Getting Started with artificialLifeR

library(artificialLifeR)

Overview

artificialLifeR is an educational R package for exploring artificial-life ideas through simplified simulations. The package focuses on agents, resources, reproduction, mutation, selection, population dynamics, and life-like complexity.

The central idea is:

Life-like patterns can arise from local interaction, variation, inheritance, and environmental constraint.

The simulations are toy models. They are designed for teaching and conceptual exploration, not for realistic biological prediction.

What the package helps you explore

Model family	Main question	Core function
Agents and traits	What are the artificial individuals?	create_agents()
Resource competition	How do agents interact with an environment?	simulate_resource_competition()
Reproduction	How can offspring inherit traits?	simulate_reproduction()
Mutation	How does variation enter a population?	simulate_mutation()
Selection	How do some agents persist more than others?	simulate_selection()
Population dynamics	How does population size change over time?	simulate_population_dynamics()
Measurement	How can outputs be summarized?	measure_life_like_complexity()

First example: create agents

agents <- create_agents(
  n_agents = 50,
  seed = 1
)

head(agents)
#>   agent         x          y    energy      speed efficiency
#> 1     1 0.2655087 0.47761962 1.0597159 0.03759267  0.5450187
#> 2     2 0.3721239 0.86120948 0.9081960 0.05084232  0.4981440
#> 3     3 0.5728534 0.43809711 1.0511680 0.03178157  0.4681932
#> 4     4 0.9082078 0.24479728 0.8305955 0.05316058  0.4070638
#> 5     5 0.2016819 0.07067905 1.2149536 0.03690831  0.3512540
#> 6     6 0.8983897 0.09946616 1.2970600 0.08534575  0.3924808
#>   reproduction_threshold age alive
#> 1               1.540940   0  TRUE
#> 2               1.668887   0  TRUE
#> 3               1.658659   0  TRUE
#> 4               1.466909   0  TRUE
#> 5               1.271476   0  TRUE
#> 6               1.749766   0  TRUE

Interpretation

Each row represents one artificial agent. The agents have positions, energy, and simple traits such as speed and efficiency.

These agents are not organisms. They are simplified units for exploring artificial-life ideas.

Resource competition

competition <- simulate_resource_competition(
  n_agents = 50,
  steps = 40,
  resource_regen = 0.20,
  seed = 2
)

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

Plot average energy

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

Measure life-like complexity

measure_life_like_complexity(
  competition$agents,
  trait_col = "energy",
  time_col = "step"
)
#>      n unique_values entropy     mean        sd temporal_variability
#> 1 2000          1999 2.90035 1.130187 0.5258798           0.06078271
#>   mean_abs_change
#> 1     0.005784354

Population dynamics

pop <- simulate_population_dynamics(
  initial_population = 40,
  steps = 60,
  carrying_capacity = 100,
  seed = 3
)

head(pop$summary)
#>   step population mean_energy mean_efficiency  trait_sd
#> 1    1         40    1.200125       0.4912442 0.1216771
#> 2    2         51    1.088529       0.5132234 0.1215461
#> 3    3         63    1.000476       0.5186715 0.1169869
#> 4    4         65    1.043270       0.5173388 0.1159313
#> 5    5         68    1.071840       0.5147596 0.1142854
#> 6    6         71    1.088787       0.5247883 0.1217227

plot_alife_sim(
  pop$summary,
  x = "step",
  y = "population",
  type = "line"
)

Suggested learning path

1. Start with create_agents() to understand traits and populations.
2. Use simulate_resource_competition() to explore agents in an environment.
3. Use simulate_reproduction() and simulate_mutation() to explore inheritance and variation.
4. Use simulate_selection() to explore fitness-based survival.
5. Use simulate_population_dynamics() to study population-level change.
6. Use measure_life_like_complexity() and plot_alife_sim() to summarize and visualize outputs.
7. Read the Theory Guide to connect the code to artificial life, emergence, origin-of-life research, and responsible interpretation.

Responsible interpretation

It is better to say:

The model illustrates life-like dynamics in a simplified artificial system.

than:

The model creates or fully explains life.