NPS Wk9a - Agent-Based Models + Complex Systems

Mechanistic models

Uses theory to predict what will happen in the real world

Empirical Models

Uses real-world data to help develop a theory

Deterministic Models

Produces the same output for a given set of inputs

Stochastic Models

Incorporates randomness and uncertainty into the model (e.g., k-means is a stochastic model due to random initialization of the mean)

Computational models

• A computational model is a model written in terms
of a computer code that describes a system and the
rules it obeys
• We can run these models to see how the system
changes with time
• Computational models have been referred to as the
third pillar of science, after theory and experiment

What is a simulation?

• The process of running a computational model is called a simulation

• Simulations are used to better understand and predict a real- world process or scenario

When to use simulations?

• • When the physical system under study does not exist yet, or the prototype is too expensive to build

  • § In a wind tunnel, the effect of turbulence on a new aircraft model

  • § Biochemical mechanism of a candidate drug to treat cancer

• • When we are trying to predict the future

  • § Rates of deforestation after policy interventions

  • § Global population growth trends in the next century

  • § GDP forecasts for the year 2025

• • When the physical system exists but a real experiment is not feasible, due to it being too complicated, too expensive, or too dangerous

  • § The dynamics of how a virus spreads during a pandemic
    § The risks of an outbreak of nuclear war
    § How greenhouse gas concentrations in the atmosphere
    contribute to a rise in global temperatures

Limitations of simulations

• It might be difficult to verify the results of a simulation if
the actual, real-world system does not exist
• Models may be too simple to properly reflect real-world
phenomena
• The computational cost of running complex simulations
can be high
• It can be a challenge to strike a balance between
simplicity and accuracy

Steps of the modeling/simulation process

What are complex systems?

Complex systems consist of a large number of locally interacting parts that are evolving in time without any centralized control

Common properties of complex systems:

Non-linearity, emergence, feedback loops

What is Emergence behaviour in complex systems?

• In complex systems, emergent global patterns often arise from simple, local interactions and rules

• Emergence occurs when a complex entity has properties or behaviours that its constituent parts do not have on their own, and which emerge only when interactions take place as part of a wider whole

What are attractor states in complex systems?

• once in an attractor state, the system will never leave it

• State the example of the wolf sheep model

What are critical/tipping points in complex systems?

• They are thresholds at which small changes to the input parameters have a large impact on the eventual outcome

• Use the example of forest fire model