In this simple simulation we can see how with just these three parameters, population, diffusion and evaporation rates we can get a non-obvious complex behavior.
When we face a complex system like this one and we try to predict their outcomes we use to assume linear relationships among the involved factors that probably are wrong. We also tend to oversee other factors that are not so obvious at first glance, in this case I didn’t see that the distance from food sources to the hive and the strategy taken by ants to choose the order of collection affects the final total time expend in gather the food.
When I set the evaporation rate to 0, it means that the signal never disappears, so the ants keep looking for food although the source was already empty, leaving them unused to explore other sources.
Other interesting factors not found as parameters were the ant sensitivity and their return strategy to the hive. I didn’t have time to test them but also small changes on them should produce different times variations.
The beauty of this kind of models is how they show us how hard to understand is the complexity for our minds. I have found along my study of complex systems that we use to:
- Underestimate by far the complexity of reality
- Overestimate by far also our capacity to understand it.
Specially with systems involving people, we tend to simplify the complexity using assumptions and creating models that use those assumptions to try to predict future states of the system. Worst yet, we use to forget of those assumptions and never test again their true with hard data. Then we get frustrate because this never works as expected and then people start pointing each other and conflict emerge.
To be fair, test assumptions when people is involved is very difficult, control the factors is usually impossible or unfeasible.
That’s the main reason I started to study Complexity Theory. I think that the paradigm of prople organizations based on processes is all wrong and it turns a drag for the advance of all types of orgs that use such approach. Worst yet, I suspect it has become a health problem for millions of workers around the world that has to deal with that interaction model.
Processes do a lot of weak assumptions on human behavior expecting that people respond with the reliable and rational level of machines. What I’ve seen is that humans are far of being reliable or rational on most situations, even having all the information and time to decide, we use to take “bad” decisions too often. We are mostly emotional creatures that happened to be able to be rational sometimes, not the other way around.
So my point is that process should be for machines, we humans need another model that guide our interactions. We forget that our main virtue is our creative imagination that allows us to cooperate each other and face the unknow, the uncertain. This is one of our great advantages over machines, even the hyped current AI algorithms, we need to start to put more attention to it.
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