Identifying and forecasting the reverse salient in video game consoles: A performance gap ratio comparative analysis

This study uses the reverse salient methodology to contrast subsystems in video game consoles in order to discover, characterize, and forecast the most significant technology gap. We build on the current methodologies (Performance Gap and Time Gap) for measuring the magnitude of Reverse Salience, by showing the effectiveness of Performance Gap Ratio (PGR). The three subject subsystems in this analysis are the CPU Score, GPU core frequency, and video memory bandwidth. CPU Score is a metric developed for this project, which is the product of the core frequency, number of parallel cores, and instruction size. We measure the Performance Gap of each subsystem against concurrently available PC hardware on the market. Using PGR, we normalize the evolution of these technologies for comparative analysis. The results indicate that while CPU performance has historically been the Reverse Salient, video memory bandwidth has taken over as the quickest growing technology gap in the current generation. Finally, we create a technology forecasting model that shows how much the video RAM bandwidth gap will grow through 2019 should the current trend continue. This analysis can assist console developers in assigning resources to the next generation of platforms, which will ultimately result in longer hardware life cycles.

[1]  Matt Garite,et al.  The Ideology of Interactivity (or Video Games and Taylorization of Leisure) , 2003, DiGRA Conference.

[2]  Ozgur Dedehayir,et al.  Determining reverse salient types and evolutionary dynamics of technology systems with performance disparities , 2011, Technol. Anal. Strateg. Manag..

[3]  R. Katz,et al.  Anticipating disruptive innovation , 2004, IEEE Engineering Management Review.

[4]  Estela Bee Dagum,et al.  A Unified View of Signal Extraction, Benchmarking, Interpolation and Extrapolation of Time Series , 1998 .

[5]  P. Rossel.  Beyond the obvious: Examining ways of consolidating early detection schemes , 2011 .

[6]  George Basalla,et al.  The Evolution of Technology: Selection (2): Social and Cultural Factors , 1989 .

[7]  Timothy R. Anderson,et al.  Predicting U.S. jet fighter aircraft introductions from 1944 to 1982: A dogfight between regression and TFDEA☆ , 2006 .

[8]  Joseph P. Martino,et al.  A review of selected recent advances in technological forecasting , 2003 .

[9]  Kyoung-Joo Lee,et al.  Mobile music business in Japan and Korea: Copyright management institutions as a reverse salient , 2005, J. Strateg. Inf. Syst..

[10]  Jianguang Sun,et al.  Achieving Disruptive Innovation - forecasting potential technologies based upon technical system evolution by TRIZ , 2008, 2008 4th IEEE International Conference on Management of Innovation and Technology.

[11]  S. Hultén,et al.  Escaping lock-in: The case of the electric vehicle☆ , 1996 .

[12]  M. Taylor,et al.  Conclusion: International Political Economy—The Reverse Salient of Innovation Theory , 2009 .

[13]  Karel Mulder,et al.  PVC plastic: a history of systems development and entrenchment , 2001 .

[14]  Melissa A. Schilling.  Technological Leapfrogging: Lessons from the U.S. Video Game Console Industry , 2003 .

[15]  Tugrul U. Daim,et al.  Forecasting emerging technologies: Use of bibliometrics and patent analysis , 2006 .

[16]  S. Cunningham,et al.  Technology futures analysis: Toward integration of the field and new methods , 2004 .

[17]  Udayan Chanda,et al.  Modelling innovation and imitation sales of products with multiple technological generations , 2008 .

[18]  Joseph P. Martino,et al.  A comparison of two composite measures of technology , 1993 .

[19]  P. David.  Clio and the Economics of QWERTY , 1985 .

[20]  Mark Finn.  Console Games in the Age of Convergence , 2002, CGDC Conf..

[21]  T. P. Hughes.  Networks of power : electrification in Western society, 1880-1930 , 1984 .

[22]  Matthew T. Clements,et al.  Indirect Network Effects and the Product Cycle: Video Games in the U.S., 1994-2002 , 2004 .

[23]  Ozgur Dedehayir,et al.  Bibliometric study of the reverse salient concept , 2009 .

[24]  A. Kayal,et al.  Measuring the Pace of Technological Progress , 1999 .

[25]  Joseph Wolfe.  A Forecast of Business Gaming in the Year 2010 , 1993 .

[26]  Daniel Sloan.  Playing to Wiin: Nintendo and the Video Game Industry's Greatest Comeback , 2011 .

[27]  Timothy R. Anderson,et al.  Further Examination of Moore's Law with Data Envelopment Analysis , 2002 .

[28]  B. Bowonder,et al.  Predicting the future: Lessons from evolutionary theory , 1999 .

[29]  F. Bass.  A new product growth model for consumer durables , 1976 .

[30]  W. Arthur,et al.  INCREASING RETURNS AND LOCK-IN BY HISTORICAL EVENTS , 1989 .

[31]  A. Hornsby,et al.  Staying at the technology forefront by spotting the reverse salient: The case of digital video broadcasting , 2008, 2008 4th IEEE International Conference on Management of Innovation and Technology.

[32]  M. Lehtonen,et al.  History of electricity network control and distributed generation in the UK and western Denmark , 2008, 2008 First International Conference on Infrastructure Systems and Services: Building Networks for a Brighter Future (INFRA).

[33]  S. G. Deshmukh,et al.  Matching of technological forecasting technique to a technology , 2002 .

[34]  H. Dietl,et al.  Intra-system competition and innovation in the international videogame industry , 2003 .

[35]  Robert K. Colwell,et al.  INTERPOLATING, EXTRAPOLATING, AND COMPARING INCIDENCE-BASED SPECIES ACCUMULATION CURVES , 2004 .