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Agent-Based Modeling. PSC 120 Jeff Schank. Agent-Based Modeling. What Phenomena are Agent-Based Models Good for? What is Agent-Based Modeling (ABM)? What are the uses of ABM? Model Assumptions Analyzing Models Comparing Models to Data. What are they good for?. Complex systems

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agent based modeling

Agent-Based Modeling

PSC 120

Jeff Schank

agent based modeling1
Agent-Based Modeling
  • What Phenomena are Agent-Based Models Good for?
  • What is Agent-Based Modeling (ABM)?
  • What are the uses of ABM?
  • Model Assumptions
  • Analyzing Models
  • Comparing Models to Data
what are they good for
What are they good for?
  • Complex systems
  • Emergent phenomena
  • When we understand the parts better than the whole
  • When we seek mechanistic explanations
  • When we are faced with multiple levels of organization
what is abm
What is ABM?
  • ABM is a general Style of modeling that focuses on individuals
    • Agents can represent people, animals, or entities at different levels of organization
    • The modeling of agents typically require the specification of rules for agent behavior and interactions
  • ABM is a style of modeling that has features of both experimental and mathematical styles of thinking
    • When designing an ABM it is often useful to think like an experimentalist
      • What behaviors and properties do/should agents have?
      • How should the environment be designed and controlled?
      • How should experiments be designed?
    • Most ABMs have probabilistic elements, so each simulation experiment may differ considerably even for the same parameter values
    • Thus, a large number of simulated experiments are often required to analyze an ABM for a given set of parameters
    • From a mathematical style of thinking, the emphasis should be on investigating the entire parameter space or regions of interest in more complex models
what are the uses of abm
What are the uses of ABM?
  • To model complex systems in which individual behavior and properties are better understood than the behavior and properties of the system
    • Molecular and cellular biology
    • Ecology
    • Anthropology and other social sciences
    • Animal behavior
  • Exploratory modeling
    • Artificial life
    • Evolutionary game theory
  • Investigating the robustness of analytical results
    • Evolutionary game theory
    • Ecology
    • Evolutionary Biology
analysis of models
Analysis of Models
  • Parameter sweeps
    • Systematically vary one or move parameters of a model
    • The limitations are on the number of parameters
      • If there are two parameters and you want to look at 5 values for each parameter, then you must conduct 5 × 5 = 25 sets of simulations
      • As you can see, the number of sets of simulations to be conducted increases exponentially with the number of parameters to be swept
  • Another approach is to use genetic algorithms to evolve models that either fit some set of goals or data of interest
  • I’ll discuss an example of both approaches
ovarian cycle synchrony
Ovarian-Cycle Synchrony
  • Does ovarian-cycle synchrony exist in mammals?
  • The problem of cycle variability
  • Ovarian cycles and female mate choice
    • The cost of synchrony
  • Studies have reported synchrony in
    • Women
    • Norway rats
    • Golden hamsters
    • Golden lion tamarins
    • Chimpanzees
  • All are fundamentally flawed and more recent studies have found no effects
the cost of synchrony
The Cost of Synchrony
  • There are two types of fitness costs for synchronized females
    • Male quality
    • Mating opportunities
  • To explore these costs, I built an ABM, based on J. B. Calhoun’s study: The Ecology and Sociology of The Norway Rat
calhoun s rats abm
Calhoun’s Rats ABM
  • Aims and Design
    • Ecologically realistic
    • Based on data
    • 5 to 10 reproductive females at a given time
    • 61 adult males (7 high, 12 medium, 42 low)
    • Movement is determined by “collapsing” preferences into a local probability space surrounding a model rat
  • Ovarian cycles may have evolved to facilitate female mate choice
  • Synchrony has fitness costs
  • Cycle variability may have fitness benefits in promiscuous mating systems
the development of locomotion
The Development of Locomotion
  • How do animals do what they do?
  • How do we answer this question?
  • Start simple and work to the complex
  • If we want to understand how something works in space and time, it is often a good idea to build it or something like it.
  • We cannot just build animals at different stages of development, but we can build models of them, which may help us understand them better (i.e., simulation, robotic)
rat pups
Rat Pups
  • Born with very limited sensorimotor capabilities
    • Blind and deaf till days 13 to 15
    • Legs cannot lift the body off the ground till after day 10
  • However, they can aggregate in huddles and thermoregulate
  • Basic metric: tip of nosebase of tail location
  • Derived metrics
    • Activity
    • Object Contact
    • Speed
    • Aggregation
    • Conditional Probabilities
genetic algorithms
Genetic Algorithms
  • Arrange the parameters of the into a “chromosome”
  • Create a population of models
  • Perform mutation and crossover on pairs of models
  • Run a number of simulations and choose the parameters that best fit the data
locomotion kinematic results
Locomotion Kinematic Results

Day 7

Day 10



model examples
Model Examples

Day 7

Day 10