Idea Creation, Constructivism and Evolution as Key Characteristics in the Videogame Artifact Design Process

We provide a broad characterization of how videogame design results from individuals' creative actions. Relying on qualitative data from a variety of sources including our own interviews and ethnographic work, and, a sourcebook on videogames, we are assured of the existence of three facets of creativity-based game design: idea creation, constructivism and evolution. The implications of a creativity-based framework for design are that game design features might result from conventionally known creative processes such as insight or inspiration, or from the form of creativity that 'blends' disparate concepts together in novel ways by adapting, adding or combining them. This latter form is what we term 'constructivism' or 'constructivist thinking' - something which increasingly digital or content-driven products (i.e., virtual) that are freeform in nature could require. A constructivist approach to game design suggests that games can be seen to be comprised of features from past games and other media or products, and thus as a consequence, the heritage of products are quite straightforward to discern. Furthermore, evolutionary processes can now be viewed as the outcome of these constructivist and idea creation mechanisms.

Bayesian Forecasting for Seemingly Unrelated Time Series: Application to Local Government Revenue Forecasting

One important implementation of Bayesian forecasting is the Multi-State Kalman Filter (MSKF) method. It is particularly suited for short and irregular time series data. In certain applications, time series data are available on numerous parallel observational units which, while not having cause-and-effect relationships between them, are subject to the same external forces (e.g., business cycles). Treating them separately may lose useful information for forecasting. For such situations, involving seemingly unrelated time series, this article develops a Bayesian forecasting method called C-MSKF that combines the MSKF method with the Conditionally Independent Hierarchical method. A case study on forecasting income tax revenue for each of forty school districts in Allegheny County, Pennsylvania, based on fifteen years of data, is used to illustrate the application of C-MSKF in comparison with univariate MSKF. Results show that C-MSKF is more accurate than MSKF. The relative accuracy of C-MSKF increases with decreasing length of historical time series data, increasing forecasting horizon, and sensitivity of school districts to the economic cycle.Bayesian forecasting, Kalman filtering, multivariate time series methods, seemingly unrelated time series

Comparative Study of Cross Sectional Methods for Time Series with Structural Changes

Heinz College pape

Information and communication: Alternative uses of the internet in households

Is the Internet a superhighway to information or a high-tech extension of the home telephone? We address this question by operationalizing information acquisition and entertainment as the use of the World Wide Web and interpersonal communication as the use of electronic mail (e-mail), and examine how 229 members of 110 households used these services during their first year on the Internet. The results show that e-mail drives people’s use of the Internet. Participants used e-mail in more Internet sessions and more consistently than they used the World Wide Web, and they used e-mail first in sessions where they used both. Participants used the Internet more after they had used e-mail heavily, but they used the Internet less after they had used the Web heavily. While participants ’ use of both e-mail and the Web declined with time, the decline in Web use was steeper. Those who used e-mail more than they used the Web were also more likely to continue using the Internet over the course of a year. Our findings have implications for engineering and policies for the Internet and, more generally, for studies of the social impact of new technology

Forecasting Crime

Organizations in the private sector must do strategic planning over long-term horizons to locate new facilities, plan new products, develop competitive advantages, and so forth. Consequently, long-term forecasts of demand, costs of raw materials, etc. are important in the private sector. There is no such strategic counterpart to police work; consequently, long-term forecasts are of little value to police. Police primarily need short-term forecasts; for example, crime levels one week or one month ahead. Currently, police mostly respond to new crime patterns as they occur. Client-server computing for realtime access to police records and computerized crime mapping have made it possible for police to keep abreast with crime. With short-term forecasting police may be able to get one step ahead of criminals by anticipating and preventing crime. The organization of this paper proceeds first with a description of short-term forecasting models, to provide basic terms and concepts. Next is a discussion of unique features of crime space-time series data, and the need for data pooling to handle small-area model estimation problems. Lastly are a discussion of particular forecasting requirements of police and a summary