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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:

http://edition.cnn.com/2012/07/09/tech/innovation/police-tech/

http://www.popsci.com/science/article/2011-10/santa-cruz-experiment

http://www.skyhookwireless.com

http://firstmonday.org/article/view/3663/3040

http://www.slate.com/blogs/how_not_to_be_wrong/2014/06/09/big_data_what_s_even_creepier_than_target_guessing_that_you_re_pregnant.html

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.

THE RECOMMENDATION
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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Looking at next year’s list

Posted By Administration, Monday, December 29, 2014
Updated: Saturday, February 23, 2019

Bridgette Engeler Newbury  shares her thoughts with us about the “future possibilities” 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.

It’s that time of year. Celebrations and traditions. Endings and beginnings. Promises and provocations. Reflections and resolutions. And now that the tinsel, incandescent holly and Santa-shaped shortbread are on sale, the flurry of ‘top ten’ lists will appear as quickly as the hot cross buns do (across supermarkets in the UK and Australia at least).

As Jim Carroll says here it’s relatively easy to extrapolate current trends into a ‘Top Ten for 2015’; it’s quite a different matter to look further ahead, as he does to 2025.

Some of those lists will posit that we’re in an era of innovation, entrepreneurship and technology to transform cities, economies and lives. Spurred on by wearables, rapid urbanisation, smart cities and rising popular demand for access to high-quality (and sometimes sustainable) infrastructure, it all leads to seemingly ‘good’ growth that is assumed to follow globally.

So I want to highlight Mashable’s list of notable innovations in 2014.

Few of the innovations that improved the world in 2014 will make onto the top tens for greatness in 2015 or beyond, and only a couple might be considered trend-setters. Why, I wonder? Compare it to a list of tech predictions like this one – just who are the incredible innovations on this list intended for? What worldview or model of subjectivity is inscribed in the scenarios and technologies offered by the developers of such marvellous wearables and other remarkable tech wizardry? And who stands to benefit? When you compare this with the Mashable list, it’s pretty obvious that most espouse a pronounced way of thinking about the world and civil society, with rather limited implications for people, planet and participation.

It is one thing to reinforce the beliefs, value systems and infrastructures that underpin particular ways of life; quite another to expound the importance of technologies that privilege a few when reliable access to electricity, clean drinking water, somewhere safe to sleep or sanitary facilities are not part of everyday life for too many. I’m not denying the need for or value of innovation, invention or experimentation (that Mashable list embraces all of those) but I am questioning the way value and need are prioritised, and by whom, based on what, and the kinds of futures that are being shaped by the infrastructure, innovation and technology these choices deliver.

As Andy Hines notes in his latest blog, maybe we could take some time to explore the ‘why’ of values, not just the ‘what’. Because there’s more to life in 2015 than networked information technology. Lasting change has to come from within, whether it’s individual, community or organisation. It won’t come from an app alone or something we plug in.

Tags:  future  technology  value 

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Computing / Intuiting futures?

Posted By Administration, Monday, December 22, 2014
Updated: Saturday, February 23, 2019

Sandra Geitz shares her thoughts with us about “intuiting 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.

 

Do you synthesise opinions and judgements to develop potential futures?
Alternatively, do you conduct wide-ranging data analysis for potential futures?


Recently I’ve been reflecting upon the various ways it is possible to source potential views about our futures. How there are multitudes of opinions and judgements that contest what are valid and plausible futures. How various sets of data are either universally relevant, hotly debated or ignored, depending on one’s interest of the specific issue studied. Is it ever possible to completely separate facts and opinion from one another?

This led to the diagram below, which is a synthesis of Sohail Inayatullah’s Causal Layered Analysis: litany, facts, values and myth, discussed in an earlier post, and Otto Scharmer’s Theory U process: downloading (judgement), open mind (analysis), open heart (connection), open will (insight).

Judging issues increasingly involves contested opinions, ranging from expert judgements to social media flaming. Analysis may include or exclude publicly and privately available data, especially as huge volumes of big-data are generated. How we view the world, our values and deep stories, shape which data we view as valid and relevant to an issue. Similarly, others with different perspectives will connect with alternate data and opinions for this issue. Hence, the preference for a depth method like Causal layered Analysis (CLA) in contested views of our futures. And, what issues are not contested nowadays…

Rarely, are judgements or analysis sufficient alone. Underlying assumptions, biases, or beliefs which can influence or determine either of these inputs remain hidden and unknown. Even, combining judgement and analysis, gives a similar shallow and limited future view.

Connecting with the people, understanding their outlook and values, generates a critical view of the input data and opinions. This illuminates what parts may have been included or excluded from final result. In this way, greater depth and breadth to potential future options may be perceived, enabling one to imagine interactions and potential responses by appreciating the values of each participant.

Developing an insight into the deep stories or myths of each participant, can provide the richest potential futures options. The effort to distil and synthesise participant’s values into succinct story headlines, appears to make them memorable. And then, quite often, after some time germinating, ruminating… combinations of these insights, and interactions form new stories, resolutions and potential futures… In this way, Causal Layered Analysis can be used as a prospective method, beyond analysis.

What are your experiences using judgement, data, values and stories for futures?

Does this compute or intuit with your experience?

Tags:  analysis  future  judgement 

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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.


References

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?

Notes:
http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

http://www.uschamberfoundation.org/blog/post/can-big-data-predict-future/41983

Markley, O. (2010). A new methodology for anticipating STEEP surprises. Technological Forecasting & Social Change, 78(6), 19-19. Retrieved December 1, 2014, from http://www.imaginalvisioning.com/wp-content/uploads/2010/08/Anticipating_STEEP_Surprises-TFSC2.pdf

Tags:  foresight  futurist  plausibility 

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Why should I take a step into "R"

Posted By Administration, Monday, November 24, 2014
Updated: Friday, February 22, 2019

Julian Valkieser shares his thoughts with us about “R language” 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.

Of course, the topic “Big Data” was already mentioned a few times in the Profuturist blog. Of course, we all know what it involves and consists of. We now move to a higher and higher activity on the Internet. We produce data – massive data. Worldwide, already 3 billion people are online. We spend much of our time online. The amount of data that is created, rise to a stunning 107,958 petabytes per month by 2018. For example, these are over 100 mio. hard drives with a capacity of 1 Terabyte – a drive with capacity the most of us would never use.

Companies like Google act and work with this data. Of course, they are not focused solely on this one business model. So Google is spreading in different directions. But a focus can be seen. Google is also spreading more and more offline. Why?

The data created online, are relatively negligible in comparison to the data you can still receive from the physical world. Behavior patterns online are certainly interesting, e.g. for the field of e-commerce – but behavior and properties offline are much more interesting. The greatest benefit would be to analyze all information that can be obtained and secondly to be able to deduce something. Exciting!

Here I want to present an example specifically for research-intensive areas. The start-up “Mapegy” from Berlin in Germany.

Mapegy is the compass for the high-tech world, referring to their own definition. One possible application would be the following. Let’s imagine.

I am interested in a specific topic and I would like to evaluate. Now Big Data comes into the game. Let’s take the example of a patent analysis. With tools like Mapegy I could figure out easily, who is an important stakeholder of a particular technology development, as he is related to another and what influence he has. A method of representation is about maps. Stakeholders and technological developments are illustrated via a kind of map. The larger the island, the more stakeholders gather around a particular development. The higher the mountain, the more patents were applied by a stakeholder. The closer the islands are arranged to each other, the stronger is the reference to one another. With this kind of Visual Analytic it is quite easy to illustrate how a certain subject area is connected to others.

And that is the sticking point. A lot of data is already available. But finally the correct processing and representation make this data useful.

AT THIS POINT I WANT TO MENTION “R”.
“R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls and surveys of data miners are showing R’s popularity has increased substantially in recent years.” (Wikipedia)

Someone who can program in “R” is well paid. Even at the upper end of the scale. And not for no reason. To be able to understand a context and deduce recommendations for action, not only in the economy, but also in science and research, such as in biotechnology and of course the pharmacy, is a higher aim in business and decision processes.

If you already understand some small connections, you can use it to create a network and may even explain the behavior of systems. In this specific example, it would be human behavior. Of course, the influencing factors are still too complex to be able to make reliable predictions from available data collections. But the more powerful computational resources, the closer is the opportunity to analyze all factors.

Mapegy is an example of visualizing relationships and influencing factors via big data analysis. For example, the cost of genetic testing is an indicator of how quickly data analysis will change in the next years. The costs decreased in recent years more as the price of computer chips in relation to Moore’s Law. In my next article I go further to the development in big data analysis with “R”.

Tags:  big data  R language  tec 

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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?


References

Decugis G 2014, The Desert Island: the future is the curated Web for Steve Rosenbaum in Curate This!, Scoop.it!, viewed 7Nov 2014, http://blog.scoop.it/2014/11/07/the-desert-island-the-future-of-the-curated-web-according-to-steve-rosenbaums-curate-this/

Funnell A 2014, Perspectives on the power of provocation, Future Tense, ABC Radio National program audio and transcript, viewed 3Nov 2014, http://www.abc.net.au/radionational/programs/futuretense/june-29th-segment/5548814

Meeker, M 2014, Internet Trends 2014: Code Conference, Kleiner Perkins Caulfield & Byers, slideshare, pp. 35-37, viewed on 9Nov 2014, http://www.slideshare.net/kleinerperkins/internet-trends-2014-05-28-14-pdf

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

http://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles?language=en

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, https://hbr.org/2014/11/where-to-look-for-insight/

Tags:  foresight  future  islans 

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Why do we blog?

Posted By Administration, Monday, November 17, 2014
Updated: Friday, February 22, 2019

Bridgette Engeler Newbury  reviews the logic and philosophy of blogging 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.

This isn’t some existential analysis of foresight and futures work, but a simpler question about value, purpose, intention and utility. Some pretty basic research (there is a methodology if you want it) suggests most blogs are written to raise profile, to drive traffic to a website, to build an email list, to share information, an opinion or thoughts on a subject, and/or to sell books. So leaving aside that last point, I ask myself after just over one year as an Emerging Fellow, are we doing any or all of that? And does it matter?

Two decades or so in, blogging (still) has its challenges. People have been writing about themselves and things they find interesting but it’s easier for some than others. Sharing opinions and thoughts isn’t for everyone. And we don’t have to blog. Just because we can, doesn’t mean we do.

Maybe it’s because not everyone has something to write about and share. Maybe we do, but can’t write about it. Maybe what we write isn’t getting us the sign-ups/comments/views/likes/website hits… Then later, maybe we run out of topics, ideas and clever headlines. And maybe we wonder if it’s worth all the time we spend on it.

Why would a futurist blog? Why would we share what we have to say? Would it make futures work interesting and digestible? Are we doing it to get noticed? To be read and understood? And why would we assume that others value what we have to say?

To connect with people. If our long-term goal is to build a community involved or interested in futures thinking, a blog might kick off two-way communication with people who will spread the word.

To be better communicators. Writing and honing a blog and consistently delivering (good) content is a great way to practice craft, discipline, voice and style. It’s almost inevitable that your writing will improve over time. And your ability to distil complex ideas into small sound bites.

To form relationships. There’s a community out there who want to read, learn from and challenge our ideas. People who can help us find our way. Let’s find them and have a conversation.

To find our feet. A blog can be fertile ground for idea exploration and expression.

To get noticed. Apparently a goal (or two) of every blog is to generate content that becomes a book that you then sell. Maybe not exactly true for APF, but we could suggest that our blog generates content good enough to prompt visitors to come back regularly, subscribe to our other social media outlets and perhaps other futures blogs and media. Our blog can get readers, colleagues and peers, and anyone else who may be able to offer support, discussion and/or opportunity.


So are we doing any of this? And how well are we doing it? Are we creating interesting, useful and challenging content that has value, purpose and utility? Blogs are not intrusive. No one has to respond. Reading is voluntary, and done when convenient. So who decides if we are making it worthwhile?

Tags:  blog  foresight  futurist 

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How to Evaluate a Foresight Course

Posted By Administration, Monday, November 3, 2014
Updated: Friday, February 22, 2019

Alireza Hejazi proposes a method to evaluate foresight courses 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.

Teaching foresight is both enjoyable and challenging. New and experienced teachers alike are constantly faced with making foresight theory and practice meaningful for their students. Developing and running a foresight course is a challenging job, but evaluating it can be more thought-provoking. Looking at a foresight course from different points of view, foresight instructors may find this question meaningful: “how should we evaluate a foresight course to ensure the credibility of learning outcomes?” This blog post reviews three stages of evaluation and deserves foresight coaches’ care and appropriate action.

1. Pre-evaluation
Many observers believe that an evaluation agenda can be developed only after running an educational program. However, if foresight instructors inspect these three points in their syllabi with the support of an expert, they will save much energy, time and fund for future reviews and corrections: (1) Establishing instructional objectives, (2) Planning instructional strategies, and (3) Assessing learning outcomes. Without enough care for these three items, every educational initiative is doomed to failure.

Instructional objectives are “statements describing what the student will be able to do after completing a unit of instruction” (Kibler, Cegala, Barker & Miles, 1974, p. 2). Instructional objectives are typically articulated on the course syllabus, and many teachers provide detailed instructional objectives for specific units covered in a course. They help students know what to expect. In using instructional objectives, teachers are better able to articulate what they teach, and can better help students meet those objectives. For example, we can tell our students that they will be able to lead a scenario learning process for a leadership team that tests their strategy against a range of possible future developments.

Instructional strategies that are usually used in foresight courses include futurist lectures, discussions, group activities, reflection papers, and presentations. The choice of instructional strategy depends on the particular goals of a specific lesson or unit. In the domain of strategic foresight, common education base indicates that instructional strategies should be developed so that students become skillful at learning and practicing foresight knowledge, engaging in both written and oral academic discourse, working fluently with foresight data, building environmental scanning systems, developing scenarios and problem solving effectively. All these require providing students with particular opportunities, models, and guidance needed to develop each of those sets of skills.

Learning outcomes are more determined by the motivation, skills and behaviors of the student and less by differences among instructional strategies. In other words, any single instructional strategy is inherently more effective than all other strategies. Lerner et al. (1985) found that there must be a “goodness of fit” between the instructional situation and the student. Not surprisingly, some students are in situations where they “fit well” with their instructional situation and those students excel academically; other students have a poor fit with the instructional environment and are at risk academically.

Bringing that observation into the foresight field an instructor may find certain instructional strategies effective in advancing specific learning outcomes. For example, while discussions reflect learners’ understanding and analysis of futures concepts, reflection papers and presentations show how competent they are in producing foresight outputs. A foresight teacher can facilitate assessing learning outcomes by creating a table of authorities that identifies the objectives covered by the assessment tool as well as questions corresponding to each objective. Using a flexible variety of questions in the assessment tool (to be changed occasionally) and talking friendly to the students about the test are also good techniques that can be applied.

New foresight coaches can always check the practicality of their educational programs by conducting a pilot course project and may enjoy experienced foresight teachers and gurus’ ideas and views about their project.

2. Evaluation
A foresight course can be monitored effectively by asking a number of questions like these: Is the specific need of learners in learning foresight being addressed? Are the general and special teaching methods are applied effectively? Is the instructor confident about the data presented to the students? What is running right and what is being practiced wrongly by both the teacher and the students? What major conclusions do the students make in their discussions? Are their conclusions supported by the teaching and learning materials? How are educational data being used by the students? Are there other possible explanations for students’ understandings and reflections? What are they?

At the basic level, foresight instructors might be able to answer some of the above questions, but at the expert level, they and their students need to be monitored by expert observers. A good way to do this is inviting some expert foresight teachers to inspect our courses and receive their ideas. Their appraisal would be a wealth of knowledge that can advance our teaching effort in constructive ways. Being open to critiques and welcoming necessary reforms and improvements that should be made in the course will enrich our educational experience and will satisfy our students’ expectations. The following table summarizes stages of evaluation, involved parties and sources of evaluation clearly.

In addition to involved parties and sources of evaluation mentioned above, a foresight course should be also evaluated and compared against courses conducted in similar areas such as strategic planning and management. Foresight teachers may be entitled to a wide range of knowledge and experience shared by many teachers online on strategic matters around the world. The best source of evaluation that is always available to an instructor is the students’ feedbacks. If they report cases like following items, the instructor requires a serious revision of the course material or teaching system: “You’ve left me behind. I can’t follow. The level of jargon in this course is beyond my understanding. I cannot use the LMS (Learning Managing System) easily. I don’t enjoy reading this.” Down the road, everything should be tuned according to students’ needs and level of understanding.

3. Post-evaluation
An eagle knows when a storm is approaching long before it breaks. It flies to some high spot and wait for the winds to come. When the storm hits, it sets its wings so that the wind will pick it up and lift it above the storm. While the storm rages below, the eagle is soaring above it. The eagle does not escape the storm. It simply uses the storm to lift it higher. It rises on the winds that bring the storm.

Managing a foresight course can appear as a storm and a foresight coach should be as clever as an eagle. When the course is completed and the students are graduated, it’s a good time to look back and find weak and strong points in our foresight educational program. Problems that students reported during the course period such as working with LMS (Learning Managing System), using foresight methods and tools, using and applying foresight data and preparing assigned outputs along with other unpredicted difficulties that appeared during the course all may come upon us like a storm. We can rise above them by setting our course up to higher levels of learning and teaching foresight. The storms do not have to overcome us. We can let our checking do the balancing work for us and lift us above them. Instructor’s experience coupled with students and experts’ feedbacks that had monitored our course make a compound that can enrich our educational effort.

Revisiting and post-evaluating a foresight course can be done in long middle and short runs. In long term, we should consider where our course fits into the curricular goals and course sequences. Perhaps the broad goals of our foresight course should be redefined, and a rearrangement of textbooks and study materials is necessary. For example, setting a goal such as leading a departmental team to develop strategic plans should consider developing mission, vision, and goals, appropriately matched to the near-term competitive, customer and industry environment. In middle term, learning objectives should be articulated for course and appropriate readings; videos, slides, websites, etc. need re-identification. The nature of assignments and activities should be also determined according to objectives, assessments, and instructional activities. And finally in short term, the calendar of activities, syllabus, LMS should be checked and updated.

References

Kibler, R., Cegala, D., Barker, L., & Miles, D. (1974). Objectives for instruction and evaluation. Boston: Allyn and Bacon, Inc.

Lerner, J. V., Lerner, R. M., & Zabski, S. (1985). Temperament and elementary school children’s actual and rated academic performance: A test of a “goodness-of-fit” model. Journal of Child Psychology and Psychiatry and Allied Disciplines, 26, 125-126.

Tags:  education  foresight  futurist 

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