simulating complex systems: applications to...
TRANSCRIPT
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
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, [email protected]
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
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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.
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Tesfatsion, L., and K. L. Judd (eds.), 2006, Handbook of Computational Economics, Volume II: Agent-Based Computational Economics, Elsevier/North-Holland: Amsterdam, 904 pp.
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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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