# A Model Is A Model Is A Model

## Modeling

 There are many “modelings”: Agent-Based Models Mathematical Models Statistical Models System-Dynamics Models Discrete-Event Models Stochastic Dynamic Models Physical Models … MONIAC: A physical model of the British national economy (Photo: Wm. Vandivere, Fortune, March 1952, p. 100)

## Agent-Based Modeling (ABM)

• Two elements:
1. Agent-based
2. Modeling
• Certain principles apply to all kinds of modeling
• First, consider modeling
• Then consider what distinguishes agent-based modeling from other kinds.

## What Is A Model?

• Definition (first try):
• A model is a simplified representation of reality
• Why do we simplify?

## Modeling

• Developing a model:
• Problem solving under constraints
• Most important constraints:
• Incomplete information
• Lack of time
• Lack of resources (people, money, computing power, etc.)

## Example

• You bought a six-pack of a tasty beverage last night,
• but when you get home this evening, you realize that you forgot to put it in the refrigerator.
• So you put it in the fridge, and now you want to know, without trial and error, when it will be cool enough to drink.
• How do you approach the problem?

# Heuristics

## Heuristics

• Mental shortcuts
• Rules of thumb that experience has shown to be useful.
• When solving problems under constraints, apply heuristics in modeling:
• Simplified representations

## Typical Heuristics

• Rephrase the problem
• Draw a simple diagram of the system
• Imagine that you are inside the system
• Identify essential variables
• Identify simplifying assumptions
• Use “salami tactics”: slice space and time

## What Is A Model

• Definition (second try):
• A model is a purposeful (simplified) representation
• Modeling is something we all do all the time because we never have enough data and time!

• Thinking = problem-solving = modeling

## What Is A Model?

• Modeling adaptive behavior means trying to model the models used by adaptive agents (plants, animals, humans, organizations, etc.)
• A model is a model is a model

# Is Modeling Essential?

## Is Modeling Essential?

• When trying to solve a problem, we keep asking ourselves, “is this aspect of the real system essential for solving my problem?”
• How can we know whether something is essential?
• We cannot know
• In science, we keep developing the model to test our assumptions

## Example: Model A Forest

• Without a clearly stated question or problem we cannot formulate a simplified representation.
• We don’t know the purpose of the model
• The strategy:

Model first, then think about what problems we can solve with the model

does not work!
• Forest model:
• Timber extraction
• Ecosystem preservation
• Forest fires

## Example: Checkout Queue

• Minimize waiting time
• Manager’s purpose:
• Minimize waiting time of all customers
• Manager’s solution:
• Single queue for all customers
• Airports, banks, etc.

# Lessons for Agent-Based Modeling

## Lessons for Agent-Based Modeling

• ABM requires some specific techniques (programming, math, statistics)
• But general modeling principles apply.
• Scientific modeling explicitly states heuristics, simplifying assumptions
• Use math & computer logic to rigorously explore consequences of assumptions

## Lessons for Agent-Based Modeling

• We need to simplify
• Iterative process:
• Formulate question
• Create simplified representation
• Implement model as program
• Test program
• Analyze output
• Start over with modified question/model/program/etc.
• Modeling cycle

# The Modeling Cycle

## The Modeling Cycle

### Formulate the Question

• Question or problem serves as filter for what to include in the model.
• Modeling the system first and then specifying the question does not work

### Assemble Hypotheses

• We need a conceptual (often verbal, graphical) model of how the system works and what the answer is.
• This conceptual model can be based on: empiricial experience, theory, feeling
• Discuss and revise the conceptual model thoroughly, but not forever.

### Choose Model Structure

• What are the model’s entities?
• How are they characterized (state variables)?
• How do you represent the environment?
• What are temporal and spatial resolutions and extents?

### Implement the Model

• Write down equations and/or implement model as computer program
• Choose appropriate software platform/system

### Analyze the Model

• Perform controlled experiments to understand your model
• Design & analyze simulation experiments just like real experiments
• This is the hard part (95% of the time)

### Communicate the Model

• Like lab protocol: Model development has to be documented
• Keep a notebook of what y ou do.
• Keep old versions of your model
• Name files model_1.nlogo, model_2.nlogo, etc.
• Or use revision-control software (git, mercurial, etc.)
(See “Reading Resources and Computing Tools” handout)
• Final documentation should enable peers to fully understand and re-implement model (ODD specification) (More on this next week)

# Example Models

## Examples of Agent-Based Model Research at Vanderbilt

• Impact of land-use change on Brazilian ecosystems
• Spread of solar-roof systems in California
• Adaptation to drought by rice farmers in Sri Lanka
• Interaction of land-use and sea-level rise in Bangladesh
• Interaction of environmental change and population migration in Bangladesh
• Co-evolution of mega-herbivores and steppe ecosystems at Pleistocene-Holocene boundary
• Predicting traffic congestion for navigation apps
• Impact of Nashville gentrification on mobility & access to mass transit
• Can prediction market affect belief in climate change?
• Teaching K-12 science

## Other Examples of Agent-Based Model Applications

• Predator-prey interactions
• Preserving viability of threatened species
• Interaction of public belief in global warming, engineering projects, and future vulnerability of coastal communities.
• Impact of natural disasters on cities
• Designing effective political institutions
• Designing evacuation routes from buildings
• Predicting and managing disease epidemics
• Mechanisms of septic shock (bacteria in human body)
• Developing strategies for responding to mass shootings
• Effect of opium trafficking on Taliban insurgency in Afghanistan