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Resilience.io Platform
Technical Brief on Model
Architecture & Decision Support
Ulaanbaatar - Mongolia
11 June 2015
Rembra...
Resilience.io
Technical development team
IIER team:
• Hannes Kunz, May Sule, Alfeo Ceresa, Zoltan Kis,
Nikul Pandya, Elean...
Overview
• Simulation and optimization modelling
• Resilience.io model components
• Building a UB local model
• Decision i...
Agent-based modelling (simulation)
Structure example of algorithm:
At time t = t + h {
for (A in agentSet) {
for (x,y,z in...
Agent Decision Socio-Economics
People
Institutions
(Regulatory, Planning,
Soft Policies, Culture)
Government
Decisions
Mar...
Agent factory
Population
Data
General
Rules
Synthetic population with n agents
Generates
Agent ID: xxxxx1
Individual
rules...
Optimization modelling
Approach:
To calculate an outcome by finding the
minimal or maximal value of particular
mathematica...
Overview
• Simulation and optimization modelling
• Resilience.io model components
• Building a UB local model
• Decision i...
Хувьцаа
- Эх үүсвэр
- Материал
- Бараа
Яаж – компьютерын хялбаршуулсан ертөнч
"Хүний экосистем" орон зайн загвар’
Нарны ай...
Foundation components of model
Activity simulation of the population
Activities examples
Leisure, work, food, travel,
personal care, home care, religious...
1) Population/household group
characteristics per spatial area
- Population, household numbers
- household types
- Populat...
Service consumption from activities
Model output examples
• Spatial maps of use
• Changes in space over time as a
video (s...
Processes as the means for resource
accounting of the economy
Operation of Process/Technology Networks
Underlying functions
• Simple input-output factors
• Linear equations
• Dynamic m...
By identification of Infrastructure
• Site type: commercial, industrial,
agricultural, residential etc.
• Spatial location...
Хувьцаа
- Эх үүсвэр
- Материал
- Бараа
Яаж – компьютерын хялбаршуулсан ертөнч
"Хүний экосистем" орон зайн загвар’
Нарны ай...
Market exchange for goods and services
Demand and supply in other markets/services
Aim:
Simulate influence of long term 5+
year changes in societies on
outcomes
...
Population demographics development
Data input:
Birthrates, death rates, fertility
rates, migration rates, migratory
event...
Creating linkages between the Economy,
Ecosystems functioning, and Human well-being
Human and ecotoxicity impact assessments
Aim: To incorporate indicators for
assessment of implications of
environmental fl...
Human and ecotoxicity impact assessments
EPA Eco-Health Relationship browser
http://enviroatlas.epa.gov/enviroatlas/tools/...
Ecological model linkages
Examples of data linkages:
• Spatial dispersion of pollution
in the air
• Scenarios for flow rat...
Simulation of human well-being indicators
Existing and emerging metrics:
• Gallup world poll’s well-being
index
• OECD “Be...
Overview
• Simulation and optimization modelling
• Resilience.io model components
• Building a UB local model
• Decision i...
Building an integrated data map of UB
People and household data
•Numbers per khoroolol
•Demographics changes
•Workforce, e...
Building an integrated data map of UB
Regulations and market data
• Land use planning data
• Market tariffs / fees / price...
Data development influenced by priority areas
Ecosystems (Terrestrial, Aquatic)
Construction
Energy Generation
Transportat...
Building and adjusting rulesets so that they work for the
local context
• Influence of extreme temperature
changes on beha...
Testing and validation using local data
• Uncertainty analysis – feed in
range for parameters of agent
properties and deci...
Overview
• Simulation and optimization modelling
• Resilience.io model components
• Creating a UB local model
• Decision i...
• Resilience.io is not a predictive
modelling platform which seeks to
describe the future.
• Resilience.io is normative as...
Outcome as a trajectory of key performance
indicators
Each scenario simulation provides an outcome range of
indicators (vi...
Using the model as a ‘test-bed’ to add
evidence and ideas to planning for decisions
Investment, Policy, Planning,
Impacts visible at multiple levels
Level 3 :
Underlying Indicator
system relation details
Le...
Meta overview of scoping of indicators for
complete model
Indicator Category Description
Economic Development
The sum of r...
Economic Instruments
Legislative & Public Instruments
Taxes and tax
concessions
Purchasing
Tradable
Permits
Educational
pr...
• Users can as “central planner” choose their own investment
ideas (e.g. new drainage infrastructure, water treatment
plan...
Simulating Planning Decisions
• At baseline for a region the local
spatial planning map is
reconstructed in the model.
• T...
Koppelaar@iier.ch
Resilience.io
Technical Brief on Model Architecture
& Decision Support
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resilience.io Technical Briefing for UB City – Meeting – Rembrandt Koppelaar - 11th June 2015

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Technical Brief on Model Architecture & Decision Support
Ulaanbaatar -Mongolia
11 June 2015
Rembrandt Koppelaar –Senior Researcher
Institute for Integrated Economic Research (IIER)

Published in: Government & Nonprofit
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resilience.io Technical Briefing for UB City – Meeting – Rembrandt Koppelaar - 11th June 2015

  1. 1. Resilience.io Platform Technical Brief on Model Architecture & Decision Support Ulaanbaatar - Mongolia 11 June 2015 Rembrandt Koppelaar – Senior Researcher Institute for Integrated Economic Research (IIER)
  2. 2. Resilience.io Technical development team IIER team: • Hannes Kunz, May Sule, Alfeo Ceresa, Zoltan Kis, Nikul Pandya, Eleanor Watkins, Stavros Pyrotis Imperial College team: • James Keirstead, Nilay Shah, Koen van Dam, Charalampos Triantafyllidis Foundations in SynCity and SmartCity models developed at Imperial College London* Keirstead, J., Shah, N., Fisk, D., 2013. Urban Energy Systems: an integrated approach. Routledge
  3. 3. Overview • Simulation and optimization modelling • Resilience.io model components • Building a UB local model • Decision insight use
  4. 4. Agent-based modelling (simulation) Structure example of algorithm: At time t = t + h { for (A in agentSet) { for (x,y,z in conditionSet) { If condition X,Y,Z…. updateState (Aa to Ab) } } } Approach: To model behaviour of an ‘object’ in a flexible way using language decision logic based algorithms Use in resilience.io: • Activities of people • Transportation • Well-being indicators • Factory operation • Educational decisions Software use: • Java coding • Repast symphony libraries • http://repast.sourceforge.net/ • BSD License
  5. 5. Agent Decision Socio-Economics People Institutions (Regulatory, Planning, Soft Policies, Culture) Government Decisions Markets Outcomes (Production, Investment, Activities, Well-being as happiness and health, etc.) Demographics Firmographics Companies Households Labour Supply & Demand Supply & Demand Shape Shape Shape Make Make Make Influence Influence External World Regulate Supply & Demand
  6. 6. Agent factory Population Data General Rules Synthetic population with n agents Generates Agent ID: xxxxx1 Individual rules Individual data System level behaviour (emergence) Generates Validation Scenario analysis (transport, energy, etc.) Generates Spatial Socio- demographic Technical parameters External data Scenario definition (van Dam and Bustos-Turu, in print)
  7. 7. Optimization modelling Approach: To calculate an outcome by finding the minimal or maximal value of particular mathematical functions, including a set of constraints. Use in resilience.io: • Technology/Process operation • Service network flows • Market equilibrium Software use: • Java coding • GLPK solver • http://www.gnu.org/software/glpk/ • GNU GPL License
  8. 8. Overview • Simulation and optimization modelling • Resilience.io model components • Building a UB local model • Decision insight use
  9. 9. Хувьцаа - Эх үүсвэр - Материал - Бараа Яаж – компьютерын хялбаршуулсан ертөнч "Хүний экосистем" орон зайн загвар’ Нарны аймгийн хөдөлгөөн, “Байгаль” - - Цаг хугацаа, орон зай Урт хугацааны хүчин зүйл - Хүн ам зүй - Хөдөлмөр эрхлэлт - Боловсрол Дэд бүтэц - Барилга - Үйлчилгээний сүлжээ - Аж үйлдвэр Хэрэглэгчийн оролцоо Технологийн хөрөнгө оруулалт Зах зээлийн бодлого Төлөвлөлтийн журам Гадаадын - импортын - экспортын - шилжих хөдөлгөөн - хөрөнгө оруулалт Оролцогч - Хэрэглэгчид - Ажилчид - Эзэмшигчид - Худалдаачид Явцын урсгал - Технологийн мэдээлэл - Эрчим хүч, олон нийтийн тэнцэл - Хөдөлмөрийн хувь нэмэр
  10. 10. Foundation components of model
  11. 11. Activity simulation of the population Activities examples Leisure, work, food, travel, personal care, home care, religious practice, sleep Aim: Simulate activities by socio- economic groups of people in time and space Activity transition rulesets: APi= {(ACTj, MDTj, SDj, PDj)} ACTj : Activity j MDTj : Mean departure time SDj : Standard deviation PDj : Probability of departure * Keirstead J, Sivakumar A, 2012, Using Activity- Based Modeling to Simulate Urban Resource Demands at High Spatial and Temporal Resolutions, Journal of Industrial Ecology, Vol:16, ISSN:1088-1980, Pages:889-900
  12. 12. 1) Population/household group characteristics per spatial area - Population, household numbers - household types - Population gender, age distribution - Employment, - Educational enrollment 2) Activity time data from surveys to establish and validate activity transition rulesets Activity data needs: Population characteristics and activity dataset
  13. 13. Service consumption from activities Model output examples • Spatial maps of use • Changes in space over time as a video (sequence of maps) • Electricity use profiles Aim: Simulate consumption of services caused by population activities Calculation example: • Calculate the total population in each area based on density • Calculate occupancy % in buildings for each area per period (based on activities profile) • Calculate electricity demand from occupancy based on use rates, building size, base electricity use, peak electricity * Keirstead J, Sivakumar A, 2012, Using Activity-Based Modeling to Simulate Urban Resource Demands at High Spatial and Temporal Resolutions, Journal of Industrial Ecology, Vol:16, ISSN:1088-1980, Pages:889-900
  14. 14. Processes as the means for resource accounting of the economy
  15. 15. Operation of Process/Technology Networks Underlying functions • Simple input-output factors • Linear equations • Dynamic models C CHP HDH HX WH CO2 E
  16. 16. By identification of Infrastructure • Site type: commercial, industrial, agricultural, residential etc. • Spatial location • Outputs produced • Infrastructure/technology type Add ‘process blocks’ from the IIER process database • Mass inputs and outputs • Energy inputs and outputs • Labour inputs • Input to output relationships Process data needs: Spatial identification of sites/outputs Distribution centre Meat process factory Football stadium Hospital Residences
  17. 17. Хувьцаа - Эх үүсвэр - Материал - Бараа Яаж – компьютерын хялбаршуулсан ертөнч "Хүний экосистем" орон зайн загвар’ Нарны аймгийн хөдөлгөөн, “Байгаль” - - Цаг хугацаа, орон зай Урт хугацааны хүчин зүйл - Хүн ам зүй - Хөдөлмөр эрхлэлт - Боловсрол Дэд бүтэц - Барилга - Үйлчилгээний сүлжээ - Аж үйлдвэр Хэрэглэгчийн оролцоо Технологийн хөрөнгө оруулалт Зах зээлийн бодлого Төлөвлөлтийн журам Гадаадын - импортын - экспортын - шилжих хөдөлгөөн - хөрөнгө оруулалт Оролцогч - Хэрэглэгчид - Ажилчид - Эзэмшигчид - Худалдаачид Явцын урсгал - Технологийн мэдээлэл - Эрчим хүч, олон нийтийн тэнцэл - Хөдөлмөрийн хувь нэмэр
  18. 18. Market exchange for goods and services
  19. 19. Demand and supply in other markets/services Aim: Simulate influence of long term 5+ year changes in societies on outcomes Markets/services to include: • Change in occupations and jobs from labour markets. • Change in physical capital from investment decisions • Change in Human Capital from Education and Labour as well as Health Markets. Transactions of Goods & Services Markets Investment & Property Markets Agents as 1) Consumers 2) Processors 3) Owners 4) Traders Health services Labour Markets Educational services
  20. 20. Population demographics development Data input: Birthrates, death rates, fertility rates, migration rates, migratory events, household types, relationships change Aim: Create scenarios for change in population numbers and households Example of calculation: • Population births and deaths based on a rate per household type (births) and age (deaths) • Households can transition between types (sole-person, one- parent, couples, couplies with kids, students, etc.) • Household transitions dependent on relationship change, employment, births, deaths, education
  21. 21. Creating linkages between the Economy, Ecosystems functioning, and Human well-being
  22. 22. Human and ecotoxicity impact assessments Aim: To incorporate indicators for assessment of implications of environmental flow outputs Calculations: The model generates flow data of solids, liquids, and gasses into the atmosphere, surface, soils. These can used to assess effects using toxicity indicators (as per LCA) and dose-response functions from toxicological research* *Ritz, C. 2010. Towards a Unified Approach to Dose-Response Models in Ecotoxicology. 29(1). pp. 220-229 Concentration Emission Dose Probability of effect Severity of effect
  23. 23. Human and ecotoxicity impact assessments EPA Eco-Health Relationship browser http://enviroatlas.epa.gov/enviroatlas/tools/Eco Health_RelationshipBrowser/index.html Aim: Simulate changes in human health and subsequent linkages on society, service needs, employment, quality of life Example for human health: HSj,t+1= {(HSj,t, MDj,t, SDj.t, PEj)} HSj : Health status j MDj,t : Mean dose over time SDj,t : Standard deviation PEj : Probability of effect SIj,t= {(HSj,t, SDj,t, PSj,t)} Sij,t : Sickness from work status j SDj,t : Standard deviation PSj : Probability of sickness
  24. 24. Ecological model linkages Examples of data linkages: • Spatial dispersion of pollution in the air • Scenarios for flow rates in Tuul river in coming decades Aim: To create links to ecological models to better understand ecosystem impacts Model examples: • Hydrological models of Tuul River Basin and underground aquifers • Wind dispersion models of pollution entering the air • Ecosystem / species models of Bogd Khan mountain * Tuul River flow model in Altansukh, O., 2008. Water quality Assessment and Modelling Study in the Tuul River, Ulaanbaatar city, Mongolia. ITC Source of figure: Emerton, L., N. Erdenesaikhan, B. De Veen, D. Tsogoo, L. Janchivdorj, P. Suvd, B. Enkhtsetseg, G. Gandolgor, Ch. Dorisuren, D. Sainbayar, and A. Enkhbaatar. 2009. The Economic Value of the Upper Tuul Ecosystem. Mongolia Discussion Papers, East Asia and Pacific Sustainable Development Department. Washington, D.C.: World Bank.
  25. 25. Simulation of human well-being indicators Existing and emerging metrics: • Gallup world poll’s well-being index • OECD “Better Life Index” • ISO31720 indicators for city services and quality of life • EU/Eurostat “quality of life indicators” under development • WHO framework under development Aim: Simulate well-being of the population based on modelled relationships and outcomes Indicator examples: • Health status of agents / health service access • Services standard of living indicators • Employment and educational development • Quality of the city environment • Recreational possibilities • Fire and emergency infrastructure
  26. 26. Overview • Simulation and optimization modelling • Resilience.io model components • Building a UB local model • Decision insight use
  27. 27. Building an integrated data map of UB People and household data •Numbers per khoroolol •Demographics changes •Workforce, employment, education records •Time spent on activities per day •Health records and happiness surveys •Transportation records •…. Physical Infrastructure •Buildings and roads •Electricity, heat, water, service networks •Forests, farms, parks, grasslands •Site records: factories, warehouses, processing plants, recreational sites, schools
  28. 28. Building an integrated data map of UB Regulations and market data • Land use planning data • Market tariffs / fees / prices for services • Building regulations • Property investment data Ecological data • Soil, air, and water quality • Biomass / Ecosystem productivity • Climate records • Factories, warehouses, recreational sites Resource flow data • Consumption of water, goods, energy, food • Production of minerals, materials, goods, wastes • Imports and exports of materials and goods • Estimated losses in networks • ….
  29. 29. Data development influenced by priority areas Ecosystems (Terrestrial, Aquatic) Construction Energy Generation Transportation Human and animal Services Mineral Extraction Physical manufacturing Chemical manufacturing Recycling, disposal, remanufacturing Water Supply and Sanitation Agriculture & Seafood 2016 2017 2018 Forestry Agri-Food processing Biological processing Human consumption
  30. 30. Building and adjusting rulesets so that they work for the local context • Influence of extreme temperature changes on behaviour/technologies. • Culture and planning of activities. • Behaviour to market prices and people’s investments • Responses to policy changes in adjusting behaviour
  31. 31. Testing and validation using local data • Uncertainty analysis – feed in range for parameters of agent properties and decisions, and assess whether outcomes change. • Plausibility of results analysis – do the results make sense based on historic and current data + fundamental knowledge. • Accounting assessment – test if physical input to output values match up over space and time. Run model Improve rulesets Simulation Results Inputs Calculations Outputs Comparison Historic data Result range Logic
  32. 32. Overview • Simulation and optimization modelling • Resilience.io model components • Creating a UB local model • Decision insight use
  33. 33. • Resilience.io is not a predictive modelling platform which seeks to describe the future. • Resilience.io is normative as the aim is to create insights in how to shape the future. • Its value is the ability to simulate investment, planning, and policy decisions. • And giving users visibility on decision impact in economic, social, and environmental dimensions. Decision Support for Regional Design Model Regional Design Simulation Results Investment Planning Policies Visibility Resilience Performance State of society
  34. 34. Outcome as a trajectory of key performance indicators Each scenario simulation provides an outcome range of indicators (via numerous model runs, as opposed to a “predictive” optimal outcome) --------------------------> Time
  35. 35. Using the model as a ‘test-bed’ to add evidence and ideas to planning for decisions
  36. 36. Investment, Policy, Planning, Impacts visible at multiple levels Level 3 : Underlying Indicator system relation details Level 4: Quantitative & Qualitative Variable and Parameter values Level 1: Sector Key Performance Indicators & Spatial variation Level 2: City wide information (long-term trends)
  37. 37. Meta overview of scoping of indicators for complete model Indicator Category Description Economic Development The sum of resource flows related to an economy (or sector) in material input/outputs, energy input/outputs, and the total of resulting goods and/or services produced. Values are expressed in physical quantities, quality adjusted labour hour currency (QLH), including the reproduction of a QLH based GDP figure from quantity and market transactions. Employment The simulated number of people in the workforce and in employment (for inclusion in phase 1a at sector level). Environmental quality and services A set of indicators related to biomass productivity, air quality from gaseous emissions, and water quality of local water bodies and flows Human health The access to health services of the population and their life expectancy and the impact of health on productivity. Income inequality The distribution of household income in QLH following from employment. Quality of the living environment A set of indicators related to the amount of greenspace, recreational area, and access to luxury services. Production efficiency The conversion efficiency of materials and energy to produce goods and services and consume them in the city-region based on losses of materials and heat for different types of work. Resource access The availability of basic services related to population livelihood including access to water, energy, and transportation services. Stability of resource availability Indicators which relate to the overall physical availability of resources as extracted in the supply hinterland such as from a mine-site or a forest, and through imports from the outside world. Waste and pollution flows The generation of solid, liquid, and gaseous wastes through production and consumption across the spatial landscape. Inclusive of information on the final end-point as a non-harmful waste, as a pollutant such as GHG emissions, or as being reintroduced into production by recycling, re-manufacturing, or re-use. Well-being and happiness An index indicator initially to be based on a weighting of variables such as simulated household income, employment status, productivity ratio of work to leisure time, human health, access to utility services, and proximity to pollution. The index can be adjusted over time as the comprehensiveness of the model develops.
  38. 38. Economic Instruments Legislative & Public Instruments Taxes and tax concessions Purchasing Tradable Permits Educational programmes Standards and Penalties Covenants Accreditation systems Licensing Subsidies and grants Public service provision Simulating Policy Decisions • The model is being built with a library of policy options (put policies into effect and vary their degree). • Policy effects are simulated based on changes in market operation and decisions of the population and company agents. • Impacts become visible through changes in outcomes (production, consumption, activities) and indicators (social, economic, environmental) in space and time.
  39. 39. • Users can as “central planner” choose their own investment ideas (e.g. new drainage infrastructure, water treatment plant). • Investments options are then simulated are based on a three- step procedure, first: technology choice, second: selection of plausible efficient spatial options, third: cost-benefit type analyses. • Analyse investment condition impacts by adjusting parameters requirements (NPV, ROI, Time Horizon), value inclusion (Economic, Social, Environmental). • Aim for long term model expansion is for investment decisions to also be taken within internal model logic (by simulated companies/government) Simulating Technology Investment Decisions
  40. 40. Simulating Planning Decisions • At baseline for a region the local spatial planning map is reconstructed in the model. • The platform users can adjust planning rules as a “planning permission authority” about land use, construction, building standards, demolition etc. • Any investment or policy decision generated in the simulation will then be evaluated and accepted, adjusted, or rejected based on user set planning rules. Built environment change Planning Investment Planning consideration Simulated Planning Application Acceptance/Rejection based on user rules
  41. 41. Koppelaar@iier.ch Resilience.io Technical Brief on Model Architecture & Decision Support

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