Agent-Based Modeling Overview

• Basic ABM Components• Background for guest talks• Background for using NetLogo

• ABM Creation• Conceptual Model design• ABM implementation - a computer program

• General Programming Tips

• NetLogo basics – building blocks for ABMs

• Start Group Project Design

ABM - Represents (part of) the world

< Look at El Farol , Wolf-Sheep-Grass >

• Population of agents; often multiple types of agents• Each Agent: state (data); actions; choice mechanisms (goals);• Adaptive processes (individual; population)• Environment (non-agent): often spatial; has its own dynamics;• Dynamics: who does what, when; activity schedule; time• Interaction topology: Who interacts with whom/what• Initial state of ABM (agent/environment state; parameters )• Measures: aggregate values we are interested in!• GUI (Graphical User Interface) -- Controls and Displays

Agent Types – What kinds of agents are there?

Agent Type = Set of Variables and procedures • Defines a set of variables (fields) that all agents of this type have• Defines a set of procedures all agents of this type can carry out.

• Variables represent agent characteristics, memory, relationships• Example types and variables:

• Cell -- infected, activationLevel• Person -- sex, age, education, income, infected

priceOfCigarettesAtLocalStore, seenFriendSmoke, parents, siblings, friends• School – size, budget, teachers, students

Agents (continued)

Agent Instances• One instance for each “real world agent” being modeled• An instance’s variables contain values (the agent’s “state”) representing

one real world agent’s characteristics, memory, relationships, etc.< look at running model with agent monitors showing their state >

Population of Agent Instances• An explicit representation of distributions agent characteristics, etc.

Agent Procedures – Represent what agents can do

• All agents of a given type can execute the same procedures• What each agent actually does can differ and vary over time:

Each agent has its own state / place in environment.

Procedures defined to carry out various agent activity:• Sense their (local) environment: countSimilarNeighbors()• Communicate with other agents: askNeighborsType()• Make choices (calculations; heuristics): pickBestSite( candidates )• Move / change environment moveForward( distance )• Exchanges with other agents: trade, buy, predation grazeOnPlant( loc )• Learn: change state; change relations; change rules addFriend( person )• Reproduce, die haveOffspring()

Agent Procedures in Action

A particular agent executes a procedure when “asked” (told!)• Agent already has access to its own state

• Agent may be sent “arguments” with procedure: moveForward ( d )

• Agent carries out procedure’s specified operations: • Built from language primitives and calls to other procedures• Conditional on state of agent, its neighbors and environment

• Agent own state may change, or it may do nothing!

• Agent actions may try to change state of environment (or other agents!)

Designing / Programming agents: Think like an agent!

• Who is asking me to do what? • What do I know? • Who can I ask for information?• What actions-options do I have? • How do I choose?• How do I learn ? (Do I have offspring?)

Example: Program (conceptual) a sheep or wolf agent

The Environment (Non-agent)

• Often spatial – 2D grid of locations (easy to see what’s happening!)• Sites (“patches”) can have variables to store environment state

• Ex: occupied, grassLevel, toxinConcentration • Dynamics independent of agents: “the biophysics” of the world

• growGrass() , diffuse(toxin), • Agents interact with environment

• Ask for information: isSiteOccupied(), getListOfNeighbors()• Change state of world: eatGrass(), moveTo(x,y), addToxin(x,y)

• Spatial biases can be represented by constraining agent actions• Only sense local information• Only affect / move to local sites

• Non-spatial environment: “globally available” information• Ex: barAttendanceLastStep, govSpendingOnEducation

Adaptive Behavior

Agent behavior is a function of its “behavioral rules” (procedures)

and its current state, neighbors’ state, environment state

Agent’s can adapt (change behavior) when/if :• Conditional behavior changes because:

• Agent’s own state changes (if unhappy then move) • Neighbor agents change• Environment state changes

• Learning – individual agents change behavioral rules based on experience

• Evolution – population changes: new agents with new rules, etc.

ABM Dynamics -- Generated “Bottom Up”

• Define micro-level mechanisms: agents, environment• Set model parameters: control initialization ; set mechanism parameters• Initialize environment, agents -- System State at t=0• Run the model: System State(t+1) = F( State(t) )

• F(.) is very complicated – use computer!• Stochastic -- multiple runs generate distribution of histories

• Measure/observe macro-level behavior of system generated by agents• Aggregate measures • Patterns in space• Correlations in agents’ states • Static snapshots and over time.

Agent Dynamics -- Time

• Which agents do which actions (procedures) in what order?• When is the environment state updated?• How is model time related to “real world” time?• How are “rates” of different model components related to each other?• Can be complicated: model and modeling-goal dependent.

Typical approaches (hybrids also common):• Discrete Time – agents activated each discrete step (random order)

• Synchronous update: All change state “instantaneously”based on same information about previous state.Like playing Rock-Paper-Scissors! Or GameOfLife & EqnBasedModels

• Asynchronous update: One agent acts and changes state,Then next acts based on updated state, then next, etc.

• Discrete event: time advances as “events” (agent actions) happen.Order is dynamically determined (stochastically)

Interaction Topologies

Who interacts with whom ?

“Traditional” assumptions:• Complete mixing: everyone interacts with all others• Random mixing: interact with uniform random sample

Complex systems approaches often include:• Spatial bias: higher probability interact with spatial neighbors• Social network bias: higher probability interact with family, friends, …

Often include combinations of spatial / social interactions

ABM Projects – This week and later

Most of these comments applicable to all modeling approaches.Some especially relevant to ABM.

Keep in mind the modeling choices going from:• The World – what parts do you want to explain/understand

What questions are being addressed?• A Conceptual Model

• What agents, what actions, etc. (sketches, not details) • Hard questions (for any modeling enterprise…):

• What to include? What to leave out?• Implementation of a conceptual model – a computer program

Many choices -> many implementations of same conceptual model.(Unless you do it “by hand” as Schelling did!)

Designing a Conceptual (Agent Based) Model

Start with simple, conceptual sketches, fill in as needed as you proceed• What types of agents? What do they do? How do they decide?• What data do agents need to have? • What is the environment, its state and dynamics (if any)?• How do agents interact with each other and the environment?

Remember the KISS Principle (Keep It Simple, Stupid)• Model as political cartoon – focus on some aspects of the world,

Other parts admittedly overly simple or ignored.• ABM – great temptation to add zillions of mechanisms! (Its looks cool!)• Sketch a series of conceptual models:

• V0: has few components— probably it is too simpleInclude only the one or two key components/mechanisms

• V1, V2: Add/complicate components/mechanisms only as needed

Designing a Conceptual (Agent Based) Model (cont)

Keep asking:• What questions is the model being designed to answer?• What is model intended to explain or help us understand better?• What kind of model is desired / required / possible?

• Simple, abstract, “stylized facts” – Exploratory ModelsEx: Schelling; An’s “Toy” models (Wedn); Eisenberg etal (Wedn)

• More “realism” – data for parameters / for model evaluationStill simple: Bruch’s models of segregation (Tues)Complicated: An’s models; Galea/Kaplan model (Thur)

• Model specific situations – lots of data for initial state, etc.Ex: An’s models; models of traffic in specific cities; EpiSim (Portland)

Implementing an ABM as a computer program.

• Choose language / platform (Friday)

• Start from available program if you can! (See NetLogo Models Libraries)

• Implement program stepwise, as a series of versions• Get each version doing something, no matter how simple• Add one mechanism/feature at a time. Test…

(Makes testing and debugging later…)• See examples of sequences of versions this week (Tues,Thur)

On Programming Style

Really important to make programs understandable & debuggable!

• Use indentation, horz spacing, to emphasize program “structure”Take advantage of our eyes pattern recognition capabilities

• Name variables, procedures in some standard wayCase matters! Easier to read, understand and guess namesEx: avgAge maxIncome findNeigbhors()

• Be consistent! Be Consistent!!

• Procedure definitions should be short – see it all on one screenComplicated procedures should call sub-procedures.

Document Your Models, Programs and Experiments

• Document, Document, Document• For others (sharing programs, especially with publication)• For yourself! In 2 months you’ll wonder “why did I do it that way?”!

• Comments in programs• General comments about overall program structure• At start of each procedure

• Write it before writing procedure! (Guide your coding; save mis-starts)

• Describe anything “tricky” – again, you will forget!• Notebook files / wiki / etc

• Model design / implementation decisions• Model Testing• Model Experiments (“How did I generate that figure…?”)