1. Identify factors in the physical and biotic world that may be relevant. These factors are neither technical or societal. Examples of such factors include natural resource availability, land cover, climate trends, and risks of volcanism or earthquake.

2. Identify relevant societal, cultural, and behavioral patterns. Numerous aspects of technology, the physical world, and the biotic world are not separable from human activity, and require consideration of societal, cultural, and psychological factors.  Natural resources, for example, exist only so far as human groups define them as useful and only in so far as technology, politics, and economics can make them available (Steward 1972). Factors relevant to the foresight process include the following categories:

- Legal and political trends that may affect the foresight problem at hand. For example, historical and recent trends in environmental legislation, or trends in international agreements in areas such as trade, labor, or human rights may affect the uses and applications of technology.

- Cultural and economic processes and values that influence individual and group decision-making about technology. Some examples are profit motives (individual and corporate), standard of living goals and perceptions, power motives, values on technology, aesthetic values, social welfare values, social justice values, attitudes toward public spending, advertising, and ideologies about and interpretations of human behavior.

3. Identify possible changes in the technology suggested by the TRIZ laws of technological system evolution. These assist in understanding the possible quantum shifts in the state of technologies.

4. Identify historical analogues. Historical situations and processes may exist that are relevant to the foresight problem at hand (recall George Santayana's aphorism, "those who cannot remember the past are condemned to repeat it").  Can their outcomes inform the foresight process?  For example, the current period of economic expansion is often attributed to the ongoing information revolution. As a historical analog, the economic ramifications of the Industrial Revolution are of interest.

5. Identify relevant "must happen" factors. These are events that can be projected with high probability of accuracy, assuming no significant perturbations to the system.  For example, the number of persons retiring in the year 2050 will be strongly correlated with the number of children born between 1980 and 1995. Identify perturbations that  may modify these otherwise high-probability outcomes. For example, changes in social security laws, changes in work patterns and life cycles, and changes in employment rates are among factors that may affect retirement practices over time.

6. Identify trends revealed by historical records and bibliometric analyses.  The most famous example of this in the digital electronics field is undoubtedly Moore's law (Intel 2000), which states that the number of transistors on a chip doubles every 18 months or so. Similar trends show increasing network bandwidths, hard disk storage capacities, and so on. Usually trends are modeled as linear, exponential, or S-curve. Trends of interest are not just those relating directly to the foresight problem, but include indirect influences as well. By extrapolating trends in indirect influences, the future of these influences can be addressed.

7. Identify counteracting factors. These oppose any factors already identified as influencing the foresight problem under consideration.  For example, the forces that promote faster, more powerful computers are counterbalanced by the forces that restrain that trend, explaining why computers aren’t increasing in power even faster than they are. From that perspective, Moore’s law (that the number of transistors on a chip doubles every 18 months or so) describes an equilibrium between opposing forces which would be useful to identify, rather than as the unitary empirical trend it is popularly perceived to be.

8. Identify limiting factors. No real phenomenon goes to infinity.  For identified trends, how far might they go and what might stop them from going further? For each limit so identified, ask if it could conceivably be overcome. If so, how? If not, why? Develop alternative prediction scenarios for the case in which a limitation is overcome and the case in which it is not.

9. Identify possibilities for breakthroughs. For limits to trends identified earlier, assess the credibility of conceivable breakthroughs. Such breakthroughs are more likely when one aspect of a technology is the main bottleneck, and less likely when significant improvement in the performance of a technology requires advances in several subproblems. For each such possibility, generate prediction scenarios based on whether or not the breakthrough actually occurs.

10. Identify potentially self-fulfilling prophecies. Expectations about change can cause change.   For example, Moore's law is frequently suggested as an important impetus to advances in microelectronics manufacturing, since the industry expects it to hold and therefore a company in the industry, to remain competitive, feels it must meet that expectation in its own products. An important type of self-fulfilling prophecy is related to foresight studies themselves. When used by policy-makers the conclusions of a foresight exercise can be an integral part of a plan to reach a strategic goal.

11. Identify what relevant phenomena might end during the period of interest. Probabilistic conclusions can be made about the life spans of such phenomena based on when they began and the assumption that the current moment is a random time point in the life spans of the phenomena (Gott 1993). Use this to help understand timing issues regarding paradigm shifts and other discrete events not predictable based on trend analysis techniques.