Agent based modeling - AnyLogic...• Agent based modeling does NOT assume clock "ticks" or "steps"...

© The AnyLogic Company | www.anylogic.com

Agent based modelingDr. Andrei BorshchevCEO, The AnyLogic [email protected]

Sydney, February 2013

© The AnyLogic Company | www.anylogic.com 2

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Modeling

The solutionThe problem

The model The solution at the model level

The real world

The world of models

RISK-FREE SPACE


Types of models

Boxes connected with lines

Formulas of a sheet of paperSimulation models

Excel spreadsheets

Mental models Physical models


A simulation model

• This is an “executable” model A set of rules that allow us to obtain the next state of the system in

time from the current state

• The model produces the trajectory of the system in time Outputs are “observed” as we move

Inputs

X1

X2

X3

X4

Y1

Y2

Y3

Y4

SimulationModel


The three methods in simulation modeling

• The three modeling methods are the three different viewpoints …the modeler can take when mapping the real world system to its

image in the world of models

Low abstraction level[maximum details

Micro levelOperational level]

High abstraction level[minimum details

macro levelstrategic level]

Aggregates, global feedback loops, influences, trends…

Medium abstraction level

[medium detailsmeso level

tactical level]

Individual objects, exact sizes, velocities, distances, timing…

Discrete Event(process based)

Modeling

SystemDynamics

Agent BasedModeling


System Dynamics (J. Forrester, 1950s)

• Bass diffusion model Diffusion of a new product, innovation, or idea

Potentialclients Clients

Sales

Sales fromAdvertizing

+

Salesfrom Word of

Mouth

+

Advertizingeffectiveness

+

+

B

AdoptionFraction

ContactRate

+

+ ++

B

R


Discrete event modeling (G. Gordon 1960s)

• Entities and resources. Flowchart diagram Queues and delays

Bank

[resources]

[queue+service]

[entities]

[source] [decision]

yes

no

[sink]

[decision]

yes

no

[queue] [delay]


Agent based modeling (no “father”, 2000s)

• We focus on individual objects and describe their local behavior, local rules Sometimes – also the dynamics of the environment

EnvironmentAgent’s behavior

Child

Junior

Adult

Senior


Example: Consumer market


DON'T be misguided by "academic" literature:

• Agents are NOT the same thing as cellular automata that live in discrete space There can be no space at all. When space is needed, in most cases it is continuous, sometimes a

geographical map or a facility floor plan

• Agent based modeling does NOT assume clock "ticks" or "steps" Most well-built and efficient agent based models are asynchronous. Continuous time dynamics may

also be a part of agent or environment behavior

• Agents are NOT necessarily people Anything can be an agent: a vehicle, a piece of equipment, a project, an idea, an organization, an

investment

• Agents DON"T have to be intellectual, adaptive, learning, or exhibit goal-seeking behavior Agents can be very primitive, dumb, seeking nothing, and not able to learn

• Agents DON'T have to be active. A passive object can be an agent Consider a pipe segment of a water supply network with ist maintenance and replacement schedules,

cost, breakdown events, etc

• There can be many and there can be very few agents in an agent based model Compare a model of a consumer market and a model of a steel plant

• There are agent based models where agents do not interact at all Consider agents – patients with chronic non-contagious diseases

• There are agent based models with and without environment


Agents can be:

Non-material things:projects, products, innovations, ideas, investments …

Vehicles, equipment:trucks, cars, cranes, aircrafts, rail cars, machines, …

Organizations:companies, political parties, countries, …

People:consumers, habitants, employees,

patients, doctors, clients, soldiers, …


Example: Competition in pulp market


What is agent in my model?

• Sometimes this is obvious, sometimes not Example: agent based model of America's automotive market

Person

Car

Person

Car

Person

?

?

?

? Household

• How do I choose? In this particular case: think, e.g. where the decisions are made In general: try, see which architecture results in more elegant model


Special/standard language for AB modeling…

• …does not exist AB models are very diverse

• However, there are “design patterns” common to many AB models Object-”based” architecture Time model: asynchronous or synchronous (steps) Space: no space, continuous (2D or 3D), GIS, discrete Mobility

Networks and links Communication Between agents Between agents and environment

Agent state information and behavior Statecharts “Rules” SD inside agents DE (processes) inside agents

Statistics collection on populations of agents


Example: Field service


AB modeling has a lot in common with OO design

• Agent = object• The model is designed as a set of agent/object classes• Agents of the same class have common structure, but differ in

parameters/states Memory/state information is individual to every agent

• "Border" of agent/object, separation of interface and implementation

• Agent/object communication: message passing, function calls Message sequence diagrams are useful in AB model design

• Agents/objects can be created and deleted dynamically• [Inheritance and polymorhysm may be useful, but are not that

important in AB modeling]


Networks

• If agents have more or less stable connections, it may make sense to create a network [If there is no network, it does not mean agents do not communicate.

They can communicate randomly, or establish short-term links]

• There are standard network types supported by AnyLogic Random Ring lattice Small world Scale free Distance-based

• Standard networks have bi-directional "unnamed" links• You can create custom networks of any configuration

For example, hierarchical, with unidirectional or “named” links, etc.

• Networks can be dynamically reconfigured


Network examplesRandom network

3 links per agent 50 agents on a ring layout

Ring lattice4 links per agent

10 agents on a ring layout

Scale free networkm = 4


Small world network 3 links per agent, 75% of neighborhood links


Distance-based networkRange = 50

100 agents on a random layout

Custom network 50 initial agents small world

200 more agents linked to them


Example: HIV diffusion and syringe usage


Multi-method modeling. AnyLogic

• Support all three modeling methods on a single modern object-oriented platform

• The modeler can choose from a wide range of abstraction levels/methods and can efficiently vary them while working on the model

• The modeler can combine different methods in one model

System dynamics

Discrete event(process based)

modeling

Agent based modeling

Dynamicsystems


Model architectures

and so on in any combination…

Agents SD DE (Process model)

SD inside agent(e.g. consumer’s individual

decision making)

Process model inside agent(e.g. business process in a company

in a bigger supply chain model)

Agents + SD environment(e.g., population +city infrastructure)

Agents + process model(e.g., clients + service)

SD + process model(e.g., demand + production)

Agents become entities(e.g., patients with chronic diseases

return to hospital)


Example: Aerated concrete factory


“Academic” agent based modeling

• Discrete time

Agent 2

Agent 1

Agent 0

Time0 1 2 3

• Discrete space

rows

columns

{N,S,E,W] {N,NE,E,SE,S,SW,W,NW]


Example: Schelling segregation


Example: Air defense system


Do I need to have programming skills?

SD

Almost none

DE

Some: expressions,function calls

AB

More: expressions,function calls,

statements

Softwaredevelopment

Classes, interfaces,

inheritance,polymorphism,

…


– Same agents,– Environment

scaled down

How many agents to simulate?

• If I need to model the US population do I need to simulate 300,000,000 agents? Fortunately not!

• Two main “model scaling” techniques are used:

– Same environment– Agents represent

groups


Thank you!

• Questions?

• Links: AnyLogic website: www.anylogic.com AnyLogic models online: www.runthemodel.com

http://www.anylogic.com/

http://www.runthemodel.com/


Supplementary materials…


Bass Diffusion – Agent Based version

PotentialAdopters Adopters

AdoptionRate

Adoptionfrom

Advertising

AdvertisingEffectiveness

+

+

+

B

B

Adoptionfrom Wordof Mouth

TotalPopulation

AdoptionFraction

ContactRate

+

++

+

-

+R

ABSD

100 agents

PotentialAdopters

Adopters

10,000 agents

PotentialAdopters

Adopters

PotentialAdopter

Adopter

Rate:AdEffectiveness

“Buy!”Guard: randomTrue(AdoptionFraction)

Rate: ContactRate

<random agent>.”Buy!”


Capturing more with AB Model

• Can you build an SD model that captures such dynamics?

• Let the word-of-mouth influence of an adopter depend on how recently he has purchased

PotentialAdopters

Adopters

0 1 2 3

0.01

0.02

0.03

Adoption Fraction vsTime since purchase

“Buy it!”Guard: randomTrue(AdoptionFraction (

Now – Time Purchased ) ))

Rate: Contact Rate<random agent>.”Buy it!”

Rate: AdEffectiveness

PotentialAdopter

Adopter Time Purchased = Now

Time Purchased = Now

Time Purchased


Correspondence between DE and AB

Arrival Decision?Y

N

Service

Delay Exit

Resource

Idle

In Use

seizedreleased Dispatcher

Wait Resource

B

In Service

Requestresource

Resourcegranted

Finished

Delayed

Delay Time

Y

N

Requestresource

Releaseresource

Delete this agent

DE AB


Capturing more with AB model

Wait Resource

B

In Service

Request …

Resourcegranted

Finished

Delayed

Delay Time

Y

N

Requestresource

Releaseresource

Deletethis agent

Emergency Process…

Alarm!

Normal

Normal processEmergency process

Agent based modeling - AnyLogic...• Agent based modeling does NOT assume clock "ticks" or "steps"...

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