Speaking and listening

Agent-based modelling, Konstanz, 2024

Henri Kauhanen

30 April 2024

Plan

• Last week, we ran out of time
• Better go slowly and build a solid foundation rather than try to cover as much ground as possible
• Hence, today:
  • Finish last week’s material
  • Introduce a little bit of new material: implementing interactions between variational learners

Dropping the environment

• So far, we’ve been working with the abstraction of a LearningEnvironment:

Dropping the environment

• We will now drop this and have two VariationalLearners interacting:

Dropping the environment

• The probabilities P1 and P2 now need to be represented inside the learner:

Dropping the environment

• Hence we define:

mutable struct VariationalLearner
  p::Float64      # prob. of using G1
  gamma::Float64  # learning rate
  P1::Float64     # prob. of L1 \ L2
  P2::Float64     # prob. of L2 \ L1
end

Exercise

Write three functions:

• speak(x::VariationalLearner): takes a variational learner as argument and returns a string uttered by the learner
• learn!(x::VariationalLearner, s::String): makes variational learner x learn from string s
• interact!(x::VariationalLearner, y::VariationalLearner): makes x utter a string and y learn from that string

Exercise

Answer (speak)

using StatsBase

function speak(x::VariationalLearner)
  g = sample(["G1", "G2"], Weights([x.p, 1 - x.p]))

  if g == "G1"
    return sample(["S1", "S12"], Weights([x.P1, 1 - x.P1]))
  else
    return sample(["S2", "S12"], Weights([x.P2, 1 - x.P2]))
  end
end

speak (generic function with 1 method)

Exercise

Answer (learn!)

function learn!(x::VariationalLearner, s::String)
  g = sample(["G1", "G2"], Weights([x.p, 1 - x.p]))

  if g == "G1" && s != "S2"
    x.p = x.p + x.gamma * (1 - x.p)
  elseif g == "G1" && s == "S2"
    x.p = x.p - x.gamma * x.p
  elseif g == "G2" && s != "S1"
    x.p = x.p - x.gamma * x.p
  elseif g == "G2" && s == "S1"
    x.p = x.p + x.gamma * (1 - x.p)
  end

  return x.p
end

learn! (generic function with 1 method)

Exercise

Answer (interact!)

function interact!(x::VariationalLearner, y::VariationalLearner)
  s = speak(x)
  learn!(y, s)
end

interact! (generic function with 1 method)

Picking random agents

• rand() without arguments returns a random float between 0 and 1
• rand(x) with argument x returns a random element of x
• If we have a population of agents pop, then we can use rand(pop) to pick a random agent
• This is very useful for evolving an ABM

Aside: for loops

• A for loop is used to repeat a code block a number of times
• Similar to array comprehensions; however, result is not stored in an array

for i in 1:3
  println("Current number is " * string(i))
end

Current number is 1
Current number is 2
Current number is 3

A whole population

• Using a for loop and the functions we defined above, it is now very easy to iterate or evolve a population of agents:

pop = [VariationalLearner(0.1, 0.01, 0.4, 0.1) for i in 1:1000]

for t in 1:100
  x = rand(pop)
  y = rand(pop)
  interact!(x, y)
end

Exercise

Write the same thing using an array comprehension instead of a for loop.

Exercise

Answer

pop = [VariationalLearner(0.1, 0.01, 0.4, 0.1) for i in 1:1000]

[interact!(rand(pop), rand(pop)) for t in 1:100]

100-element Vector{Float64}:
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 ⋮
 0.09801
 0.099
 0.10900000000000001
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099
 0.099

Next time

• Next week, we will learn how to summarize the state of an entire population
• This will allow us to track the population’s behaviour over time and hence model potential language change
• This week’s homework is all about consolidating the ideas we’ve looked at so far – the variational learner and basics of Julia