The kind of levels involved in evolutionary epistemology are quite different than the kind of levels of selection which are discussed much more often in the “levels of selection” debate in evolutionary biology. In evolutionary biology, the “levels” of selection under discussion are levels of scale. The debate concerns whether genes are always the “units” or “targets” of selection, or whether selection can occur on higher levels, like organisms, groups, and species. The levels involved in evolutionary epistemology, on the other hand, are levels of the regulatory hierarchy involved in the control of behavior. These include the genetic bases of cognitive and perceptual hardware, concepts, languages, techniques, beliefs, preferences, and so forth. Note that in the case of evolutionary epistemology, the terms “levels” and “hierarchy” may be impressionistic. There is often no clear arrangement of levels at all.
There are at least two different approaches that have been taken to modeling multi-level evolution.
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[*]Dual Transmission Models: Boyd and Richerson (1985) adapted models from genetics to model a case in which a trait (cooperation) was affected both by genetic and cultural evolution. It was first shown that a genetically determined bias on cultural transmission could be selected for in a migratory population. The bias made it easier to pick up local customs, increasing the likelihood of imitation beyond that determined by the frequency and perceived value of the behavior. Once this bias was in place, its effect was strong enough to overcome the perceived costs involved in cooperative behavior. The model yielded two important results. First, it provided a novel mechanism according to which cooperative behavior can stabilize in migratory populations. But more importantly, it demonstrated that cultural evolution cannot be predicted purely on the basis of genetic fitnesses.

[*]Multiple Population Models: Harms (1997) constructed a multi-level dynamic population model of bumblebee learning. Mutual information between distributions of sensor types, overt foraging behaviors, and internal foraging preferences on the one hand and environmental states on the other was assessed and compared to average fitness of the population states. It was shown that information present in overt behaviors may be underutilized, and that exaptation of sensor mechanisms for preference formation can bring about the utilization of that information.

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2.4 Meaning

Full descriptive accounts of truth and justification both demand a theory of meaning. Until a sign has meaning, it cannot be true or false. Moreover, determining the meaning of justificatory claims may provide a descriptive theory of justification. Presumably, what makes a claim of justification true is the basis of that justification. If meaning is conventional, then the evolution of meaning becomes an instance of the evolution of conventions.
Models of the evolution of conventions have in one case been extended to apply to meaning conventions. Skyrms (1996, chapter 5) gave an evolutionary interpretation of  David Lewis' (1969) model of rational selection of meaning conventions. Skyrms was able to show that there is strong selection on the formation of “signaling systems” in mixed populations with a full set of coordinated, countercoordinated, and uncoordinated strategies. It is significant that the structure of the model and the selective process by which meaning conventions emerge and are stabilized largely parallels the account of the evolution of meaning given by Ruth Millikan (1984).
In the simplest version, the model is constructed as follows: We imagine that there are two states of affairs T, two acts A, and two signals M. Players have an equal chance of being in either the position of sender, or receiver. Receivers must decide what to do based purely on what the sender tells them. In this purely cooperative version, each player gets one point if the receiver does A1 if the state is T1 or A2 if the state is T2.
Since players will be both sender and receiver, they must have a strategy for each situation. There are sixteen such strategies, and we suppose them to be either inherited (or learned) from biological parents, or imitated on the basis of perceived success in terms of points earned. Strategies I1 and I2 are signaling systems, in that if both players play the same one of these two strategies they will always get their payoff. I3 and I4 are anti-signaling strategies, which result in consistent miscoordination, though they do well against each other. All of the other strategies involve S3, S4, R3, or R4, which results in the same act being performed no matter what the external state is.

اقتباس :

Sender Strategies:

S1:	Send M1 if T1; M2 if T2
S2:	Send M2 if T1;M1 if T2
S3:	Send M1 if T1 or T2
S4:	Send M2 if T1 or T2

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Receiver Strategies:
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R1:	Do A1 if M1; A2 if M2
R2:	Do A2 if M1; A1 if M2
R3:	Do A1 for M1 or M2
R4:	Do A2 for M1 or M2

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Complete Strategies:
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I1:	S1,R1
I2:	S2,R2,
I3:	S1,R2
I4:	S2,R1
I5:	S1,R3
I6:	S2,R3
I7:	S1,R4
I8:	S2,R4
I9:	S3,R1
I10:	S3,R2
I11:	S3,R3
I12:	S3,R4
I13:	S4,R1
I14:	S4,R2
I15:	S4,R3
I16:	S4,R4

Simulation results showed that virtually all initial population distributions become dominated by one or the other of the two signaling system strategies. The situation becomes more complex when more realistic payoffs are introduced, for instance, that the sender incurs a cost rather than automatically sharing the benefit that the receiver gets from correct behavior for the environment. Even in such situations, however, the most likely course of evolution is domination by a signaling system.