Project 1: Prediction, Uncertainty and Measures of Complexity
1st Author
1st author's E-mail
2nd Author
2nd author’s E-mail
ABSTRACT (5 points)
4-6 sentences summarizing the project and your findings.
0. Author Contributions. List what each author did for each section –who wrote code, who debugged code, who did writing, who edited, who made figures. Acknowledge any discussions or help you had with anyone else. You must list contributions and acknowledge outside help in order to get credit for your project.
1. INTRODUCTION (5 points)
1. 1st paragraph: Motivation: Why are prediction, uncertainty and measures of complexity relevant for understanding complex adaptive systems?
2. 2nd paragraph: List the questions and hypotheses addressed in this Project and explain your approach to answering them.
.
Each figure needs a caption that explains the essence of what the figure shows.
The caption should be 1-2 sentences and refer to each panel in multi-panel figures.
2. RESULTS (55 points)
2.1 Logistic Map
One paragraph 8 points
One 3 panel figure 10 points
2.2 Information Gain
2.2.1.1 Uncertainty without information
State likelihood to guess population and uncertainty in bits (1 point)
2.2.1.2 Information gain in bits as a function of R
1 paragraph (4 points) and 1 figure (5 points)
2.3 Calculating Evolutionary Probabilities
2.3.1.1 Number of Genomes length 99(1 point)
2.3.1.2 Number of Proteins in genome length 99
(1 point)
2.3.1.3 Number of Genomes & Proteins 4 mutations away (2 points)
2.3.1.4 Fraction of genomes and proteomes explored by Bob & his descendants (2 points)
2.3.1.5 Fraction of descendants who will survive (2 points)
2.3.1.6 Likelihood of finding fitness-doubling mutation (2 points)
2.3.1.7 Explain how fitness-doubling mutations could be found with higher than random probability n (7 points, 2-3 paragraphs; 5 points figure)
2.4 Define complexity
(5 points paragraph, 5 points figure)
3. DISCUSSION
3.1 1 paragraph summary relating your findings to a)what can be predicted (or not predicted) in CAS (5 points)
3.2 1 paragraph relating your results to how living systems process information (5 points)
3.3 1 paragraph relating the definitions of complexity to your experiments—how do your experiments demonstrate that these measures (5 points)
4. METHODS (12 points)
For each section of Methods explain how you made calculations and produced figures. State any assumptions you made. In a publishable paper you would give enough detail to reproduce your results. Do not provide that level of detail here, but provide sufficient detail for me to know that you understand what you did and why you did it.
4.1 Logistic Map
4.2 Information Gain
4.3 Evolutionary Probabilities
5. REFERENCES (3 points)
List any references you used. Refer to them by number (like this [1] ) in the text. List the Mitchell book, Wagner article, Gell-Mann article, any code you modified and any other source you used.
References should be published materials accessible to the public. Use the “ACM Reference format” for references – that is, a numbered list at the end of the article, ordered alphabetically and formatted accordingly. See examples of some typical reference types, in the new “ACM Reference format”, at the end of this document. References should be numbered in the text and listed alphabetically.
6. Writing Clarity (15 points)
Make sure there are no mistakes in grammar or spelling.
Paragraphs should have 3 – 6 sentences. Avoid run-on sentences.
See http://writing.colostate.edu/guides/guide.cfm?guideid=83 for guidelines for scientific writing.
7. CODE Turnin (25 points)
- Logistic Map (4)
- Information Gain (4)
- Evolutionary Probabilities (4)
- Measures of Complexity (3)
(5) if your code runs easily and generates your figures on the first try. Your code must run on the second try to get any credit for code.
(5) A readme file that clearly explains how to run your code
[1] Bowman, M., Debray, S. K., and Peterson, L. L. 1993. Reasoning about naming systems. ACM Trans. Program. Lang. Syst. 15, 5 (Nov. 1993), 795-825. DOI= http://doi.acm.org/10.1145/161468.16147.
[2] Ding, W. and Marchionini, G. 1997. A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
[3] Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (The Hague, The Netherlands, April 01 - 06, 2000). CHI '00. ACM, New York, NY, 526-531. DOI= http://doi.acm.org/10.1145/332040.332491.
[4] Tavel, P. 2007. Modeling and Simulation Design. AK Peters Ltd., Natick, MA.
[5] Sannella, M. J. 1994. Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
[6] Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
[7] Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE. In Proceedings of the 16th Annual ACM Symposium on User Interface Software and Technology (Vancouver, Canada, November 02 - 05, 2003). UIST '03. ACM, New York, NY, 1-10. DOI= http://doi.acm.org/10.1145/964696.964697.
[8] Yu, Y. T. and Lau, M. F. 2006. A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions. J. Syst. Softw. 79, 5 (May. 2006), 577-590. DOI= http://dx.doi.org/10.1016/j.jss.2005.05.030.
[9] Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender, Ed. ACM Press Frontier Series. ACM, New York, NY, 19-33. DOI= http://doi.acm.org/10.1145/90417.90738.