Denotes a System Dynamics model containing only compartments, flows and variables

ModelId:

edinburgh1

SimileVersion:

3.1+

This (entirely hypothetical model) aims to demonstrate the potential for developing urban policy simulators. The model contains a number of stocks (industrial capital, population size and landfill), and a number of policy levers that the user can manipulate (road pricing and a rubbish uplift charge). Models like this, expanded and calibrated for particular cities, can be used in a black-box manner, where stakeholders want to see the consequences of different policies.

Equations:

Industrial capital = 1000

Landfill = 1000000

Population size = 500000

Closures = 10

Construction = graph(road_pricing)

Decomposition = 0.2*landfill

Mortality = 9000

Refuse collection = graph(uplift_charge)*population_size

Reproduction = 15000

Age structure = 1

Cars per head = graph(road_pricing)

CO2 = 0.5*emissions+100*industrial_capital+0.5*decomposition Emissions = 1.0e-006*total_cars

Health = temperature*age_structure*graph(pollution)*Note: temperature and age_structure are (at this stage) 0-1 multipliers that cause the effect of pollution to be even worse if either is suboptimal*

Pollution = emissions*graph(wind)

Road pricing = 0

Temperature = 1

Total cars = cars_per_head*population_size

Uplift charge = 0

Wind = 2

Model tags:

- Read more about Urban model (trivial example)
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ModelId:

DegreeDays1

SimileVersion:

4.0

The development rate of plants and invertebrate animals often depends on amount of heat received while the ambient temperature is between minimum and maximum thresholds.

The number of degree-days is a commonly used statistic related to the development of plants and invertebrates. Degree-days, DD, is given by:

DD = ∫ Tt.dt

- T is temperature in degrees Celsius (could be Fahrenheit)
- t is time in days.

degree-days is then ^{o}C days or ^{o}F days.

Equations:

Compartment DegreeDays : Degree days

Initial value = 0

Rate of change = + dDt on dt

Comments:

Also called temperature sum or less accurately "heat sum".

Flow dDt on dt : Rate of accumation of degree-days (degC)

dDt on dt = max(T_m-T_thresh,0)

Where:

T_m=../Environment/T_m

Variable Sdevelopment : State of development

Sdevelopment = min(DegreeDays/DDreq,1)

Comments:

Value = 0 to 1

Equations in Environment

Variable T_m : Mean daily temperature

Results:

Weblinks:

University of California Integrated Pest Management Program [1] [1] http://www.ipm.ucdavis.edu/WEATHER/ddconcepts.html

ModelId:

daisyworld1

SimileVersion:

3.1+

This is an implementation of James Lovelock’s DaiyWorld model. This aims to show that even a very simple, artificial system is capable of regulating conditions to suit itself.

We consider a planet on which can grow black amd white daisies. Black daisies heat up the air around them; white daisies cool it down. The proportion of black and white changes as solar output increases, resulting in a reasonably uniform value for global planet temperature.

Model tags:

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ModelId:

competition1

SimileVersion:

3.1+

This is the classic textbook model of the population dynamics of two competing species.

It is based on a pair of differential equations, each having the same mathematical form:

dX_{1}/dt = r_{1}.X_{1}.(1-b_{1}.X_{1}-c_{1}.X_{2})

dX_{2}/dt = r_{2}.X_{2}.(1-b_{2}.X_{2}-c_{2}.X_{1})

Results:

Weblinks:

Lotka-Volterra model of competitionSally Otto, Zoology, UBC, Canada. Rather more mathematically-based analysis of the model.

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ModelId:

chemical1

SimileVersion:

5.x

On the face of it, System Dynamics should be a good notation for modelling chemical reactions: chemical reactions are continuous processes involving amounts of substances, and System Dynamics is suitable for modelling continuous processes involvinga mounts of substances.

Equations:

Compartments:

CO2: initial value = 100

H2O: initial value = 100

C6H12O6: initial value = 0

O2: initial value = 0

Flows:

CO2_used = 6*rate

H2O_used = 6*rate

sugar_made = rate

O2_made = 6*rate

from_atmos = 0

Variables:

rate = 0.01*CO2*2*(temperature/10)

Results:

Model tags:

- Read more about Modelling chemical reactions: CO2 + H2O → Sugar and O2
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ModelId:

century_gc_weekly

SimileVersion:

5.x

This implemements the litter and soil organic matter submodels from the Century model (see http://www.nrel.colostate.edu/projects/century5/).

Development history:

- Century2 in Stella by Georg Cadisch;
- Re-implementation of the Stella model in Simile 2.91 by Ed Rowe;
- Converted to Simile version 4.0 by Robert Muetzelfeldt.

Weblinks:

Model tags:

- Read more about Century model (weekly) (from Cadisch/Rowe)
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ModelId:

broomgrass1

SimileVersion:

5.x

Co-View is a tool developed by CIFOR (the Centre for International Forestry Research) to help facilitators of natural resource management and stakeholders to articulate and explore a shared vision of the future and to develop strategies to achieve it. The following explanation of the background to the broom grass model is adapted from the CoView (Collaborative Vision Exploration Workbench) User Manual (see Web Links below).

Equations:

**Compartments**

compartment: broomgrass = 75000

**Flows**

destroyed = (if burning_internsity==2 then Broom_Grass__in_Machije_Vlei else agriculture+decomposition+element([proportion_of_grass_by__digging_uprooting__or_cutting],1)*total_harvesting/300)

growth = (if frequency_of_burning*burning_internsity>0 and element([proportion_of_grass_by__digging_uprooting__or_cutting],3)>0 then grass_growth*Broom_Grass__in_Machije_Vlei+germination else 0)

total harvesting = (if Broom_Grass__in_Machije_Vlei==0 then 0 elseif harvest then(element([proportion_of_grass_by__digging_uprooting__or_cutting],1)+element([proportion_of_grass_by__digging_uprooting__or_cutting],2))*0.008*number_of_harvesters*quantity_harvested_per_person+element([proportion_of_grass_by__digging_uprooting__or_cutting],3)*0.012*number_of_harvesters*quantity_harvested_per_person else 0)

**Variables**

agriculture = 2

Percent of Machije converted to agriculture

alternative income sources = (1==0)

availability of transport = (if transport_cost<=10 then 1 else 0)

beauty = processing+bundling_with_tube_or_fibre

bundling with tube or fibre = 1

1 for tube

0 for fibre

burning events = 1

burning internsity = (if season>=6 and season<=10 and frequency_of_burning>0 then 2 else 1)

1 = low internsity burning

2 = high internsity burning

comfort = length

decomposition = 5

expected income after harvesting = number_of_brooms_made*market_price_per_broom-permit_price-transport_cost

expected income before harvesting = market_price_per_broom*2000-permit_price-availability_of_transport

frequency of burning = (if rules_enforcement>0.5 then 0 elseif season>=6 and season<10 then burning_events else 0)

germination = (if soil_moisture==1 then seeds else 0)

grass growth = 1-growth_rate*soil_moisture/K

growth rate = 5

harvest = any([time_of_harvesting]==season)

income = number_of_brooms_sold*market_price_per_broom-permit_price

K = 1000000

length = 1

life of broom = bundling_with_tube_or_fibre*ripe_or_unripe*length*whether_cut__or_dug_or_uprooted

maximum number of broom harvest per day = 100

no_of_permits = 150

number of brooms made = 0.99*total_harvesting

Number of brooms sold = availability_of_transport*number_of_brooms_made

transport]

market price per broom = 3+last(quality_of_broom)*season/(supply_from_other_sources+1)

number of harvesters = (if alternative_income_sources then 0 elseif permit_price0.5 then[5,10,85]else[80,10,10]) quality of broom = beauty*life_of_broom*comfort quantity harvested per person = maximum_number_of_broom__harvest_per_day rainfall = element([rainfall_this_month],season) rainfall this month = [193,185,70,32,7,2,0,0,4,28,91,128] Months of the year when harvesting is permitted ripe or unripe = (if element([time_of_harvesting],1)==6 then 1 else 0) 1 = ripe 0 = unripe season = fmod(time(1),12)+1 seeds = (if element([time_of_harvesting],1)==6 and burning_internsity==1 and season==6 then produce else 0) set harvesting season = [6,7,8,9,10] soil moisture = (if rainfall>100 then 1 else 0) supply from other sources = 1 1 = yes there is supply from elsewhere 0 = there is no availabiility time of harvesting = (if rules_enforcement>0.5 then [set_harvesting_season] elseif number_of_people_not_farming<0 then[set_harvesting_season]-3 else[set_harvesting_season]) time of sale = market_price_per_broom transport_cost = 2 Price in dollars to transport a bundle of brooms

Weblinks:

Model tags:

- Read more about Broomgrass model, Zimbabwe
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ModelId:

besn1

SimileVersion:

3.1+

Equations:

Compartments:

canopy: initial value = 0

harvest: initial value = 0

litter: initial value = 0

wood: initial value = 0

soil: initial value = 0

Flows:

flow1 = 0.9*canopy

flow2 = 50

flow3 = 0.9*canopy

flow4 = 0.9*litter

flow5 = 0.01*wood

flow6 = 0.5*canopy

flow7 = 0.5*soil

flow8 = 0.01*soil

Results:

Model tags:

Tags:

- Read more about Simple teaching model of forest nitrogen dynamics
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