NetLogo 老化/预热问题

Question

使用 NetLogo 5.3.1，我尝试设置BehaviorSpace，以便其所有模型运​​行在完全相同的 500 个刻度预热期之后开始。然而，结果对我来说并不直观。

出于说明目的，我将使用模型库中的“Flocking.nlogo”模型。下面是模型代码，在设置末尾添加了 2 行代码，用于在 500 个周期后保存模型的状态。

turtles-own [
  flockmates         ;; agentset of nearby turtles
  nearest-neighbor   ;; closest one of our flockmates
]

to setup
  clear-all
  create-turtles population
    [ set color yellow - 2 + random 7  ;; random shades look nice
      set size 1.5  ;; easier to see
      setxy random-xcor random-ycor
      set flockmates no-turtles ]
  reset-ticks

  ; Now execute a 500-tick warm-up period and save the model's state
  repeat 500 [ go ]
  export-world "Flocking-after-500ticks.csv"
  
end

to go
  ask turtles [ flock ]
  ;; the following line is used to make the turtles
  ;; animate more smoothly.
  repeat 5 [ ask turtles [ fd 0.2 ] display ]
  ;; for greater efficiency, at the expense of smooth
  ;; animation, substitute the following line instead:
  ;;   ask turtles [ fd 1 ]
  tick
end

to flock  ;; turtle procedure
  find-flockmates
  if any? flockmates
    [ find-nearest-neighbor
      ifelse distance nearest-neighbor < minimum-separation
        [ separate ]
        [ align
          cohere ] ]
end

to find-flockmates  ;; turtle procedure
  set flockmates other turtles in-radius vision
end

to find-nearest-neighbor ;; turtle procedure
  set nearest-neighbor min-one-of flockmates [distance myself]
end

;;; SEPARATE

to separate  ;; turtle procedure
  turn-away ([heading] of nearest-neighbor) max-separate-turn
end

;;; ALIGN

to align  ;; turtle procedure
  turn-towards average-flockmate-heading max-align-turn
end

to-report average-flockmate-heading  ;; turtle procedure
  ;; We can't just average the heading variables here.
  ;; For example, the average of 1 and 359 should be 0,
  ;; not 180.  So we have to use trigonometry.
  let x-component sum [dx] of flockmates
  let y-component sum [dy] of flockmates
  ifelse x-component = 0 and y-component = 0
    [ report heading ]
    [ report atan x-component y-component ]
end

;;; COHERE

to cohere  ;; turtle procedure
  turn-towards average-heading-towards-flockmates max-cohere-turn
end

to-report average-heading-towards-flockmates  ;; turtle procedure
  ;; "towards myself" gives us the heading from the other turtle
  ;; to me, but we want the heading from me to the other turtle,
  ;; so we add 180
  let x-component mean [sin (towards myself + 180)] of flockmates
  let y-component mean [cos (towards myself + 180)] of flockmates
  ifelse x-component = 0 and y-component = 0
    [ report heading ]
    [ report atan x-component y-component ]
end

;;; HELPER PROCEDURES

to turn-towards [new-heading max-turn]  ;; turtle procedure
  turn-at-most (subtract-headings new-heading heading) max-turn
end

to turn-away [new-heading max-turn]  ;; turtle procedure
  turn-at-most (subtract-headings heading new-heading) max-turn
end

;; turn right by "turn" degrees (or left if "turn" is negative),
;; but never turn more than "max-turn" degrees
to turn-at-most [turn max-turn]  ;; turtle procedure
  ifelse abs turn > max-turn
    [ ifelse turn > 0
        [ rt max-turn ]
        [ lt max-turn ] ]
    [ rt turn ]
end


; Copyright 1998 Uri Wilensky.
; See Info tab for full copyright and license.

BehaviorSpace 窗口如下所示:

添加的 2 行代码在 500 个刻度后保存模型的状态，来自 Railsback & Grimm 2012:基于代理和基于个体的建模(第一版)第 9 章中问题 6 的答案。答案继续说明下一步:“然后，在行为空间中，更改“设置命令”以仅导入已保存的世界并再运行 1000 个刻度”。

我这样做了，然后将文件导入到 R 中，通过计算刻度 100、200、300、400 和 500 处的同伴数量的平均值和 SD 来汇总数据。R 代码下方:

df <- read.csv("ibm_table_output-test.csv", skip = 6)

    df1 <- df %>%
      rename(run_number = X.run.number.,
             time_step = X.step.,
             mean_flockmates = mean..count.flockmates..of.turtles
      ) %>%
      select(run_number,
             time_step,
             mean_flockmates,
             vision) %>%
      arrange(run_number,
              time_step) %>%
      filter(time_step == 100 | 
               time_step == 200 |
               time_step == 300 |
               time_step == 400 |
               time_step == 500)
    
    df1_long <- melt(df1,                                      # Apply melt function
                     id.vars = c("run_number", "time_step","vision"))
    
    # Calculate a summary table
    df1.summ <- df1_long %>%
      group_by(time_step, vision) %>%
      summarise(avg = mean(value),
                sd = sd(value))

输出如下:

 # A tibble: 15 × 4
# Groups:   time_step [5]
   time_step vision   avg    sd
       <int>  <int> <dbl> <dbl>
 1       100      1  8.34     0
 2       100      2  8.34     0
 3       100      3  8.34     0
 4       200      1  7.83     0
 5       200      2  7.83     0
 6       200      3  7.83     0
 7       300      1  7.95     0
 8       300      2  7.95     0
 9       300      3  7.95     0
10       400      1  7.45     0
11       400      2  7.45     0
12       400      3  7.45     0
13       500      1  7.92     0
14       500      2  7.92     0
15       500      3  7.92     0

对我来说这个输出没有意义。

我的问题是，为什么同一 time_step 组内不同视力水平的平均同伴数量相同？为什么SD全是0呢？换句话说，为什么模型运行会产生相同的输出？我认为启动老化期将为所有模拟启动相同的起始位置，但由于使用不同的随机数，为每次运行创建不同的平均值和 SD 值？还是我理解错了？

---

编辑:SD 为 0 的原因是平均值没有变化，但我不明白为什么没有变化。以下是 df1_long 数据框:

   run_number time_step vision        variable    value
1           1       100      1 mean_flockmates 8.340000
2           1       200      1 mean_flockmates 7.833333
3           1       300      1 mean_flockmates 7.953333
4           1       400      1 mean_flockmates 7.446667
5           1       500      1 mean_flockmates 7.920000
6           2       100      1 mean_flockmates 8.340000
7           2       200      1 mean_flockmates 7.833333
8           2       300      1 mean_flockmates 7.953333
9           2       400      1 mean_flockmates 7.446667
10          2       500      1 mean_flockmates 7.920000
11          3       100      2 mean_flockmates 8.340000
12          3       200      2 mean_flockmates 7.833333
13          3       300      2 mean_flockmates 7.953333
14          3       400      2 mean_flockmates 7.446667
15          3       500      2 mean_flockmates 7.920000
16          4       100      2 mean_flockmates 8.340000
17          4       200      2 mean_flockmates 7.833333
18          4       300      2 mean_flockmates 7.953333
19          4       400      2 mean_flockmates 7.446667
20          4       500      2 mean_flockmates 7.920000
21          5       100      3 mean_flockmates 8.340000
22          5       200      3 mean_flockmates 7.833333
23          5       300      3 mean_flockmates 7.953333
24          5       400      3 mean_flockmates 7.446667
25          5       500      3 mean_flockmates 7.920000
26          6       100      3 mean_flockmates 8.340000
27          6       200      3 mean_flockmates 7.833333
28          6       300      3 mean_flockmates 7.953333
29          6       400      3 mean_flockmates 7.446667
30          6       500      3 mean_flockmates 7.920000

Answer 1

我的理解是，您手动运行一次setup，然后运行您的BehaviorSpace 实验。您将遇到的问题是，随机数生成器种子包含在您通过运行setup 过程生成的export-world 数据中。然后，当您在每次实验运行的设置命令:中调用import-world时，您也将获得导入的RNG种子。导出实际上包括 RNG 的完整状态，但将其视为相同的种子就足够接近了。

LeirsW 是正确的，Flocking(与大多数 NetLogo 模型一样，也可能是您遇到问题的原始模型)是完全确定性的。因此，每次使用相同的 RNG 种子，结果都会相同。

解决方法很简单，只需在运行 random-seed new-seed 的 import-world 之后向您的BehaviorSpace 实验中添加第二行设置命令: 。这将确保每个模型运行都有一个新的、独特的 RNG 种子可用于其运行的其余部分。