302 unit1 forecasting


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This file provides additional content for the forecasting paper assignment in TECH 3020: Technology Systems in Societies, at BGSU.

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  • Example: planning for Y2K bug back in late 1990s. No one was entirely certain what would definitely happen if we did nothing, so most companies did as much as possible to overcompensate against any potential problems that might arise as a result of it.
  • El Nino: Name given to the occasional development of warm ocean surface waters along the coast of Ecuador and Peru. When this warming occurs the tropical Pacific trade winds weaken and the usual upwelling of cold, nutrient rich deep ocean water off the coast of Ecuador and Peru is reduced. The El Nino normally occurs around Christmas and lasts usually for a few weeks to a few months. Sometimes an extremely warm event can develop that lasts for much longer time periods.
  • For presidential election: “As Ohio goes, so goes the nation”
  • A leading indicator for PC sales would be that there is usually more demand from July - September. A causal model would examine why this is the case: back-to-school shopping season
  • 302 unit1 forecasting

    1. 1. February 5, 2013 Volti, Unit 1 information, chapters 1-3 • The Nature of Technology • Winners and Losers: The Differential Effects of Technological Change • The Sources of Technological Change
    2. 2. February 5, 2013 Forecasting • Any individual or organization affected by technological change inevitably engages in forecasting (financial, economic, etc.) • Goal is not always to predict future • examine trends • predict likely scenarios • develop contingency plans
    3. 3. February 5, 2013 Methods of Forecasting • Summarized from Martino’s Technological Forecasting: An Introduction handout (PDF available in Course Documents area) • Examples may overlap with more than one method
    4. 4. February 5, 2013 Extrapolation • Projecting a pattern that has been found in the past, to anticipate potential outcomes in the future • Examples: Moore’s Law / El Nino • Examples?
    5. 5. February 5, 2013 Leading Indicators • Using one time series to anticipate / obtain information another time series • Assumption is that both time series share similar behaviors, but with a time-lag • Example: “What the barometer is doing today is what the rain clouds will do tomorrow”
    6. 6. February 5, 2013 Causal Models • Finding cause and effect relationships • Contextualizing first two methods • Example: understanding why the barometer itself works, in order to better understand why there will be rain clouds tomorrow.
    7. 7. February 5, 2013 Probabilistic Methods • Forecasting using any combination of the first three methods, then arriving at a range of possible values • Example: 70% chance of showers