Thinking is Social
The chapter begins by talking about the story of the blind men and the elephant. The point of the story here is to show the societies are able to benefit from the sharing of individuals partial knowledge, which results in a large body of knowledge that alloww the group to develop strategies no individual could formulate. They then begin talking about the three levels of adaptation, the point of these three levels are the development of optima processes in a group. The adaptive culture model is the next topic of interest. First they briefly review their earlier discussion of Axelrod, and his contribution to evolutionary computing. Then they begin talking about speculations that have been made regarding the development optimums through the use of cognitive optimization. Axelrod’s recent simulations are touched on before they address the next topic.
Axelrod’s culture model is the next topic of interest in the chapter. It begins by talking about how similarities between individuals can be used to spread culture. In the ACM model individuals adopt non-matching features from their neighbors stochastically. It then talks about how simulations are conducted by repeated iteration until regions of the matrix contain matching patterns. The following sections detail various experiments that were conducted using ACM.
The first experiment deals with Axelrods theory that similarity is a precondition for social interaction and subsequent exchange of cultural features. There is then discussion on about the “birds of a feather flock” idea where self-similar individuals group, another idea presented where by people are more interested in group with people that share the same ideas. In this experiment the effect of similarity as a casual influence was removed. The result of the experiment was unanimity. Thus, it appears that the effect of casual similarity in ACM results in polarization.
In the second experiment they substituted a simple arbitrary function for the similarity test previously used. The rule was “if (the neighbor’s sum is larger than the targeted individual’s sum) then interact.” In this experiment the population converged on the global optimum every time, though the number “9999” was never included in the initial population.
The task of the third experiment was to find a set of five numbers that represented the features of an individual, within which the sum of the first three numbers equaled the sum of the last two. They go into discussion about why this is interesting, and relevant. Then they explained the details of the experiment. The result of the experiment was that all the individuals solved the problem, and parts of the solution were distributed through definite regions of the matrix. They believe the point of this experiment is to show the spread of features throughout a culture.
The fourth experiment deals with “hard” problems, also known as NP problems. In this particular experiment, they looked at the traveling salesman problem. They talk about the details of setting up the TSP to work within the simulation, and then make some observations about the results they received across multiple tests. It seems that at best half of the population would find an optima path, but throughout the simulations they found five different paths that all yielded the shortest distance.
Parallel constraint satisfaction was the focus of the fifth experiment. It began by discussion how features of ACM can be used to represent constraint satisfaction networks. They then go into discussion about parallel constraint satisfaction networks. Discussion of the advances and disadvantages of various aspects of these networks followed. An example, and setup for the experiment was the next topic of discussion. They go into detailing how these networks were encoded for the sample, and then talk about their observations from the experiment.
The sixth experiment focuses on symbol processing. There is discussion about traditional AI and navigation through symbolic nodes. Then, there is a more detailed discussion of how a network of nodes is transformed into a hierarchical tree. From the hierarchical tree they example the properties that are used in this experiment. There is some discussion at the end about the relevance of the experiment.
The chapter ends with a discussion about the ACM, and important questions related to it. Then they begin to talk about the relative insignificance of the individual in the system. And, finish by trying to make a global comparison to human thinking, and cognition.