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Distancing our futures

Posted By Administration, Monday, March 16, 2015
Updated: Saturday, February 23, 2019

Sandra Geitz  shares her thoughts with us about “Distancing our Futures” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.


Along a similar theme to the last post, I’m exploring enhancing and enabling futures thinking. This post is concerned with Bridging Psychological Distance, from Rebecca Hamilton’s HBR article this week, and how this may impact facilitating foresight.

What is psychological distance?

People directly experience only the here and now. It is egocentric. In order to think about the future, another person’s perspective, remote locations and/or understand hypothetical options, people need to transcend their self, or their individual present experiences. This is termed by psychologists, Nira Liberman and Yaacoc Trope as overcoming psychological distance. People are able to do this, to varying degrees of ability, by creating distant abstractions, or mental constructs.

Psychological distance can occur as one or in several dimensions. Social distance is the gap between yourself and other people. Temporal distance is the gap between the present experience and the future. Spatial distance occurs between your present location and some far away distance. Experiential distance is the gap between one’s direct experience and an hypothetical or imaginary situation.

Why may psychological distance be important to foresight?

Liberman and Trope’s research shows that the farther removed an object is from direct experience, the more abstract one represents the distant object. Also, their research shows that each of the four psychological distances are cognitively related to each other, that they similarly influence and are influenced by the level of abstraction, and that they similarly affect they way we preference, predict, perceive and take action.

If the psychological distance is large, we tend to think in more abstract ways; we focus on the big picture, the why or purpose of our choices, and the desirability of our options. Large distances and abstract language are associated with power and visionary thinking.

When the psychological distance is small, we think in more concrete terms,; we are focused on the details, the how and what of our choices, and the feasibility of each option. Small distances are synonymous with familiar, concrete tasks. From this research, Hamilton advises that the optimal strategy is adjusting the psychological distance to suit the needs of the particular task at hand.

Social distance can be reduced by taking into account the perspectives of others, employing the ability to step into another’s shoes. Similarly, social distance can also be reduced by reducing temporal distance, through immediate task deadlines, or by meeting others onsite, reducing spatial gaps.

Temporal distance can be reduced by adopting milestones or internal deadlines, to reduce overwhelm of the distant project completion, or visualising the future state.Temporal distance can be reduced through less social and/or spatial distance, such as meeting with stakeholders of the large project task.

Spatial gaps are reduced by face-to face meetings and travelling onsite. And experiential distances can be minimised via role plays, prototyping experiences to enable more concrete thinking or action to occur. Similarly, experiential distance can be reduced via social distance, by peer group word of mouth recommendations to encourage us to take similar actions.

However, if big-picture thinking, creativity or authority is the desired goal, increasing social distance by using abstract language helps. Deploying greater spatial distance by moving meetings offsite or to open, lofty and spacious surroundings can assist expansive thinking. Increasing temporal distance for long-term planning horizons can encourage more ambitious goal-setting. And, increasing experiential distance with hypothetical questions and imagery may encourage a broader range of scenarios to be considered.

How can we use greater psychological distance to expand our futures options?

How might we minimize distances to enable concrete actions towards our preferred futures?

Tags:  foresight  future  psychology 

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How much should the stakeholders budget for the unknown?

Posted By Administration, Monday, March 9, 2015
Updated: Saturday, February 23, 2019

Alireza Hejazi shares his thoughts with us about “budget for the unknown” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

Attending a summit on the investment in R&D, I found the majority of R&D outputs discussed in the summit were professionally polished secondary research. A panel of experts was tasked to evaluate a strategic framework documenting a baseline, as well as alternative futures for a number of stakeholders active in the construction industry. An interesting debate was ignited in the panel when I suggested three points to be considered in their appraisal: originality, quality and timeliness. Coming back home from the summit, I asked myself how much the stakeholders should really budget for the unknown—the future. To answer that question I wrote this post and I assume those three points may make general criteria in budgeting foresight projects.

Primary or Secondary?

How much should the stakeholders pay for insights offered by futurists? In my view, a criterion can be made based on the primary or secondary nature of research. Secondary research means using other researchers’ data rather than generating one’s own statistics. Using data produced by well-known institutes such as ILO, WTO, UNESCO, Gallup, and etc. a futurist can conduct secondary research. Futurists do more secondary research than primary explorations and most of scanning jobs are based on secondary sources of information.

While secondary research can be precious in the right place, like many other researchers, futurists are expected to create their own data. Normally, primary research offers a better taste of trustworthiness to stakeholders. Governments and NGOs collect and publish statistics, researchers and authors write books and articles based on their observations, speakers write speeches according to their ideas and information, but what do futurists produce? Generally speaking, futurists find, interpret and represent the results of all that data for their clients, books and articles and also their speeches.

The missing point in judging research outputs produced by futurists is that primary data does not interpret itself. A dexterous interpreter is needed to make sense of that data. The collection of the data from various sources can be done by every researcher, but futurists enliven the collected data by suggesting alternative futures. Collecting and interpreting are both necessary, but what is the best data in foresight profession?

According to Gordon (2009), “The best data is primary data—data researched and presented by the original researcher—and the best use is primary use” (p. 14). Results from scientific research which are based on primary data are usually published in top research journals and are sometimes delayed for publishing due to the sensitivity of issues for investors who sponsored the research project and perhaps never published.

The value of primary data can be also revealed in the light of inherent limitations of using secondary data. Those limitations are identified by Burnett (2008) in this manner: “First, the information is frequently dated. Second, seldom are secondary data collected for precisely the same reasons that the information is sought to solve the current marketing problem” (p. 61). The stakeholders want fidelity and they prefer the primary source. The futurists can lead that sense of preference skillfully towards original authentic foresight outputs produced by their own reliable and valid research.

Quantitative or Qualitative?

Potent futurists are expected to organize and conduct both quantitative and qualitative researches. A noteworthy foresight output is expected to open up a window through which readers may peer into the world of foresight to learn more from the findings. Strong foresight works engage the audience by displaying and discussing correlations, values, and other details both quantitatively and qualitatively.

The choice of using a qualitative or quantitative design (or both), for a given research problem is mainly related to the nature of problem. Basically, quantitative methods are appropriate when: “(1) measurement can offer a useful description of whatever you are studying, (2) when you may wish to make certain descriptive generalizations about the measures, and (3) when you wish to calculate probabilities that certain generalizations are beyond simple, chance occurrences” (Williams & Monge, 2001, p. 5).

While most quantitative researches create generalizations that transcend the immediate situation or particular setting, qualitative researches often do not try to generalize beyond the particular situation, but may leave it to the reader to assess applicability (Fraenkel, 2009, p. 15). The history of futures research shows that the majority of studies have been conducted through qualitative approaches. The main reason is that the future is unknown and less quantitative data are normally available compared to other fields of study.

The research perspective, approach, and method should be determined as a consequence of deciding upon the objectives of the investigation. Thus, one particular perspective, approach, or method is neither better nor worse than another, just simply more or less appropriate within the specific circumstances and objectives of a foresight project. What matters for a fair payment are time, fund, knowledge, skill and energy that are devoted by a futurist or a team of futurists to a foresight project through both quantitative and qualitative approaches.

On time or Late?

The importance of each foresight output at any given time depends on aspects of the situation, such as the type of industry and the amount of volatility in the external environment. The consequent is the timeliness of a foresight report that is set up for submission to related stakeholders. The futurists are not the only ones who need time to accomplish research; the stakeholders also need enough time to devise their companies with foresight insights or new strategies proposed by the futurists.

The amount of budget that investors offer to know the unknown is tightly related to available time for decision making or change management. Firms that consistently establish a management reserve for foresight projects can tell us how much time is needed and how valuable a foresight output will be over time. Certainly a specific percentage of the performance budget should emerge as the right amount, but it is directly related to timeliness, potential risks, and the degree of predictability of the industry. As observed by Verzuh (2005, p. 106), “high-risk industries such as software development may add as much as 30 percent to the budget. More predictable projects will use an amount closer to 5 percent of the performance budget.”

The factor of time determines how much should be paid for a foresight output. Over multiple foresight projects, a normal range will appear for both futurist and client. Imagine an alternative scenario like this: A construction company is interested in a particular topic and the CEO decides to hire a futurist to research the topic for them, but time is a determining factor in the success of company. The futurist spends six months researching the issue, and six months doing and writing up the research. How much do you think the futurist could charge for this report? If the CEO needs the final report six months earlier, then how quickly should the job get done? How about the quality of research and how about the payment? Many clients pay considerable outlays for private research reports. They pay not just because of the worthiness of information, but because of its timeliness. Quick and qualified futurists are brilliant gems in every company.

In my view, the budgetary value of a foresight output depends on its originality, quality and timeliness, but its intellectual value and the contribution that it will make to building better corporate futures may not be determined by such means of assessment easily.


Burnett, J. (2008). Core concepts of marketing. Zurich: Jacobs Foundation.

Fraenkel, J. R. (2009). How to design and evaluate research in education. New York, NY: McGraw-Hill.

Gordon, A. (2009). Future savvy: Identifying trends to make better decisions, manage uncertainty, and profit from change. New York: American Management Association.

Verzuh, E. (2005). The fast forward MBA in project management. Hoboken, New Jersey: John Wiley & Sons.

Williams, F., & Monge, P. (2001). Reasoning with statistics: How to read quantitative research (5th ed.). Fort Worth: Harcourt College.

Tags:  budget  foresight  futurist 

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Benefits of Big Data – predictions vs. foresight

Posted By Administration, Monday, February 16, 2015
Updated: Saturday, February 23, 2019

Julian Valkieser  shares his thoughts with us about “Benefits of Big Data” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

In my last articles, I have already mentioned the power of Big Data. My blog colleague Jason adopted it and expressed his own thoughts. In his last article, he has shown wonderfully how technology has already overturned business models and efficiency in other sectors and renewed them. In comparison to this, it could happen in the area of futurist and industry’s foresight as well.

Now, there are foresight methods that work well or best with uncertainty. Indeed, Delphi-Interviews are planned preciously, e.g. interviewees are pre-selected. But this does not mean that the statements can be processed for hard facts of future reality. And, they should not. That’s the exciting thing about scenarios. They give a way to stimulate the imagination and to derive recommendations for action.

But again, you try to keep the “cone of plausibility” as narrow as possible. (See Jason’s blog). You are looking for certain experts. You force certain issues. This is done in order to build the scenario reasonably.

Now you can imagine how neutral subjective responses and subjective questions are. Anyone who read “Thinking Fast and Slow“ from Daniel Kahneman knows what I mean. And right here data comes into play. Information could passively express motives and interests of groups. I have already indicated this in my last article.

In this article, I already referred to the fact that you can only get the most out of Big Data, if one applies the prediction to a trigger event. One extracts motives and interests out of big data for one or more so-called, trigger events. These are events that can be relatively easily predicted in the near future based on data, because the circumstances are (should be) less complex. Based on these trigger events you can create a scenario. In principle, this is nothing new. Just the basic information is extracted out of big data instead of interviews and subjective insights.

Let’s take an example. A major mobile phone company has 50 million customers. Each customer has a phone and moves every day with this turned-on phone – in this case between different radio towers (See Triangulation). Let’s suppose further that the company receives 20-100 motion information’s by any customer. Provided the company may cache this information for a longer period of time, the result is a huge amount of data information, how people move, how long they stay in which locations, etc. Of course, each individual could now be afraid of privacy. But the individual is not of interest. It’s about the mass.

Imagine what you can do with this information now available. Road offices could optimize the logistics. Infrastructure projects could be optimized. Where should the new stadium be built? How is the highway to be calculated? How many trains must be set on this track?

In a rising urban environment, where sheer masses of people are moving, all these data are exciting as the basis for trigger events and scenarios.

And finally, I have another wonderful example for these ideas. Eric Fischer has evaluated geo-tagging data from photo cameras. He compared where locals and tourists take pictures in certain cities in the world and displayed this information on maps.

Tags:  big data  foresight  scenarios 

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Is a Ranking System Feasible for Futurists?

Posted By Administration, Monday, January 26, 2015
Updated: Saturday, February 23, 2019

Alireza Hejazi shares his thoughts with us about the feasibility of establishing a "Ranking System” for Futurists in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

Attending an international exhibition on a marketing mission recently, I was asked to score service and product providers there and nominate my preferred candidate at that expo. After reviewing many pavilions, I made up my top ten list and scored them according to my checklist. I voted for a European company that met most of my desired factors of presenting their services in a client-friendly manner. On the final day, my nominee won the cup not just because of my vote, but due to many other votes that other evaluators had given in favor of them. What looked nice in my eyes was also fine in the eyes of others. I asked myself whether such scorings and rankings could be also made for professional futurists. The idea made me write this blog post.

I think that ranking the futurists can be a challenging task due to a number of reasons. First of all, there is no universally agreed system of scoring for futurists. Secondly, futurists normally come from different fields of expertise and they cannot be ranked similarly. And thirdly, ranking the futurists may be done validly by institutions that might be authorized for such rankings. I would like to share some of my assumptions and questions about the feasibility of such a scoring system in this post. I should remind that the goal of ranking is not to drive low scores away but to claim them as candidates of high rank through professional development.

The first question that comes to mind is this: “What is the benefit of ranking?” or “Why should the futurists be ranked?” In my view, futurists can benefit easily from their own personal branding without ranking; but if they are going to be entitled to the merits of professional recognition, they should be identified by the degree of excellence they provide with their services. In other words, ranking is a means of qualification in terms of knowledge, skill and the quality of service that professional futurists provide for their clients. In my view, professional recognition and related merits logically belong to those who provide high-quality foresight outputs. Fortunately, the APF’s Most Significant Futures Works program has been serving this idea since 2013.

Another question that will arise concerning a ranking system is this: “Can the futurists be ranked according to their academic degrees, the number of their published or referenced works, the number of their students, the efficiency of methods and techniques they have developed or the number of their daily Tweets?” or “Should they be judged according to the values they bring to their own nations and the entire humanity?” Conventional methods of ranking may sound useful for scoring the futurists who live in societies where thinking and acting about the future is respectful, but how about futurists who live in regions where futurism is nonsense in the eyes of local decision-makers who are positioned based on aristocracy, not a meritocracy?

Any conceivable scoring system for futurists should recognize the fact that futurists are various in their talents and capabilities. While many of them are competent in applying qualitative methods of research, there are some who are brilliant in using quantitative methods of inquiry. Many futurists are good communicators and some of them are skillful in communicating what is ahead in innovative ways. Most of them are open-minded and lifelong learners, but what makes them valuable for themselves and the societies they serve? What are the social impacts of futurists and how can a ranking system measure them in national and international scales?

The first step that should be taken in this line is to provide a clear and detailed description of the knowledge, skills and attributes expected of a competent futurist or foresight practitioner. A competency framework like what is developed by the International Manipulative Physical Therapy Federation (Rushton, 2013) can be also made for professional futurists based on these components:

(1) Dimensions: The dimensions are the major functions for foresight performance at postgraduate level. The functioning of strategic foresight and futures studies graduates should be evaluated after their graduation in practice.

(2) Competencies: The competencies are the components of each dimension stated as a performance outcome. The competencies linked to a dimension indicate the standardized requirements to enable a professional futurist to demonstrate each major function for performance at postgraduate level.

Competencies can be divided into competencies related to knowledge, skills, and attributes.

(a) Knowledge: Encompasses the theoretical and practical understanding, use of evidence, principles, and procedures.

(b) Skills: Encompasses the cognitive, psychomotor and social skills needed to carry out pre-determined actions.

(c) Attributes: Encompasses the personal qualities, characteristics, and behavior in relation to the environment.

There are other concerns in the workplace that should be addressed. Research shows that ranking systems are often viewed negatively by people. However, many major corporations such as General Electric (GE), Intel, and Yahoo! use relative rankings and believe in their advantages. For example, Jack Welch, the former CEO of General Electric, instituted a forced ranking system at GE in which 20% of employees would be in the top category, 70% would be in the middle, and 10% would be at the bottom rank. Employees who were repeatedly ranked at the lowest rank would be terminated (Ryan, 2007). Corporate futurists or foresight practitioners might be ranked internally within the corporations they work, but how should they be ranked externally in a larger scale within the global community of futurists?

Relative rankings may create a culture of performance at corporation level by making it clear that low performance is not tolerated, but how about rankings that might be made by scoring futurists at a professional level? Should a low scorer be expelled out of international futurist communities? Or should he/she be prohibited from practicing the foresight profession without receiving required certifications? More importantly, what are the potential downsides to such rankings? Should a ranking encourage the futurists to upgrade their academic education in foresight and develop their professional skills, or conversely discourage them and deprive them from professional recognition?

There are many other questions and assumptions like what are mentioned above that make a long list. They highlight a special attention that should be paid to all the details of any effort that would be likely made towards ranking the futurists. Until the completion of a standardized ranking system, conducting self-other rating agreement surveys can be the easiest way to capture a better understanding of futurists’ standing in companies and organizations they serve.


Rushton, A. (2013). Educational Standards in Orthopaedic Manipulative Therapy, Part A: Educational Standards. International Manipulative Physical Therapy Federation.

Ryan, L. (2007, January 17). Coping with performance-review anxiety. Business Week Online, 6.

Tags:  foresight  futurist  professionalization 

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Towards Disintermediation

Posted By Administration, Monday, January 12, 2015
Updated: Saturday, February 23, 2019

Jason Swanson shares his thoughts with us about the “Disintermediation” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

In my post last month I explored a few ideas of how big data might affect the futures field in terms of both practice and business. This post will continue that exploration, this time focusing more on the potential implications for the business side of things as big data tools such as R and Python come to fore, as well as academic programs such Udacity’s nanodegree in Data Analysis come to market.

I would like to explore the implications of big data on the futures field using a lens of scarcity, abundance, and disintermediation. There are quite a few examples of industries that have experienced line of development. The retail industry comes to mind with consumers once relegated to seemingly few retail outlets, then an abundance of options, and now retail is becoming increasingly disintermediated as the internet has allowed for increased opportunities for peer to peer transactions. The music industry is has followed this path, and even public education here in the United States is dealing the changes that a system and its stakeholders have to contend with as it moves from the abundance period in terms of information access into disintermediation.

Apply this lens to the futures field, one might make the argument that thinking about the future is indeed disintermediated. Everyone person alive thinks about the future in some capacity. It is part and parcel of living. When we define the futures field as the professional practice of studying the future using a defined methodology, an argument can be made that the field is still at the scarcity stage. The number of professional futurists is tiny in comparison to other professions. I can’t help but reflect on how many times I have given my futurist elevator speech during my brief time in this profession whenever I am asked what it is I do.

What might it take to move the futures field from scarcity to abundance or even all the way to disintermediation? If we are to consider the examples of retail, music, and education, the key additive was technology, particularly when moving from the abundance stage to the disintermediation phase. Concerning the futures field, there are signs that the field may be beginning to move from the scarcity stage to the abundance stage. Among those signals are the small but growing numbers of academic programs offering courses and degrees on the topic of foresight, growing interest in existing foresight courses and programs, and even growing interest in the application of foresight methodology in disciplines such as design.

If these signals might signify a slow move to the abundance stage for the foresight field, what might that look like? Let’s use the retail industry at the abundance stage as an example once more. At the stage of abundance, the consumer had a high degree of choices in retail outlets, with a high degree of choice in terms of items, and many price points for those items. The foresight field at the abundance stage may look similar to the retail industry; a high degree of choice in terms of foresight services, many practitioners, an increase in organizational or internal futurists, more choices in training programs, in short, more. Of course a critical uncertainty here is will there be demand to account for all this “more” beyond simply an interest in methods.

As I mentioned in my last post, big data has the potential to change our practice and our industry. As big data tools continue to simplify and improve, there will be an effect on the futures field. One of those effects might be speeding futures through the abundance stage into disintermediation. As these tools develop, become easier to use and more accurate, the by product may be a growing interest in what’s next. Using predictive analytics and modeling to give a client, company, or organization accurate peaks into the future could push the field into adopting these tools (Again, please check out Julian’s blog post for an excellent use case). The move towards incorporating this type of data into our work may have the effect of “legitimizing” the work in the eyes of clients who in the past may have been standoffish, or who may shy away from more qualitative pursuits.

As big data tools continue to develop over time, these tools have the potential to be a factor in moving the futures field into the disintermediation phase. We can expect over time that big data tools, like nearly all forms of technology, will become cheaper and easier to use. As the ease of use increases and price points fall there is the potential for new users. Given enough time, a user might be able to utilize these tools through apps on a phone or other personal devices. A person might be able to one day run predictive models with the same ease of sending a text message. This sort of breakthrough could be compared to having a futurist in your pocket, crunching massive sets of data and giving highly plausible scenarios back to the end user. At this point the futures field may be considered disintermediated, with users being able to directly use methods and tools in developing images of what the future may be like.

Could big data and big data tools be a catalyst to push the futures field towards disintermediation?

Tags:  disintermediation  foresight  futurist 

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R and Python for the evaluation of trigger events

Posted By Administration, Monday, January 5, 2015
Updated: Saturday, February 23, 2019

Julian Valkieser shares his thoughts with us about the “R and Python” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

In my last article, I referred to the importance of Big Data as it has become more and more important for decisions in medium-term periods. Big Data is an often used buzzword – especially by large corporations and middle management levels.

I have mentioned R programming, claiming that everyone in the area of Foresight should learn it in the near future. Now we have to add the programming language Python. For people with a lot of self-discipline I would like to recommend a Google search and a good book. For myself, I have gone the way of Coursera, a Massive Open Online Course (MOOC), which I can highly recommend.

It is not so much about being a programmer. After all, it is not our field of interest. Rather, it’s about using these programming languages to play with a large amount of data so that you can develop an understanding of the benefits. Of course, there are also tools that require no programming skills. Maybe you have heard of NeuroBayes or RapidMiner? But someone who wants to sell a car should also know how a car works.

Especially the tool RapidMiner shows very clearly what makes this kind of tools and what Big Data is all about: The visual presentation or summary of large amounts of data. Only a good representation and summary can be a benefit from Big Data.

Beautiful examples of where data analysis for short-term forecasts are used are as follows:

Of course, these examples are not transferable or all reality based. But – to get back to the metaphor of the car – in terms of data analysis, we find ourselves status quo in the early days of the Ford Model T.

There are certainly countless more of such examples. All more or less well understood and scientifically correct. Another example: Nate Silver Predicting an election.

One thing you can say now: Forecasts based in the past are less reliable, or partially obsolete, for example, if you are emanating from seasonal recurring events, such as the flu or the purchase of heaters in winter time. If you can analyze data in terms of motives and interests (See also Computing and Intuiting futures from Sandra Geitz), then it gains a different picture. Motives and interests provide information representing “we are going to…,”, situations such as “I’ll buy a car if I get a raise.”

This could be transmitted at the macro level, e.g. if the Democrats are elected in 2020, they will finally put through a specific law, because we all know that they are still working on this. It is very likely that they will do it if external circumstances allow it. This is when Big Data comes into play. The Democrats re-election depends in turn on the people’s interests which can be reflected, e.g. on Google queries.

All of this relates only to medium-term time horizons and Foresight is less about making a prediction, rather likedepicting a scenario. However, a scenario could be represented more closely or exactly, as already hinted by Jason with his, “A Shrinking Cone of Plausibility” blog. Big Data could serve to draw the “so called trigger events” in this case to create scenarios based on these trigger events. For example: The next US president election, Jason used a Cone of Plausibility in a familiar example. I like this approach. But for me, Big Data is used for the representation of starting points or trigger events with which you can create scenarios in the distant future.

Existing Scenarios are mostly based on the current day or status quo. At this point, let’s go back to the Big Data analysis where Democrats will be re-elected. Based on this forecast with a certain probability we can build a scenario that is not mirrored from today’s point of view, but from the status of the so-called trigger event that a particular party is elected. Of course, this should not be the only factor for our scenario. Other trigger events could be used such as other interests and motives. What are the media interests? In what way have the most protests been expressed? Which governments were overthrown and which companies enjoy continuously high investments in the market? How have prices developed for this and that? This information be more precisely reflected in the near future with Big Data analytics. Of course, not 100% accurately – but more accurately than if not used, or only subjectively evaluated.

Try to engage in R and Python. Look at tools above with which you can analyze data and represent it visually, even without programming skills. The former and the latter tend to be the same.

A pretty manageable article on R and Python in terms of big data is from the DC data community.

But finally – why R and Python? R is primarily used for visual analysis of structured data sets, such as you already know from an Excel spreadsheet. Corresponding programming packages could complement R. Python is a little more powerful, albeit with the appropriate packages the functionality of both languages overlap. The scene will still argue which tool is more appropriate. Using Python for the analysis of texts are getting really exciting. Essentially, it is mostly a matter of counting words. How often is a corresponding keyword mentioned in a particular text or even more interesting, how often is it mentioned in a specific timetable in the whole web? Since most of the texts can be classified according to one author, and date etc., it is exciting here to see who mentioned what, when, where and why. And that’s what makes the data analysis so exciting: text analysis. As mentioned above, interests and motives are the valuable insights as they represent a target of individuals and groups. I might tend to buy more bio in the future or try to travel without a car? Of course, most of us won’t write it down digitally. But who else is active in clubs, google-searching, mailing and shopping online? It’s all about your interests!

Have a nice easy entry case in R and Python offered by Beautiful Data Blog.

Tags:  foresight  future  Python 

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How Can Corporate Foresight Create Value?

Posted By Administration, Monday, December 15, 2014
Updated: Saturday, February 23, 2019

Alireza Hejazi shares his thoughts with us about “creating value by foresight” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

Talking to an architecture company CEO recently, I was confronted with this question: “How can corporate foresight create value in my company?” I wanted to offer a “business-as-usual” response, but I changed my mind by remembering Rohrbeck and Schwarz’s (2013) clear-cut response identifying four faces of value creation through corporate foresight. Basing my response on their view, I told my CEO friend that corporate foresight may create an enhanced capacity to perceive, interpret and respond to change, an enhanced capacity for organizational learning, and more impacts on other actors.

In fact, the philosophy of applying corporate foresight is to reduce the uncertainty by scanning the unknown in the environment. If this is the least and perhaps the most value it can create, then employing corporate foresight is worthy enough to be considered by managers and leaders. I also suggested my CEO pal to form a multi-disciplinary team who might lower the risk of disregarding and misunderstanding the change factors. In this way, his company wouldn’t fall into the traps that might be made by personal biased assumptions about future.

My suggestion for shaping a multi-disciplinary team originated from Gracht and Stillings’ (2013) observation maintaining that interdisciplinary cooperation not only could solve the problem of biases, but also satisfies the future needs of the target customer. In this sense, techniques like scenario planning may sound useful as far as they depict the picture of the future market and introduces new product concepts that might provide new opportunities and development routes for the market and the technology. Corporation decision makers can enrich their short-, medium- and long- term decisions significantly through alternative scenarios or by technology road-mapping.

However, as Rohrbeck and Schwarz admit, the implementation of corporate foresight activities is still limited due to uncertainty in getting desirable outcomes and return on investment and the degree of their value creation for strategic planning. On the other hand, too much focus on current conditions and activities makes the organizations inattentive to small changes that are taking place in the wider environment but impactful in the future.

Rohrbeck and Schwarz’s review of foresight research in the European context reveals that foresight can create value for innovation and strategic management through utilizing appropriate methods in the process of decision-making and strategic planning. Companies who practice foresight in different sectors gradually find out that foresight is a tool of value-creation. It contributes to their survival in the competitive business environment, especially in time of discontinuous change. More importantly, the application of corporate foresight methods can lead to the improvement of organizational responses and thereby improving values in innovation management. This shapes Rohrbeck and Schwarz’s (2013) paradigm that links knowledge creation to value generation.

In my view, if the value of foresight is to influence decision, then foresight practitioners should extend their efforts beyond conventional business decision making to discover alternative methods and analyses that might enrich businesses, organizations and policy makers with new solutions. The simple world of Shell Company and its well-known six scenarios in oil crisis is evolved into a complex world of STEEPV interactions and interpersonal relations where the survival of values is tested every day. Today, value networks are drenched in intangible value exchanges that create their strategic advantage in the market.

Corporate foresight is able to aid companies which create value by connecting clients and customers that prefer to depend on each other. These companies create and distribute tangible and intangible values through networks that are webs of dynamic relationships and exchanges between two or more individuals, groups or organizations. In my view, the success of corporate foresight in the future depends on the contributions that it would make to the development and management of these networks. For such success to happen, effective interpersonal networks must be built on a foundation of expertise, trust and shared understanding. I think that APF is exactly established to build that foundation now and in the future.


Rohrbeck, R. & J. O. Schwarz. (2013). The value contribution of strategic foresight: Insights from an empirical study on large European companies. Technological Forecasting and Social Change, 80(8), 1593-1606.

Von der Gracht, H. A., & Stillings, C. (2013). An innovation-focused scenario process: A case from the materials producing industry. Technological Forecasting & Social Change, 80, 599-610.

Tags:  company  foresight  value 

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“Warming up your brain” using the Theory of Inventive Problem Solving

Posted By Administration, Monday, December 8, 2014
Updated: Saturday, February 23, 2019

Daniel Bonin  shares his thoughts with us about “TRIZ” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

The Theory of Inventive Problem Solving (TRIZ)

Some weeks ago I learned about the basics of TRIZ (Theory of Inventive Problem Solving). I find the method itself and also the history of its development fascinating. The development of TRIZ started during the mid 1940s in Russia. Round about 40.000 patents were analyzed to reveal patterns, similarities, differences and laws in order to formulate methods that help to standardize the problem solving processes*. One of the inventors TRIZ, Genrich Altshuller had to endure years in the gulag after he criticized the ignorance of the leadership regarding innovation and invention (Mishra 2006). During this time, he continued to develop TRIZ and made friends with other prisoners by telling them science fiction stories he analyzed as well. The TRIZ toolkit finally made its way to Europe and the U.S. after the end of the cold war.

The theory TRIZ assumes that typical solutions can be found for recurring problems and that psychological barriers like inertia hinder problem solving. Thus algorithmic problem solving methods and creativity techniques were developed to overcome such problems. One can say that in contrast to brainstorming or trail and error, TRIZ relies on solutions that have proven to be useful in the past. Famous methods of the TIRZ toolkit include the 40 TRIZ Principles (described later on) or the Algorithm of Inventive Problem Solving (ARIZ).

Clearly, TRIZ aims to find solutions to technical problems and does not intend to describe possible futures. But the inventors of TRIZ believed that creativity techniques are helpful to over overcome psychological inertia and can increase the degree of inventiveness of ideas. For instance the Size-Time-Cost-Operator method assumes that material, space, time and money/costs are (a) unlimited or (b) limited/ nonexistent to find new solutions to problems (Hentschel et al. 2010, Savransky). I believe that approaches like the Size-Time-Cost-Operator could be used to imagine or invent unusual and extreme futures. And what I find particularly interesting is the idea to use some of the TRIZ creativity techniques to create a “warming up and stretching program” for workshops in order to familiarize participants with outside of the box thinking.

Using TRIZ to facilitate creativity and encourage out of the box thinking in workshops

Imagine you have to carry out a workshop with participants that have never thought about the future. To make the topic easily understandable, a simplified perspective might be presented. Reading a book of Savransky (2002) on TRIZ, I came across some methods and games that might be used to create such a “warm up and stretching program”.

The Value Changing Method confronts participants with the question of what if an object (e.g. technology or societal values and norms) with an extraordinary value is rendered useless. One could then possibly use the Good Bad Game, a game that requests to find something good in a bad situation (or the other way around) to direct the focus toward positive implications and thus further facilitate creativity. The Snow Ball Method could then finally be used as a warming up activity to introduce the basics of system dynamics. Here you think about interrelationships and ask questions like: what happens to X if Y is changed and how does this affect Z.

Other application fields of TRIZ

Furthermore the more technical parts like the 40 TRIZ Principles might be used to simplify foresight methods. The 40 TRIZ Principles are usually applied to reduce complexity and increase effectiveness of systems. Foresight methods can be undoubtedly considered complex. The 40 TRIZ principles (e.g. “Taking out”, “Merging of Objects”, “Periodic Action” (replace continuous action with a periodic one), Skipping”, “Cheap Short-Lived Objects”) consist of reoccurring solutions that were used in the patents analyzed to solve problems and cut through complexity**. As foresight processes are labor and time intensive small and medium sized companies might struggle to deploy the necessary resources. A simplification of foresight methods might be desirable when educating or establishing foresight processes for such clients. Bannert and Warschat (2007) used the principles to modify management methods like the scenario analysis (click here for a illustration of their simplified method and a brief overview on some TRIZ principles).

The methods described in this blog post aim to create novel ideas by changing an existing object or its function. I am wondering if the TRIZ toolkit could be used to invent Wild Cards based on the present by using tools such as the 40 TRIZ Principles or the so called Fantogram. The Fantogram describes two dimensions: (a) the way an object is changed and (b) the methods used (see figure below; click to enlarge). The advantage of this method is that you create more creative ideas. Normally you would tend to come up with a new based on only one dimension (Zhuravleva 2005). The invention of Wild Cards will be a covered in another blog post.

Tags:  Fantogram  foresight  TRIZ 

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A Shrinking Cone of Plausibility?

Posted By Administration, Monday, December 1, 2014
Updated: Saturday, February 23, 2019

Jason Swanson shares his thoughts with us about “the cone of plausibility” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

In my colleague Julian Valkieser’s latest blog post, Julian wrote about the start-up Mapegy, the programming language “R”, and Big Data analysis as they relate to creating systems models and possible applications in foresight. It was a fascinating post and I look forward to reading more of his analysis as I am excited about the uses for Big Data in the foresight. The potential for Big Data to be disruptive is massive. One of the potential disruptions could to the foresight field.

With the development of “R” and start-ups like Mapegy, along with the generation and capture of more and more data, and new tools for analysis, our ability to analyze massive data sets is growing in leaps and bounds. Analysis of complex data sets combined with predictive analytics is allowing us to create increasingly accurate models and predict outcomes and behaviors. By now most people are familiar with the story of Target using data analysis to correctly predict that one of their customers was pregnant. A more recent example could be found with HealthMap , a project of Harvard Medical School and Boston Children’s Hospital, which predicted an Ebola outbreak 9 days before the World Health Organization began reporting irregular spikes in cases.

While neither of these are long range predictions, as we capture and analyze larger and larger data sets the ability to predict outcomes and behaviors with accuracy, at least in the near term, goes up. Even though Futurists are not in the prediction business, will being able to accurately assess the near term cancel out the need for long range thinking in multiple narratives? Furthermore, would an increasing reliance on Big Data analysis and prediction affect not only the business side of foresight, but also the the study or practice of foresight itself? Would the cone of plausibility shrink as we develop the ability to analyze larger data sets with increasing sophisticated tools? Would we see a rise in a rise in wild cards?

While I can only speculate on these questions, there is a possible implication that as we gain the ability to use data analysis and models to predict outcomes with greater accuracy there is the potential for the cone of plausibility to shrink. The highest probability in terms of outcome or behavior might become a major piece, or the piece, in terms of a baseline future, with variability from the models in terms of outcomes or behaviors as your alternative futures, or greatly influencing alternative futures. Those probabilities could create or influence the bounds of the cone of plausibly. The greater the degree of accuracy, even in the near term, could potentially act to focus or tighten the cone, in effect shrinking the bounds of plausibility.

As the cone of plausibility shrinks, there might also be a potential rise in wild cards, specifically Type 2 wild cards. Introduced by Dr. Oliver Markley in his article, “A New Methodology for Anticipating STEEP Surprises” , Dr. Markley defines type 2 wilds cards as “having high probability and high impact as seen by experts if present trends continue, but low credibility for non-expert stakeholders of importance”. If the bounds of plausibility were to tighten, even some alternative futures which in the past might have been considered plausible alternate futures might fall out of the bounds of plausibility. By falling out of the bounds of plausibility, those same alternative futures have the potential to fall out of creditably for non-expert stakeholders of importance and as a result could be classified as type 2 wild cards if the impact were thought to be enough. In the event that the potential impact is possibly too low to be considered a wild card, a new term may be needed for the alternative futures that do not fit inside of the bounds of the predictive models.

It will be interesting to see the effect that Big Data will have on the foresight field. Will clients shy away from long term thinking in favor of near or short term predication? Will increasingly accurate models add to or possibly alter our foresight toolboxes? How is the futures community currently utilizing big data and predictive analytics?


Markley, O. (2010). A new methodology for anticipating STEEP surprises. Technological Forecasting & Social Change, 78(6), 19-19. Retrieved December 1, 2014, from

Tags:  foresight  futurist  plausibility 

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Desert island futures?

Posted By Administration, Thursday, November 20, 2014
Updated: Saturday, February 23, 2019

Sandra Geitz shares her thoughts with us about “desert island futures” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.


Who and what would you bring to your desert island?

Imagine for a second, that you’re planning your own island retreat… a self-imposed, indefinite island retreat. Who would you take on your journey? Whose skills are most useful? What seems essential to bring along?

Now, is this scenario really so far-fetched? Let’s consider emerging social dynamics. Both the pace and volume of social media streams and vast hidden forces like globalisation and digitisation promote increased competitive and attention-seeking behaviours. How do we tend to respond to all this? By withdrawing to the familiar, comfortable and well-known? Are we retreating into closed worlds, hostages within reassuring personalisation algorithms, Eli Pariser’s filter bubbles, with a world outside hostile to our comforting ideas and worldviews, filled with those shouting, trolling and blocking any chance of real debate and learning?

“Both Whatsapp and Secret represent the ascendency of the phone book over the friend graph. It’s back to the future,” tweeted Yammer CEO/ Founder, David Sacks (Meeker 2014).

Ever more sophisticated filtering will reduce external noise in our social media feeds, and the potential for proliferating private desert islands of our close friends and genuine interests, according to Steven Rosenbaum, content curation author and promoter (Decugis 2014). Naturally, he advises business to curate quality content or face extinction via irrelevance. Seth Godin’s concept of permission marketing on steroids.

So what, you may ask?

Although, it appears an attractive solution in the current carcophany of noise, attention-seeking and celebrity trivia, there are significant downsides to this future of private retreat. Antony Funnell’s (2014) recent Future Tense program on ABC Radio National, examined this in perspectives on the power of provocation.

Funnell’s (2014) first guest, Graeme Turner, Emeritus Professor of Cultural Studies at the University of Queensland explained that the purpose of provocation used to be about challenging and debating ideas. Now, modern provocation has become a competition for attention, rather than ideas. It is about promotion and entertainment, requiring greater shock value and/or engagement over time to be noticed by provocation- immune audiences and/or participants. Turner believes the future of public debate and innovative ideas seems quite bleak (in Australia, at least). There are enormous competitive media pressures to entertain, whilst countering public dis-engagement with more complex or sophisticated issues.

Another perspective was offered by Scott Stephens, Religion and Ethics program editor for ABC Online (Funnell 2014). In his studies of the spread of philosophy, provocation and innovation were the product of dialogue and debate within historical constraints. Stephens suggests a future of greater discernment and discrimination is possible, if we are able to overcome cultural relativism or permissiveness for anything goes. Potential awaits for futures of value, integrating judgement with broad social acceptance.

Very similar conclusions to those of Alex Pentland’s (2014) Social Physics, were reviewed in a prior post. Pentland designed experiments that measued the productive output of different groups and the patterns of groups interactions. He found that innovation was optimised with iterative patterns of exploration for novelty interspersed with the socialisation of these ideas for acceptance. Pentland believes a diversity of shared experiences and history builds a stores of both trust and experiences to associate with for future application.

“Feedstock for innovation is insight – an imaginative understanding of an internal or external opportunity that can be tapped to improve efficiency, generate revenue, or boost engagement,” states the recent HBR article of Mohanbir Sawhney and Sanjay Khosla (2014). Similarly, foresight can be thought of as the imaginative understanding of potential impacts of internal and/or external factors in the future. The purpose of foresight is to help make decisions, solve problems, identify and adapt to changes by thinking about what could happen and how to influence and enable what should happen.

Future implications?

Both foresight and innovation introduce novel ideas for social acceptance to organisations and/or the public. They involve challenge existing ways of thinking, provocation of current thinking to generate alternative ideas, perspectives and spark imagination.

In current social dynamics, can foresight practitioners and the field expect a desert island welcome?

How might we further socialise foresight?


Decugis G 2014, The Desert Island: the future is the curated Web for Steve Rosenbaum in Curate This!,!, viewed 7Nov 2014,

Funnell A 2014, Perspectives on the power of provocation, Future Tense, ABC Radio National program audio and transcript, viewed 3Nov 2014,

Meeker, M 2014, Internet Trends 2014: Code Conference, Kleiner Perkins Caulfield & Byers, slideshare, pp. 35-37, viewed on 9Nov 2014,

Pariser E 2011, Beware online “filter bubbles”, TED Talks, viewed 9Nov 2014,

Pentland A 2014, Social Physics: How Good Ideas Spread – the lessons from a new science, Scribe Publications Pty Ltd, Brunswick, Australia and London, United Kingdom.

Sawhaney M and Khosla S 2014, Managing Yourself: Where to Look for Insight, Harvard Business Review, November 2014, pp.126-129, viewed 5Nov 2014,

Tags:  foresight  future  islans 

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