By Siv Helen Gjerstad

I went to the library the other day, looking for a new book on environmental psychology that I wanted to read. When I discovered that neither of the two copies of the book were checked out, I was happy that I could pick it up right away. But as I kept thinking about it, the fact that nobody else was reading the book was just disappointing. Why don’t we care more about the psychological aspects of sustainable transitions?

Eighty percent of Norwegians believed that climate change is caused by human activity in 2015. So most of us do admit that we are the source of environmental changes. Yet we don’t feel much personal responsibility to do anything about it, nor are we willing to make significant sacrifices to make a change.

At the Center for Technology, Innovation and Culture (TIK) we talk as if climate change and global warming is something that will have significant impact on our lives if we don’t take serious action. We talk about the policy changes that we need to make in order to be able to adapt our fossil fuel-based lifestyle to the goals of sustainable development. But do we know enough about how people react to and act upon this information about climate change, and the policies that are put in place to counter it?

Research on environmental psychology and behaviour is not a large academic field, but it is growing. The interesting thing about exploring these aspects of human mind and behaviour is that they are often less intuitive than we might think. One would assume that people who care about the environment would act more environmentally friendly, having a lifestyle with less carbon emissions than those who do not care. However, recent studies suggest that there is no link between the desire to project an environmentally friendly image and environmentally friendly behaviour. Neither energy use in travel nor consumption with significant impact on the environment seems to be affected by people’s attitudes. Denial and irrationality are important aspects of the human mind, and so we need to address them.

Another example I find interesting is how studies indicate negative spillover effects from one environmental activity to another. Festinger’s popular theory of cognitive dissonance would predict a catalyst effect between one environmentally friendly behaviour and another, but on the contrary people can actually be less prone to do more environmentally friendly measures when already doing some. It seems like instead of being motivated to adopt a more environmentally friendly behaviour profile, people feel like they contributed enough to the ‘common good’ already. Being a student at TIK and a cautious optimist I do believe that technology plays a significant role in successful sustainable transitions. That being said, it turns out that those of us who believe in new technology as the key to the reduction of carbon emissions are less willing to change our own consumption behaviour.

Most people acknowledge the fact that our behaviour is involved in climate change, yet we worried less about the greenhouse effect in 2013 than in 1989. That is a strange discovery, considering how scientific evidence has been significantly strengthened the last two decades. Psychologist and economist Per Espen Stoknes describes how information about risk, like the risk of global warming, is too abstract and too distant for us to actually be frightened, and how that impacts our willingness to adjust our behaviour.

These are just a few examples, but they illustrate how human environmental behaviour is very complex, and that it is difficult to anticipate people’s perceptions and behaviour. Stoknes elaborates on the paradox of being aware of global warming, and yet keeping up what can be considered self-destructive behaviour. He puts it very accurately: We pretend to be rational, while behaving irrationally. We need to understand the relevant psychological processes and the barriers in our ways of thinking before attempting to modify human actions. Certain measures might even have negative effects on carbon emissions, thus not knowing anything about environmental psychology may lead to ineffective measures and policy.

Though I am loath to admit it, an army of environmental psychologists is not going to save us from destroying our planet. Nevertheless, I think it is important that we take people’s behaviour and the underlying processes of that behaviour more into consideration. After all, it is nothing but human behaviour that will determine whether we succeed or fail in stopping global warming.

Life after graduating

Vegard worked as a research assistant before he started working
in a market analytics firm. Martin has travelled and been working
at the Norwegian Embassy in Havana, Cuba.


MA specialization: Innovation Studies
Bachelor’s degree: North-American Studies

What did you write about in your master thesis?

My master thesis was about how a company (Telenor Norway) can use customer feedback in their innovation processes. I analyzed around 50 000 free form text messages from Telenor customers, 5000 tweets written about the company, as well as interviewing employees working with innovation in the company. I found that customers can be a source of smaller innovations and inform major decisions that lead to larger organizational innovations.

What have you done since graduating from TIK?

After graduating, I first spent a little over a year working with research at the TIK Centre. First on a paper about organizational innovations, and second on a project on how ICT affects happiness. Now I work as a Junior Consultant at Opinion, a market analytics firm.

In what ways has your TIK education been useful for your career?

Without the TIK education, I could not have had any of the jobs I’ve had. I have benefitted from practical skills that I use every day such as interviewing people, structuring a text properly, writing better and presenting better. Perhaps most importantly, my TIK education made me sit down for a whole year and focus on a problem, banging my head against the wall until something good enough came out. I know that is a skill I will always need going forward.


MA specialization: Innovation Studies
Bachelor’s degree: Social Anthropology

What did you write about in your master thesis?

My thesis was about how the Norwegian hospitals works to follow up new and possibly system changing ideas by hospital employees to enhance and facilitate improvements. The innovation literature emphasizes the importance of innovation actors and contributors within hospitals, but at the same time, there is lack of qualitative knowledge on how these processes occur and develop. I wanted to contribute to this literature by studying eight different ideas and innovations at Sunnaas sykehus, a Norwegian hospital.

What have you done since graduating from TIK?

This last year I have been living abroad. First in Argentina where I did some post-studying, but also some traveling. I have learned the importance of a good, heart-breaking and sexy tango and the incredible atmosphere at an Argentinian football match. Since Christmas, I have been working at the Norwegian embassy in Havana, Cuba, an extremely interesting and special country to live in.

In what ways has your TIK education been useful for your career?

To be quite honest, this question comes a little early in my career. At this stage, I am not even sure I can call it a career? Nevertheless, what I have experienced is that there is an interest in persons with our particular background, and our expertise is relevant for a wide range of working fields.

Bias-debasing Bayes

By Jørgen Tresse


“Prediction is very difficult, especially if it’s about the future.” – Niels Bohr.

We, as individuals and as a species, make predictions about future events all the time. Yet we keep getting many of them wrong, and it often seems like we’re unable to improve our predictive abilities. I present here a roadmap to uncertainty, risk and the failure of predictions, hoping to leave us all a bit wiser regarding this everyday activity.

Cognitive biases

First a word on the difference between risk and uncertainty. A risk is something you take when you know the probability of different outcomes. Uncertainty is what you have when you don’t know the odds of different outcomes. Walking home from work today, you take a chance of dying in a traffic related accident. However, you know that the risk of this happening is low, so you deem it an acceptable risk for walking home. Compare this to the fear many have of flying, or of terrorism. These risks are certainly much lower, but there are so many uncertainties involved, that the perceived risk is higher. This leads us to adopt anti-terrorism measures much more quickly than traffic safety measures. This in turn says something about how we perceive the probability of future events happening – in other words, our predictions about the future.

In addition to perceived risk, we have other cognitive biases – failures, if you will – that affect our ability to clearly and accurately predict outcomes of events. Take for example availability bias. Many studies suggest that we have an easier time remembering and drawing upon things that we are more exposed for, in uncertain situations. This makes sense, but it skews our predictions. Survivorship bias is another one, which is a sampling bias based on only looking at the survivors of an event. During World War II, Abraham Wald famously helped the American Navy build sturdier airplanes. Before Wald, they were reinforcing planes based on where returning planes had been hit, not realizing that they were reinforcing them where a plane could sustain damage and still survive the trip. Wald recognized that their samples consisted only of survivors, and they had hence failed to consider where fallen planes had been hit. Reinforcing the planes instead where the survivors had not been hit, drastically reduced the number of fatalities.

Furthermore, we have the gambler’s fallacy – the belief that just because something has had the same outcome many times in a row, the outcome is bound to soon change. The chance of a coin toss coming up heads is 50%, regardless of how many times you have thrown heads in a row. When simulating coin tosses – that is, writing down what is considered a reasonable result of coin tosses without actually tossing them – people have been found to write down too short streaks. After maybe four or five heads, they feel that they have to switch to tails, even though with real coin tosses you can easily get eight or more heads before tails show up. Humans are very good at finding patterns in data, and reacting accordingly, which gives us many evolutionary advantages. As many a gambler will have experienced, however, being good at finding signal in the noise does not always work to our advantage.

The unknown unknowns

All these biases and pitfalls can be summed up as snap judgements – cognitive heuristics, or shortcuts, that allow us to make decisions quickly, without having to stop and consciously process all the information we are bombarded with everyday. The Nobel laureate Daniel Kahneman, along with his collaborator Amos Tversky, is one of the best-known scientists within the field of heuristics and biases. In his 2011 book, Thinking, fast and slow, he lays out the differences between two “systems” we all have – system 1, which calls all the snap decisions, based on heuristics we already have discussed, and system 2, which consciously processes information before acting on it. When it comes to making predictions about future events, we could all benefit from slowing down, recognizing our blind spots, and putting system 2 in charge.

Of course, recognizing our blind spots requires us to be aware of them in the first place – so-called known unknowns. Former US Secretary of Defence Donald Rumsfeld also gave us two other “knowns”: known knowns – which is simply what we’re aware of that we know – and unknown unknowns, which is the real kicker. You can’t correct a prediction for unknown unknowns, because, well, you don’t know what to correct for. Yet being aware of the fact that there are unknown unknowns, is a giant leap for making more accurate predictions. A famous study by Philip Tetlock found that experts were often no better than amateurs at predicting future events, even though they stated their predictions with great confidence. This is in part based on the wisdom of the crowd, which experts almost by definition try to stand out from, but also because experts like to make predictions in others’ fields (catchily named ultracrepidarianism). This leads many experts to disregard common sense, in a way inadvertently creating their own blind spots, without even realizing it. Just being aware of your blind spots, so to speak, allows us to counteract some of their effect. My proposition? Think probabilistically.

Predicting the unpredictable

One of the great proponents for increasing the accuracy of predictions is Nate Silver. He started the political website FiveThirtyEight, and correctly predicted the results in 49 out of 50 states in the 2008 US presidential election. This improved to 50 out of 50 in the 2012 election, solidifying Silver and his team as top forecasters in the game. In statistics, the reigning paradigm for more than a half century has been testing a null-hypothesis (which posits that there is no relation between the variables you are examining), and disregarding it if a certain value passes a critical threshold, thereby strengthening your belief in there being a relationship between said variables. (All statistics nerds, please disregard my oversimplification of the method.) While a mathematically sound way of finding correlations, it has a few shortcomings.

Firstly, the critical value may in some cases seem arbitrary, and indeed it is. The critical value simply represents how accepting you are of being wrong. Second, your results really only tell you something about your sample of the population. If we could do the impossible task of testing every individual in the population, there would be no need for the prediction in the first place, so you are always operating with a part of the whole. Thirdly, as the more savvy of you will have noticed, I use the word “correlation” instead of “causation” for a reason. A correlation does not ensure a causation, and that leads me to the final point: statistical generalization is good for saying something about the, but not always a good predictor of the future. For predictions we’re better off using another tool, which has gained a lot of traction lately: Bayesian statistics.

Bayesian statistics, named for Thomas Bayes, encourages looking at the world through Bayesian probabilities, which is simply the act of assessing the chance of an event based on the chance of it occurring and your prior expectation of said event occurring. If said event occurs, you update your prior to fit the new data. Sounds intuitive? Bayes reportedly thought so little of his findings that he didn’t even find it worthwhile publishing. Imagine you test positive for a rare disease. Your doctor tells you that it’s correct 99% of the time. So how likely do you think it is that you have the disease? This depends on your prior, which is how likely you thought it was that you had the disease in the first place. If the disease is sufficiently rare, even a 1% error margin will amount to many people getting a false positive, so maybe you don’t need to be so worried. Of course, if you have a second, independent test that also turns up positive, you can be quite sure that you indeed have the disease. Your prior in this case is not the chance of having the rare disease (say 1/1000), but the chance of having the disease and having been tested positive for it before.

Updating your probabilities for future events after an event happens seems like a no-brainer. As they say: once bitten, twice shy. However, this may lead us to premature conclusions. The most difficult part of prediction is figuring out your priors, which is hard to do post-event. This leads us back to our cognitive blind spots. After being hit by lightning you might never go out during a thunderstorm again, even though the chance of being hit is small. Your experience trumps your prior, and suddenly a one-off event defines how you interact with the world. Keeping in mind that events have a probability of happening, and that the happening of an event should only make you update your belief in the probability of it happening, we might all make both more accurate forecasts, and keep the door open for a discussion about events, the future, and the truth.

So how did Silver’s FiveThirtyEight do in the 2016 election? Well, they missed the fact that Trump would win, but they gave him a much larger chance of winning than most other forecasters. As election day rolled around they had Trump at around a 30% chance of winning. This would imply him winning three out of every ten elections, which is far from an assured loss. Having a grasp of probabilities and error margins, this led to the team at FiveThirtyEight to not be surprised by his victory. Understanding probabilities like these is something we do everyday, even if we’re unaware of it. If you predicted that three out of every ten times you went outside, you would get hit by a car, you would probably start staying indoors.

But hey, what do I know? I’m no expert. And perhaps that’s for the best.

The death of the American Dream

By Sondre Jahr Nygaard

Wealth tends to accumulate. Growing inequality of income is threatening the order of society and is one of the biggest failures of our time. The mechanisms that are contributing to this increasing inequality can be seen on all levels: from inequality between countries to the same disparity between neighbourhoods in a city. The idea of the American Dream is that every person has the potential to realize his or her aspirations through hard work. The American Dream is not limited to the United States. It has become a widespread mode of thought in Western European countries as well. Despite the fact that these values are widely embraced, the map is very different from the terrain.

Some years ago, the book “Capital in the 21st century” by French economist Thomas Piketty created a big stir among policymakers and researchers alike. Piketty showed that the income gap between the rich and the poor is ever widening. The richest 1% are accumulating much more wealth than the rest can make through labour efforts alone. This results in a cycle where those who are very rich have the means to accumulate even more wealth at the expense of others.

An important aspect of the American Dream is upward social mobility, making a better life for yourself and your family. Using statistical analysis, the economist Raj Chetty claims that mobility between classes is not just a matter of personal skills, but also a matter of geography. Chetty says that the chances of moving between social classes are highly dependent on which neighbourhood you are living in. He explains it as the importance of the environment around you. Neighbourhoods in different social strata vary in the quality of their schools, education levels of residents and quality of life in general. Can one therefore say that people from lower and higher class backgrounds are equal and have the same opportunities to succeed? Higher education is a good indicator of upward social mobility. With higher education, you have access to a wider range of jobs with better pay and increased cultural capital. Research shows that the more you are exposed to books from an early age, the more likely you are to undertake higher education. University entry, though, is dependent in part on results from earlier education. Higher education can also be expensive, such as in the UK or the US, which may create barriers for potential students coming from low-income families. Evidence in Norway however suggests that parent income is not as important for choice of education, where the government provides free education.

While Chetty’s studies are based in the US, similar trends can be seen by looking at cases in Norway. If you were born and raised in Finnmark, for example, the likelihood that you will undertake higher education and move to a social class higher than your parents is much lower than for someone born and raised in Oslo. The proportion of people having more than four years of higher education in Oslo is about 19.3 percent, the highest in the country. In Finnmark, this amount is 5,9 percent. Combine this with the flow of people moving from Finnmark into urban areas further south, and the future of Finnmark might look grim – as is the case for any other sparsely populated place in Norway.

But what if we break these numbers down on a local level? Are you destined to a have a future in society’s elite if you are born and raised in Oslo? Well, that depends largely on which part of the city you are born and raised. Though the data are a bit rough, the trend is still clear: education levels vary considerably among neighbourhoods in the city. In Stovner, the least educated part of the city, the population with higher education is approximately 23 percent. At the opposite end of the scale, St.Hanshaugen´s higher educated population is almost three times greater, with over 60 percent of residents having higher education.

Ever-increasing inequality in our society is not just a problem in and of itself, but it also leads to more distrust between people, and between people and the government. Distrust may lead to people abstaining from paying taxes, such as we have seen in Southern European countries, or civil unrest and subsequent police brutality, like we have seen lead to the black lives matter campaign in the US. Trust is among the critical pillars for a democracy to function well. Norway enjoys a high degree of trust, but this trust does not come from nowhere. It comes in part from giving people chances and opportunities to make use of their skills and potential.

To solve the problem of inequality, we cannot sit back idly and hope for a resolution to come. We need to enact policy which can work to minimize extreme income inequality. The mechanisms which strengthen this inequality exist on global, national and local levels, which calls for a holistic perspective on the problem. Achieving the American Dream may prove extremely hard, if not impossible, if you aren’t born in the right place at the right time.

Identity crisis

By Emilie Skogvang

Earlier this year, Innovation Norway and the UN agency UN Women signed an innovation agreement that will promote gender equality by exploring the possibilities in new technologies. The purpose of the partnership is to connect the challenges identified by UN Women to Norwegian solutions that can help empower women all over the world.

Double jeopardy

UN data shows that female refugees are less likely to survive than male refugees. Women who are unaccompanied, pregnant or elderly are especially at risk. Girls and women are more likely to be left behind in humanitarian crises, and they represent a big part of the total number of people who do not have access to identity papers. When women cannot document their identity, it makes it impossible for them to acquire basic health- and financial services. They will not get residence permit or work permit, access to education or health services, and basically not be able to take part of the society or get help in any way. UN data also shows that there are 1,5 billion people in the world who are not included in the financial infrastructure because they do not have access to identity papers. Being able to prove their identity, women will be empowered to take charge of their own lives Today, women are left in a double jeopardy, not only being refugees, but also being women with fewer possibilities and rights than male refugees. As a result of the innovation agreement between Innovation Norway and UN Women, some of the greatest Norwegian and international tech-minds, creatives and social forces will gather in Oslo in May 2017 for a so called 36 hour hackathon with the aim to “crack the code” to come up with a solution to the issue of identity through blockchain technology.

The blockchain revolution

What is blockchain technology? You have probably heard about the virtual crypto currency Bitcoin. And no, it is not just money for nerds. Bitcoin is a digital currency based on blockchain technology, which blockchain enthusiasts claim will revolutionize the economy. A blockchain is a decentralized, distributed database with built-in validation. The blockchain is a ledger of records arranged in data packages or blocks that use cryptographic validation to link themselves together. The blocks form a chain, and each block has a timestamp and a link to the previous block.

What is so interesting, and some would say revolutionary about blockchain, is that it eliminates the third party middle man from all transactions. In the case of Bitcoin, this means that the banks do not have to validate the transactions because the blocks are self-validating and totally secure. The blockchain ledger is not stored in one location like a bank, but is distributed and public. The block validation system ensures that nobody can tamper with the records. Old transactions are preserved in the ledger, and new are added irreversibly. Anyone in the blockchain network can check the ledger and see the exact same transaction history as everyone else. However, blockchain is a technology which is not limited to financial transactions. It can also be applied to a wide range of areas including digital proof of identity.

Taking refuge in blockchain

In the humanitarian context, blockchain technology can be used to build up a digital proof of identity. The blocks in the chain can validate each other without intervention from intermediates. In cultures where men control women’s financials, this can liberate women in many ways. Blockchain makes it easy and safe for anyone to build and maintain immutable and secure personal records and to transfer digital assets directly without intermediates or additional costs. In this way girls and women in humanitarian crises will be able to have safe records of important documents that are needed to rebuild their life and participate in economic activities after a crisis. If this project succeeds, it can empower refugees all over the world to take charge of their own lives and get the medical and financial aid they need.

We believe that technological innovations – so called blockchain technology- can contribute to giving women the help they need in a crisis. Many refugees fail to prove their identity, and by giving women the chance to identify themselves, they can be included in the financial infrastructure. It will make it easier for the people giving and receiving help, and also for women’s work – and financial possibilities after a disaster, explains Ingvild von Krogh Strand, responsible for the blockchain project in Innovation Norway.

Author’s note: This article was written in April, about three weeks before the hackathon took place.

CSI: Maleficio

y Joar Kvamsås

Serial, The Jinx, Making a Murderer. The last few years has seen an upsurge of documentary serials that deal with the complexities of figuring out what and who perpetuated crimes. And how easily eyewitness evidence can be forged, falsified, twisted and in general unreliable. Oftentimes, the question of guilt comes down to the opposing stories of a witness and the accused. What do we do in cases where the crime is too serious to ignore, but the truth about it is hidden from us?

The modern day response is to hope that forensic science can give us the answer. Prosecutors havealready for a few years lamented the supposed»CSI-effect», claiming that shows such as CSI:Crime Scene Investigations have shaped the public imagination and made jurors expect widelyavailable and technically advanced forensicevidence in order to convict. With its fingerprints, ballistics identification and DNA evidence, the sterile and futuristic forensics lab can apparently provide the objectivity and accuracy that eyewitness testimony lacks.

The problem of whether to trust testimony is not a new one, and neither is the impulse to seek the truth by some impersonal and objective force. Perhaps the most infamous of these was the medieval practice of Trial by Ordeal, in which the accused were put through some kind of painful and wounding treatment, such as walking across red-hot ploughshares, or walking a number of steps while holding a red-hot iron. Interestingly, the ability of the accused to withstand this torturous treatment was rarely the factor deciding guilt or innocence. Rather, these trials functioned more like empirical experiments; one common practice was to bandage the burn wounds, and guilt was determined by inspecting them three days later to see whether healed healthily, or were infected.

In his book Strange Histories the British historian Darren Oldridge emphasises how medieval belief systems were not, contrary to common perception, dominated by irrationality and hysteria. Rather, he claims, people of the medieval period were both moral and reasonable – the difference lies in the baseline axioms that made their world view so different from ours. The first axiom entailed that truth could be found by the authority of ancient texts, the most prominent being the scriptures of the Bible. Secondly, it was assumed that the world was inhabited by a mass of invisible forces, including spirits, demons, witchcraft and curses. In a magic-induced universe ruled by an omniscient and interventionist God, trial by ordeal was not an unreasonable practice.

Trial by ordeal was usually a last-case solution where no other testimony or evidence was available than that of the accused or the accuser, such as sexual infidelity or heresy. It was not uncommon for the accused themselves to request trial by ordeal, in order to prove their piety and innocence. While trial by ordeal is nowadays most often associated with witch trials, they were actually rarely used in these cases. Instead, confessions were usually obtained through torture. Ironically, those accused of witchcraft would probably have had better chances of acquittal had they undergone trial by ordeal instead. Interestingly, the practice of trial by ordeal was not ended because of a changing understanding of truth and reality in the medieval period. Rather, its outlawing was based on the axioms that inspired it: Holy scriptures did not actually sanction judicial ordeal, and the church declared that no court could demand a miraculous result from God. What then about the axioms that leads us to increasingly rely on forensic science in modern court rooms? The chemistry sets and apparatuses of the forensics lab hold the promise of being everything that human testimony is not: Objective, unbiased, not open to interpretation. In recent years, many of the most used forensic techniques have come under scientific scrutiny, including fingerprint matching, the practice of matching bullets to firearms, and burn patterns in fire investigations. A 2009 report by the National Academy of Sciences in the US claimed that among the most common techniques in forensic science, the only methods that have passed the standards of modern scientific scrutiny are those that have been recently developed, such as DNA testing and drug screenings.

Much like the medieval condemnation of trial by ordeal, modern criticisms of forensic techniques is not brought on by a paradigm shift in how we think about truth and judgment. Like the medieval Christians, we do not reject our source of truth and its nature – rather, we acknowledge that our power to reveal the truth by manipulating the world is a lot more limited than we would like.