Agent-Based Modeling PSC 120 Jeff Schank

Introduction 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 ModelWhat we discover

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 –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

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 1.Material Cause: The material of which a thing is made 2.Efficient Cause: The conditions that combine to produce an effect from a cause 3.Formal Cause: The shape, configuration or type of thing something is 4.Final Cause: The purpose or end of a thing or process

Aristotelian Causation

Works well for explaining artifacts that we build Does not work well for explaining biological systems from an evolutionary- developmental 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 1.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 2.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) 3.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) 4.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.System function: This concerns what organized components do (e.g., a heart as an organized collection of cells pumps blood) in a larger system b.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