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# Homework 4

1. Observe the results of running the model with a variety of populations and population sizes. For example, can you get cooperate’s average payoff to be higher than defect’s? Can you get Tit-for-Tat’s average payoff higher than cooperate’s? What do these experiments suggest about an optimal strategy? (2-3 sentences)
2. Relate your observations from this model to real life events. Where might you find yourself in a similar situation? How might the knowledge obtained from the model influence your actions in such a situation? Why? (3-4 sentences)

Read/skim “Strong and Weak Emergence” by David Chalmers from 2006 (PDF; full citation). Answer these questions:

1. Chalmers writes, “We can think of strongly emergent phenomena as being systematically determined by low-level facts without being deducible from those facts.” Give an example (1-2 sentences) that may possibly satisfy this definition of strong emergence.
2. Are the NetLogo models we have been using examples of strong or weak emergence? Provide a 1-2 sentence argument.
3. What is the relation between weak emergence, as described by Chalmers (second half of the reading) and the knowledge level? This is a “compare and contrast” type of question. Answer in one paragraph (4-5 sentences).

Execute the k-means algorithm by hand on the following data:

item #wxyztrue label
13.01.01.01.0A
20.02.01.00.0B
32.02.02.02.0A
41.01.01.01.0B
53.02.03.02.0A
60.00.01.03.0B

Use $$k=2$$. Show the centroids as they change, and give the final centroids. You must choose random (or not so random) starting centroid values. Finally, give the confusion matrix.

Run the k-means algorithm in Weka using this dataset: iris.arff (iris species clustering).

Find the best value of $$k$$. Give the confusion matrix for this $$k$$. Also report the percent of correctly classified instances for each class.

Execute the k-nearest neighbor algorithm by hand on the clusters found from task 1 (or make up random clusters by labeling the points from task 1). Use $$k = 2$$. Classify the data point: $$<1, 0, 1, 2>$$.

Run the k-nearest neighbor in Weka using this dataset: letter.arff (handwritten letter classification). Find the best value of $$k$$. Report the accuracy and give the confusion matrix.