Reef Fish example: one dimensional stock

Description

The GOM provides data to replicate the Gulf of Mexico Reef Fish example in Fenichel and Abbott (2014). This dataset is consisted of parameters and functions.From Fenichel and Abboott(2014),

catch effort: x(s)=ys^\gamma,

harvest: h(s,x)=q((ys^\gamma)^\alpha)s=q(y^\alpha)(s^{\gamma\alpha}),

profit: w(s,x)=price \cdot h(s,x) - cost \cdot x(s), and

sdot: \dot{s} = rs \left( 1 - \frac{s}{k} \right) - q(y^\alpha)(s^{\gamma \alpha + 1}).

The parameters in detal are in below.

Usage

## Load dataset
data("GOM")
## Demonstration of example
# demo(GOM, package="capn")
## R-script location
# system.file("demo", "GOM.R", package = "capn")

Format

param: a data.frame of parameters

• r intrinsic growth rate (=0.3847)

• k carrying capacity (=359016000)

• q catchability coefficient (=0.00031729344157311126)

• price price (=2.70)

• cost cost (=153.0)

• alpha technology parameter (=0.5436459179063678)

• gamma pre-ITQ management parameter (=0.7882)

• y system equivalence parameter (=0.15745573410462155)

• delta discount rate (=0.02)

• order Chebyshev polynomial order (=50)

• upperK upper bound of Chebyshev polynomial nodes (=k)

• lowerK lower bound of Chebyshev polynomial nodes (=5*10^6)

• nodes the number of Chebyshev polynomial nodes (=50)

functions: functions for generate simulation data for each nodes

• effort effort function

• catch catch function

• profit profit function (w in Fenichel and Abbott (2014))

• sdot evaluated \frac{dst}{dt}

• dsdotds evaluated \frac{dsdot}{ds}

• dsdotdss evaluated \frac{d}{ds}(\frac{dsdot}{ds})

• dwds evaluated \frac{dw}{ds}

• dwdss evaluated \frac{d}{ds}(\frac{dw}{ds})

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

Prey-Predator (Lotka-Volterra) example: two stocks

Description

The LV provides the data and functions to simulate prey-predator (Lotka-Volterra) model. The original code was written by Joshua Abbott in MATLAB and Seong Do Yun adapted it to a package example. The prey-predator model is:

Prey (X): \dot{X} = rX \left( 1 - \frac{X}{K} \right) - aXY - \theta X , and

Predator (Y): \dot{Y} = bXY - mY - \gamma Y .

The parameters are given as:
r = 0.025: intrinsic growth rate for prey,
K = 1: carrying capacity for prey,
a = 0.08: predator-related mortality parameter for prey,
b = 0.05: predator/prey uptake parameter for predator,
m = 0.01: natural mortality for predator,
\gamma = 0.005: slope for linear predator harvest control rule, and
\theta = 0.005: slope for linear prey harvest control rule

The predator with no economic value (unharvested) is designed for the economic program as:

W = harv.prey(p.prey-c.prey/X)\theta X + harv.pred*(p.pred-c.pred/Y)\gamma Y.

The paramters are:
p.pred = 0: price per unit harvest of predator,
p.prey = 25: price per unit harvest of prey,
c.prey = 0.1 p_prey: cost /per unit of prey effort in Schaefer model (really c/q with q=1), and
c.pred = c_prey: cost per unit of predator effort in Schaefer model (really c/q with q=1).

Usage

## Load dataset
data("lvdata")
## Demonstration of example
# demo(LV, package="capn")
## R-script location
# system.file("demo", "LV.R", package = "capn")

Format

lvaproxdata: a data.frame for approximation (evaluated on (20 x 20) Chebyshev nodes)

• xs prey stock

• ys predator stock

• xdot evaluated xdot \frac{dx}{dt}

• ydot evaluated ydot \frac{dy}{dt}

• wval profit (W in Fenichel and Abtott (2014))

lvsimdata.time: a data for time simulation (101 ODE solution)

• tseq time sequence from 0 to 100

• xs prey stock

• ys predator stock

Defining Approximation Space

Description

The function defines an approximation space for all three approximation apporoaches (V, P, and Pdot).

Usage

aproxdef(deg, lb, ub, delta)

Arguments

Details

For the i-th dimension of i = 1, 2, \cdots, d, suppose a polynomial approximant s_{i} over a bounded interval [a_{i},b_{i}] is defined by Chebysev nodes. Then, a d-dimensional Chebyshev grids can be defined as:

\mathbf{S} = \left\{ (s_{1},s_{2},\cdots,s_{d}) \vert a_{i} \leq s_{1} \leq b_{i}, i = 1, 2, \cdots, d \right\} .

Suppose we impletement n_{i} numbers of polynomials (i.e., (n_{i}-1)-th order) for the i-th dimension. The approximation space is defined as:

deg = c(n_{1},n_{2},\cdots,n_{d}),
lb = c(a_{1},a_{2},\cdots,a_{d}), and
ub = c(b_{1},b_{2},\cdots,b_{d}).

delta is the given constant discount rate.

Value

A list containing the approximation space

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

vaprox, vsim, paprox, psim, pdotaprox, pdotsim

Examples

## Reef-fish example: see Fenichel and Abbott (2014)
delta <- 0.02
upper <- 359016000     # upper bound on approximation space
lower <- 5*10^6        # lower bound on approximation space
myspace <- aproxdef(50,lower,upper,delta)
## Two dimensional example
ub <- c(1.5,1.5)
lb <- c(0.1,0.1)
deg <- c(20,20)
delta <- 0.03
myspace <- aproxdef(deg,lb,ub,delta)

catch function of GOM dataset

Description

The function calulates the catchment in the reef-fishy example of GOM dataset (Fenichel and Abbott, 2014).

Usage

catch(s,Z)

Arguments

Details

This catch function is adopted in GOM dataset.

Value

Quantity of catchment

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

GOM

Generating Unidimensional Chebyshev polynomial (monomial) basis

Description

The function calculates the monomial basis of Chebyshev polynomials for the given unidimensional nodes, s_{i}, over a bounded interval [a,b].

Usage

chebbasisgen(stock, npol, a, b, dorder = NULL)

Arguments

Details

Suppose there are m numbers of Chebyshev nodes over a bounded interval [a,b]:

s_{i} \in [a,b], for i = 1,2,\cdots,m.

These nodes can be nomralized to the standard Chebyshev nodes over the domain [-1,1]:

z_{i} = \frac{2(s_{i} - a)}{(b - a)} - 1.

With normalized Chebyshev nodes, the recurrence relations of Chebyshev polynomials of order n is defined as:

T_{0} (z_{i}) = 1,
T_{1} (z_{i}) = z_{i}, and
T_{n} (z_{i}) = 2 z_{i} T_{n-1} (z_{i}) - T_{n-2} (z_{i}).

The interpolation matrix (Vandermonde matrix) of (n-1)-th Chebyshev polynomials with m nodes, \Phi_{mn} is:

\Phi_{mn} = \left[ \begin{array}{ccccc} 1 & T_{1} (z_{1}) & \cdots & T_{n-1} (z_{1})\\ 1 & T_{1} (z_{2}) & \cdots & T_{n-1} (z_{2})\\ \vdots & \vdots & \ddots & \vdots\\ 1 & T_{1} (z_{m}) & \cdots & T_{n-1} (z_{m}) \end{array} \right] .

The partial derivative of the monomial basis matrix can be found by the relation:

(1-z_{i}^{2}) T'_{n} (z_{i}) = n[ T_{n-1} (z_{i}) - z_{i} T_{n} (z_{i}) ].

The technical details of the monomial basis of Chebyshev polynomial can be referred from Amparo et al. (2007) and Miranda and Fackler (2012).

Value

A matrix (number of nodes (m) x npol (n)) of (monomial) Chebyshev polynomial basis

References

Amparo, Gil, Javier Segura, and Nico Temme. (2007) Numerical Methods for Special Functions. Cambridge: Cambridge University Press.
Miranda, Mario J. and Paul L. Fackler. (2002) Applied Computational Economics and Finance. Cambridge: The MIT Press.

See Also

chebnodegen

Examples

## Reef-fish example: see Fenichel and Abbott (2014)
data("GOM")
nodes <- chebnodegen(param$nodes,param$lowerK,param$upperK)
## An example of Chebyshev polynomial basis
chebbasisgen(nodes,20,0.1,1.5)
## The partial derivative of Chebyshev polynomial basis with the same function
chebbasisgen(nodes,20,0.1,1.5,1)

Generating Chebyshev grids

Description

This function generates a grid of multi-dimensional Chebyshev nodes.

Usage

chebgrids(nnodes, lb, ub, rtype = NULL)

Arguments

Details

For the i-th dimension of i = 1, 2, \cdots, d, suppose a polynomial approximant s_{i} over a bounded interval [a_{i},b_{i}] is defined by Chebysev nodes. Then, a d-dimensional Chebyshev grids can be defined as:

\mathbf{S} = \left\{ (s_{1},s_{2},\cdots,s_{d}) \vert a_{i} \leq s_{1} \leq b_{i}, i = 1, 2, \cdots, d \right\} .

This is all combinations of s_{i}. Two types of results are provided. 'rtype = list' provides a list of d dimensions wherease 'rtype = grids' creates a \left( \displaystyle \prod_{i=1}^{d} n_{i} \right) \times d matrix.

Value

A list with d elements of Chebyshev nodes or a \left( \displaystyle \prod_{i=1}^{d} n_{i} \right) \times d matrix of Chebyshev grids

See Also

chebnodegen

Examples

## Chebyshev grids with two-dimension
chebgrids(c(5,3), c(1,1), c(2,3))
# Returns the same results
chebgrids(c(5,3), c(1,1), c(2,3), rtype='list')
## Returns a matrix grids with the same domain
chebgrids(c(5,3), c(1,1), c(2,3), rtype='grid')
## Chebyshev grids with one-dimension
chebgrids(5,1,2)
chebnodegen(5,1,2)
## Chebyshev grids with three stock
chebgrids(c(3,4,5),c(1,1,1),c(2,3,4),rtype='grid')

Unidimensional Chebyshev nodes

Description

The function generates uni-dimensional chebyshev nodes.

Usage

chebnodegen(n, a, b)

Arguments

Details

A polynomial approximant s_{i} over a bounded interval [a,b] is constructed by:

s_{i} = \frac{b+a}{2} + \frac{b-a}{2}cos (\frac{n - i + 0.5 }{n} \pi ) for i = 1,2,\cdots,n.

More detail explanation can be refered from Miranda and Fackler (2002, p.119).

Value

An array n Chebyshev nodes

References

Miranda, Mario J. and Paul L. Fackler. (2002) Applied Computational Economics and Finance. Cambridge: The MIT Press.

Examples

## 10 Chebyshev nodes in [-1,1]
chebnodegen(10,-1,1)
## 5 Chebyshev nodes in [1,5]
chebnodegen(5,1,5)

first derivative function of sdot in GOM dataset

Description

dsdotds evaluated \frac{dsdot}{ds} in the reef-fishy example of GOM dataset (Fenichel and Abbott, 2014).

Usage

dsdotds(s,Z)

Arguments

Details

This function is adopted in GOM dataset.

Value

The first derivative of sdot with respect to s

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

GOM

second derivative function of sdot in GOM dataset

Description

dsdotdss evaluated \frac{d}{ds}(\frac{dsdot}{ds}) in the reef-fishy example of GOM dataset (Fenichel and Abbott, 2014).

Usage

dsdotdss(s,Z)

Arguments

Details

This function is adopted in GOM dataset.

Value

The second derivative of sdot with respect to s

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

GOM

first derivative function of profit in GOM dataset

Description

dwds evaluated \frac{dw}{ds} in the reef-fishy example of GOM dataset (Fenichel and Abbott, 2014).

Usage

dwds(s,Z)

Arguments

Details

This function is adopted in GOM dataset.

Value

The first derivative of w with respect to s

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

GOM

second derivative function of profit in GOM dataset

Description

dwdss evaluated \frac{d}{ds}(\frac{dw}{ds}) in the reef-fishy example of GOM dataset (Fenichel and Abbott, 2014).

Usage

dwdss(s,Z)

Arguments

Details

This function is adopted in GOM dataset.

Value

The second derivative of w with respect to s

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

GOM

effort function of GOM dataset

Description

The function calulates the catchment effort in the reef-fishy example of GOM dataset (Fenichel and Abbott, 2014).

Usage

effort(s,Z)

Arguments

Details

This effort function is adopted in GOM dataset.

Value

catchment effort values

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

GOM

Prey-Predator (Lotka-Volterra) example in LV dataset

Description

The lvaproxdata provides the data in LV dataset to simulate prey-predator (Lotka-Volterra) model. The original code was written by Joshua Abbott in MATLAB and Seong Do Yun adapted it to a package example. The prey-predator model is:

Prey (X): \dot{X} = rX \left( 1 - \frac{X}{K} \right) - aXY - \theta X , and

Predator (Y): \dot{Y} = bXY - mY - \gamma Y .

The parameters are given as:
r = 0.025: intrinsic growth rate for prey,
K = 1: carrying capacity for prey,
a = 0.08: predator-related mortality parameter for prey,
b = 0.05: predator/prey uptake parameter for predator,
m = 0.01: natural mortality for predator,
\gamma = 0.005: slope for linear predator harvest control rule, and
\theta = 0.005: slope for linear prey harvest control rule

The predator with no economic value (unharvested) is designed for the economic program as:

W = harv.prey(p.prey-c.prey/X)\theta X + harv.pred*(p.pred-c.pred/Y)\gamma Y.

The paramters are:
p.pred = 0: price per unit harvest of predator,
p.prey = 25: price per unit harvest of prey,
c.prey = 0.1 p_prey: cost /per unit of prey effort in Schaefer model (really c/q with q=1), and
c.pred = c_prey: cost per unit of predator effort in Schaefer model (really c/q with q=1).

Usage

## Load dataset
data("lvdata")

Format

lvaproxdata: a data.frame for approximation (evaluated on (20 x 20) Chebyshev nodes)

• xs prey stock

• ys predator stock

• xdot evaluated xdot \frac{dx}{dt}

• ydot evaluated ydot \frac{dy}{dt}

• wval profit (W in Fenichel and Abtott (2014))

See Also

LV,vsim

Prey-Predator (Lotka-Volterra) example in LV dataset

Description

The lvsimdata.time provides the time simulation data in LV dataset to simulate prey-predator (Lotka-Volterra) model. The original code was written by Joshua Abbott in MATLAB and Seong Do Yun adapted it to a package example. The prey-predator model is:

Prey (X): \dot{X} = rX \left( 1 - \frac{X}{K} \right) - aXY - \theta X , and

Predator (Y): \dot{Y} = bXY - mY - \gamma Y .

The parameters are given as:
r = 0.025: intrinsic growth rate for prey,
K = 1: carrying capacity for prey,
a = 0.08: predator-related mortality parameter for prey,
b = 0.05: predator/prey uptake parameter for predator,
m = 0.01: natural mortality for predator,
\gamma = 0.005: slope for linear predator harvest control rule, and
\theta = 0.005: slope for linear prey harvest control rule

The predator with no economic value (unharvested) is designed for the economic program as:

W = harv.prey(p.prey-c.prey/X)\theta X + harv.pred*(p.pred-c.pred/Y)\gamma Y.

The paramters are:
p.pred = 0: price per unit harvest of predator,
p.prey = 25: price per unit harvest of prey,
c.prey = 0.1 p_prey: cost /per unit of prey effort in Schaefer model (really c/q with q=1), and
c.pred = c_prey: cost per unit of predator effort in Schaefer model (really c/q with q=1).

Usage

## Load dataset
data("lvdata")

Format

lvsimdata.time: a data for time simulation (101 ODE solution)

• tseq time sequence from 0 to 100

• xs prey stock

• ys predator stock

See Also

LV,vsim

Calculating P-approximation coefficients

Description

The function provides the P-approximation coefficients of the defined Chebyshev polynomials in aproxdef. For now, only unidimensional case is developed.

Usage

paprox(aproxspace, stock, sdot, dsdotds, dwds)

Arguments

Details

The P-approximation is finding the shadow price of a stock, p from the relation:

p(s) = \frac{W_{s}(s) + \dot{p}(s)}{\delta - \dot{s}_{s}},

where W_{s} = \frac{dW}{ds}, \dot{p}(s) = \frac{dp}{ds}, \dot{s}_{s} = \frac{d\dot{s}}{ds} , and \delta is the given discount rate.

Consider approximation p(s) = \mathbf{\mu}(s)\mathbf{\beta}, \mathbf{\mu}(s) is Chebyshev polynomials and \mathbf{\beta} is their coeffcients. Then, \dot{p} = diag (\dot{s}) \mathbf{\mu}_{s}(s)\mathbf{\beta} by the orthogonality of Chebyshev basis. Adopting the properties above, we can get the unknown coefficient vector \beta from:

\mathbf{\mu}\mathbf{\beta} = diag \left( \delta - \dot{s}_{s} \right)^{-1} \left( W_{s} + diag (\dot{s}) \mathbf{\mu}_{s} \mathbf{\beta} \right) , and thus,

\mathbf{\beta} = \left( diag \left( \delta - \dot{s}_{s} \right) \mathbf{\mu} - diag (\dot{s}) \mathbf{\mu}_{s} \right)^{-1} W_{s} .

In a case of over-determined (more nodes than approaximation degrees),

\left( \left( diag \left( \delta - \dot{s}_{s} \right) \mathbf{\mu} - diag (\dot{s}) \mathbf{\mu}_{s} \right)^{T} \left( diag \left( \delta - \dot{s}_{s} \right) \mathbf{\mu} - diag (\dot{s}) \mathbf{\mu}_{s} \right) \right)^{-1} \left( diag \left( \delta - \dot{s}_{s} \right) \mathbf{\mu} - diag (\dot{s}) \mathbf{\mu}_{s} \right)^{T} W_{s}

For more detils see Fenichel et al. (2016).

Value

A list of approximation resuts: deg, lb, ub, delta, and coefficients. Use results$item
(or results[["item"]]) to import each result item.

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.
Fenichel, Eli P., Joshua K. Abbott, Jude Bayham, Whitney Boone, Erin M. K. Haacker, and Lisa Pfeiffer. (2016) "Measuring the Value of Groundwater and Other Forms of Natural Capital." Proceedings of the National Academy of Sciences .113:2382-2387.

See Also

aproxdef,psim

Examples

## 1-D Reef-fish example: see Fenichel and Abbott (2014)
data("GOM")
nodes <- chebnodegen(param$nodes,param$lowerK,param$upperK)
simuDataP <- cbind(nodes,sdot(nodes,param),
                   dsdotds(nodes,param),dwds(nodes,param))
Aspace <- aproxdef(param$order,param$lowerK,param$upperK,param$delta)
pC <- paprox(Aspace,simuDataP[,1],simuDataP[,2],
             simuDataP[,3],simuDataP[,4])

the parameter vector adopted in GOM dataset

Description

The GOM provides data to replicate the Gulf of Mexico Reef Fish example in Fenichel and Abbott (2014).

Usage

## Load dataset
data("GOM")

Format

param: a data.frame of parameters

• r intrinsic growth rate (=0.3847)

• k carrying capacity (=359016000)

• q catchability coefficient (=0.00031729344157311126)

• price price (=2.70)

• cost cost (=153.0)

• alpha technology parameter (=0.5436459179063678)

• gamma pre-ITQ management parameter (=0.7882)

• y system equivalence parameter (=0.15745573410462155)

• delta discount rate (=0.02)

• order Chebyshev polynomial order (=50)

• upperK upper bound of Chebyshev polynomial nodes (=k)

• lowerK lower bound of Chebyshev polynomial nodes (=5*10^6)

• nodes the number of Chebyshev polynomial nodes (=50)

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

GOM

Calculating Pdot-approximation coefficients

Description

The function provides the Pdot-approximation coefficients of the defined Chebyshev polynomials in aproxdef. For now, only unidimensional case is developed.

Usage

pdotaprox(aproxspace, stock, sdot, dsdotds, dsdotdss, dwds, dwdss)

Arguments

Details

The Pdot-approximation is finding the shadow price of a stock, p from the relation:

p(s) = \frac{W_{s}(s) + \dot{p}(s)}{\delta - \dot{s}_{s}},

where W_{s} = \frac{dW}{ds}, \dot{p}(s) = \frac{dp}{ds}, \dot{s}_{s} = \frac{d\dot{s}}{ds} , and \delta is the given discount rate.

In order to operationalize this approach, we take the time derivative of this expression:

\dot{p} = \frac{ \left( \left(W_{ss}\dot{s} + \ddot{p} \right) \left( \delta - \dot{s}_{s} \right) + \left( W_{s} + \dot{p} \right) \left(\dot{s}_{ss} \dot{s} \right) \right) } { \left( \delta - \dot{s}_{s} \right)^{2} }

Consider approximation \dot{p}(s) = \mathbf{\mu}(s)\mathbf{\beta}, \mathbf{\mu}(s) is Chebyshev polynomials and \mathbf{\beta} is their coeffcients. Then, \ddot{p} = \frac{ d \dot{p}}{ds} \frac{ds}{dt} = diag (\dot{s}) \mathbf{\mu}_{s}(s) \mathbf{\beta} by the orthogonality of Chebyshev basis. Adopting the properties above, we can get the unknown coefficient vector \beta from:

\mathbf{\mu \beta} = diag \left( \delta - \dot{s}_{s} \right)^{-2} \left[ \left(W_{ss}\dot{s} + diag (\dot{s}) \mathbf{\mu}_{s} \mathbf{\beta} \right)\left( \delta - \dot{s}_{s} \right) + diag \left(\dot{s}_{ss} \dot{s} \right) \left( W_{s} + \mathbf{\mu \beta} \right) \right] , and

\mathbf{\beta} = \left[ diag \left( \delta - \dot{s}_{s} \right)^{2} \mathbf{\mu} - diag \left( \dot{s}\left( \delta - \dot{s}_{s} \right) \right) \mathbf{\mu}_{s} - diag (\dot{s}_{ss} \dot{s} ) \mathbf{\mu} \right]^{-1} \left( W_{ss} \dot{s} \left( \delta - \dot{s}_{s} \right) + W_{s} \dot{s}_{ss} \dot{s} \right) .

If we suppose A = \left[ diag \left( \delta - \dot{s}_{s} \right)^{2} \mathbf{\mu} - diag \left( \dot{s}\left( \delta - \dot{s}_{s} \right) \right) \mathbf{\mu}_{s} - diag (\dot{s}_{ss} \dot{s} ) \mathbf{\mu} \right] and
B = \left( W_{ss} \dot{s} \left( \delta - \dot{s}_{s} \right) + W_{s} \dot{s}_{ss} \dot{s} \right) , then over-determined case can be calculated:

\mathbf{\beta} = \left( A^{T}A \right)^{-1} A^{T}B .

For more detils see Fenichel and Abbott (2014).

Value

A list of approximation results: deg, lb, ub, delta, and coefficients. Use results$item
(or results[["item"]]) to import each result item.

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

aproxdef, pdotsim

Examples

## 1-D Reef-fish example: see Fenichel and Abbott (2014)
data("GOM")
nodes <- chebnodegen(param$nodes,param$lowerK,param$upperK)
simuDataPdot <- cbind(nodes,sdot(nodes,param),
                      dsdotds(nodes,param),dsdotdss(nodes,param),
                      dwds(nodes,param),dwdss(nodes,param))
Aspace <- aproxdef(param$order,param$lowerK,param$upperK,param$delta)
pdotC <- pdotaprox(Aspace,simuDataPdot[,1],simuDataPdot[,2],
                   simuDataPdot[,3],simuDataPdot[,4],
                   simuDataPdot[,5],simuDataPdot[,6])

Simulation of Pdot-approximation

Description

The function provides the Pdot-approximation simulation.

Usage

pdotsim(pdotcoeff, stock, sdot, dsdotds, wval, dwds)

Arguments

Details

Let \hat{\beta} be the vector of approximation coefficents from the results of pdotaprox function. The estimated shadow price (accounting) price of stock over the given approximation interval of s \in [a,b], \hat{p} can be calculated as:

\hat{p} = \frac{ W_{s} + \mathbf{\mu \beta} }{ \delta - \dot{s}_{s} } .

The estimated value function is:

\hat{V} = \frac{1}{\delta} \left( W + \hat{p} \dot{s} \right) .

For more detils see Fenichel and Abbott (2014) and Fenichel et al. (2016).

Value

A list of approximation resuts: shadow (accounting) prices, inclusive wealth, and value function, stock, and W values. Use results$item (or results[["item"]]) to import each result item.

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.
Fenichel, Eli P., Joshua K. Abbott, Jude Bayham, Whitney Boone, Erin M. K. Haacker, and Lisa Pfeiffer. (2016) "Measuring the Value of Groundwater and Other Forms of Natural Capital." Proceedings of the National Academy of Sciences .113:2382-2387.

See Also

pdotaprox

Examples

## 1-D Reef-fish example: see Fenichel and Abbott (2014)
data("GOM")
nodes <- chebnodegen(param$nodes,param$lowerK,param$upperK)
simuDataPdot <- cbind(nodes,sdot(nodes,param),
                      dsdotds(nodes,param),dsdotdss(nodes,param),
                      dwds(nodes,param),dwdss(nodes,param))
Aspace <- aproxdef(param$order,param$lowerK,param$upperK,param$delta)
pdotC <- pdotaprox(Aspace,simuDataPdot[,1],simuDataPdot[,2],
                   simuDataPdot[,3],simuDataPdot[,4],
                   simuDataPdot[,5],simuDataPdot[,6])
GOMSimPdot <- pdotsim(pdotC,simuDataPdot[,1],simuDataPdot[,2],simuDataPdot[,3],
                      profit(nodes,param),simuDataPdot[,5])
# Shadow Price
plotgen(GOMSimPdot, xlabel="Stock size, s", ylabel="Shadow price")

# Value function and profit
plotgen(GOMSimPdot,ftype="vw",
        xlabel="Stock size, s",
        ylabel=c("Value Function","Profit"))

Plot Generator for Shadow Price or Value Function

Description

The function draws shadowp or vfun-w plot from the simulation results of vsim, psim, or pdotsim.

Usage

plotgen(simres, ftype = NULL, whichs = NULL, tvar = NULL, xlabel = NULL,
  ylabel = NULL)

Arguments

Details

This function provides an one-dimensional plot for "shadow price-stock", "shadow price-time", "Value function-stock", "Value function-time", "Value function-stock-W value", or "Value function-time-W value" depending on input arguments.

Value

A plot of approximation resuts: shadow (accounting) prices, inclusive wealth, and Value function

See Also

vsim, psim, pdotsim

Examples

## 1-D Reef-fish example: see Fenichel and Abbott (2014)
data("GOM")
nodes <- chebnodegen(param$nodes,param$lowerK,param$upperK)
simuDataP <- cbind(nodes,sdot(nodes,param),
                   dsdotds(nodes,param),dwds(nodes,param))
Aspace <- aproxdef(param$order,param$lowerK,param$upperK,param$delta)

# p-approximation
pC <- paprox(Aspace,simuDataP[,1],simuDataP[,2],
             simuDataP[,3],simuDataP[,4])

# Without prividing W-value
GOMSimP <- psim(pC,simuDataP[,1])
# With W-value
GOMSimP2 <- psim(pC,simuDataP[,1],profit(nodes,param),simuDataP[,2])

# Shadow price-Stock plot
plotgen(GOMSimP)
plotgen(GOMSimP,ftype="p")
plotgen(GOMSimP,xlabel="Stock Size, S", ylabel="Shadow Price (USD/Kg)")

# Value-Stock-W plot
plotgen(GOMSimP2,ftype="vw")
plotgen(GOMSimP2,ftype="vw",xlabel="Stock Size, S", ylabel="Value Function")
plotgen(GOMSimP2,ftype="vw",xlabel="Stock Size, S", ylabel="Value Function")

## 2-D Prey-Predator example
data("lvdata")
aproxdeg <- c(20,20)
lower <- c(0.1,0.1)
upper <- c(1.5,1.5)
delta <- 0.03
lvspace <- aproxdef(aproxdeg,lower,upper,delta)
lvaproxc <- vaprox(lvspace,lvaproxdata)
lvsim <- vsim(lvaproxc,lvsimdata.time[,2:3])

# Shadow price-Stock plot
plotgen(lvsim)
plotgen(lvsim,ftype="p")
plotgen(lvsim,whichs=2,xlabel="Stock Size, S",ylabel="Shadow Price (USD/Kg)")

# Shadow price-time plot
plotgen(lvsim,whichs=2,tvar=lvsimdata.time[,1])

# Value Function-Stock plot
plotgen(lvsim,ftype="vw")
plotgen(lvsim,ftype="vw",whichs=2,
        xlabel="Stock Size, S",ylabel="Shadow Price (USD/Kg)")

# Value Function-time plot
plotgen(lvsim,ftype="vw",tvar=lvsimdata.time[,1])
plotgen(lvsim,ftype="vw",whichs=2,tvar=lvsimdata.time[,1],
        xlabel="Stock Size, S",ylabel="Shadow Price (USD/Kg)")

profit function in GOM dataset

Description

profit (w) function in the reef-fishy example of GOM dataset (Fenichel and Abbott, 2014).

Usage

profit(s,Z)

Arguments

Details

This function is adopted in GOM dataset.

Value

profit

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

GOM

Simulation of P-approximation

Description

The function provides the P-approximation simulation.

Usage

psim(pcoeff, stock, wval = NULL, sdot = NULL)

Arguments

Details

Let \hat{\beta} be the vector of approximation coefficents from the results of paprox function. The estimated shadow price (accounting) price of stock over the given approximation interval of s \in [a,b], \hat{p} can be calculated as:

\hat{p} = \mathbf{\mu}(s) \mathbf{\hat{\beta}}.

The estimated value function is:

\hat{V} = \frac{1}{\delta} \left( W + \hat{p} \dot{s} \right) .

For more detils see Fenichel and Abbott (2014) and Fenichel et al. (2016).

Value

A list of approximation resuts: shadow (accounting) prices, inclusive wealth, value function, stock, and W values. Use results$item (or results[["item"]]) to import each result item.

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.
Fenichel, Eli P., Joshua K. Abbott, Jude Bayham, Whitney Boone, Erin M. K. Haacker, and Lisa Pfeiffer. (2016) "Measuring the Value of Groundwater and Other Forms of Natural Capital." Proceedings of the National Academy of Sciences .113:2382-2387.

See Also

aproxdef, paprox

Examples

## 1-D Reef-fish example: see Fenichel and Abbott (2014)
data("GOM")
nodes <- chebnodegen(param$nodes,param$lowerK,param$upperK)
simuDataP <- cbind(nodes,sdot(nodes,param),
                   dsdotds(nodes,param),dwds(nodes,param))
Aspace <- aproxdef(param$order,param$lowerK,param$upperK,param$delta)
pC <- paprox(Aspace,simuDataP[,1],simuDataP[,2],
             simuDataP[,3],simuDataP[,4])
GOMSimP <- psim(pC,simuDataP[,1],profit(nodes,param),simuDataP[,2])

# Shadow Price
plotgen(GOMSimP, xlabel="Stock size, s", ylabel="Shadow price")

# Value function and profit
plotgen(GOMSimP,ftype="vw",
        xlabel="Stock size, s",
        ylabel=c("Value Function","Profit"))

growth function of GOM dataset

Description

The function calulates the growth rate in the reef-fishy example of GOM dataset (Fenichel and Abbott, 2014).

Usage

sdot(s,Z)

Arguments

Details

This function is adopted in GOM dataset.

Value

growth rate

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.

See Also

GOM

Generating unifrom grids

Description

This function generates a grid of multi-dimensional uniform grids.

Usage

unigrids(nnodes, lb, ub, rtype = NULL)

Arguments

Details

For the i-th dimension of i = 1, 2, \cdots, d, suppose a polynomial approximant s_{i} over a bounded interval [a_{i},b_{i}] is defined by evenly gridded nodes. Then, a d-dimensional uniform grids can be defined as:

\mathbf{S} = \left\{ (s_{1},s_{2},\cdots,s_{d}) \vert a_{i} \leq s_{1} \leq b_{i}, i = 1, 2, \cdots, d \right\} .

This is all combinations of s_{i}. Two types of results are provided. 'rtype = list' provides a list of d dimensions wherease 'rtype = grids' creates a \left( \displaystyle \prod_{i=1}^{d} n_{i} \right) \times d matrix.

Value

A list with d elements of Chebyshev nodes or a \left( \displaystyle \prod_{i=1}^{d} n_{i} \right) \times d matrix of uniform grids

Examples

## Uniform grids with two-dimension
unigrids(c(5,3), c(1,1), c(2,3))
## Returns the same results
unigrids(c(5,3), c(1,1), c(2,3), rtype='list')
## Returns a matrix grids with the same domain
unigrids(c(5,3), c(1,1), c(2,3), rtype='grid')
## Uniform grid with one-dimension
unigrids(5,1,2)
## Uniform grids with three stock
unigrids(c(3,4,5),c(1,1,1),c(2,3,4),rtype='grid')

Calculating V-approximation coefficients

Description

The function provides the V-approximation coefficients of the defined Chebyshev polynomials in aproxdef.

Usage

vaprox(aproxspace, sdata)

Arguments

Details

The V-approximation is finding the shadow price of i-th stock, p_{i} for i=1,\cdots,d from the relation:

\delta V = W(\mathbf{S}) + p_{1}\dot{s}_{1} + p_{2}\dot{s}_{2} + \cdots + p_{d}\dot{s}_{d},

where \delta is the given discount rate, V is the intertemporal welfare function, \mathbf{S} = (s_{1}, s_{2}, \cdots, s_{d}) is a vector of stocks, W(\mathbf{S}) is the net benefits accruing to society, and \dot{s}_{i} is the growth of stock s_{i}. By the definition of the shadow price, we know:

p_{i} = \frac{\partial V}{\partial s_{i}}.

Consider approximation V(\mathbf{S}) = \mathbf{\mu}(\mathbf{S})\mathbf{\beta}, \mathbf{\mu}(\mathbf{S}) is Chebyshev polynomials and \mathbf{\beta} is their coeffcients. Then, p_{i} = \mathbf{\mu}_{s_{i}}(\mathbf{S})\mathbf{\beta} by the orthogonality of Chebyshev basis. Adopting the properties above, we can get the unknown coefficient vector \beta from:

\delta \mathbf{\mu}(\mathbf{S})\mathbf{\beta} = W(\mathbf{S}) + \displaystyle \sum_{i=1}^{d} diag (\dot{s}_{i}) \mathbf{\mu}_{s_{i}}(\mathbf{S})\mathbf{\beta}, and thus,

\beta = \left( \delta \mathbf{\mu}(\mathbf{S}) - \displaystyle \sum_{i=1}^{d} diag (\dot{s}_{i}) \mathbf{\mu}_{s_{i}}(\mathbf{S}) \right)^{-1} W(\mathbf{S}) .

In a case of over-determined (more nodes than approaximation degrees),

\beta = \left( \left( \delta \mathbf{\mu}(\mathbf{S}) - \displaystyle diag (\dot{s}_{i}) \sum_{i=1}^{d} \mathbf{\mu}_{s_{i}}(\mathbf{S}) \right)^{T} \left( \delta \mathbf{\mu}(\mathbf{S}) - \displaystyle \sum_{i=1}^{d} diag (\dot{s}_{i}) \mathbf{\mu}_{s_{i}}(\mathbf{S}) \right) \right)^{-1}
\times \left( \delta \mathbf{\mu}(\mathbf{S}) - \displaystyle \sum_{i=1}^{d} diag (\dot{s}_{i}) \mathbf{\mu}_{s_{i}}(\mathbf{S}) \right)^{T} W(\mathbf{S}) .

For more detils see Fenichel and Abbott (2014), Fenichel et al. (2016), and Yun et al. (2017).

Value

A list of approximation resuts: deg, lb, ub, delta, and coefficients. Use results$item
(or results[["item"]]) to import each result item.

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.
Fenichel, Eli P., Joshua K. Abbott, Jude Bayham, Whitney Boone, Erin M. K. Haacker, and Lisa Pfeiffer. (2016) "Measuring the Value of Groundwater and Other Forms of Natural Capital." Proceedings of the National Academy of Sciences.113:2382-2387.
Yun, Seong Do, Barbara Hutniczak, Joshua K. Abbott, and Eli P. Fenichel. (2017) "Ecosystem Based Management and the Welath of Ecosystems" Proceedings of the National Academy of Sciences. (forthcoming).

See Also

aproxdef, vsim

Examples

## 1-D Reef-fish example: see Fenichel and Abbott (2014)
data("GOM")
nodes <- chebnodegen(param$nodes,param$lowerK,param$upperK)
simuDataV <- cbind(nodes,sdot(nodes,param),profit(nodes,param))
Aspace <- aproxdef(param$order,param$lowerK,param$upperK,param$delta)
vC <- vaprox(Aspace,simuDataV)

## 2-D Prey-Predator example
data("lvdata")
aproxdeg <- c(20,20)
lower <- c(0.1,0.1)
upper <- c(1.5,1.5)
delta <- 0.03
lvspace <- aproxdef(aproxdeg,lower,upper,delta)
vaproxc <- vaprox(lvspace,lvaproxdata)

Simulation of V-approximation

Description

The function provides the V-approximation simulation by adopting the results of vaprox. Available for multiple stock problems.

Usage

vsim(vcoeff, adata, wval = NULL)

Arguments

Details

Let \hat{\beta} be the approximation coefficent from the results of vaprox function. The estimated shadow (accounting) price of i-th stock over the given approximation intervals of s_{i} \in [a_{i},b_{i}], \hat{p}_{i} can be calcuated as:

\hat{p}_{i} = \mathbf{\mu}(\mathbf{S})\mathbf{\hat{\beta}} where \mathbf{\mu}(\mathbf{S}) Chebyshev polynomial basis.

The value function is:

\hat{V} = \delta \mathbf{\mu}(\mathbf{S})\mathbf{\hat{\beta}}.

For more detils see Fenichel and Abbott (2014), Fenichel et al. (2016a), Fenichel et al. (2016b), and Yun et al. (2017).

Value

A list of simulation resuts: shadow (accounting) prices, inclusive wealth, Value function,
stock, and W values. Use results$item (or results[["item"]]) to import each result item.

References

Fenichel, Eli P. and Joshua K. Abbott. (2014) "Natural Capital: From Metaphor to Measurement." Journal of the Association of Environmental Economists. 1(1/2):1-27.
Fenichel, Eli P., Joshua K. Abbott, Jude Bayham, Whitney Boone, Erin M. K. Haacker, and Lisa Pfeiffer. (2016a) "Measuring the Value of Groundwater and Other Forms of Natural Capital." Proceedings of the National Academy of Sciences.113:2382-2387.
Fenichel, Eli P., Simon A. Levin, Bonnie McCay, Kevin St. Martin, Joshua K. Abbott, and Malin L. Pinsky. (2016b) "Wealth Reallocation and Sustainability under Climate Change." Nature Climate change.6:237-244.
Yun, Seong Do, Barbara Hutniczak, Joshua K. Abbott, and Eli P. Fenichel. (2017) "Ecosystem Based Management and the Welath of Ecosystems" Proceedings of the National Academy of Sciences. (forthcoming).

See Also

aproxdef, vsim

Examples

## 1-D Reef-fish example: see Fenichel and Abbott (2014)
data("GOM")
nodes <- chebnodegen(param$nodes,param$lowerK,param$upperK)
simuDataV <- cbind(nodes,sdot(nodes,param),profit(nodes,param))
Aspace <- aproxdef(param$order,param$lowerK,param$upperK,param$delta)
vC <- vaprox(Aspace,simuDataV)
# Note vcol function requries a data.frame or matrix!
GOMSimV <- vsim(vC,as.matrix(simuDataV[,1],ncol=1),profit(nodes,param))

# plot shadow (accounting) price: Figure 4 in Fenichel and Abbott (2014)
plotgen(GOMSimV, xlabel="Stock size, s", ylabel="Shadow price")

## 2-D Prey-Predator example
data("lvdata")
aproxdeg <- c(20,20)
lower <- c(0.1,0.1)
upper <- c(1.5,1.5)
delta <- 0.03
lvspace <- aproxdef(aproxdeg,lower,upper,delta)
lvaproxc <- vaprox(lvspace,lvaproxdata)
lvsim <- vsim(lvaproxc,lvsimdata.time[,2:3])

# plot Biomass
plot(lvsimdata.time[,1], lvsimdata.time[,2], type='l', lwd=2, col="blue",
     xlab="Time",
     ylab="Biomass")
lines(lvsimdata.time[,1], lvsimdata.time[,3], lwd=2, col="red")
legend("topright", c("Prey", "Predator"), col=c("blue", "red"),
       lty=c(1,1), lwd=c(2,2), bty="n")

# plot shadow (accounting) prices
plot(lvsimdata.time[,1],lvsim[["shadowp"]][,1],type='l', lwd=2, col="blue",
     ylim = c(-5,7),
     xlab="Time",
     ylab="Shadow price")
lines(lvsimdata.time[,1],lvsim[["shadowp"]][,2], lwd=2, col="red")
legend("topright", c("Prey", "Predator"), col=c("blue", "red"),
       lty=c(1,1), lwd=c(2,2), bty="n")

# plot inclusive weath and value function
plot(lvsimdata.time[,1],lvsim[["iw"]],type='l', lwd=2, col="blue",
     ylim = c(-0.5,1.2),
     xlab="Time",
     ylab="Inclusive Wealth / Value Function ($)")
lines(lvsimdata.time[,1],lvsim[["vfun"]], lwd=2, col="red")
legend("topright", c("Inclusive Wealth", "Value Function"),
       col=c("blue", "red"), lty=c(1,1), lwd=c(2,2), bty="n")