Agent-Based Modeling, the PDC and DiveArch:What Is Susanne Doing Here, Anyway?

Susanne Jul, PhDPacific Disaster Center

sjul@pdc.org

Overview

• Agent-based modeling– What is it and why is it better than sliced bread?

• Agents at the PDC– A fantasy

• DiveArch project– What is Susanne doing here?

Agent-Based Modeling(What is it and why is it better than

sliced bread?)

But First

An exercise!

Exercise(High school attraction)

• Select another person (anyone)• Try to keep someone between you and the

person you selected at all times

Predictions?

Exercise(College attraction)

• Select another person (anyone)• Try to keep no one between you and the person

you selected at all times

Predictions?

Point?

Changes to individual behaviors yielded changes in system behavior!

• Describe individual components of system– Behaviors, including responses to internal and

external stimuli

• Observe emergent phenomena arise from actions of and interactions among component parts

• Not describing interactions• Not describing system behavior

Essence of Agent-Based Modeling

Why ABM Is Better than Sliced Bread

• Describe the loaf• Describe the knife

The bread slices itself!(Or not)

An Agent-Based Model Is

A set of agents,embedded in an environment

An Agent Is

• A software module that encapsulates state and behavior representing a single conceptual entity

• Defining properties:

autonomous Controls its own actions and behaviors

embedded Exists within and can sense a specific environment

reactive Responds in a timely fashion to changes in the environment

goal-oriented Does not simply act in response to the environment

temporally continuous Is a continuously running processAgent-based modeling is a programming

technique

An Environment Is

• The runtime “ether” in which agents operate• Always provides unified concept of time

– May be as simple as “clock tick”

• May or may not have other properties, – E.g., concept of space

• Agents may have environmental properties– E.g., age, location

• Agents can detect (some) properties of environment

An Agent-Based Simulation Is

A set of agents embedded in an environment

going about,doing their thing(s)

(reacting to internal and external events)

Agents at the PDC(A Fantasy)

Scenario

• Maui County has received $100,000 for tsunami mitigation/preparedness

• Considering using the money toa. Upgrade warning sirens dependent on power grid,

orb. Develop effective self-rescue procedures and

educate public (including visitors) about them

• PDC has been contracted to develop a model to help answer the question

– Which strategy is more likely to lead to fewer casualties?

Today?

Real Question

Which strategy is more likely to get more people to higher ground

faster, safely?

Factors to Model

• Possible and probable warnings Sirens Ground tremors Visible water recession

• Possible and probable responses Do nothing Go look to see what’s going on Run for the hills Drive for the hills

• Possible and probable impacts Siren failure Water surge Debris damage (casualties)

Agents

• Hazards– Earthquake– Tsunami

• Structures– Power generator– Warning siren

• Humans– Probably at household scale

Hazards

• Tsunami does not “know” about earthquakes• Earthquake can be as simple as table lookup

Earthquake Tsunami

Spatial location Modeled after real-world data

Derived from trigger

Temporal location Modeled after real-world data

Derived from trigger

Environmental effect (behavior)

Ground displacement Water movement

Triggers Programmatic input Water (ground) displacement

Probability of triggering

100% Modeled after real-world data

Structures

Power Generator Sirens

Spatial location Modeled after real-world data

Modeled after real-world data

Failure behavior, possible response to

• Ground displacement

• Flooding

• Ground displacement

• Failure of associated power source

Environmental effect (behavior)

Power output Audible signal

Triggers Always on if not in failed state

Water (ground) movement(PTWC siren controlling agent)

Probability of triggering NA 100%, if not in failure mode

Humans

Household

Spatial location Initial state modeled after real-world data

Perception behavior, yielding

• Possible detection of ground movement• Possible detection of water recession• Possible detection of audible signal

Response behavior • Do nothing• Makai movement• Mauka movement

Movement behavior • Run at 2.5 mph (anywhere)• Drive at 25 mph (along road network)• Cannot occupy same space as other

Household

Environment

• Geo-referenced space and time• Elevation topology of Maui County• Road network topology of Maui County

Add Agents and Shake

• Earthquake(s) occurs (location, magnitude, type generated from real-world data)

• Tsunami may or may not trigger• Power generators, sirens may or may not fail• Siren may or may not sound• Household may or may not feel quake, see water

recede, hear siren• Household may or may not react

And so forth and so on…

Possible Results

a. More sirens independent of power generators More households hearing siren warnings But fewer responding with mauka behavior

b. More households interpreting all warnings and responding “correctly” Fewer households hearing siren warnings But more responding with mauka behavior

Traffic jams? “False positives”?

PDC

• Agents– Earthquake, tsunami probability, effect– Power generators, sirens distribution– Population configuration, distribution

• Environment– Elevation topology– Road network topology

• Results output

End User (Maui County)

• Experiment with– Number and location of upgraded sirens– Number and type of households with modified

behaviors

• May come back with request for contra-flow traffic patterns– (Hey look, we have half a storm/wildfire/whatever

evacuation model!)

Agent-Based Modeling Revisited

Agent-Based Modeling

• Most sophisticated models are non-deterministic– Outcome cannot be predicted from inputs

• Model configuration may change– Stochastic environmental configuration– Stochastic population-generation

• Environment may change– Stochastic environmental change over time

• Agent behaviors may change– Stochastic decision-making– Combinatorics of environmental change and behaviors of other

agents

A butterfly flaps its wings…

Uhm, So How Do I Get Answers?

• Run simulation many times to identify patterns of possible dependency

• Change system state interactively– As active agent (participant)– As controlling deity

Same effect Different effect

Same inputs Is effect a fundamental system property?

Is there a fundamental pattern to effects?

Different inputs Input factors unrelated to effect?

Are differences correlated?

Benefits of Agent-Based Modeling

• Natural way of thinking about and describing complex system– Many entities – Many types of entities

• Emergent phenomena generated, not specified explicitly– Don’t need to predict dependencies in advance

• Modularity allows distributed model development– Plug and play agents– Can incorporate other computational modeling techniques within agents

(equational modeling, production-rule system, random guessing, etc.)• Independent nature of agents allow dynamic model and agent

modifications– Model is live and can be made interactive

Difficulties with Agent-Based Modeling

• Model and agents must be at right level of abstraction and detail– “Perhaps the most common typical modeling mistake is developing a

model that is not well disciplined and that has too many pieces. ... One may think adding more detail yields a more accurate picture, but the modeler's job is to discover the system's essential core dynamics.”

– True for all types of modeling, but easier to misjudge in agent-based modeling

• “Correctness” (validity, calibration, determination of confidence) of model depends on “correctness” of individual agents– Typically not quantifiable

• Potentially computationally intensive

“That all sounds really complicated!”

-- Bryan Boruff, Nov 6, 2006

The DiveArch Project(Why Susanne is Here)

What Do You Need to Make It Happen?

• Sets of agents and environmental representations

• A framework (computational infrastructure) that provides building blocks, and ties agents and environments together

• A (computational superstructure) that allows control of and experimentation with simulations, supports data collection and results generation

Books

Bookcases

Books and Bookcases

What Do You Need to Make It Happen?

• Sets of agents and environmental representations

• A framework (computational infrastructure) that provides building blocks, and ties agents and environments together

• A (computational superstructure) that allows control of and experimentation with simulations, supports data collection and results generation

Bookcases

Bookcase Components

Books and Bookcases

“Don't try to do everything for everyone, make it easy for them to do for themselves.”

– Ben Bederson, October 18, 2006

DiveArch Research Question

What does a framework for disaster-specific

agent-based models need to be?

In Other Words

• What are the different kinds of agents are needed in disaster-related modeling?

• What are the specialized needs of those kinds agents?• What are the specialized needs of disaster-related

models?

Hazards

Earthquake Tsunami

Spatial location Modeled after real-world data

Derived from trigger

Temporal location Modeled after real-world data

Derived from trigger

Environmental effect (behavior)

Ground displacement Water movement

Triggers Programmatic input Water (ground) displacement

Probability of triggering

100% Modeled after real-world data

Hazards

Earthquake Tsunami

Spatial location Modeled after real-world data

Derived from trigger

Temporal location Modeled after real-world data

Derived from trigger

Environmental effect (behavior)

Ground displacement Water movement

Triggers Programmatic input Water (ground) displacement

Probability of triggering

100% Modeled after real-world data

Iterative Approach

Problem Analysis

Design Generation

Design Realization

Design Evaluation

Think about it• Look at literature and existing

disaster-related models• Talk to developers

Do it• Design and build a simple framework• Test the design against a small

number of models

First Step

• Find a suitable general-purpose agent-based framework on which to build– Understand issues in agent-based modeling, in

general– Understand which issues might be critical to disaster-

related agent-based modeling

Preliminary Result

• Have comprehensive list of criteria for evaluating general-purpose agent-based frameworks, ~160 criteria in 7 categories:– General criteria– Model development– Model validation & calibration– Model interaction & visualization– Results assembly & publication– Model publication– Modeling options

Preliminary Result

• The “right” framework doesn’t exist (yet)– Integration between agent-based and GIS functionality is critical to

disaster-related modeling, but is an unsolved problem– Many designs make the “books and bookcases” mistake:

• “Finding, understanding and adapting the available libraries to our needs took by far more time, than developing everything from scratch. And even worse: Even for simple changes often complex programming in the system itself was necessary.”

– Robert Tobias, October 20, 2006

Next Steps

• Identify most suitable “starter” framework– Rate significance of criteria to PDC (long term) and

DiveArch (short term) needs– Experiment with existing agent-based frameworks to

determine which is the best “starter” framework

• Develop similar understanding of criteria for evaluation of GIS systems

• Develop more detailed “straw models” to use in framework design

Summary

• Agent-based modeling– Extension of object-oriented programming– Describes a system by describing its constituent parts– Phenomena generated from the bottom up, i.e.,

emerge from behaviors of and interactions among constituent parts

The whole is more than the sum of its parts

Susanne Is Here to

• Begin to understand what a disaster-specific computational framework for agent-based modeling and simulation needs to be– Tap into PDC knowledge of disaster-related modeling

and simulation– Tap into PDC network of knowledge and resources

• Design and prototype a proof-of-concept framework

And we all lived happily ever after!