successes of the project; without the players, there would be no game.
Scientific discovery games are an exciting part of the growing effort towards engaging the public in science. I look forward to new ways for anyone with an interest and a passion to contribute to scientific discovery.
Acknowledgments
There are many people to thank for helping and supporting me during the creation of this book.
•my advisor, Zoran Popović, for his guidance, support, and the opportunity to work on such a great video game as part of my research;
•David Baker for his expertise and time for helping make this a successful project;
•my committee members, David Salesin, John Zahorjan, and Ethan Merrit, for their valuable feedback throughout the project;
•the Foldit development team—Adrien Treuille and Janos Barbero were instrumental at the very beginning of the project, starting off on the considerable task of creating Foldit;
•the members of the Baker lab for lending their considerable biochemical expertise to the project. Firas Khatib contributed excellent biochemical insight and interaction with the Foldit community, and Andrew Leaver-Fay was always available and helpful in understanding the Rosetta codebase;
•the community of Foldit players for their enormous contribution to this work, for always surprising me with their ingenuity, and for their patience with the development of the game; and
•my parents, Harris and Beth Cooper, my sister, Emily Cooper, and my wife, Fayette Shaw, for all their help and support throughout school.
My work was supported by the National Science Foundation, DARPA, the Howard Hughes Medical Institute, NVIDIA, Electronic Arts, Sony, Microsoft, and Adobe.
Seth Cooper
July 2014
1 Introduction
1.1 Motivation
Despite the massive amounts of computational power available, many difficult scientific problems still remain computationally intractable. Fortunately, people possess great skill in problem solving and creativity, and individual problem solving skills can be augmented by working together in groups. However, only a small population of people are involved in scientific inquiry and advancing science.
The following questions arise: (1) How much more scientific advancement would be possible if more people were involved? (2) Can we integrate what people and computers, respectively, do well? We would like to maximize the effectiveness of this human-computer symbiosis, to find places were computational power is most useful and where human ability can best be applied. People and computers are often good at solving different types of problems; for example, a person would likely translate a passage of text more naturally, and a computer would likely be able to numerically optimize a function faster. We would like to be able to combine the best abilities of each in order to solve challenging problems that neither could alone.
The goal of this book is to determine if it is possible to design the coevolution of human-computer symbiosis to solve currently open problems in science. Two particular areas where humans can excel are spatial reasoning and creativity. People are able to reason spatially by forming mental models of objects, their environment, and the spatial relationships between them [Byrne and Johnson-Laird 1989, Tversky 1993 ].
People enjoy expressing their creativity, and many successful video games require players to think about objects in space and their spatial relationships to each other. Tetris1 is a popular example of this. There are many physical puzzles that rely on spatial reasoning as well, such as Rubik’s Cube2 and many sliding block puzzles. Recently, there has been a rise in popularity of video games whose explicit purpose is to help people train their cognitive skills, such as spatial reasoning. Brain Age3 and Big Brain Academy4 are examples of this.
A scientific field that naturally requires spatial reasoning and creativity for problem solving is biochemistry. Many problems in biochemistry are fundamentally spatial structural problems, particularly when dealing with protein structures. Proteins are important to biochemistry and our understanding of life itself, because they are indispensable to living systems and perform many important tasks in the cell, including structural, transport, defensive, and catalytic roles. The way proteins achieve their function is due to their shapes and how they interact with other molecules. They involve the relationships between physical objects in three-dimensional space; a protein’s structure determines its function [Zhang and Kim 2003 ].
To explore the potential of this human-computer framework for solving scientific problems, we have developed Fold it,5 an online videogamethat casts protein structure manipulation as a puzzle solving competition. The game tries to predict naturally occurring protein structures and to design novel proteins not previously seen in nature. In order to achieve this goal, the game gives players the ability to manipulate and optimize protein structures while competing and collaborating with other players to discover the best structures. Foldit’s YouTube channel can be found at http://www.youtube.com/user/uwfoldit; http://www.youtube.com/watch?v=lGYJyur4FUA gives a good introduction to the game.
1.2 Problem Statement
1.2.1 Game Design Problem
Designing a game for scientific discovery presents many distinct challenges. Oneof the primary purposes of using a game is to maximize the engagement and retention of the players. However, it is not enough to simply make the game as fun as possible; this goal must also be balanced with the need for relevant scientific outcomes. For most games, the designer is free to make decisions based only on what will make the game fun. In a scientific discovery game, the tension between the freedom to design for engagement and the realism of the scientific constraints is a key challenge. Thus, an important question is, how can we design a game that is both engaging and produces useful results? We would also like to know what kinds of problems would lend themselves to such contributions by non-scientists, and how can we identify these problems and map them onto a game.
We presume that the game players begin without any knowledge of the scientific field the game is based in. Given this, we would like for the players to gain the domain knowledge necessary to make a contribution to a challenging scientific problem quickly. This is not necessarily general expertise in the subject area or formal scientific expertise. Players may develop their own specialized form of expertise unique to the problem presentation within the game. How can the game best support the training of players to the point where they can make a contribution, and integrate players into the scientific process? We would like to use structures from games to teach players, and keep players interested and involved long-term. We would also like to spread the expertise gained by experienced players and to help new players learn from it.
The game’s architecture should support the coevolution of both the players and the game itself. In this way, as the players adapt to the game by gaining experience in how the game works and solving the problems presented, the game can also adapt to how the players best use it to become a better tool. How can we allow for this coevolution of the game and the player base?
1.2.2 Biochemistry Discovery Problem
Predicting protein structures computationally is a central goal for computational biochemists
