Simulating Complex Systems:

Applications to Energy Charles Macal, PhD, PE

Decision & Information Sciences Division, Argonne National Laboratory

Energy Policy Institute, University of Chicago

Computation Institute, University of Chicago

macal@anl.gov

University of Texas at Austin’s Energy Symposium (UTES)

Energy Institute at UT Austin

March 29, 2012

Outline

Quick Intro to Complex Systems / Agent-based Modeling

Energy Systems as Complex Systems

Applications

– Restructuring Electric Power Markets

– Adopting Solar Photovoltaics

– Biofuels Supply Chains

Energy Analysis for the Future

2

Complex Systems Agent-based Modeling

3

Agent-based Modeling – Thesis…

Agent-based modeling (ABM) is a relatively new approach to modeling systems comprised of autonomous, interacting agents.

Growing number of ABM applications in a variety of fields

– Predicting the spread of epidemics to

– … modeling consumer behavior and technology adoption to

– …modeling crowd and pedestrian movements

Hardly a simulation publication exists now without an article on ABM

Continuing interest in ABM by sponsors – DOE, DOD, DOT, NIH, NSF, Industry, Others

4

AgentAttributes: Static: identifier, name,... Dynamic: memory, resources Neighbors in neighborhood,...Behaviors Behaviors Behaviors that modify behaviorsOther Methods:

Update rules for dynamic attributes, ...

Agent Interactions with the Environment

Agent Interactions with Other Agents

A Typical Agent

5

The Basic Idea of Agent-based Modeling…

Agents – Decentralization: Agents have behaviors

– Agents are “autonomous”

– There is no central authority

Interactions – Local Information: The idea is that

everyone does not interact with everyone else, all of the time.

– Agents live in a dynamic “environment”

Agent Model

– Simulates the dynamics of agent interactions

– Generally, this process occurs over time

Agent Interaction Network

Some Recent ABMS Applications Using Repast (2004-2008)

Application Area Model Description Reference

Air Traffic Control Air traffic control to analyze control policies and performance of a capacity constrained air traffic management facility.

Conway, 2006

Anthropology Prehistoric settlement patterns and political consolidation in the Lake Titicaca basin of Peru and Bolivia.

Model of linguistic diversity.

Griffin and Stanish, 2007 De Bie and de Boer, 2007

Ecology Predator-prey relationships between transient killer whales and other marine mammals.

Aphid population dynamics in agricultural landscapes

Mock and Testa, 2007

Parry, et al., 2004

Energy Analysis Scenario development of offshore wind energy. Residential energy generation.

Mast, et al. , 2007 Houwing & Bouwmans, 2007

Epidemics MIDAS (Models of Infectious Disease Agent Study) Program www.nigms.nih.gov/Research/FeaturedPrograms/MIDAS/

Retrospectively simulate the spread of the 1918-1919 influenza epidemic through the small fur-trapping community of Norway House in Manitoba, Canada.

Carpenter, 2004

Market Analysis An agent-based simulation to model the possibilities for a future market in sub-orbital space tourism.

A multi-agent based simulation of news digital markets. An agent-based model of Rocky Mountain tourism. An agent-based computational economics model to study market

mechanisms for the secondary use of the radio spectrum.

Charania et al., 2006 López-Sánchez, et al., 2005 Yin, 2007 Tonmukayakul, 2007

Organizational Decision Making

Approach to allow negotiations in order to achieve a global objective, specifically for planning the location of intermodal freight hubs.

Evaluation framework for supply chains based on corporate culture compatibility

van Dam, et al., 2007 Al-Mutawah and Lee, 2008

6

Energy as a complex system

7

Imagine modeling the energy systems from the

“bottom-up” – using agent-based modeling

Electric Power

Natural Gas

Petroleum

Coal

Ethanol

Nuclear

Other

Energy Infrastructure and

Interdependent Energy Supply Chains

Imports

/ Exports

Business and Decision-Making Units

Consumers

Energy Consumption

and Production Energy Prices

Energy Markets

8 8

Demand Sectors: • Industrial • Commercial • Residential • Transportation

Supply Sectors:

Energy systems analysis involves multiple layers

Environmental Layer

Physical Layer

Economic/Business Layer

Regulatory Layer

9

The Physical Layer analysis focuses on the physical

infrastructure

•Oil •Natural Gas •Coal •Nuclear

•Transportation •Industry •Residential •Agriculture

Physical Layer

Energy Supply Energy Infrastructure Energy Utilization

•Electricity •Renewables •Other

10

The Economic/Business Layer addresses company and

consumer behavior

Cost •Capital •Operating

Finance •Loans •Debt

Micro/behavioral economics •Business behavior •Consumer behavior

Markets •Energy markets •Commodities markets

Macroeconomics •GDP Growth •Employment •Inflation

Economic/Business Layer

11

Why agents?

The rules of business and social interaction are at least

as important as the rules of physics when it comes to the

generation, sale, pricing and delivery of energy.

12

Agent-based modeling of the

energy system “from the ground up”

Agents – Have behaviors, make decisions

– Are heterogeneous over a population – Agents learn and adapt

– Example: Consumers making decisions about solar PV adoption

– Example: Consumers deciding on PHEV adoption

Agent Interactions – Agents receive information, compete for resources

– Example: Social networks through which agents receive information from trusted sources

– Example: Power markets, bilateral trading arrangements

Environment – The physical infrastructure constrains agents in space and time

– Example: Power grid, transport network, energy resources and distribution

13

Application: EMCAS

EMCAS, Electricity Market Complex Adaptive Systems Model

14

Electricity Market Restructuring

Question: When the Illinois electric power market is deregulated, what will happen to:

– Electricity prices?

– Reliability of service?

– Market power?

Electricity power deregulation

Centralization - Before

– Single electricity price for whole state

– Rate of return regulated by Illinois

Commerce Commission (ICC)

Decentralization – As of January 1, 2007

– Companies free to price their production

by bidding into power pools

– Independent System Operator (ISO)

matches supply and demand and clears

the market

– People make their own decisions on

consumption

New ways to calculate electricity prices

– Locational marginal pricing (LMP)

– 30 separate pricing zones on the grid

These issues can only be addressed through

agent-based modeling Illinois electric power transmission grid

and service areas

16

Market structure under deregulation

17 17

Deregulation questions

Will power transmission

capacity be adequate, or is

congestion likely?

Will congestion create regional

imbalances in supply and

demand?

Will imbalances create pockets

of market power, potentially

driving up locational electricity

prices?

Under what conditions are these

situations possible, likely?

18

AMRN A

AMRN B

AMRN C

AMRN D

AMRN E

CILC

CWLP

EEI

IP A

IP B

IP C

IP D

NI A NI B

NI C

NI D

NI E

NI F

NI G

SIPC

Buses : 1856

Bus locations : 441

Links displayed : 1283

Transmission Line Capacity

0 kV 125

125 kV 150

150 kV 300

300 kV 400

400 kV

18

There is enough capacity in the State to satisfy load

requirements

19

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Hour

5000

10000

15000

20000

25000

30000

35000

MW Total Load by Hour for Year

Peak Load MW : 33181

19

Illinois timeline 1997

In Illinois, electricity restructuring is mandated by the Electric Service Customer Choice and

Rate Relief Law of 1997.

The law provides for a transition period up to January 1, 2007, in which the electric power

system is to move toward a competitive market.

2000 - 2001

California Electricity Crisis occurs

Illinois Commerce Commission commissions study with Argonne and the University of Illinois

– The problems experienced elsewhere in the country emphasize the need for an evaluation of

how Illinois might fare under a restructured electricity market.

– Despite the current adequacy of the generation and transmission system in Illinois, there is

concern that the uncertainties of electricity restructuring warrant a more detailed analysis to

determine if there might be pitfalls that have not been identified under current conditions.

2003-2006

Data Collection, EMCAS Model Application, Analysis, Draft Reports

April 2006

Final Report

May 2006

Testified before the ICC on model results and report

Jan 1, 2007

Deregulation of the Illinois electric power market completed

20

*EMCAS: Electricity Market Complex Adaptive Systems Model, developed by Argonne National Laboratory

20

The electric power system data "problem"

Large Data Sets

– 2522 transmission lines, 1908 buses

– 66 plants, 237 generating units, 638 total generator blocks

– 20 genCos

– 852 buses with loads, for 8760 hours (1 year), collected into 18 load zones

– 113 geospatial objects

Heterogeneous Data Sources

– Standard data sets (FERC)

– Reference data sets (hourly load profiles)

– Hand tailored data sets (new announced capacity additions)

– Generally available data sets (geo-spatial)

Necessarily, assembled data sets have gaps, inconsistencies and

anomalies.

21 21

Generating plants

66 plants

237 generating units

638 total generator blocks

20 GenCos

22

Ameren

Calpine

Calumet Energy LLC

City of Springfield

Constellation Power

Dominion Energy

Duke Energy

Dynegy Midwest Generation Inc.

Dynegy /NRG Energy

Electric Energy Inc.

Exelon Nuclear

Exelon Nuclear /Midamerican EnergyMid America

Midwest Generation LLC

NRG EnergyPower Energy Partners

Reliant Energy

Southern Illinois Power Coop

Soyland Power Coop Inc

22

Transmission network

2522 transmission line segments

1908 buses

852 buses with loads for 8760 hours (1 year)

23

Deriving an equivalent grid representation

An equivalent DC Optimal Power Flow (DC-OPF) model for Illinois is

derived from a Midwest regional AC Power Flow Model

– Includes an out-of-state connections to the Eastern Interconnect

– DC model allows locational marginal prices to be computed for the

Illinois power grid

917

896

896

542

922

891

895

811

895

891

893

200

200

2663

542

1109

2003441

468

1604

2146

1973

2622

2685

2489

815

2269

2718

112

1409

1348

1289

1193

569

200

2551

717

3010

200

531

1109

200

1008

9977

6377

1751

2409

200

578

468

1973

2394

2971

2975

2409

569

717

3081

3694

2717

200

2509

538

920

1728

200

535

504

1604

200

920

2409

186

186

1751

27958

543

1664

200

6576

200

1621

786

968

9681497

1604

14742

11414

18667

195

195

2152

2146

2409

2188

3105

2409

836

434

AECI

AEP

ALTE

ALTW

AMRNA

AMRNB

AMRNC

AMRND

CILC

CIN

CWLP

DPC

EEI

ENLC

IPA

IPB

IPC

IPD

MEC

MIPU

NIA

NIB

NIC

NID

NIE

NIF

NIG

NIPS

SIPC

TVA

WEC

TRANSFER CAPABILITIES: ATC FROM TO

Network Reduction from regional AC to state DC

* PowerWorld from the University of Illinois

24

Load zones

852 buses with loads

8760 hours (1 year)

Collected into 18 load zones

with Locational Marginal

Prices (LMP) averaged across

associated buses

25 25

GenCo agents have a complex yet realistic decision

process for proposing bids to the day-ahead market

26

27

Illinois Electric Power Market Elements

Illinois electric power market elements, showing generating companies

and ownership relationships (left), electric generators and transmission

network (center), and service area loads (right). 27

GenCos may have many plausible strategies

Witholding to increase prices

– Physical Witholding: Capactiy

– Economic Witholding: Price

Agents could employ many strategies:

– Incremental pricing

– Bid production cost

– Bid low to ensure dispatch (as in EDF,

for spot market)

– Bid high to increase the market clearing

price

– Bid last increment of capacity at high

price

Price probing to probe the market for

weaknesses or flaws

– Discover if you are the marginal

supplier

– Discover who is the marginal supplier

28

0

10

20

30

40

50

60

70

80

90

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

LM

P [

$/M

Wh

]

Max

LMPs

Min

LMPs

High LMPs due to

Forced Outages

High LMPs due to high load

Spread of LMPs due to

transmission congestion

Projected Monthly Minimum and Maximum Hourly LMPs for All Zones (Reference Case)

Identifying market power

GenCo with Market Power

29

GenCo bid prices to various levels above production cost

– All-day

– Afternoon only

0

50

100

150

200

250

1 2 3 4 5 6 7 8

Pe

ak-D

ay

24

-Ho

ur

Ge

ne

rati

on

[G

Wh

]

0.0

2.0

4.0

6.0

8.0

10.0

Pe

ak-D

ay

24

-Ho

ur

Op

era

tin

g P

rofi

t [$

mil

lio

n]

Generation (2pm-6pm Strategy)

Generation (All-Day Strategy)

Profits (2pm-6pm Strategy)

Profits (All-Day Strategy)

Base

Case

25%

Above

Cost

50%

Above

Cost

100%

Above

Cost

150%

Above

Cost

400%

Above

Cost

650%

Above

Cost

200%

Above

Cost

GenCo without Market Power

-20

0

20

40

60

80

100

Pe

ak

-Da

y 2

4-H

ou

r G

en

era

tio

n [

GW

h]

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Pe

ak

-Da

y 2

4-H

ou

r O

pe

rati

ng

Pro

fit

[$ m

illi

on

]Generation (2pm-6pm Strategy)

Generation (All-Day Strategy)

Profits (2pm-6pm Strategy)

Profits (All-Day Strategy)

Base

Case

25%

Above

Cost

50%

Above

Cost

100%

Above

Cost

400%

Above

Cost

200%

Above

Cost

30

EMCAS impact

The findings: There is the potential for some companies to exercise

market power (i.e., raise prices and increase profitability by unilateral

action) and raise consumer costs under selected conditions, particularly

when there is transmission congestion.

EMCAS results* have been entered into the public record of the Illinois

Commerce Commission (ICC), 6 June 2006.

Report available from the ICC web site http://www.icc.illinois.gov/

EMCAS is an example of an agent-based model that has been

successfully applied to a real-world policy issue and provided information

that would otherwise have not been available using any other modeling

approach.

*Cirillo, R., P. Thimmapuram, T. Veselka, V. Koritarov, G. Conzelmann, C. Macal, G. Boyd, M. North, T. Overbye and X. Cheng. 2006. Evaluating the Potential Impact of Transmission Constraints on the Operation of a Competitive Electricity Market in Illinois, Argonne National Laboratory, Argonne, IL, ANL-06/16 (report prepared for the Illinois Commerce Commission), April.

30

Application: BE-Solar

BE-Solar: A Behavior-Based Agent Model for Assessing Market Adoption of Solar Photovoltaics

31

Solar Photovoltaics

Question: What are the factors, aside from the

economics, limiting the rate of residential consumer

adoption of solar photovoltaics?

Secondary Questions:

– Can we explain them?

– Can we understand them well enough to make better

“forecasts”?

BE-Solar Model – building adoption curves from the ground up

We model the solar adoption decisions of thousands of consumers based on a behavioral model, in a large-scale agent-based model (ABM) – called BE-Solar

We are applying the model in an attempt to replicate the rapid market adoption of residential solar PV in the Southern California market

Lease-vs.-buy decisions

33

Behavior – What are we going to do with it?

How do we make consumer behavioral data and theories of decision

making relevant to policy making?

Current models of solar

photovoltaic market adoption

o Do not consider individual

decision makers and their

situations

o Do not capture diversity in

realistic decision behaviors

Consumer Decision Model

34

Behavioral economics and behavioral science

Market adoption evolves from the behaviors, decisions, and interactions of many market participants

Behavioral scientists have shown that individuals and organization rarely conform to rational economic thinking in their decision making

Recognition

Informationsearch

Evaluation of

alternatives

Purchase decision

Post-decisionbehavior

Residential Consumer Five-Step Decision Process

Ideas from Behavioral Economics can

help make better models of consumer

decision-making

o Bounded rationality

o Social interactions and networks:

Information and influence

o Consumer learning

35

Agents and Objects

Owners (decision makers) – Owner, rents out, renter pays utilities (owner has no incentive for

solar adoption)

– Renter (does not decide on solar adoption)

– Owner-occupied (decides on solar adoption)

Installers (decision makers)

– Initiate contact with potential customers (Internet, door-to-door)

– Activities may limit adoption capacity

BE-Solar Model: Solar PV Adoption in Southern California

36

Housing units – by parcel,

housing type, associated with owners

Agent: Homeowner Decision-makers

37

Table 1 Housing Unit Owner Attributes (Class owner)

Attribute Description Data Source

ownerId Owner ID (integer)

ownerZip Owner zipcode (same as housing unit zipcode) CSI data

occupierStatus Whether the building is ownerOccupied or renterOccupied: {own, rent, vacant}

LA parcel data

ownAge Owner age (years)

ownIncome Owner income ($K/year)

ownEd Owner education: {< 9th grade, high school, some college, associates degree, bachelor degree, graduate degree}

ownRace Race: {White, Black, Hispanic, American Indian, Pacific Islander, Other}

ownEnergyAttitudewEA Propensity toward green/clean energy issues: (Yes,No} based on vote on Prop 53

ownpvAffectwPA Affect for solar PV (owner knows someone who adopted, adopter experience is positive)

ownadopterType Adopter category: {"innovator","early adopter","early majority","late majority","laggard"}

(Rogers 2007)

ownminStayTime Minimum time owner expects to stay in home (years)

ownreqdPaybackTimewMP Required payback time for solar PV investestment by owner (years). reqdPaybackTimewMP <= minStayTime

networkStreet Neighbors on street

Residential consumer agent decision making

Adoption Attributes

– Ability to Pay (up-front cost)

– Energy Attitude (Prop 23 Yes, No)

– Adoption Affect (social contact with previous adopters)

– Adopter Threshold (per Rogers’ adopter types)

– Perceived Reliability

– Financial Metrics: Minimum Payback (Buy) / Monthly Savings (Lease)

– Demographics: Income, Age, Education (per study*)

*Drury, E., M. Miller, C. Macal, D. Graziano, D. Heimiller, J. Ozik, T. Perry. 2011. The Transformation of Southern California's Residential Photovoltaics Market through Third-Party Ownership, submitted to Energy Policy.

38

Better predictions based on behavior

We have incorporated behavioral approaches to improve models for the market adoption of solar photovoltaics

As better data on consumer behavior becomes available, better predictions should result

Validation and modeling uncertainty are key areas for interfacing with policy makers

BE-Solar Model Results for Solar PV Adoption in Southern California

39

Application: Modeling biofuels with AAF

AAF - The Advanced Analysis Framework

40

Biofuels

Question: How much and how soon will biofuels be a

factor in displacing petroleum as a transportation fuel?

Secondary Questions:

– What are the roles of new fuels and technology

developments on biofuels adoption?

– What are the impacts of biofuels development ... on

employment? … on the environment?

Biomass supply sector

T

Biomass

Processing

Owner

BGE Plant

Owner

Poplar

Farmer

Switchgrass

Farmer

Farmer

Biomass

Storage Owner E10 Terminal

Owner

E85 Terminal

Owner

CDFEl Plant

Owner

E85 Blending

terminalE85Blending

Biomass Bulk Storage

storageBiomass

Corn Farm

farmCorn

Ethanol Plant

BGE (Biomass Gasification to

Ethanol )

plantBiomassToEthanolBGE

E10 Blending

terminalE10Blending

Ethanol Plant

CDFE (Corn Dry

Fractionation to Ethanol)

plantCornToEthanolCDEF

Res

Owner

Comm

Owner

Ind

Owner

Corn

Storage

Owner

BIOMASS SUPPLY CHAIN – PROCESS CONNECTIVITY

M, R, TTCar T

o

Eco

no

my

Fueling

Station Owner

S

Household

VehicleCommeric

al

Truck Industry

To

Eco

no

my

To

Eco

no

my

S

S

To

Eco

no

my

P, R, T

Fro

m

Na

tura

l Ga

s

Inputs:

cultivated

land

labor

capital

corn seed

diesel

LPG

electricity

fertilizer

pesticide

herbicide

water

Corn

Biomass

Ethanol mill waste

Ethanol

Fro

m

Pe

trole

um

Liquid fuel

Service

Switchgrass Farm

farmSwitchgrass

Fueling Station

fuelingStation

T Truck

R Rail

P Pipeline (future)

M Marine

S Self-transport

Transport Mode:Commodity/Flow:

Poplar Farm

farmPoplar

T

T

O On-site

Outputs:

corn

CO2

VOC

NOx

PM2.5

PM10

SOx

CH4

N2O

Biomass

Processing

processBiomassT

DDGS

P

P, R, T

P, R, T

P

T

TNote: includes drying

process

Note: includes cogeneration

process

P, R, T

P

truckCorn

truckBiomass

truckEthanol

truckBlendedFuel

Corn Bulk Storage

storageCorn

42

Application: How to build a model of an energy sector

Define the business objects (decision-making agents)

Add ownership relations and connect with physical layer

Identify the relevant business decisions:

– Decision on pricing

– Decision on what to offer into the market (q, p)

– Decision on which market to participate in

– Decision on time frame: long-run vs. short run

– Where or from whom to get inputs and to market outputs

– Decision on capacity expansion (decommissioning)

– Decision on acquisition, investment and growth

Model the business decisions

43

Step 1: Model the physical layer of the energy system

Step 2: Model the business / decision / economic layer of the energy system:

44

Individual process models

45

Decision making affects multiple facilities operations

and investment

Business

Layer

Physical

Layer

Company

Plants

Ownership Links

46

The Future of Energy Analysis

ABM and Energy Applications

47

In summary, there is much recent work on agent-based

models of the energy system

Agent-based modeling literature (see

below: BIBLIOGRAPHY: Agent-based Modeling and Energy):

– Energy consumer demand and decision making

– Energy investment decisions and markets

– Energy systems and subsystems

– Transportation and energy

– Electricity markets

– And many more

48 48

BIBLIOGRAPHY:RecentPublicationsonAgent-BasedModelsofEnergyAgent-BasedModelsofEnergyConsumerDemandandDecisionMakingAgent-BasedModelsofEnergyInvestmentDecisionsandMarketsAgent-BasedModelsofEnergySystemsandSubsystemsAgent-BasedModelsofTransportationandEnergy

Agent-BasedModelsofElectricityMarketsAgent-BasedModelsofEnergyConsumerDemandandDecisionMaking

AnABMtoidentifyinterventionspromotingwood-pelletheating(Sophaetal.2011). Sopha,B.M.,C.A.Klockner,andE.G.Hertwich(2011)“ExploringPolicyOptionsforaTransitionToSustainableHeatingSystemDiffusionUsinganAgent-BasedSimulation.”EnergyPolicy,39(5):2722-2729,May,ISSN0301-4215,10.1016/j.enpol.2011.02.041.

AnABMtomodeltheenergyuseofbuildingoccupants(ElieAzarandMenassa2011).

ElieAzar,S.M.,andCarolC.Menassa(2011)“Agent-BasedModelingofOccupants’ImpactonEnergyUseinCommercialBuildings,”JournalofComputinginCivilEngineeringdoi:http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000158.

AnABMtomodelelectricityconsumptioninofficebuildings(2011). Zhang,T.,etal.(2011)“Modellingelectricityconsumptioninofficebuildings:Anagentbasedapproach.”EnergyBuildings(2011),doi:10.1016/j.enbuild.2011.07.007

AnABMforresidentialspaceheatingdemand(ChingcuancoandMiller2011). Chingcuanco,Franco,andEricJ.Miller(2011)“Amicrosimulation

modelofurbanenergyuse:ModellingresidentialspaceheatingdemandinILUTE,”Computers,EnvironmentandUrbanSystems,Availableonline28December.

AnABMofenergyconsumptiondecision-makinginbuildingoccupantpeernetworks(Chen,TaylorandWei2011). JiayuChen,JohnE.Taylor,Hsi-HsienWei(2011)“ModelingBuildingOccupantNetworkEnergyconsumptionDecision-making:theinterplaybetweennetworkstructureandconservation,”EnergyandBuildings,Availableonline26December.

…

Data challenges for ABM energy systems analysis

Data – Local, national, international

– Anonymous

– Cleaned, internally consistent, validated

– Secure

For ABM, need “constructed” data sets – Synthetic populations

– Cross tabulations

– Existing data sets

– New data sets

– New surveys

49

Need to augment the Rational Choice Model with

individual and behavioral elements

Rational economic agents

Well-defined rational models maximizing utility

Bounded rationality, satisficing, behavioral models based on experiment

Economic agents are homogeneous

Identical characteristics and rules of behavior

Heterogeneous individuals (limited aggregation), Asymmetric Information

Decreasing returns to scale

Strict assumptions on functional forms

Relaxed assumptions lead to feedback amplification, increasing returns, and lock-in

Emergence

Specified organizational forms and interrelationships

Dynamic emergence of self-organizing structures

Equilibrium

Long-run equilibrium

Transient dynamics, Generalized notion of equilibrium, Non-existence of equilibria

Agent-based computational economics (ACE) employs ideas from complexity science, behavioral economics, psychology, cognitive sciences, et al. to understand the formation and evolution of markets

50

51

Charles M. Macal, macal@anl.gov

Web Info:

Paper: Tutorial on Agent-Based Modelling and Simulation (Journal of Simulation):

www.palgrave-journals.com/jos/journal/v4/n3/abs/jos20103a.html

Book: Managing Business Complexity web site (Oxford University Press, 2007):www.oup.com/us/catalog/general/subject/Business/Management/StrategicManagement/~~/dmlldz11c2EmY2k9OTc4MDE5NTE3MjExOQ==

Training Course: ABMS 2012 - Annual Course on Business Applications of Agent-Based Modeling and Simulation, with the Santa Fe Institute: www.dis.anl.gov/conferences/abms/info.html

CAS2 web site: www.dis.anl.gov/exp/cas/index.html

Agent 20xy Conference series: www.dis.anl.gov/agent20XY/

Repast Agent-Based Modeling and Simulation Toolkit (on sourceForge): http://repast.sourceforge.net/

© Charles M. Macal, All Rights Reserved

Questions?

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Capturing Business Complexity with Agent-Based Modeling

and Simulation:

Useful, Usable, and Used Techniques

General Description: An intensive business applications-oriented introduction

to agent-based modeling and simulation (ABMS) based on Michael North and

Charles Macal’s new book Managing Business Complexity: Discovering

Strategic Solutions with Agent-Based Modeling and Simulation (Oxford 2007).

The first half of the course will focus on ABMS concepts from the perspective

of company managers and analysts. The second half of the course will focus

on ABMS implementation from the perspective of company software

developers and will include extensive hands-on exercises. Participants are

invited to attend the first session, the second session, or both depending on

their interests. Each participant will receive a copy of Managing Business

Complexity and break refreshments as part of their course fee.

Format and Topics: An intensive series of lectures and hands-on laboratories

are used to introduce the foundational ideas and tools of ABMS and their

application to business questions. Topics include the definition of agents, the

design and construction of agents, the design and construction of agent

environments, understanding of ABMS results, effective presentation of ABMS

results, and applications of these core topics to specific examples. A Microsoft

Excel retail store model and a Repast Simphony supply chain ABMS are

discussed in detail. Registrants are asked to provide a paragraph on the

ABMS applications they are most interested in to help focus instruction on the

issues of greatest relevance to the audience.

Who Should Attend: Three groups should attend the course: managers

involved in strategic planning or operations, analysts who design and operate

models, and software developers who build models. The course introduces

managers to ABMS, shows them how ABMS can be useful to their businesses,

and describes how managers can present ABMS results to senior decision

makers. Managers should attend the first session of the course. The course

gives analysts the principles of ABMS design, discusses the fundamental

features of the leading ABMS development tools and how these features affect

ABMS design, and teaches them how to present ABMS results to decision

makers. Analysts should attend both sessions of the course. The course gives

software developers the basic principles of ABMS design and shows how to

effectively use the leading ABMS development tools. Software developers

should attend both sessions of the course.

Decision and Information Sciences - ABMS Workshop http://www.dis.anl.gov/conferences/abms/info.html

1 of 3 2/22/12 11:38 AM

52

The course dates are Monday through Friday, May 14–18, 2012

http://www.dis.anl.gov/conferences/abms/info.html

ABM/Repast Course at Argonne in May

Repast Resources

Our book on agent-based modeling "Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation" (Oxford 2007)

http://bit.ly/xg9XHP

The Repast toolkit is an free and open source platform for writing agent models

http://repast.sourceforge.net

The Repast's ReLogo approach is an easy way to begin

http://repast.sourceforge.net/docs/ReLogoGettingStarted.pdf

Argonne-sponsored Agent 20xy Conferences, 1999-2007

http://www.dis.anl.gov/agent20XY/

53

Some Recent ABM Applications

54

Table1:Sampleofrecentagent-basedapplicationsavailableontheweb(allapplicationuseRepasttoolkit)

ApplicationArea: ModelDescription:

Agriculture Aspatialindividual-basedmodelprototypeforassessingpotentialexposureoffarm-workersconductingsmall-scaleagriculturalproduction(Leyk,Binder,andNuckols2009).

AirTrafficControl Agent-basedmodelofairtrafficcontroltoanalyzecontrolpoliciesandperformanceofanairtrafficmanagementfacility(Conway2006)

Anthropology

Agent-basedmodelofprehistoricsettlementpatternsandpoliticalconsolidationintheLakeTiticacabasinofPeruandBolivia(GriffinandStanish2007)

BiomedicalResearch TheBasicImmuneSimulator,anagent-basedmodeltostudytheinteractionsbetweeninnateandadaptiveimmunity(Folcik,An,andOrosz2007)

CrimeAnalysis Agent-basedmodelthatusesarealisticvirtualurbanenvironment,populatedwithvirtualburglaragents(Malleson2010).

Ecology Agent-basedmodeltoinvestigatethetrade-offbetweenroadavoidanceandsaltpoolspatialmemoryinthemovementbehaviorofmooseintheLaurentidesWildlifeReserve(Grosmanetal.2011).

Agent-basedmodelofpredator-preyrelationshipsbetweentransientkillerwhalesandothermarinemammals(MockandTesta2007).

Arisk-basedapproachforanalyzingtheintentionalintroductionofnon-nativeoystersontheUSeastcoast(Opaluch,Anderson,andSchnier2005).

EnergyAnalysis Agent-basedmodeltoidentifypotentialinterventionsfortheuptakeofwood-pelletheatinginNorway(Sophaetal.2011).

Agent-basedmodelforscenariodevelopmentofoffshorewindenergy(Mastetal.2007).

Some Recent ABM Applications

55

Table1(cont’d):Sampleofrecentagent-basedapplications

Epidemiology Syntheticage-specificcontactmatricesarecomputedthroughsimulationofasimpleindividual-basedmodel(Iozzietal.2010).

Evacuation AsimulationoftsunamievacuationusingamodifiedformofHelbing’ssocial-forcemodelappliedtoagents(Puckett2009).

MarketAnalysis Alarge-scaleagent-basedmodelforconsumermarketingdevelopedincollaborationwithaFortune50firm(Northetal.2009).

Anillustrativeagent-basedmodelofaconsumerairlinemarkettoderivemarketsharefortheupcomingyear(Kuhnetal.2010).

Agent-basedsimulationthatmodelsthepossibilitiesforafuturemarketinsub-orbitalspace

tourism(Charaniaetal.2006).

OrganizationalDecisionMaking

Anagent-basedmodeltoallowmanagerstosimulateemployeeknowledge-sharingbehaviors(Wangetal.2009).

Anagent-basedmodeltoevaluatethedynamicbehaviorofaglobalenterprise,consideringsystem-levelperformanceaswellascomponents'behaviors(Behdanietal.2009).

Agentbasedmodelingapproachtoallownegotiationsinordertoachieveaglobalobjective,specificallyforplanningthelocationofintermodalfreighthubs(vanDametal.2007).

SocialNetworks Anagent-basedmodelofemail-basedsocialnetworks,inwhichindividualsestablish,maintainandallowatrophyoflinksthroughcontact-listsandemails(Menges,Mishra,andNarzisi2008).

Why agent-based modeling?

Several of the papers contend that using agent-based modeling versus other

modeling techniques is necessary because agent-based models:

“can uniquely and explicitly capture the complexity arising from individual

actions and interactions that exist in the real-world”

56

Agent-based Modeling

Agents 1. Modular

2. Autonomous

3. Behaviors

4. Agent state

5. Social, dynamic interactions

– Adaptive

– Goal-directed, reactive

Agent-based Model 1. Agents

2. Relationships

3. Environment

4. Computational engine: Toolkit, programming language

Typical Agent

57

Characteristics of large-scale ABMs

1. “Synthetic” populations composed of “synthetic” agents

2. Agents have realistic, dynamic behaviors

3. Geography and geo-spatial representations

4. “Validated” or otherwise credible

5. Provide essential information for making decisions or setting policies

6. Impacting decisions or ways of doing business

58

Application: Modeling the transition to hydrogen

Hydrogen Model

59

Widespread Interest in Prospects for the Hydrogen

Economy

The Hydrogen Economy After Oil, Clean Energy From a Fuel Cell-Driven Global Hydrogen Web by Jeremy Rifkin, E/The Environmental Magazine, Vol. XIV, No. 1 (Jan-Feb 2003) http://emagazine.com

The landscape model illustrates how the electricity from wind turbines and solar cells are distributed into an electrolyzer plant to produce hydrogen for distribution. http://minihydrogen.com/

HYDROGEN™ Magazine, Fall 2003, explains relevant issues pertaining to the adoption of a hydrogen-based economy.

http://www.hydrogen.com

60

The Emergence of the Hydrogen Economy Is a Chicken

and Egg Problem

Should the hydrogen infrastructure be developed first?

or

Should the hydrogen market come first?

61

Funded by DOE/EERE, in

partnership with Ford Motor

Company and RCF Economic and

Financial Consulting

Models driver and infrastructure

investment agents in the Los

Angeles basin

Physical world attributes:

– Home and work locations for

consumer agents

– Highway network

Fueling decisions are influenced

by location and other factors

Investments decisions are

influenced by profit, uncertainty and

experience

62

GIS-based grid 100x50 miles, includes 25-mile buffer zone

Investigating Market Acceptance of Hydrogen-fuels Vehicles, Using Agent Based Modeling

62

Consumer Modeling for Hydrogen The population contains a mix of adopter types.

– Early adopters obtain greater utility from acquiring new technologies, such as H2

vehicles

– Go-with-the-crowd adopters only copy what others have done

A mass of early adopters (5-10%) are needed to start early vehicle adoption

63

Input: Different Adoption Propensities

0%

10%

20%

30%

40%

50%

60%

0 2 4 6 8 10 12 14 16 18 20

H2

Ve

hic

le F

lee

t P

en

etr

atio

n (

%)

Years

20% Early Adopters / 80% Go with the Crowd

10% Early Adopters / 90% Go with the Crowd

5% Early Adopters / 95% Go with the Crowd

1% Early Adopters / 99% Go with the Crowd

-$8,000

-$6,000

-$4,000

-$2,000

$0

$2,000

$4,000

$6,000

$8,000

0% 20% 40% 60% 80% 100%

Dri

ver

Age

nt'

s H

ydro

gen

Uti

lity

Infl

ue

nce

(Do

llar

s)

Hydrogen Vehicle Fleet Penetration (%)

Innovator Early Adopter Fast Follower Go with the Crowd Laggard

63

BIBLIOGRAPHY: Agent-based Modeling and Energy Agent-Based Models of Energy Consumer Demand and Decision Making

An ABM to identify interventions promoting wood-pellet heating (Sopha et al. 2011).

Sopha, B. M., C. A. Klockner, and E. G. Hertwich (2011) “Exploring Policy Options for a Transition To Sustainable Heating System Diffusion Using an Agent-Based Simulation.” Energy Policy, 39(5): 2722-2729, May, ISSN 0301-4215, 10.1016/j.enpol.2011.02.041.

An ABM to model the energy use of building occupants (Elie Azar and Menassa 2011).

Elie Azar, S. M., and Carol C. Menassa (2011) “Agent‐Based Modeling of Occupants’ Impact on Energy Use in Commercial Buildings,” Journal of Computing in Civil Engineering doi:http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000158.

An ABM to model electricity consumption in office buildings (2011).

Zhang, T., et al. (2011) “Modelling electricity consumption in office buildings: An agent based approach.” Energy Buildings (2011), doi:10.1016/j.enbuild.2011.07.007

An ABM for residential space heating demand (Chingcuanco and Miller 2011).

Chingcuanco, Franco, and Eric J. Miller (2011) “A microsimulation model of urban energy use: Modelling residential space heating demand in ILUTE,” Computers, Environment and Urban Systems, Available online 28 December.

An ABM of energy consumption decision-making in building occupant peer networks (Chen, Taylor and Wei 2011).

Jiayu Chen, John E. Taylor, Hsi-Hsien Wei (2011) “Modeling Building Occupant Network Energy consumption Decision-making: the interplay between network structure and conservation,” Energy and Buildings, Available online 26 December.

An ABM to model adoption of residential heat pumps (Houwing and Bouwmans).

Houwing, Michiel , and Ivo Bouwmans (undated) “Agent-based modelling of residential energy generation with micro-CHP,” Delft University of Technology.

Agent-Based Models of Energy Investment Decisions and Markets

A book on ABM approaches to investment decisions (Wittmann 2008).

Wittmann, Tobias (2008) Agent-based models of energy investment decisions, Physica-Verlag, Heidelberg.

An ABM for scenario development of offshore wind energy (Mast et al. 2007).

Mast, E. H. M., G.A.M. van Kuik, and G.J.W. van Bussel (2007) “Agent-Based Modelling for Scenario Development of Offshore Wind Energy.” Delft University of Technology, The Netherlands.

64

BIBLIOGRAPHY: Agent-based Modeling and Energy Agent-Based Models of Energy Systems and Subsystems

An ABM of energy infrastructure transitions (Chappin and Dijkema 2010).

Chappin, E. J. L., and G. P. J. Dijkema (2010) “Agent-based modelling of energy infrastructure transitions,” Int. J. of Critical Infrastructures, 6(2):106-130.

An ABM of energy infrastructure transitions (Chappin and Dijkema 2010).

Chappin, E.J.L., R. Praet and G.P.J. Dijkema (2010) “Transition in LNG Markets – Combining Agent-Based Modeling and Equation Based Modeling,” pp. 1-21. Proc. of the 33st IAEE International Conference, The Future of Energy: Global Challenges, Diverse Solutions, 6-9 June. Rio de Janeiro, Brazil.

An ABM of a distributed energy system (Hou and Zhou (2010).

Hou, Jianmin, and Dequn Zhou (2010) "Agent-Based Modeling of Distributed Energy System," 2010 Third International Conference on Information and Computing (ICIC), vol.1, no., pp.166-169, 4-6 June doi: 10.1109/ICIC.2010.48.

An ABM of an oil refinery supply chain (Van Dam, et al., 2008)

Van Dam, K.H.; A. Adhitya; R. Srinivasan and Z. Lukszo (2008) “Benchmarking numerical and agent-based models of an oil refinery supply chain,” Computer-Aided Chemical Engineering 25 (2008) International Proceedings (refereed).

A method for developing agent-based models of socio-technical systems (Ghorbani and Nikolic 2011).

Ghorbani, A., and I. Nikolic (2011) “A Method for Developing Agent-based Models of Socio-technical Systems,” pp. 44-49. Proc. of the 2011 IEEE International Conference on Networking, Sensing and Control (ICNSC), 11-13 April. Delft, The Netherlands. ISBN: 978-1-4244-9570-2.

Agent-Based Models of Electricity Markets

An ABM for modeling the Smart Grid (Jackson 2010).

Jackson, J. (2010) “Improving energy efficiency and smart grid program analysis with agent-based end-use forecasting models.” Energy Policy, doi:10.1016/j.enpol.2010.02.055.

A survey of agent-based electricity market models (Weidlich and Veit 2008).

Weidlich, Anke, and Daniel Veit (2008) “A critical survey of agent-based wholesale electricity market models,” Energy Economics, 30(4): 1728-1759, July, ISSN 0140-9883, 10.1016/j.eneco.2008.01.003.

A paper on using ABM to model electricity markets (Weidlich and Veit 2008).

Weidlich, Anke, Daniel Veit (2008) "Agent-Based Simulations for Electricity Market Regulation Advice: Procedures and an Example," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 228(2+3), pages 149-172, June.

65

BIBLIOGRAPHY: Agent-based Modeling and Energy

Agent-Based Models of Transportation and Energy

An ABM for assessing the demand for hydrogen vehicles (Mahalik et al. 2007).

Mahalik, M.R., G. Conzelmann, C.H. Stephan, M.M. Mintz, T.D. Veselka, G.S. Tolley, D.W. Jones (2007) “Modeling The Transition To Hydrogen-Based Transportation,” in Proc. Agent 2007 Conference on Complex Interaction and Social Emergence, ANL/DIS-07-2, ISBN 0-9679168-8-7, M.J. North, C.M. Macal, and D.L. Sallach (editors), pages 407-420, available from http://www.dis.anl.gov/agent20XY/ (accessed Feb. 20, 2012).

An ABM of energy demand and emissions from plug-in hybrid electric vehicles (Thomas 2010).

Stephens, Thomas (2010) “An Agent-Based Model of Energy Demand and Emissions from Plug-In Hybrid Electric Vehicle Use.” Master's Thesis, University of Michigan: Ann Arbor: 1-118.

An ABM to study market penetration of plug-in hybrid electric vehicles (Eppstein et al 2011).

Eppstein, Margaret J., David K. Grover, Jeffrey S. Marshall, Donna M. Rizzo (2011) “An agent-based model to study market penetration of plug-in hybrid electric vehicles,” Energy Policy, 39(6): 3789-3802, June, ISSN 0301-4215, (http://www.sciencedirect.com/science/article/pii/S0301421511002904).

An ABM of consumer choice of new cars (Mueller and de Haan 2009).

Mueller, Michel G., and Peter de Haan (2009) “How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars—Part I: Model structure, simulation of bounded rationality, and model validation,” Energy Policy, 37(3): 1072-1082, March, ISSN 0301-4215, (http://www.sciencedirect.com/science/article/pii/S0301421508006599)

66

BIBLIOGRAPHY: Repast Applications

Al-Mutawah, K. and V. Lee, 2008, An Evaluation Framework for Supply Chains Based on Corporate Culture Compatibility, in Supply Chain, Theory and Applications, Kordic, V., (ed.) pp. 59-72, Vienna, Austria.

Carpenter, C., 2004, Agent-Based Modeling of Seasonal Population Movement and the Spread of the 1918-1919 Flu: The Effect on a Small Community, University of Missouri-Columbia, Master's Thesis, Department of Anthropology.

Charania, A. C., J. R. Olds and D. DePasquale, 2006, Sub-Orbital Space Tourism Market: Predictions of the Future Marketplace Using Agent-Based Modeling, SpaceWorks Engineering, Inc., Atlanta, GA, Available online at http://www.sei.aero/uploads/archive/IAC-06-E3.4.pdf.

Conway, S. R., 2006, An Agent-Based Model for Analyzing Control Policies and the Dynamic Service-Time Performance of a Capacity-Constrained Air Traffic Management Facility, ICAS 2006 - 25th Congress of the International Council of the Aeronautical Sciences Hamburg, Germany, 3-8 Sep. 2006, Availabe online at http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20060048296_2006250468.pdf.

de Bie, P. and B. de Boer, 2007, An Agent-Based Model of Linguistic Diversity, Proc. ESSLLI 2007 Workshop on Language, Games, and Evolution, Benz, A., C. Ebert and R. van Rooij (eds.), pp. 1-8, Available online at http://frim.frim.nl/Dublin.pdf.

Griffin, A. F. and C. Stanish, 2007, An Agent-Based Model of Prehistoric Settlement Patterns and Political Consolidation in the Lake Titicaca Basin of Peru and Bolivia, Structure and Dynamics: eJournal of Anthropological and Related Sciences, 2(2) Availabe online at http://repositories.cdlib.org/imbs/socdyn/sdeas/vol2/iss2/art2.

Houwing, M. and I. Bouwmans, 2007, Agent-Based Modelling of Residential Energy Generation with Micro-CHP, Delft University of Technology, Availabe online at http://wiki.smartpowersystem.nl/images/d/dc/M_Houwing&I_Bouwmans_Napa2006_FIN.pdf.

López-Sánchez, M., Xavier Noria, Juan A. Rodríguez and N. Gilbert, 2005, Multi-Agent Based Simulation of News Digital Markets, International Journal of Computer Science & Applications, II(I), Available online at http://www.tmrfindia.org/ijcsa/v21.html.

BIBLIOGRAPHY: Repast Applications (cont’d.)

Mast, E. H. M., G.A.M. van Kuik and G.J.W. van Bussel, 2007, Agent-Based Modelling for Scenario Development of Offshore Wind Energy, Delft University of Technology, The Netherlands.

Mock, K. J. and J. W. Testa, 2007, An Agent-Based Model of Predator-Prey Relationships between Transient Killer Whales and Other Marine Mammals, University of Alaska Anchorage, Anchorage, AK, May 31, 2007, Available online at http://www.math.uaa.alaska.edu/~orca/.

Narzisi, G., V. Mysore and B. Mishra, 2006, Multi-Objective Evolutionary Optimization of Agent-Based Models: An Application to Emergency Response Planning, New York University, Available online at http://www.cs.nyu.edu/mishra/PUBLICATIONS/06.ci06PlanC.pdf.

Parry, H., A. J. Evans and D. Morgan, 2004, Aphid Population Dynamics in Agricultural Landscapes: An Agent-Based Simulation Model, International Environmental Modelling and Software Society iEMSs 2004 International Conference University of Osnabrück, Germany, 14-17 June 2004, Available online at http://www.iemss.org/iemss2004/pdf/landscape/parraphi.pdf.

Tonmukayakul, A., 2007, An Agent-Based Model for Secondary Use of Radio Spectrum, University of Pittsburgh, Ph.D. thesis, School of Information Sciences.

van Dam, K. H., Z. Lukszo, L. Ferreira and A. Sirikijpanichkul, 2007, Planning the Location of Intermodal Freight Hubs: An Agent Based Approach, Proceedings of the 2007 IEEE International Conference on Networking, Sensing and Control, pp. 187-192, London, UK, 15-17 April 2007.

Wragg, T., 2006, Modelling the Effects of Information Campaigns Using Agent-Based Simulation, DSTO Defence Science and Technology Organisation, Edinburgh South Australia, DSTO-TR-1853, April.

Yin, L., 2007, Assessing Indirect Spatial Effects of Mountain Tourism Development: An Application of Agent-Based Spatial Modeling, The Journal of Regional Analysis & Policy, 37(3):257-265, Available online at http://www.jrap-journal.org/pastvolumes/2000/v37/F37-3-8.pdf.

69

BIBLIOGRAPHY: Selected References Related to ABMS Axelrod, R., 1984, The Evolution of Cooperation, Basic Books: New York.

Axtell, R., 2000, “Why Agents? On The Varied Motivations for Agent Computing in the Social Sciences,” Working Paper 17, Center on Social and Economic Dynamics, Brookings Institution, Washington, D.C.

Bonabeau, E., 2001, "Agent-Based Modeling: Methods and Techniques for Simulating Human Systems," Proc. National Academy of Sciences, 99(3):7280-7287.

Carley, K. M., D. B. Fridsma, E. Casman, A. Yahja, N. Altman, L.-C. Chen, B. Kaminsky and D. Nave, 2006, “Biowar: Scalable Agent-Based Model of Bioattacks,” IEEE Transactions on Systems, Man and Cybernetics, Part A, 36(2):252 - 265.

Emonet, T., C. M. Macal, M. J. North, C. E. Wickersham and P. Cluzel, 2005, “AgentCell: A Digital Single-Cell Assay for Bacterial Chemotaxis,” Bioinformatics 21(11):2714-2721.

Epstein, J. M., 2002, “Modeling Civil Violence: An Agent-based Computational Approach,” Proc. National Academy of Sciences 99(90003): 7243-7250.

Epstein, J. M., and R. Axtell, 1996, Growing Artificial Societies: Social Science from the Bottom Up, MIT Press: Cambridge, MA.

Gallagher, R. and T. Appenzeller, 1999, "Beyond Reductionism," Science, 284(2):79.

Gardner, M., 1970, “The Fantastic Combinations of John Conway's New Solitaire Game ‘Life’", Scientific American 223:120-123.

Gilbert, N. and K. G. Troitzsch, 2006, Simulation for the Social Scientist, Open University Press: Buckingham, 2nd edition.

GMU (George Mason University), 2009, MASON Home Page, Available at <http://cs.gmu.edu/~eclab/projects/mason/>.

Grimm, V., U. Berger, F. Bastiansen, S. Eliassen, V. Ginot, J. Giske, J. Goss-Custard, T. Grand, S. K. Heinz, G. Huse, A. Huth, J. U. Jepsen, C. Jørgensen, W. M. Mooij, B. Müller, G. Pe'er, C. Piou, S. F. Railsback, A. M. Robbins, M. M. Robbins, E. Rossmanith, N. Rüger, E. Strand, S. Souissi, R. A. Stillman, R. Vabø, U. Visser and D. L. DeAngelis, 2006, “A Standard Protocol for Describing Individual-based and Agent-based Models,” Ecological Modelling, 198 (1-2), pp. 115-126.

Holland, J. H., 1995, Hidden Order: How Adaptation Builds Complexity, Addison-Wesley: Reading, Mass.

Jennings, N. R., 2000, “On Agent-Based Software Engineering,” Artificial Intelligence, 117:277-296.

Kaufmann, S. A., 1995, At Home in the Universe: The Search for the Laws of Self-Organization and Complexity, Oxford: Oxford University Press.

© Charles M. Macal, All Rights Reserved

Selected References Related to ABMS (cont’d.) Kohler, T. A., G. J. Gumerman and R. G. Reynolds, 2005, “Simulating Ancient Societies,” Scientific American, 293(1): 77-

84, July 2005.

Macal, C.M., 2009, “Agent-based Modeling and Artificial Life,” in Encyclopedia of Complexity and System Science, Robert A Meyers (ed.), ISBN: 978-0-387-75888-6, available at http://www.springer.com/physics/book/978-0-387-75888-6.

Macal, C. M., 2004, “Emergent Structures From Trust Relationships In Supply Chains,” in Proc. Agent 2004: Conf. on Social Dynamics: Interaction, Reflexivity and Emergence, Eds., C. Macal, D. Sallach and M. North, Chicago, IL, Oct. 7-9, pp. 743-760, Argonne National Laboratory.

Macal, C. M., and M. J. North (in press) “Tutorial on Agent-Based Modeling and Simulation,” Journal of Simulation.

Macal, C., and M. J. North, 2009, “Agent-Based Modeling and Simulation,” Proc. 2009 Winter Simulation Conference, M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin and R. G. Ingalls, eds., Austin, TX (Dec. 14-16).

Macal, C., and M. J. North, 2008, “Tutorial on Agent-based Modeling and Simulation: ABMS Examples,” Proc. 2008 Winter Simulation Conference, S. J. Mason, R. Hill, L. Moench, and O. Rose, eds., Miami, FL (Dec. 15-17).

Macal, C., and M. North, 2007, “Tutorial on Agent-based Modeling and Simulation: Desktop ABMS,” Proc. 2007 Winter Simulation Conference, S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds., Washington, DC (Dec. 9-12).

Macal, C., and M. North, 2006, “Tutorial on Agent-based Modeling and Simulation, Part 2: How to Model with Agents,” Proc. 2006 Winter Simulation Conference, L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, eds., Monterey, CA (Dec. 3-6).

Macal, C., and M. North, 2005, “Tutorial on Agent-based Modeling and Simulation,” Proc. 2005 Winter Simulation Conference, M. E. Kuhl, N. M. Steiger, F. B. Armstrong, and J. A. Joines, eds., Orlando, FL, pp. 2-15 (Dec. 4-7).

Macal, C. M., and M. J. North, 2005, “Validation of an Agent-based Model of Deregulated Electric Power Markets,” Proc. 2005 North American Association for Computational Social and Organizational Science (NAACSOS) Conference, South Bend, IN, June 22–24, available on CD.

70 © Charles M. Macal, All Rights Reserved

Selected References Related to ABMS (cont’d.)

Macal, C., and M. North, C. Drugan, and G. Pieper, 2008, “Overview of Agent-Based Modeling and Simulation,” SciDAC Review. Summer 2008, No. 8, 34-41.

Marsh, W. E. and R. R. Hill. 2008. An Initial Agent Behavior Modeling and Definition Methodology as Applied to Unmanned Aeriel Vehicle Simulation. International Journal of Simulation and Process Modeling, 4(2): 119-129.

Minar, N., R. Burkhart, C. Langton, and M. Askenazi. 1996. The Swarm Simulation System, A Toolkit for Building Multi-Agent Simulations, Working Paper 96-06-042, Santa Fe Institute, Santa Fe, NM. <http://www.santafe.edu/projects/swarm/overview/overview.html>.

NetLogo. 2009. NetLogo Home Page. Available at <http://ccl.northwestern.edu/netlogo/>.

Nikolai, C. and G. Madey. 2009. “Tools of the Trade: A Survey of Various Agent Based Modeling Platforms,” Journal of Artificial Societies and Social Simulation 12(2)2, <http://jasss.soc.surrey.ac.uk/12/2/2.html>.

North, Michael J., Charles M. Macal, 2009, “Agent-based Modeling and Systems Dynamics Model Reproduction,” International Journal of Simulation Process Modeling, 5(3)256-271.

North, M.J., and C.M. Macal, 2009, “Foundations of and Recent Advances in Artificial Life Modeling with Repast 3 and Repast Simphony,” in Artificial Life Models in Software, 2nd ed, A. Adamatzky and M. Komosinski, eds., Springer, Heidelberg, FRG, ISBN: 978-1-84882-284-9, available at http://www.springer.com/computer/mathematics/book/978-1-84882-284-9.

North, M. J., and C.M. Macal, 2009, “Agent-based Modeling and Computer Languages,” in Encyclopedia of Complexity and System Science, Robert A Meyers (ed.), ISBN: 978-0-387-75888-6, available at http://www.springer.com/physics/book/978-0-387-75888-6.

North, M., C. Macal, J. St. Aubin, P. Thimmapuram, M. Bragen, J. Hahn, J. Karr, N. Brigham, M. E. Lacy, and D. Hampton, 2010, “Multi-scale Agent-based Consumer Market Modeling,” Complexity, 15(5):37-47.

71 © Charles M. Macal, All Rights Reserved

Selected References Related to ABMS (cont’d.)

North, M.J., T.R. Howe, N.T. Collier, E.R. Tatara, J. Ozik, and C.M. Macal, 2009, “Search as a Tool for Emergence,” Chapter XXIII in Handbook of Research on Agent-Based Societies: Social and Cultural Interactions, Eds. G. Trajkovski and S. Collins, Information Science Reference (IGI Global), Hershey PA, pp. 341-363, Available at http://www.igi-global.com/reference/details.asp?ID=33015&v=tableOfContents, February.

North, M. J., and C. M. Macal, 2007, Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, Oxford: Oxford University Press.

Pan, X., C. S. Han, K. Dauber, and K. H. Law, 2007, A Multi-Agent Based Framework for the Simulation of Human and Social Behaviors During Emergency Evacuations, AI & Society 22(2): 113-132.

Resnick, M., 1997, Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds, MIT Press: Cambridge, MA.

ROAD (Repast Organization for Architecture and Design), 2009, Repast Home Page. Available at <http://repast.sourceforge.net/>.

Sakoda, J. M., 1971, The Checkerboard Model of Social Interaction, Journal of Mathematical Sociology, 1:119-132.

Samuelson, D.A., and C. M. Macal, 2006, “Agent-based Simulation Comes of Age,” OR/MS Today, 33(4):34-38, August.

Schelling, T. C., 1978, Micromotives and Macrobehavior, New York: Norton.

Sun, R., (ed.), 2006, Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, Cambridge University Press.

Tesfatsion, L., and K. L. Judd (eds.), 2006, Handbook of Computational Economics, Volume II: Agent-Based Computational Economics, Elsevier/North-Holland: Amsterdam, 904 pp.

Valluri, A., M. North, C. Macal, 2009, “Reinforcement Learning in Supply Chains,” International Journal of Neural Systems, 19(5)331-344.

Wilensky, U., 1999, Netlogo, Center for Connected Learning and Computer-Based Modeling, Northwestern University:Evanston, IL USA, <http://ccl.northwestern.edu/netlogo/>.

Wilkinson, T. J., M. Gibson, J. H. Christiansen, M. Widell, D. Schloen, N. Kouchoukos, C. Woods, J. Sanders, K.-L. Simunich, M. Altaweel, J. A. Ur, C. Hritz, J. Lauinger, T. Paulette and J. Tenney, 2007, Modeling Settlement Systems in a Dynamic Environment, in The Model-Based Archaeology of Socionatural Systems, Kohler, T. A. and S. E. v. d. Leeuw (eds.), pp. 175-208, School for Advanced Research Press: Santa Fe, NM.

XJ Technologies, 2009, AnyLogic Home Page, Available at http://www.xjtek.com/.

72 © Charles M. Macal, All Rights Reserved

Getting Started with Agent-based Modeling Resources

Personal Recommendations by Chick Macal

Repast: http://repast.sourceforge.net/ – Repast is a free and open source general purpose agent-based modeling and simulation toolkit developed

and supported by Argonne (originally by the University of Chicago) and available from Source Forge. Repast is being used for applications ranging in size from desktop applications to enterprise-wide modeling systems.

NetLogo: http://ccl.northwestern.edu/netlogo/ – NetLogo is a free (for non-commercial use) agent-based modeling and simulation toolkit geared to

educational uses (not open source). It is being developed and supported by Northwestern University’s Center for Connected Learning.

Agent-based Computational Economics (ACE) web site at Iowa State (Leigh Tesfastion)

– On-Line Guide for Newcomers to Agent-Based Modeling in the Social Sciences

– http://www.econ.iastate.edu/tesfatsi/abmread.htm

73 © Charles M. Macal, All Rights Reserved

Agent: Solar PV Leasers/Installers

Attribute Description Data Source

huId Housing unit ID (integer) LA parcel data

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Table 2 Housing Unit Attributes (Class housingUnit)

Object: Housing Units

Attribute Description Data Source

huId Housing unit ID (integer) LA parcel data

huZip Zipcode CSI data

huType

Housing type residential building: {1:Detached homes, "detachedHome”, 2:Attached homes, "attachedHome”, 3:Mobile homes, "mobileHome”, 4:Apartments with 2 - 4 tenants, "small apartment bldg 2-4 tenants”, 5:Apartments with more than 4 tenants, "large apartment bldg >4 tenants”}

LA parcel data

buildingSize Building size

financeType Finance type: {lease, buy} NREL lease vs. buy data

electricityRateAve Electricity rate, average during solar period ($/kWh)

electricityUsage Electricity usage, average (kWh/day)

N/A rateBin Electricity rate bin

N/A rateType Electricity rate type

N/A TMY site TMYsite (SolarDS)

N/A priceRegion priceRegion (SolarDS)

N/A orientation orientation (SolarDS)

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Table 2 Housing Unit Attributes (Class housingUnit)

Energy systems analyses are carried out at

different scales and granularities

Geographical resolution – National, regional

– State, local, neighborhood

– Continental, global

Time resolution – Hourly, daily, monthly, annual, decadal

– Life cycle

System resolution – Total energy system

– Fuel cycle, Technology supply chain

– Facility

– Process

– Interdependencies with other infrastructure systems

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Complex Systems

Agent-based Modeling

Applications to Energy

The Basic Idea

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