globals [ age ] ;; Set two breeds of agents, one for the bugs and one for food breed [ food a-food ] breed [ bugs bug ] bugs-own [ energy ;; The resource bugs will be depleting and refilling by moving and eating speed ;; The "gene" of movement speed for each bug vision ;; The "gene" that decides how far a bug can see ] to setup clear-all reset-ticks setup-world setup-bugs end to setup-world ask patches [ set pcolor green + 2 ] end to setup-bugs ;; Creating the bugs and setting their initial states to numbers that, after trials, suits the model create-bugs 60 [ set color 10 set speed .5 set energy 50 set vision 3 ] ask bugs [ set shape "bug" setxy random-xcor random-ycor] end to go ;; Two criteria for stop: One if no bugs are alive and one if any setting results in uncontrolled reproduction if not any? bugs [ stop ] if count bugs >= 2000 [ stop ] move-bug ;; Bugs move forward, change direction or towards food within their vision. eat-food ;; If standing on a patch where there is food, eats it eat-prey ;; If standing on a patch where there is prey, eats it check-death ;; Bugs check if their energy is zero, if yes they die reproduce ;; Bugs check if their energy is at or above the reproduction threshold. If yes they reproduce ;; with a 20% chance to mutate each gene and direction. regrow-food ;; Food grows at random places at a rate set in the interface tick end to move-bug ask bugs [ ;; Make bugs consider the nearest bug with half the size of self "prey". If there is no such bug within vision, set prey to the closest piece of food within vision. let prey min-one-of other ((bugs in-radius vision) with [ (size * 2) < [size] of myself ]) [distance myself] if prey = nobody [ set prey min-one-of (food in-radius vision) [distance myself] ] ;; If there is any prey, bug or food, within vision: Move towards it. ifelse prey != nobody [ face prey ifelse distance prey < speed [ move-to prey ][ fd speed ] ] [ ;; If no prey within vision, move forward at respective speed with a 20% chance of changing direction fd speed if random 100 < 20 [right random 360] ] ;; Set energy to decrease relative to current size and speed as well as the decided cost of each set energy energy - ((size * cost-of-size) * (speed * cost-of-speed)) ] end to eat-food ;; If there is any food at current position, eat it and set energy to increase by current food-value ask bugs [ let prey one-of food-here if prey != nobody [ ask prey [ die ] set energy energy + food-value ] ] end to eat-prey ask bugs [ ;; If there is any bugs with half the size of self at current position, eat it and set energy to increase by current prey-value let prey one-of other bugs-here with [ (size * 2) < [size] of myself ] if prey != nobody [ ask prey [ die ] set energy energy + prey-value ] ] end to check-death ;; If energy is at zero, the bug dies ask bugs [ if energy <= 0 [ die ] ] end to reproduce ask bugs [ ;; If energy is above 100, decrease it by 50 and hatch a bug identical to self if energy > 100 [ set energy energy - 50 hatch 1 [ ;; Each "gene" has a set chance of increasing or decreasing a set amount when being passed on to the offspring if random 100 < mutation-rate [ ifelse random 100 < 50 [ set speed speed * (1 + mutation-amount / 100) ][ set speed speed * (1 - mutation-amount / 100) ] ] if random 100 < mutation-rate [ ifelse random 100 < 50 [ set size size * (1 + mutation-amount / 100) ][ set size size * (1 - mutation-amount / 100) ] ] if random 100 < mutation-rate [ ifelse random 100 < 50 [ set vision vision * (1 + mutation-amount / 100) ][ set vision vision * (1 - mutation-amount / 100) ] ] ] ] ] end to regrow-food ;; Create food at the rate of food-growth at random places. if random 3 > 1 [ create-food food-growth [ setxy random-xcor random-ycor set shape "circle" set color red set size 0.5 ] ] end @#$#@#$#@ GRAPHICS-WINDOW 340 10 838 509 -1 -1 14.85 1 10 1 1 1 0 0 0 1 -16 16 -16 16 1 1 1 ticks 30.0 BUTTON 95 215 161 248 NIL setup NIL 1 T OBSERVER NIL NIL NIL NIL 1 BUTTON 170 215 236 248 NIL go T 1 T OBSERVER NIL NIL NIL NIL 1 SLIDER 20 40 140 73 food-value food-value 0 100 20.0 1 1 NIL HORIZONTAL SLIDER 20 265 310 298 food-growth food-growth 0 20 15.0 1 1 NIL HORIZONTAL PLOT 25 325 315 510 Population NIL NIL 0.0 10.0 0.0 10.0 true true "" "" PENS "Bugs" 1.0 0 -16777216 true "" "plot count bugs" "Food" 1.0 0 -2674135 true "" "plot count food" SLIDER 20 80 141 113 prey-value prey-value 0 100 40.0 1 1 NIL HORIZONTAL SLIDER 185 40 307 73 cost-of-speed cost-of-speed 0 5 1.4 0.1 1 NIL HORIZONTAL SLIDER 185 80 310 113 cost-of-size cost-of-size 0 5 1.4 0.1 1 NIL HORIZONTAL PLOT 1015 145 1175 265 Mean size NIL NIL 0.0 2.0 0.0 2.0 true false "" "" PENS "default" 1.0 0 -16777216 true "" "plot ifelse-value (count bugs = 0) \n[ 0 ]\n[ mean [size] of bugs ]" "pen-1" 1.0 0 -4539718 true "" "plot food-growth / 20" PLOT 850 145 1010 265 Mean speed NIL NIL 0.0 1.0 0.0 1.0 true false "" "" PENS "default" 1.0 0 -16777216 true "" "plot ifelse-value (count bugs = 0) \n[ 0 ]\n[ mean [speed] of bugs ]" "pen-1" 1.0 0 -4539718 true "" "plot food-growth / 20" PLOT 970 305 1236 509 Bugs plotted by size and speed Speed Size 0.0 1.0 0.0 1.0 true false "ask bugs [ plotxy speed size ]" "\n\n" PENS "pen-0" 0.01 2 -7500403 true "ask bugs [ plotxy speed size ]" "clear-plot\nask bugs [ plotxy speed size ]\n" PLOT 1180 145 1340 265 Mean vision NIL NIL 0.0 2.0 0.0 2.0 true false "" "" PENS "default" 1.0 0 -16777216 true "" "plot ifelse-value (count bugs = 0) \n[ 0 ]\n[ mean [vision] of bugs ]" "pen-1" 1.0 0 -4539718 true "" "plot food-growth / 20" MONITOR 850 465 945 510 Bug Population count bugs 0 1 11 TEXTBOX 970 270 1275 296 (Black pen: Mean value. Grey pen: Food-growth/20 9 0.0 0 SLIDER 20 125 310 158 mutation-rate mutation-rate 0 100 20.0 1 1 % HORIZONTAL SLIDER 20 165 310 198 mutation-amount mutation-amount 1 100 20.0 1 1 % HORIZONTAL @#$#@#$#@ ## WHAT IS IT? A model of natural/artificial selection with bugs who compete for food. By controlling different aspects of the environment and gene mutation one can observe how the population of bugs adapt and the selection of genes differ. The model shows adaption of a population through natural selection. There is no adaption on the agent level, rather it is probability that drives mutation of the genes which manifests as a battle of who can get more food and reproduce. ## HOW IT WORKS Bugs wander around the area looking for food. Moving consumes energy and if energy reaches zero the bug dies. Eating refills energy. If energy reaches 100 the bug will reproduce and lower it's energy to 50. When reproducing, an exact copy of the bug will hatch. However, there is a chance set by sliders that each "gene" of the bug will mutate. Speed, size and vision can mutate higher or lower. - Higher speed can help a bug reach more food in shorter time but comes at a higher cost of energy. - Larger size allows bugs to eat smaller sized bugs (possible when double the size of another bug) which effectively supplies the environment with more possible food. Larger size also comes with a higher cost of energy. - Greater vision allows bugs to see food at a further distance and move towards it and has no higher cost of energy. ## HOW TO USE IT # Sliders FOOD-VALUE and PREY-VALUE controls how much energy a bug receives from eating a piece of food and another bug (prey) respectively. COST-OF-SPEED and COST-OF-SIZE controls how the cost of energy co-varies with speed and size. Meaning, how much more energy should be consumed by increasing size/speed. MUTATION-RATE controls how likely it is that a gene will mutate between two generations. MUTATION-AMOUNT controls how much a gene can change between two generations. FOOD-GROWTH controls the regrowth of food. This could be said to indicate if the bugs live in an environment of abundance or scarcity. # Plots and monitors The POPULATION plot on the left side shows population of bugs and food. The SPEED, SIZE and VISION histograms show the distribution of values of the three genes. The MEAN plots beneath them plots the mean value in the population of each gene. It also has a line for food-growth to visualize where changes were made during a run. The BUGS PLOTTED BY SPEED AND SIZE plot each bug as a dot on a xy-plane with speed and size on the axes. ## THINGS TO NOTICE Remember, evolution takes a very long time. Thus, there is no end to the model. It is exploratory. For each run of the model, let it run for at least a couple thousands, or even tens of thousands of ticks to see the long term effect on the population. There is a tradeoff between size and speed which makes it energy expensive to be both. Predators appear as the difference in size between bugs reaches a ratio of 2 when the larger bugs can eat the smaller. When this happens, the large "family" of bugs tends to reproduce quickly and take over parts or the whole environment. Notice how vision, without having a cost of energy, is still subject to natural selection and is not always optimal to increase. Why? When reaching extreme states the population takes longer to adapt to a change in FOOD-GROWTH. Why? ## THINGS TO TRY - Investigate how the population evolves at different levels of food-growth. - See if you can produce two different species co-existing e.g. one big slow and one small fast type of bug. - Let the population adapt to one level of food growth then set it to a new level and see how the population adapts. Note that this can take many thousands of ticks. - Move between the extreme values of food-growth. - Change the cost-of-speed/size and prey/food-value to see how this affects the propensity of adaptation and predation. - Explore how mutation rate/amount affects evolution. ## EXTENDING THE MODEL The bugs have no flight behaviour in this model. This could be added to make the predator/prey relationship more authentic. A cone of vision could be used instead of 360 which would be more realistic and decrease the risk of bugs changing direction too often. Further sliders for starting energy and reproduction thresholds could be added. ## RELATED MODELS - Vision evolution - Bug Hunt Coevolution - Wolf sheep predation ## CREDITS AND REFERENCES This model was created as a part of the Introduction to Agent-Based Modeling course on Complexity Explorer: https://www.complexityexplorer.org/courses/96-introduction-to-agent-based-modeling. @#$#@#$#@ default true 0 Polygon -7500403 true true 150 5 40 250 150 205 260 250 airplane true 0 Polygon -7500403 true true 150 0 135 15 120 60 120 105 15 165 15 195 120 180 135 240 105 270 120 285 150 270 180 285 210 270 165 240 180 180 285 195 285 165 180 105 180 60 165 15 arrow true 0 Polygon -7500403 true true 150 0 0 150 105 150 105 293 195 293 195 150 300 150 box false 0 Polygon -7500403 true true 150 285 285 225 285 75 150 135 Polygon -7500403 true true 150 135 15 75 150 15 285 75 Polygon -7500403 true true 15 75 15 225 150 285 150 135 Line -16777216 false 150 285 150 135 Line -16777216 false 150 135 15 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