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Selection Tutorial

Source: vignettes/selection-tutorial.Rmd
selection-tutorial.Rmd

Purpose

This tutorial introduces simulate_selection(). Selection is a process in which some agents persist or reproduce more than others because of differences in traits or fitness.

In this package, selection is simplified. It helps learners explore how population composition can change when survival depends on energy, efficiency, or a fitness proxy.

Create agents 

agents <- create_agents(
  n_agents = 80,
  seed = 6
)

summary(agents$efficiency)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.1969  0.4230  0.4767  0.4825  0.5496  0.7068

Apply selection 

selected <- simulate_selection(
  agents,
  survival_fraction = 0.40,
  stochastic = FALSE,
  seed = 6
)

nrow(agents)
#> [1] 80
nrow(selected)
#> [1] 32
head(selected)
#>    agent          x         y   energy      speed efficiency
#> 17    17 0.51789573 0.5056782 1.148549 0.01593736  0.6723470
#> 64    64 0.61779527 0.7483514 1.198532 0.07897580  0.5720911
#> 37    37 0.38618286 0.4545822 1.006295 0.06575135  0.6694586
#> 15    15 0.76993170 0.9659677 1.014320 0.05411230  0.6633885
#> 13    13 0.09523258 0.3920638 1.177253 0.06594654  0.5512273
#> 3      3 0.26435207 0.3327492 1.334079 0.05862615  0.4783738
#>    reproduction_threshold age alive   fitness
#> 17               1.635668   0  TRUE 0.7722238
#> 64               1.506365   0  TRUE 0.6856696
#> 37               1.358510   0  TRUE 0.6736726
#> 15               1.604649   0  TRUE 0.6728883
#> 13               1.346680   0  TRUE 0.6489338
#> 3                1.433508   0  TRUE 0.6381886

Compare before and after selection 

rbind(
  before = measure_life_like_complexity(agents, trait_col = "efficiency"),
  after = measure_life_like_complexity(selected, trait_col = "efficiency")
)
#>         n unique_values  entropy      mean         sd temporal_variability
#> before 80            80 2.944390 0.4825008 0.09878952                   NA
#> after  32            32 3.031446 0.5632640 0.06906184                   NA
#>        mean_abs_change
#> before              NA
#> after               NA

Plot energy before and after 

comparison <- data.frame(
  group = c(rep("before", nrow(agents)), rep("after", nrow(selected))),
  energy = c(agents$energy, selected$energy),
  index = seq_len(nrow(agents) + nrow(selected))
)

head(comparison)
#>    group    energy index
#> 1 before 1.3912147     1
#> 2 before 0.9923440     2
#> 3 before 1.3340791     3
#> 4 before 0.9979192     4
#> 5 before 0.7678900     5
#> 6 before 0.7930166     6

Stochastic selection

Selection is not always deterministic. In stochastic selection, higher-fitness agents have a better chance of being selected, but chance still matters.

stochastic_selected <- simulate_selection(
  agents,
  survival_fraction = 0.40,
  stochastic = TRUE,
  seed = 7
)

rbind(
  deterministic = measure_life_like_complexity(selected, trait_col = "fitness"),
  stochastic = measure_life_like_complexity(stochastic_selected, trait_col = "fitness")
)
#>                n unique_values  entropy      mean         sd
#> deterministic 32            32 2.742537 0.5901508 0.06185067
#> stochastic    32            32 3.123778 0.4900943 0.13647232
#>               temporal_variability mean_abs_change
#> deterministic                   NA              NA
#> stochastic                      NA              NA

Interpretation

Selection changes the composition of the population. In deterministic selection, the highest-fitness agents are kept. In stochastic selection, fitter agents are more likely to persist, but lower-fitness agents may sometimes remain.

This illustrates a key artificial-life idea:

Population-level change can arise when individual differences affect survival or reproduction.

Suggested exercises

  • Change survival_fraction and compare results.
  • Use deterministic and stochastic selection.
  • Create a custom fitness column and select using fitness_col.
  • Ask which traits become more common after selection.

Responsible interpretation

This function illustrates selection in a simplified artificial system. It does not model full natural selection, genetics, development, or ecology.