revision

Revised Paragraph:

Johnson gives an example of individual decisions reflecting back onto the grand scale of a whole population. “A city is a kind of pattern-amplifying machine: its neighborhoods are a way of measuring and expressing the repeated behavior of larger collectivities… Because those patterns are fed back to the community, small shifts in behavior can quickly escalate into larger movements,” (Johnson, 199). In other words, the behavior of the group is dependent on the individual’s actions, and collective intelligence is possibly accumulated from the different pieces of knowledge that the individuals contribute to the group. This is where Johnson and Lethem converge their arguments and Davidson diverges. Clearly, throughout Johnson’s paper, Johnson is more interested in the effects on the group, the general population. He is not interested in how the group affects the individual, and solely focuses on how the individual contributes to the group. Lethem as well, endorses individuals to set aside their personal property as first priority and embrace a sharing mindset so that greater works can be produced. This is all for the culture of the society, the greater good. However, Davidson only looks at crowdsourcing and the group collaboration model for the individual student and his or her learning. She shuns the “one-size-fits-all” model for education and wants to cater to each student’s unique needs as we can see with her anecdote of the girl with the green hair. Clearly, Davidson’s first priority is improving the individual’s performance whilst Johnson and Lethem are more concerned with the society or the greater good.

Original:

Johnson gives an example of collective intelligence: ant colony behavior. Because the queen ant has the name of “queen”, most people think of ant colonies as a hierarchy, but that is most certainly not the case. The queen does not make any decisions; all she does is lay eggs, which is the only reason why the ants protect her. Ant colonies display a lot of decentralized behavior. Johnson marvels the decentralized behavior that ants exhibit. Although self-organizing systems feel unnatural and we want to build centralized models and find the decision-makers or those in charge, because of the organized complexity we are able to learn and develop without limits. So, just by understanding what organized complexity and crowdsourcing are, we can see at a glance that the two concepts are very much related. Crowdsourcing depends on diversity and avoids expertise whereas organized complexity states that “intelligence emerges in the absence of leadership or authority,” (Johnson, 192). There is order in the chaos that forms when there is no leader or hierarchy, and the best solution or learning arises when people from all different backgrounds are working together. 

I think it is clear that I tried to make a lot of connections between the three authors whereas the original one is more of summarizing the Johnson paper and its arguments. Although I have a lot to improve like putting my opinion there and making it more of an analysis rather than comparing and contrasting the authors, I think that the comparisons will help me develop an argument and a stronger thesis.