MoA Master
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Elizabeth Lehtonen
Media Lab
1978, USA

Complexity in Gaming: What Game Developers Need to Know A Hypothetical Model for Comparing Player
Computer games are played by a wide range of people, of both sexes, from three to ninety-three.  This thesis is a search to understand these player’s motivations for playing games.  The challenge was to understand how to categorize both the human psyche and the massive space of computer gaming in the best way. The results of the thesis can be used to understand and predict player motivation; that is to answer the question of why one person plays a game where they kill hundreds, while another prefers to build their own neighborhood.
Game developers, currently, must make educated guesses as to what their potential players are motivated by.  In light of this, the thesis has formed a model to help under­stand these motivations and tie them into games.  This is done by building on PhD Nick Yee’s work related to mapping gamer characteristics and expanding it to encom­pass a structured system of game aspects (complexities in games). The end result of a study done for the thesis to map games against their target audience gives clear numbers for how the target audience would react to increased complexity.  
For the MoA Exhibition the data found from the thesis’ study and within the model has been reverse engineered to create a tool to analyze what games various people might find most interesting.

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This is an example of one of the eighteen questions used as part of a study on complexity preferences done for this thesis. This question focuses on the amount of endings in games as one type of complexity. Participants are asked to rank their choices from most desired to least desired, with A being the least complex and C being the most complex.

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This is an example of the output from the thesis’ model. The numbers for ‘A’, ‘B’ and ‘C ’ reflect the target audience’s feelings toward low, medium and high complexity respectively. The most interesting output is of course the different results gained by increasing complexity (seen under A to B, and B to C). Dark orange is a very positive reaction and dark blue a very nega­tive reaction to increased complexity. From this, it is easy to see where effort would be spent to the best effect simply by looking at the color coding.
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