Resource Competition Tutorial

library(artificialLifeR)

Purpose

This tutorial introduces simulate_resource_competition(). Resource competition is a core artificial-life idea because agents must interact with an environment to maintain energy.

In this tutorial, you will run a competition simulation, inspect agent and resource outputs, plot average energy, compare resource regeneration settings, and interpret results carefully.

Basic simulation

competition <- simulate_resource_competition(
  n_agents = 50,
  steps = 50,
  n_resources = 30,
  resource_regen = 0.20,
  seed = 2
)

names(competition)
#> [1] "agents"    "resources" "summary"

Inspect outputs

head(competition$agents)
#>   agent         x           y    energy      speed efficiency
#> 1     1 0.2098611 0.074092349 0.9125861 0.07148919  0.5975891
#> 2     2 0.6856852 0.003262435 1.3371467 0.05521196  0.4830577
#> 3     3 0.5455454 0.717155232 0.9520106 0.04371456  0.5722192
#> 4     4 0.1354033 0.942352902 1.2122130 0.03500740  0.4155581
#> 5     5 0.9259978 0.247930549 0.8222090 0.03275603  0.6277294
#> 6     6 0.8847294 0.780429684 0.7198679 0.09096081  0.3656889
#>   reproduction_threshold age alive step
#> 1               1.529798   1  TRUE    1
#> 2               1.398045   1  TRUE    1
#> 3               1.787090   1  TRUE    1
#> 4               1.521871   1  TRUE    1
#> 5               1.403345   1  TRUE    1
#> 6               1.538384   1  TRUE    1
head(competition$resources)
#>   resource         x          y    amount step
#> 1        1 0.6920055 0.08084101 1.0000000    1
#> 2        2 0.5599569 0.95563857 0.5526803    1
#> 3        3 0.3426912 0.97326925 1.0000000    1
#> 4        4 0.2344975 0.14015762 0.3896781    1
#> 5        5 0.4296028 0.67780596 1.0000000    1
#> 6        6 0.7367007 0.79119735 0.6611856    1
head(competition$summary)
#>   step n_alive mean_energy mean_resource total_resource
#> 1    1      50   0.9920413     0.7355619       22.06686
#> 2    2      50   1.0104519     0.7045157       21.13547
#> 3    3      50   1.0265316     0.6795264       20.38579
#> 4    4      50   1.0365931     0.6723698       20.17109
#> 5    5      50   1.0469975     0.6577956       19.73387
#> 6    6      50   1.0557799     0.6461239       19.38372

The result is a list with:

Object	Meaning
agents	Agent states over time
resources	Resource locations and amounts over time
summary	Population-level summary by time step

Plot average energy

plot_alife_sim(
  competition$summary,
  x = "step",
  y = "mean_energy",
  type = "line"
)

Plot number alive

plot_alife_sim(
  competition$summary,
  x = "step",
  y = "n_alive",
  type = "line"
)

Compare resource regeneration

low_regen <- simulate_resource_competition(
  n_agents = 50,
  steps = 50,
  resource_regen = 0.05,
  seed = 2
)

high_regen <- simulate_resource_competition(
  n_agents = 50,
  steps = 50,
  resource_regen = 0.40,
  seed = 2
)

rbind(
  low_regen = measure_life_like_complexity(low_regen$agents, trait_col = "energy", time_col = "step"),
  high_regen = measure_life_like_complexity(high_regen$agents, trait_col = "energy", time_col = "step")
)
#>               n unique_values  entropy      mean        sd temporal_variability
#> low_regen  2500          2408 3.135088 0.7603476 0.4187126            0.1812062
#> high_regen 2500          2500 2.881435 1.4541690 0.5888900            0.2640396
#>            mean_abs_change
#> low_regen       0.01159387
#> high_regen      0.01842246

Compare population summaries

data.frame(
  scenario = c("low regeneration", "high regeneration"),
  final_alive = c(tail(low_regen$summary$n_alive, 1), tail(high_regen$summary$n_alive, 1)),
  final_mean_energy = c(tail(low_regen$summary$mean_energy, 1), tail(high_regen$summary$mean_energy, 1)),
  final_total_resource = c(tail(low_regen$summary$total_resource, 1), tail(high_regen$summary$total_resource, 1))
)
#>            scenario final_alive final_mean_energy final_total_resource
#> 1  low regeneration          35         0.4671489              9.00000
#> 2 high regeneration          50         1.8947420             21.34703

Interpretation

Resource regeneration changes environmental constraint. If resources regenerate slowly, agents may lose energy or die. If resources regenerate quickly, more agents may maintain energy.

This illustrates an artificial-life principle:

Agent behavior cannot be understood separately from the environment.

Suggested exercises

• Increase movement_cost and observe survival.
• Increase n_agents while keeping resources fixed.
• Change consumption_rate and compare average energy.
• Ask whether the environment is resource-rich or resource-limited.

Responsible interpretation

This model illustrates resource competition in a simplified world. It is not a full ecological model, but it helps users explore energy, constraint, and agent-environment interaction.