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)

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

Overview

• Simulation and optimization modelling

• Resilience.io model components

• Building a UB local model

• Decision insight use

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

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

MakeM

ake

Influence

Influence

External World

Regulate

Supply & Demand

Agent factory

PopulationData

General Rules

Synthetic population with n agents

Generates

Agent ID: xxxxx1

Individualrules

Individual data

System level behaviour (emergence)

Generates

Validation

Scenario analysis (transport, energy, etc.)

Generates

SpatialSocio-

demographicTechnical

parameters

External

data

Scenario definition

(van Dam and Bustos-Turu, in print)

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

Overview

• Simulation and optimization modelling

• Resilience.io model components

• Building a UB local model

• Decision insight use

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 оролцооТехнологийн хөрөнгө

оруулалтЗах 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

Foundation components of model

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 jMDTj : Mean departure timeSDj : Standard deviationPDj : 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

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

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

Processes as the means for resource accounting of the economy

Operation of Process/Technology Networks

Underlying functions

• Simple input-output factors

• Linear equations

• Dynamic models

C

CHP

HDH HX

WH

CO2

E

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

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 оролцооТехнологийн хөрөнгө

оруулалтЗах 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

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

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 as1) Consumers 2) Processors

3) Owners4) Traders

Health services

Labour

Markets

Educational

services

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

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 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

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 jMDj,t : Mean dose over timeSDj,t : Standard deviationPEj : Probability of effect

SIj,t= {(HSj,t, SDj,t, PSj,t)}

Sij,t : Sickness from work status jSDj,t : Standard deviationPSj : Probability of sickness

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.

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

Overview

• Simulation and optimization modelling

• Resilience.io model components

• Building a UB local model

• Decision insight use

Building an integrated data map of UBPeople 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

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

• ….

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

ForestryAgri-Food processing

Biological processing

Human consumption

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

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

SimulationResults

Inputs

Calculations

Outputs

Comparison

Historic data

Result range

Logic

Overview

• Simulation and optimization modelling

• Resilience.io model components

• Creating a UB local model

• Decision insight use

• 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

SimulationResults

Investment

Planning

Policies

Visibility

Resilience

Performance

State of society

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

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

Level 4:

Quantitative & Qualitative Variable and Parameter values

Level 1:

Sector Key Performance Indicators & Spatial

variation

Level 2:

City wide information (long-term trends)

Meta overview of scoping of indicators for complete model

Indicator Category Description

Economic DevelopmentThe 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 servicesA 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 efficiencyThe 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 accessThe availability of basic services related to population livelihood including access to water, energy, and transportation

services.

Stability of resource availabilityIndicators 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 flowsThe 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 happinessAn 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.

Economic Instruments

Legislative & Public Instruments

Taxes and tax concessions

PurchasingTradable 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.

• 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

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

Koppelaar@iier.ch

Resilience.io

Technical Brief on Model Architecture

& Decision Support