Neuroeducational Approaches on Learning
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nonyms Evolutionary learning in networks; Imitation in networks; Local imitation
Definition In the following, we use the term naı¨ve learning in networks to describe simple models of behavior in strategic situations.
Theoretical Background Behavior in strategic situations can be modeled in a static or in a dynamic way. An example for a static concept is the concept of equilibrium. In a (hypothetical) world of perfectly rational decision makers, we assume that decision makers form expectations about their mutual behavior. Given these expectations, decision makers optimize. A situation is an equilibrium if all decision makers behave optimally, that is, play a best reply given their expectations, and expectations are in line with actual decisions of the other participants. This analysis assumes that decision makers understand the situation immediately and that no learning takes place. To understand all strategic aspects of a situation requires sometimes a high degree of sophistication. The alternative is a dynamic approach where decision makers do not find the “correct” choice instantly but, instead, adjust their behavior incrementally and learn over time. We will call this behavior more naı¨ve.
An extreme example for this approach is evolutionary learning which assumes a world of decision makers who possess no rationality at all but rather follow a preprogrammed strategy (like plants or simple animals). In such a world, we assume that more successful strategies grow as the result of an evolutionary process. This evolutionary dynamics requires a definition of a reference group with respect to which decision makers compare their choices and their behavior and, eventually, learn. Technically, such a population can be seen as a network. In an unstructured network, where potentially all members interact with all other members in the same way, the reference group is the entire population. In a highly structured network, where each member is connected to only a small number of other members, the reference group is rather local. If fitness on the local level matters, then we assume that strategies that outperform local competitors grow (at least locally), regardless how they compare globally. Such a local dynamics is not only plausible in a biological context, but also in the context of human decision makers. Here, the paradigm of learning replaces that of evolution. Stategies that perform well do not grow due to their evolutionary fitness but rather as a result of imitation of successful strategies. Regardless whether learning or evolution is our guiding paradigm, the dynamics of a population with a local reference group can differ markedly from one that is based on global and unstructured interaction.
Important Scientific Research and Open Questions A Theoretical Consideration of Naı¨ve Learning on Networks To see that naı¨ve learning in networks can generate patterns of behavior that are different from global learning, let us look at the following prisoners’ dilemma game.
N. Seel (ed.), Encyclopedia of the Sciences of
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