Predicting Survivor 6: A Theory of Success on Survivorby Jeffrey D. Sadow -- 02/07/2003
Here comes Survivor: The Amazon and a round of predictions about how the players will do. While past articles have identified some general factors concerning success and failure, we now have enough data to predict in a statistical fashion who are likely winners and losers.
This premise is based upon the behaviorialist view of social science. Simply, this states that nothing in the world happens by chance, that attitudes are formed and actions taken because of previous actions and antecedent attitudes. In fact, what appears to us as "chance" is merely a product of undiscovered or unmeasurable causal agents.
Thus, theory may explain why things happen they way they do, and, since Survivor features the actions of human beings, we may predict their success in the game based upon theories we build that determine game success. Further, we may test these theories statistically using quantifiable concepts. So to apply this approach to Survivor, let us first identify the theoretical constructs that promote success or failure. This article shall do so, with succeeding articles focusing on the testing of the model built from these theories, and on using the model to predict the outcome of Survivor: The Amazon.
From the previous five versions, the following factors can be assumed to influence ability in playing the game:
1. Age. While youth probably makes for a less-strenuous time of it, age does bring wisdom, and since the game is more mental than physical we can predict that older players will do better than younger players.
(There is an alternative hypothesis - that the rigors involved do penalize older players. That also can be tested for by running the analysis with age coded to put middle-aged players in one category and the younger and older in another.)
2. Sex. By the winners, final two, final four, and jury slots, men do slightly better than do women, perhaps reflecting the physical aspect of the game as a theoretical explanation.
3. Race. While the percentage of winners (20) actually exceeds the percentage of all minorities who have played (16.25), that one winner (Vecepia, S4) was the only minority to make it into the final four (10%), so possibly a subtle bias exists in favor of the majority ethnic group (it could be that players are more comfortable keeping around people that share their ethnic background, and there is more of the majority).
4. Attitudes about wealth. Some contestants (most recently Helen in S5) have shown they harbor biases against players perceived as wealthier. Thus, we can anticipate that players whose occupations convey a potentially lucrative background to some degree will face sanctions by other players and thus have reduced their chances of going further.
5. Attitudes about religion. Past shows have indicated that players who very publicly express religious attitudes encourage feelings of discomfort among some players. Thus, these players may face sanctions and have less success in the game.
6. Travel experience. Players who have more experience here may do better because of their wider exposure to the world, different people, and to new situations.
7. Degree of fitness. Fitter contestants we can predict will do better, for obvious reasons.
8. Family composition. Those individuals who have a more varied family experience may do better, given the greater diversity of people and situations they deal with, meaning that those married with children will do best, those divorced with children or married (or engaged) only are not as well prepared, and singles are the least prepared (notice that this interacts with age above; younger people are more likely to be single and childless).
These previous factors are fairly easy to quantify, and this information appears on the official CBS web site. Somewhat harder to measure are the following:
9. Occupation. It's easy to know what the contestants do, but harder to rank order their jobs theoretically. One would presume that the occupations and experiences they bring that would best prepare someone to win Survivor would be individuals in positions of authority who must deal with people on a regular basis. The least prepared would be those who work in menial positions unaccustomed to leadership and/or working with others. It would seem those closer to the first endpoint deal with more people in more varied ways potentially necessitating more need to build skills involving teamwork.
10. Luxury item shareability. A luxury item which can bring benefits to others as well as its owner (and some, such as Frisbees, can only bring benefits to the owner interacting with another unless the owner uses it only as a plate) can be figured to bring its owner brownie points with other players. The more an item can be utilized by others, the better its owner ought to do.
11. Leadership. Assuming that this increases chances of success (and real leaders lead without others knowing), those who engage in activities on or off the job requiring this ought to do better in the game.
12. Interactivity. The same applies here as for leadership, those who experiences feature situations where they must have much interaction with many types of others should do better in the game since they must live with others under appalling circumstances guaranteed to rankle everybody.
13. Learning. Those with more education ought to do better for two reasons, they probably have more useful knowledge and native intelligence than those that do not, but, perhaps more importantly, their willingness to pursue more education indicates a greater desire to learn and to be open to acquiring knowledge, a learning and adaptability trait theoretically valuable in the game.
(Since the above three concepts are all attitudes related to life experiences, for analytical purposes they may be best combined into a single indicator of game-playing abilities.)
14. Facial muscles. Some of my fellow RNO writers noted that in their evaluations of contestants, pictures of faces on the web site were worth a thousand words. One could argue that contestants, who no doubt must authorize to CBS which picture of them will be used for publicity, who pose with and release for public consumption smiles on their faces represent more relaxed, open, less-threatening individuals, all traits which ought to curry favor with fellow contestants and buy them more time on the show.
15. Covering clothes. In the same vein, players whose photos show more revealing, more garish clothing probably denote more open people less likely to raise suspicion and will be seen as friendlier and thus more acceptable by other players.
16. Wearing jewelry. Yet, the wearing of jewelry (earrings excluded) may be a sign that this person feels it necessary to obscure themselves with some distraction, or perhaps it denotes a lack of authenticity to which, presumably, other players will react negatively.
(Since the above three concepts are all attitudes related to personality, for analytical purposes they may be best combined into a single indicator of personality traits as revealed by appearance.)
Even if the last half of these indicators do not present obvious objective quantification, subjective estimations of these indicators surely can measure well the underlying concepts. The explanation attached to each of the concepts above should have outlined the theoretical relationship they share with game success. Together, they present my theory about playing the game, and I'm sticking to it.
Notice that these factors include only items known generally about the contestants before the installment starts. We can predict only through public information. Further, chance events cannot be modeled at all (such as Michael falling into the fire during S2), nor information discovered during the series. These items become relevant when we try to explain rather than predict.
Now having explained the concepts, how (in general) they are measured and related to winning and losing, the next article, coming Monday, will explain the testing procedure used to build a predictive model for Survivor success.
Jeffrey D. Sadow is an associate professor of political science at Louisiana State University in Shreveport where he teaches, among other things, classes in international politics, international organizations, and diplomatic history. He has published in the area of gaming simulations in international politics.
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