One seeks to develop predictive models to map between a set of predictor variables and an outcome. Statistical tools such as multiple regression or neural networks provide mature methods for computing model parameters when the set of predictive covariates and the model structure are pre-specified. Recent research is providing new tools for inferring the structural form of non-linear predictive models, given good input and output data. The task of choosing which potentially predictive variables to study is largely a qualitative task that requires substantial domain expertise. A survey designer must have domain expertise to choose questions that will identify predictive covariates. An engineer must develop substantial familiarity with a design in order to determine which variables can be systematically adjusted in order to optimize performance. The need for the involvement of domain experts can become a bottleneck to new insights. The wisdom of crowds could be harnessed to produce insight into difficult problems, one might see exponential rises in the discovery of the causal factors of behavioral outcomes, mirroring the exponential growth on other online collaborative communities. The goal of this research was to test an alternative approach to modeling in which the wisdom of crowds is harnessed to both propose potentially predictive variables to study by asking questions, and respond to those questions, in order to develop a predictive model. The first time, a method by which non domain experts can be motivated to formulate independent variables as well as populate enough of these variables for successful modeling. This is accomplished as follows. Users arrive at a website in which a behavioral outcome (such as household electricity usage or body mass index, BMI) is to be modeled. Machine science is a growing trend that attempts to
automate as many aspects of the scientific method as possible.Automated generation of models from data has a long history,but recently robot scientists have been demonstrated that can physically carry out experiments. The rapid growth in user-generated content on the Internet is an example of how bottom-up interactions can, under some circumstances, effectively solve problems that previously required explicit management by teams of experts.