A couple of weeks ago, if you randomly woke me in the middle of the night and demanded to know the fundamental difference between evolution and learning as adaptive processes, I would probably respond: “how did you get into my house? and umm… I guess they are mostly the same, it is just a matter of time-scales and domain.” This answer stems from my urge to generalize and find the overarching similarities between ideas, and evolution and learning share a lot in common. Both are more likely to propagate effective behaviors than ineffective and both generate novelty in randomized and often unguided process: mutation and innovation. In fact, in evolutionary game theory social imitation and reproduction are used almost interchangeably in mathematical models. Most computational models can be interpreted either as biological or cultural evolution without changing any code, just the words used to describe the agents.

In the Hammond & Axelrod (2006) model of ethnocentrism, for example, we can stretch the whole range of biological to cultural evolution depending on our interpertation:

If we interpret the agents as single bacteria and the tags are quorum markers, then we are obviously in the standard green-beard effect regime and our evolution can only be interpreted as biological.

If we interpret the agents as humans (or other animals) and tags as skin color (or other physical trait) then our strategy transmission might be biological or cultural, but the tag transmission is clearly biological.

If we interpret the agents as humans and tags as language accents, then both transmissions are cultural with only a little room to argue for biology.

Finally, if we interpret agents as villages and tags as their religion then it is almost impossible to argue for biology and the dynamics must clearly be of cultural evolution.

But, we never changed any specifications of the model, just the language we used to describe it so the dynamics were invariant. I usually view this generality as an advantage of the model; we can reason about either dynamic: cultural or biological. However, it can also be a weakness, the dynamics are underspecified and inaccurate representations of both!

From a practical point of view, if I want to combine evolution and learning in one model then it doesn’t make sense to do so (and expect anything interesting) if they follow the same exact dynamics. Since I am becoming more interested in social learning and its potential analogies to evolutionary game theory, it is important to figure out what fundamental differences the two adaptive process might have. Thankfully, evolutionary economists have already thought about this.

For an evolutionary economist: the agents are corporations and the heritable material is business-practices. In domain they are squarely working with learning and cultural evolution, but they view the resulting dynamics as analogous to the biology from which they borrow name. Since agent-based modeling is an important methodology for these economists, they have thought about the similarities and differences of evolutionary and learning models carefully.

Brenner (1998) explicitly compares models of evolutionary and learning. For evolution, he takes the EGT model of replicator-mutator dynamics, and for learning he looks at his earlier Variation-Imitation-Decision (VID) model (Brenner 1996). Since the VID model doesn’t seem to be a standard approach, I won’t go into the details of the technical comparison. I will instead highlight the distinction Brenner draws that I think generalize to most models of evolution and learning: objectivity of fitness.

In a biological settings, we have a clear objective measure of fitness: number of offspring. As such, it is relatively uncontroversial to associate a given behavior with a fitness value. In a lot of social learning settings, the same approach is also followed, but it is not as obvious. The fitness of a meme is subjective and varies between potential adopters. Some agents might be more susceptible to a given idea given the ideas they already hold, their past history with various behaviors (invidiual historicity), or just general outlook; other agents might be less so. Two agents might observe the same behavior, and the first might think the behavior is good (and thus maybe worth imitating) and another will conclude that it is not a helpful behavior (and thus probably not worth imitating). In a general social settings, we cannot view a heritable trait as having an inherent fitness, it depends on the agents that will consider it for copying.

If we wanted to incorporate a lack of objective fitness into an EGT model, we could do this in the objective versus subjective rationality model. In this model, each agent has a different subjective conception of what game the objective game of the environment is. As such, if Alice and Bob views the behavior of Eve then they will judge its effectiveness not by Eve’s conception of the game (that they doesn’t know) but by their own, as such Alice might calculate one utility for the behavior she saw Eve display, and Bob could calculate a completely different utility. From the point of view of imitation, Eve’s behavior would not have an inherent fitness. At the same time, the obj-vs-subj model also has elements of standard evolution (in the vertical transmission of conceptions of the game) and can be a good groundwork for building models that capture the different dynamics of evolution and learning.

Now, if you break into my house in the middle of the night to question me about evolution and learning then — while I wait for the cops to come remove you — I might explain the importance of objective versus subjective measures of fitness.

References

Brenner, T. (1996) Learning in a repeated decision process: A mutation-imitation-decision model. Papers on Economics and Evolution #9603, Max-Planck-Institut, Jena.

Brenner, T. (1998). Can evolutionary algorithms describe learning processes? Journal of Evolutionary Economics, 8 (3), 271-283 DOI: 10.1007/s001910050064

Hammond, R., & Axelrod, R. (2006). The Evolution of Ethnocentrism. Journal of Conflict Resolution, 50(6): 926-936