST, I-38050 Povo (Trento) Italy, b. Univ. of Trento, Physics Dept., I-38050 Povo (Trento) Italy INFN Gruppo

More information

A Low-Complexity Congestion Control and Scheduling Algorithm for Multihop Wireless Networks with Order-Optimal Per-Flow Delay

A Low-Complexity Congestion Control and Scheduling Algorithm for Multihop Wireless Networks with Order-Optimal Per-Flow Delay A Low-Coplexity Congestion Control and Scheduling Algorith for Multihop Wireless Networks with Order-Optial Per-Flow Delay Po-Kai Huang, Xiaojun Lin, and Chih-Chun Wang School of Electrical and Coputer

More information

Homework 3 Solutions CSE 101 Summer 2017

Homework 3 Solutions CSE 101 Summer 2017 Hoework 3 Solutions CSE 0 Suer 207. Scheduling algoriths The following n = 2 jobs with given processing ties have to be scheduled on = 3 parallel and identical processors with the objective of iniizing

More information

The impact of online sales on centralised and decentralised dual-channel supply chains

The impact of online sales on centralised and decentralised dual-channel supply chains European J. Industrial Engineering, Vol. 12, No. 1, 2018 67 The ipact of online sales on centralised and decentralised dual-channel supply chains Ilkyeong Moon Departent of Industrial Engineering, Seoul

More information

COULD A VARIABLE MASS OSCILLATOR EXHIBIT THE LATERAL INSTABILITY?

COULD A VARIABLE MASS OSCILLATOR EXHIBIT THE LATERAL INSTABILITY? Kragujevac J. Sci. 3 (8) 3-44. UDC 53.35 3 COULD A VARIABLE MASS OSCILLATOR EXHIBIT THE LATERAL INSTABILITY? Nebojša Danilović, Milan Kovačević and Vukota Babović Institute of Physics, Faculty of Science,

More information

5.1 The derivative or the gradient of a curve. Definition and finding the gradient from first principles

5.1 The derivative or the gradient of a curve. Definition and finding the gradient from first principles Capter 5: Dierentiation In tis capter, we will study: 51 e derivative or te gradient o a curve Deinition and inding te gradient ro irst principles 5 Forulas or derivatives 5 e equation o te tangent line

More information

Chapter 6 1-D Continuous Groups

Chapter 6 1-D Continuous Groups Chapter 6 1-D Continuous Groups Continuous groups consist of group eleents labelled by one or ore continuous variables, say a 1, a 2,, a r, where each variable has a well- defined range. This chapter explores:

More information

Is Walras s Theory So Different From Marshall s?

Is Walras s Theory So Different From Marshall s? Is Walras s Theory So Different Fro Marshall s? Ezra Davar (Independent Researcher) Anon VeTaar 4/1, Netanya 40, Israel E-ail: ezra.davar@gail.co Received: July 17, 014 Accepted: August 5, 014 Published:

More information

On Constant Power Water-filling

On Constant Power Water-filling On Constant Power Water-filling Wei Yu and John M. Cioffi Electrical Engineering Departent Stanford University, Stanford, CA94305, U.S.A. eails: {weiyu,cioffi}@stanford.edu Abstract This paper derives

More information

The Thermal Dependence and Urea Concentration Dependence of Rnase A Denaturant Transition

The Thermal Dependence and Urea Concentration Dependence of Rnase A Denaturant Transition The Theral Dependence and Urea Concentration Dependence of Rnase A Denaturant Transition Bin LI Departent of Physics & Astronoy, University of Pittsburgh, Pittsburgh, PA 15260, U.S.A Feb.20 th, 2001 Abstract:

More information

8.1 Force Laws Hooke s Law

8.1 Force Laws Hooke s Law 8.1 Force Laws There are forces that don't change appreciably fro one instant to another, which we refer to as constant in tie, and forces that don't change appreciably fro one point to another, which

More information

INTELLECTUAL DATA ANALYSIS IN AIRCRAFT DESIGN

INTELLECTUAL DATA ANALYSIS IN AIRCRAFT DESIGN INTELLECTUAL DATA ANALYSIS IN AIRCRAFT DESIGN V.A. Koarov 1, S.A. Piyavskiy 2 1 Saara National Research University, Saara, Russia 2 Saara State Architectural University, Saara, Russia Abstract. This article

More information

The Weierstrass Approximation Theorem

The Weierstrass Approximation Theorem 36 The Weierstrass Approxiation Theore Recall that the fundaental idea underlying the construction of the real nubers is approxiation by the sipler rational nubers. Firstly, nubers are often deterined

More information

On the Existence of Pure Nash Equilibria in Weighted Congestion Games

On the Existence of Pure Nash Equilibria in Weighted Congestion Games MATHEMATICS OF OPERATIONS RESEARCH Vol. 37, No. 3, August 2012, pp. 419 436 ISSN 0364-765X (print) ISSN 1526-5471 (online) http://dx.doi.org/10.1287/oor.1120.0543 2012 INFORMS On the Existence of Pure

More information

Ph 20.3 Numerical Solution of Ordinary Differential Equations

Ph 20.3 Numerical Solution of Ordinary Differential Equations Ph 20.3 Nuerical Solution of Ordinary Differential Equations Due: Week 5 -v20170314- This Assignent So far, your assignents have tried to failiarize you with the hardware and software in the Physics Coputing

More information

PAC-Bayes Analysis Of Maximum Entropy Learning

PAC-Bayes Analysis Of Maximum Entropy Learning PAC-Bayes Analysis Of Maxiu Entropy Learning John Shawe-Taylor and David R. Hardoon Centre for Coputational Statistics and Machine Learning Departent of Coputer Science University College London, UK, WC1E

More information

A proposal for a First-Citation-Speed-Index Link Peer-reviewed author version

A proposal for a First-Citation-Speed-Index Link Peer-reviewed author version A proposal for a First-Citation-Speed-Index Link Peer-reviewed author version Made available by Hasselt University Library in Docuent Server@UHasselt Reference (Published version): EGGHE, Leo; Bornann,

More information

F = 0. x o F = -k x o v = 0 F = 0. F = k x o v = 0 F = 0. x = 0 F = 0. F = -k x 1. PHYSICS 151 Notes for Online Lecture 2.4.

F = 0. x o F = -k x o v = 0 F = 0. F = k x o v = 0 F = 0. x = 0 F = 0. F = -k x 1. PHYSICS 151 Notes for Online Lecture 2.4. PHYSICS 151 Notes for Online Lecture.4 Springs, Strings, Pulleys, and Connected Objects Hook s Law F = 0 F = -k x 1 x = 0 x = x 1 Let s start with a horizontal spring, resting on a frictionless table.

More information

Modeling the Structural Shifts in Real Exchange Rate with Cubic Spline Regression (CSR). Turkey

Modeling the Structural Shifts in Real Exchange Rate with Cubic Spline Regression (CSR). Turkey International Journal of Business and Social Science Vol. 2 No. 17 www.ijbssnet.co Modeling the Structural Shifts in Real Exchange Rate with Cubic Spline Regression (CSR). Turkey 1987-2008 Dr. Bahar BERBEROĞLU

More information

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence Best Ar Identification: A Unified Approach to Fixed Budget and Fixed Confidence Victor Gabillon Mohaad Ghavazadeh Alessandro Lazaric INRIA Lille - Nord Europe, Tea SequeL {victor.gabillon,ohaad.ghavazadeh,alessandro.lazaric}@inria.fr

More information

Algorithms for parallel processor scheduling with distinct due windows and unit-time jobs

Algorithms for parallel processor scheduling with distinct due windows and unit-time jobs BULLETIN OF THE POLISH ACADEMY OF SCIENCES TECHNICAL SCIENCES Vol. 57, No. 3, 2009 Algoriths for parallel processor scheduling with distinct due windows and unit-tie obs A. JANIAK 1, W.A. JANIAK 2, and

More information

Nonlinear Dynamic Analysis of Urban Roads and Daytime Population

Nonlinear Dynamic Analysis of Urban Roads and Daytime Population Urban and Regional Planning 07; (): -6 http://www.sciencepublishinggroup.co/j/urp doi: 0.648/j.urp.0700. Nonlinear Dynaic Analysis of Urban Roads and Daytie Population Shiqing Yan College of Urban and

More information

MSEC MODELING OF DEGRADATION PROCESSES TO OBTAIN AN OPTIMAL SOLUTION FOR MAINTENANCE AND PERFORMANCE

MSEC MODELING OF DEGRADATION PROCESSES TO OBTAIN AN OPTIMAL SOLUTION FOR MAINTENANCE AND PERFORMANCE Proceeding of the ASME 9 International Manufacturing Science and Engineering Conference MSEC9 October 4-7, 9, West Lafayette, Indiana, USA MSEC9-8466 MODELING OF DEGRADATION PROCESSES TO OBTAIN AN OPTIMAL

More information

Endowment Structure, Industrialization and Post-industrialization: A Three-Sector Model of Structural Change. Abstract

Endowment Structure, Industrialization and Post-industrialization: A Three-Sector Model of Structural Change. Abstract Endowent Structure, Industrialization Post-industrialization: A Three-Sector Model of Structural Change Justin Lin Zhaoyang Xu China Center for Econoic Research Peing University Deceber 006 (Preliinary

More information

Analyzing Simulation Results

Analyzing Simulation Results Analyzing Siulation Results Dr. John Mellor-Cruey Departent of Coputer Science Rice University johnc@cs.rice.edu COMP 528 Lecture 20 31 March 2005 Topics for Today Model verification Model validation Transient

More information

An improved self-adaptive harmony search algorithm for joint replenishment problems

An improved self-adaptive harmony search algorithm for joint replenishment problems An iproved self-adaptive harony search algorith for joint replenishent probles Lin Wang School of Manageent, Huazhong University of Science & Technology zhoulearner@gail.co Xiaojian Zhou School of Manageent,

More information

Experimental Design For Model Discrimination And Precise Parameter Estimation In WDS Analysis

Experimental Design For Model Discrimination And Precise Parameter Estimation In WDS Analysis City University of New York (CUNY) CUNY Acadeic Works International Conference on Hydroinforatics 8-1-2014 Experiental Design For Model Discriination And Precise Paraeter Estiation In WDS Analysis Giovanna

More information

Quantum algorithms (CO 781, Winter 2008) Prof. Andrew Childs, University of Waterloo LECTURE 15: Unstructured search and spatial search

Quantum algorithms (CO 781, Winter 2008) Prof. Andrew Childs, University of Waterloo LECTURE 15: Unstructured search and spatial search Quantu algoriths (CO 781, Winter 2008) Prof Andrew Childs, University of Waterloo LECTURE 15: Unstructured search and spatial search ow we begin to discuss applications of quantu walks to search algoriths

More information

Approximation in Stochastic Scheduling: The Power of LP-Based Priority Policies

Approximation in Stochastic Scheduling: The Power of LP-Based Priority Policies Approxiation in Stochastic Scheduling: The Power of -Based Priority Policies Rolf Möhring, Andreas Schulz, Marc Uetz Setting (A P p stoch, r E( w and (B P p stoch E( w We will assue that the processing

More information

a a a a a a a m a b a b

a a a a a a a m a b a b Algebra / Trig Final Exa Study Guide (Fall Seester) Moncada/Dunphy Inforation About the Final Exa The final exa is cuulative, covering Appendix A (A.1-A.5) and Chapter 1. All probles will be ultiple choice

More information

Outperforming the Competition in Multi-Unit Sealed Bid Auctions

Outperforming the Competition in Multi-Unit Sealed Bid Auctions Outperforing the Copetition in Multi-Unit Sealed Bid Auctions ABSTRACT Ioannis A. Vetsikas School of Electronics and Coputer Science University of Southapton Southapton SO17 1BJ, UK iv@ecs.soton.ac.uk

More information

Estimation of Korean Monthly GDP with Mixed-Frequency Data using an Unobserved Component Error Correction Model

Estimation of Korean Monthly GDP with Mixed-Frequency Data using an Unobserved Component Error Correction Model 100Econoic Papers Vol.11 No.1 Estiation of Korean Monthly GDP with Mixed-Frequency Data using an Unobserved Coponent Error Correction Model Ki-Ho Ki* Abstract Since GDP is announced on a quarterly basis,

More information

Principles of Optimal Control Spring 2008

Principles of Optimal Control Spring 2008 MIT OpenCourseWare http://ocw.it.edu 16.323 Principles of Optial Control Spring 2008 For inforation about citing these aterials or our Ters of Use, visit: http://ocw.it.edu/ters. 16.323 Lecture 10 Singular

More information

1 Identical Parallel Machines

1 Identical Parallel Machines FB3: Matheatik/Inforatik Dr. Syaantak Das Winter 2017/18 Optiizing under Uncertainty Lecture Notes 3: Scheduling to Miniize Makespan In any standard scheduling proble, we are given a set of jobs J = {j

More information

MULTIAGENT Resource Allocation (MARA) is the

MULTIAGENT Resource Allocation (MARA) is the EDIC RESEARCH PROPOSAL 1 Designing Negotiation Protocols for Utility Maxiization in Multiagent Resource Allocation Tri Kurniawan Wijaya LSIR, I&C, EPFL Abstract Resource allocation is one of the ain concerns

More information

Course Notes for EE227C (Spring 2018): Convex Optimization and Approximation

Course Notes for EE227C (Spring 2018): Convex Optimization and Approximation Course Notes for EE227C (Spring 2018): Convex Optiization and Approxiation Instructor: Moritz Hardt Eail: hardt+ee227c@berkeley.edu Graduate Instructor: Max Sichowitz Eail: sichow+ee227c@berkeley.edu October

More information

In this chapter, we consider several graph-theoretic and probabilistic models

In this chapter, we consider several graph-theoretic and probabilistic models THREE ONE GRAPH-THEORETIC AND STATISTICAL MODELS 3.1 INTRODUCTION In this chapter, we consider several graph-theoretic and probabilistic odels for a social network, which we do under different assuptions

More information

Now multiply the left-hand-side by ω and the right-hand side by dδ/dt (recall ω= dδ/dt) to get:

Now multiply the left-hand-side by ω and the right-hand side by dδ/dt (recall ω= dδ/dt) to get: Equal Area Criterion.0 Developent of equal area criterion As in previous notes, all powers are in per-unit. I want to show you the equal area criterion a little differently than the book does it. Let s

More information

Numerical Studies of a Nonlinear Heat Equation with Square Root Reaction Term

Numerical Studies of a Nonlinear Heat Equation with Square Root Reaction Term Nuerical Studies of a Nonlinear Heat Equation with Square Root Reaction Ter Ron Bucire, 1 Karl McMurtry, 1 Ronald E. Micens 2 1 Matheatics Departent, Occidental College, Los Angeles, California 90041 2

More information

Ufuk Demirci* and Feza Kerestecioglu**

Ufuk Demirci* and Feza Kerestecioglu** 1 INDIRECT ADAPTIVE CONTROL OF MISSILES Ufuk Deirci* and Feza Kerestecioglu** *Turkish Navy Guided Missile Test Station, Beykoz, Istanbul, TURKEY **Departent of Electrical and Electronics Engineering,

More information

An Approximate Model for the Theoretical Prediction of the Velocity Increase in the Intermediate Ballistics Period

An Approximate Model for the Theoretical Prediction of the Velocity Increase in the Intermediate Ballistics Period An Approxiate Model for the Theoretical Prediction of the Velocity... 77 Central European Journal of Energetic Materials, 205, 2(), 77-88 ISSN 2353-843 An Approxiate Model for the Theoretical Prediction

More information