Population Dynamics - McGill Universitymsdl.cs.mcgill.ca/people/hv/teaching/MoSIS/lectures/... · 2017-12-14 · Population Dynamics • Deductive modelling: based on physical laws

Population Dynamics

• Deductive modelling: based on physical laws

• Inductive modelling: based on observation + intuition

• Single species:

Birth (in migration) Rate, Death (out migration) Rate

dP

dt= BR−DR

• Rates proportional to population

BR = kBR × P ;DR = kDR × P

dP

dt= (kBR − kDR)P

Hans Vangheluwe [email protected] Modelling and Simulation: Forrester System Dynamics 1/43

kBR = 1.4, kDR = 1.2 : Exponential Growth

population

trajectory

0 10 20 30 40 50

1000000

2000000


kBR = 1.4, kDR = 1.2 : log(Exponential Growth)

population

trajectory

0 10 20 30 40 50

1E2

1E3

1E4

1E5

1E6

1E7


kBR = 1.2, kDR = 1.4 : Exponential Decay

population

trajectory

0 10 20 30 40 50

20

40

60

80

100


Logistic Model

• Are kBR and kDR really constant ?

• Energy consumption in a closed system → limits growth

Epc =Etot

P

P ↑→ Epc ↓→ kBR ↓ and kDR ↑ until equilibrium

• “crowding” effect:

ecosystem can support maximum population Pmax

dP

dt= k × (1−

P

Pmax

)× P

• crowding is a quadratic effect


kBR = 1.2, kDR = 1.4, crowding = 0.001

population

trajectory

time0 20 40 60

popu

latio

n

0

100

200


Disadvantages

• NO physical evidence for model structure !

• But, many phenomena can be well fitted by logistic model.

• Pmax can only be estimated once steady-state has been reached.

Not suitable for control, optimisation, . . .

• Many-species system: Pmax, steady-state ?


Multi-species: Predator-Prey

• Individual species behaviour + interactions

• Proportional to species, no interaction when one is extinct:

product interaction Ppred × Pprey

dPpred

dt= −a× Ppred + k × b× Ppred × Pprey

dPprey

dt= c× Pprey − b× Ppred × Pprey

• Excess death rate a > 0, excess birth rate c > 0,

grazing factor b > 0, efficiency factor 0 < k ≤ 1

• Lotka-Volterra equations (1956): periodic steady-state


Predator Prey (population)

predator

prey

trajectories

0 10 20 30

200

400

600


Predator Prey (phase)

predprey

phaseplot

200 400 600

100

200

300


Competition and Cooperation

• Several species competing for the same food source

dP1

dt= a× P1 − b× P1 × P2

dP2

dt= c× P2 − d× P1 × P2

• Cooperation of different species (symbiosis)

dP1

dt= −a× P1 + b× P1 × P2

dP2

dt= −c× P2 + d× P1 × P2


Grouping and general n-species Interaction

• Grouping (opposite of crowding)

dP

dt= −a× P + b× P 2

• n-species interaction

dPi

dt= (ai +

n∑

j=1

bij × Pj)× Pi, ∀i ∈ {1, . . . , n}

• Only binary interactions,

no P1 × P2 × P3 interactions


Forrester System Dynamics

• based on observation + physical insight

• semi-physical, semi-inductive methodology


Methodology

1. levels/stocks and rates/flows

Level Inflow Outflow

population birth rate death rate

inventory shipments sales

money income expenses

2. laundry list: levels, rates, and causal relationships

birth rate → birth → population

3. Influence Diagram (+ and -)

4. Structure Diagram (functional relationships)

dP

dt= BR−DR


Causal Relationships

latent variable

beerconsumption

graduates

standardof

living(SOL)

time

graduates

SOL

beerconsumption

SOL


Archetypes

• Bellinger http://www.outsights.com/systems/

• influence diagrams

• Common combinations of reinforcing and balancing structures


Archetypes: Reinforcing Loop

state1 state2


Archetypes: Balancing Loop

adjustment state

desired state

action


Forrester System Dynamics

Predator Prey

Grazing_efficiency

uptake_predatorloss_prey

predator_surplus_DR

prey_surplus_BR

2−species predator−prey system


Inductive Modelling: World Dynamics

• BR: BirthRate

• P : Population

• POL: Pollution

• MSL: Mean Standard of Living

• . . .


Inductive Modelling: Structure Characterization

BR = f(P, POL,MSL, . . .)

BR = BRN × f (1)(P, POL,MSL, . . .)

BR = BRN × P × f (2)(POL,MSL, . . .)

BR = BRN × P × f (3)(POL)× f (4)(MSL) . . .

• f (3)(POL) inversely proportional

• f (4)(MSL) proportional

• compartmentalize to find correllations

• . . . Structure Characterization !


Structure Characterisation: LSQ fit

X(t) = - gt /2 + v_0 t2

X(t) = A sin (b t )

t

X

LSQ (sin) < LSQ (t ) 2


Feature Extraction

1. Measurement data and model candidates

2. Structure selection and validation

3. Parameter estimation

4. Model use


Feature Rationale

Minimum Sensitivity to Noise

Maximum Discriminating Power


Throwing Stones

Candidate Models

1. x = − 12gt

2 + v0t

2. x = Asin(bt)


Feature 1 (quadratic model)

gi =2xi

t2i−

2xi

ti, i = A,B

F1 = gA/gB


Feature 2 (sin model)

1

btg(bt) =

xi

xi

solve numerically for b

F2 = 200|bA − bB|

bA + bB


Feature Space Classification

Feature t2

feature sin

F1 = gA/gBgi = 2xi/ti^2 - 2xi_der/ti

F2 = 200 |bA -bB|/(bA + bB)1/b(tg(bt)) = xi/xi_der


Forrester’s World Dynamics model

• “Club of Rome” World Dynamics model

• Few “levels”, note the depletion of natural resources

• implemented in Vensim PLE (www.vensim.com)


Population

birth rate normalbirth rate normal 1

population initial

births crowdingmultiplier

births food multiplier

births deathsbirths material multiplier

births pollution multiplier

switch time 1<Time>

death rate normaldeath rate normal 1

deathscrowdingmultiplier

deaths food multiplier

deaths material multiplier

deaths pollution multiplier

switch time 3

crowding

land areapopulation

density normal

Population & Food

birthscrowdingmult tab

deathscrowdingmult tab

food ratio

food coefficientfood coefficient 1

food crowding multiplier

food percapita normal

food per capita potential

food pollutionmultiplier

switch time 7

<Time>food

pollutionmult tab

<pollution ratio>

<capital ratio agriculture>

food per capitapotential tab

<foodcrowdingmult tab>

<Time>

capital agriculture fraction indicated

births foodmult tab

deaths foodmult tab

capital agriculturefraction indicated tab

<material standard ofliving>

deathsmaterialmult tab

birthspollutionmult tab

births materialmult tab

<materialstandard of

living>

deaths pollutionmult tab

<pollution ratio>


Capital

capital initial

capital ratio

capital depreciation

capitalinvestment

rate normal 1

capital depreciationnormal

capital depreciationnormal 1

switch time 5

capital ratioagriculture

capitalagriculture

fraction normal

capitalinvestmentmultiplier

effective capital ratio

capitalinvestment

mult tab

capital investment

capitalinvestmentrate normal

<Population>

switch time 4<Time>

Capital & Quality of Life

CapitalAgriculture

Fraction

capital agriculturefraction adjustment

time

<capitalagriculture fraction

indicated>

capitalagriculture

fraction initial

capital investmentfrom quality ratio

<natural resource extraction multiplier>

quality material multiplier

qualitymaterialmult tab

material standard of living

effectivecapital ratio

normal

capitalinvestment

quality ratio tab

<quality foodmultiplier>

<natural resourceextraction

multiplier> quality of life

qualitycrowdingmultiplier quality food

multiplier

quality of lifenormal

quality pollutionmultiplier

<crowding>quality

crowdingmult tab

<food ratio>qualityfood

mult tab

<pollution ratio>quality

pollutionmult tab

<Time>


NaturalResources

pollutionabsorption

time tab

naturalresources

initial

pollution ratio

pollution standard

nat res matl multiplier natural resourceutilization normal

natural resourceutilization normal 1

<capital ratio>

switch time 2

pollutioncapital

mult tab

natural resourceutilization

Pollution & Natural Resources

Pollution

pollutioninitial

pollution capitalmultiplier

pollution percapita normal

pollution percapita normal 1

<Population>

switch time 6<Time>

pollutionabsorption time

pollutionabsorption

pollutiongeneration

<material standard of living> natural resource material mult tab

natural resourceextractionmultiplier

natural resourceextraction mult tab

natural resourcefraction remaining <Time>


World Model Results

6 B Person10 B Capital units40 B Pollution units

1e+012 Resource units2 Satisfaction units

0 Person0 Capital units0 Pollution units0 Resource units

0.4 Satisfaction units1900 1929 1957 1986 2014 2043 2071 2100

Time (Year)

Population : run1 PersonCapital : run1 Capital unitsPollution : run1 Pollution unitsNatural Resources : run1 Resource unitsquality of life : run1 Satisfaction units


The Process influences Productivity

“Adding manpower to a late software project makes it later”

Fred Brooks. The Mythical Man-Month.

(www.ercb.com/feature/feature.0001.html)


Why Brooks’ Law ? Team Size.

Model in Forrester System Dynamics

using Vensim PLE (www.vensim.com)

development rate =

nominal_productivity* (1-C_overhead*(N*(N-1)))*N


Team Size N = 5


Team Size N = 3 . . . 9

Optimal Team Size between 7 and 8


The Effect of Adding New Personnel (FSD model)

development rate = nominal_productivity*

(1-C_overhead*(N*(N-1)))* (1.2*num_exp_working + 0.8*num_new)


5 New Programmers after 100 days


5 New Programmers after 100 days


0 . . . 6 New Programmers after 100 days


Individual-based (discrete-event) model:Productivity.


Individual-based (discrete-event) model:Remaining work.


Documents

Population Dynamics - McGill Universitymsdl.cs.mcgill.ca/people/hv/teaching/MoSIS/lectures/... · 2017-12-14 · Population Dynamics • Deductive modelling: based on physical laws