Agent-Based Modeling
PSC 120
Jeff Schank
Introduction
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What are Models?
Models are Scaffolds for Understanding
Models are always false, but very useful
Emergence, Complexity, and Agent-Based
Modeling
• Levels of Organization
• Causation and Mechanisms
What are Models?
• Models are not:
• A scientific model is a representation
that we use to better understand,
explain, or predict something
• A model as representation can be
physical or formal
Examples of Physical Models
Examples of Formal Models
Model
What we discover
N 0 K rt
K - N 0 + N 0rt
Examples of Formal Models
• Mathematical models work well when we
have an understanding or a plausible
idea about how a system behaves
• But, what if we only have some
understanding about how the parts of a
system work—but not how the behavior
of the system emerges from the behavior
of the parts?
A Simple Model
• Consider a system consisting of particles on a flat surface
• They exhibit Brownian motion, but stop moving when they
contact another particle that is not moving
• How will this system behave over time?
• Let’s formally model the system as N particles moving on a
2D torus with the following rules
– Rule 1: Particles (agents) move randomly
– Rule 2: If a moving agent contacts an agent that is not moving, it stops
at that location permanently
• Initial Conditions: A single non-moving particle is placed
in the middle of the space at the start of a simulation. The
other N–1 particles are all moving and normally distributed
at the north and south of the stationary particle
Particles in Space at the Start
What Shape will Form?
Models as Scaffolds for
Understanding
• A scaffold (ordinary sense) is a structure built for
repairing or constructing other structures (e.g.,
buildings, bridges)
• A scaffold (instructional sense) consists of
resources and methods that facilitate the learning
of skills
• A model is a scaffold in so far as it promotes
understanding, discovery, explanation, and
prediction of what it is intended to represent
Models are Always False
• Useful models, whether physical or formal
never correspond to all of the properties of a
system
• All models are false in the above, but this
isn’t the only way that models can be false
Models can be false by
• resulting in false predictions
• inaccurate explanations
• biased understanding
Models are Useful
• When they yield good predictions, good
explanations, and clear understanding
• But, what about most of the time when they result
in (at least partially) bad predictions, bad
explanations, or biased understanding?
• In these cases, models are useful if we can see a
way forward
• Can we diagnose what went wrong or how we
could improve a model?
• If not, it is best to abandon the model for now
Aggregating and Flocking
Emergence and Complexity
Agent-Based Modeling and
Emergence
• Agent-Based Modeling is most appropriate for
modeling complex-emergent systems
• Complex-emergent systems are ubiquitous in the
world, examples include
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Multicellular organisms
Populations
Social Systems
Societies
Ecosystems
Economic Systems
Levels of Organization
• Fall Webworms
Levels of Composition
• A thing X is at a higher level of composition
than a thing Y if X is composed of Ys (and
possibly other things)
• For example,
– A brain is at a higher level of composition than
a neuron because brains are composed of
neurons (among other things)
– A cell is at a higher level of composition than
DNA molecules, because among other
subcellular structures, cells are composed of
DNA
Levels of Composition
Interactions
• Levels of organization emerge from interactions among
components at one or possibly more levels of composition
• For example, a brain and its psychological abilities emerge
as a level of organization from the vast number of
interaction (via connections) among neurons and possible
astrocytes
Levels of Organization
Levels of Organization
Levels of Organization
Levels of Organization
Causation
• Our ordinary meaning of causation is a relationship
between events: cause and effect such that certain
conditions that bring about certain effects
• Causation in this sense is proximate, which
means that events and conditions that bring about
an effect are near their effects in space, time, and
typically at the same level of organization
Aristotle
• He thought of causes as reasons or factors that
explain the objects and processes that exist in the
world
• For Aristotle there were four basic causes
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2.
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4.
Material Cause: The material of which a thing is
made
Efficient Cause: The conditions that combine to
produce an effect from a cause
Formal Cause: The shape, configuration or type of
thing something is
Final Cause: The purpose or end of a thing or
process
Aristotelian Causation
Aristotelian Causation
• Works well for explaining artifacts that we
build
• Does not work well for explaining biological
systems from an evolutionarydevelopmental view
• No corresponding sense formal cause in
biological systems
• No corresponding sense of final cause in
biological systems
A Reinterpretation of Aristotle’s
four causes: System Causation
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2.
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4.
Components, which corresponds closely to Aristotle’s notion of material
cause, are the parts, entities, and processes that compose a system at some
level of organization
Proximate Cause, which corresponds closely to Aristotle’s notion of efficient
cause (i.e., actions and interactions among the components of a system such
as the firing of neuron the release of cyclic AMP)
Organization, which is somewhat related to one aspect of Aristotle’s notion
of formal cause (i.e., the spatiotemporal arrangement of components, their
behaviors and interactions)
Function, which is the biggest departure from Aristotle’s system. In biology
and psychology we can replace Aristotle’s notion of final cause with
a.
b.
System function: This concerns what organized components do (e.g., a heart as
an organized collection of cells pumps blood) in a larger system
Evolutionary adaptive function: This concerns how the organization promotes
the survival and reproduction of itself or as a part of a larger system. Typically,
adaptive functions are ascribed to components and characteristic of individuals,
but they may apply to entities at other levels of organization such as groups,
ecosystems, and species
Mechanisms
• A mechanism is a system of causally interacting parts
that produce one or more effects
• The effects (phenomena) produced by biological
mechanisms are often emergent
• Mechanisms are explained by System Causation
• Thus, we will be using Agent-Based modeling to
understand the mechanisms of phenomena such as
– Aggregation and Flocking
– Mating Systems
– Cooperation