Apr 22

Nudging to Safety

 

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A recent article in The Economist paints a rather grim picture of Road Safety. Fatalities due to road accidents could surpass HIV related deaths by 2030. Bulk of these fatalities happen in developing and underdeveloped nations.

BBC Future carried a story that features Final Mile’s work in using Behavioral Sciences to improve Road Safety. Human error is the overwhelming cause of accidents and fatalities. Nudging or Behavioral design interventions aimed at reducing driver errors is one effective way to deal with the human errors. There is an urgent need to go beyond plain awareness campaigns. Driving is a highly over-learned and non-conscious activity, one that is very complex involving many senses and skills. Interventions that impact at a non-conscious level, therefore are likely to make a better impact.

The story by BBC Future collates some of the interesting examples of such interventions. Read more here

Apr 16

Noun or Verb

We are not referring to the grammatical usage here.

Consider a research context where participants are placed in a situation where they are tempted to cheat for personal gain. However, subtle change in instructions impacted their likelihood of cheating. When the instruction was in verb form (Please don’t cheat), participants cheated more than when the instruction was given in a noun form (Please don’t be a cheater).

Our earlier blogpost explains this behaviour. The primary idea is that the noun form invokes group identity while the verb form only refers to the action or the effort. People may downplay the action but that becomes very difficult when it comes a self-relevant noun.

So framing a praise in noun form may have a much more sustainable behaviour impact.  A recent New York Times article  also corroborated this idea.

But can we apply this rule universally? The book Social gives an interesting counter perspective. In his research on Altruistic behaviour, Dale Miller – a social psychologist at Stanford University, consistently found that people prefer not to accept an altruistic identify of self. So, when someone asks us why we decided to help, more often we tend to ascribe it as a selfish behaviour. Miller explains this as our tendency to conform to the cultural norm that human beings are self-interested.

Thus, sometimes we prefer focusing on the action while in other cases the identity works stronger. And these contradicting studies highlight the most important perspective of human behaviour – Context is critical.

Context-is-king-1024x768

Explanation of human decision making will always be incomplete without considering the influence of the contextual elements. The context of Cheating is different to the context of Altruism and as a result our appraisal of the stimulus also varies.

Impact of behaviour change communication will always be impacted by the context - be it using noun or verb.

 

Image Source: Here

Jan 03

Rise in ski helmet use, but no reduction in fatalities. Why?

 

UPDATE: Schumacher still in coma following skiing accident

The recent skiing accident of Michael Schumacher has shook the sporting world. The F1 world and many more people are praying for him. We also heard the impact was so severe that the helmet broke in to two. The doctors were sure that if it weren’t for the helmet, Schumi wouldn’t have even made it to the hospital. In this case, the helmet has played a big part in reducing the impact of the crash.

But, if we look beyond this individual case and examine the overall statistics on skiing accidents, helmet usage, head injuries and fatalities, there is a rather disturbing trend. There are news reports that suggest that while ski helmet usage has improved significantly, the fatalities due to head injuries have remained pretty much the same over the last 10 years. Helmet usage has tripled as compared to 2003. While there is a reduction in head injuries, the fatalities have remained the same. Ten year average fatalities from 2000 has been 41.5 fatalities per year, 54 fatalities having occurred in 2011/12. (NSSA facts). Of these 54, 36 were wearing helmets. (Skiing now might suddenly seem like a dangerous sport, but at 54 fatalities a year, its lot less dangerous than riding bicycles which lead to about 800 fatalities a year.)

Also, most of the fatalities involved Men, especially in the age group of late teens to 30′s. And a majority of these accident victims are above average at skiing, they’re not amateurs.

What would explain this ?

Overconfidence bias / Private optimism could be a credible explanation as far as the demographic of fatalities is concerned. There is research that supports a hypotheses that men tend to be more overconfident than women. An amateur would be cautious and hence ski at low speeds. An above average would be more susceptible to private optimism. Some 90% of motorists think that they are excellent/above average at driving. The believe that they are better than other drivers and that their chance of meeting an accident is lower than that of others. This could lead to people taking more risk.

The most intriguing observation though is this lack of correlation between increase in helmet usage and fatalities.  Here’s where John Adams’ take on individual risk management – the ‘Risk Thermostat’ could helpin making sense of this.  According to Mr. Adams, each individual has a specific level of risk-taking with which they are comfortable. If their sense of safety is increased, say by protective gear like seatbelts, helmets or systemic changes like ABS, their behaviour becomes riskier – they compensate for this increase in safety till the set-level is reached again. The safer we feel, therefore, the more risky our behaviour. There are studies that indicate that drivers who wear seat belts tend to drive faster. The net impact of ABS on car collisions has been negligible because drivers have driving faster and braking late. We have compensated for the safety feature. This is often referred to as “Offset hypothesis”

So, its likely here that those wearing helmets are skiing faster, taking more risks and becoming more adventurous, simply because there is an increase in overall sense of safety. So, in a strange way, the more safer we feel, the more risky our behaviour.

How can we solve this ?

While we improve the safety features, its important to induce a sense of vulnerability, a feeling of being unsafe. This could be through better design of safety systems and signage or by having interventions at point of action that introduce an element of vulnerability. The idea is not to take regressive steps like reducing safety gear or features. Safety standards should be high, but the place should feel a little unsafe.

For now, lets hope that Schumi pulls through.

Dec 30

IbIn progress report 2013

The India Backbone Implementation Network (IbIn) was launched in April 2013 by The Planning Commission of India, with the primary objective of promoting widespread capabilities in the country to systematically convert the manifest confusion to coordination, and rampant contention to collaboration, so that intentions can be converted into implementation. Final Mile is one of the partners in IbIn.

As 2013 draws to a close, Planning Commission has put out a 2013 progress report on the status of the Initiative. IbIn has made remarkable progress in spite of lack of any seed capital or a formal institutional structure.

The report also features 2 projects of Final Mile. One is a project to improve usage of toilets built under the Nirmal Bharat Abhiyaan aimed at making our villages free of open defecation. This project is currently in progress in Karnataka and is sponsored by Arghyam

The other project is involves changing the behaviour of government agencies on ground with a view to ensure better adoption of internet based government services. The report can be downloaded here

IBIN_Progress Report_Dec2013

Dec 02

First Impressions Last

Ever wonder what is the maximum number of items a drop down list can have? Does the sequence of the items in the list affect the interaction of the viewer?

The Serial Position Effect explains the phenomenon of memory where items presented at the beginning and ends of lists are more likely to be remembered than those in the middle of the list. The term ‘Serial Position Effect’ was first coined by Hermann Ebbinghaus based on studies on himself and others, where he best recalled items from the end of a list (the recency effect) and more frequently among the first items than among the middle ones (the primacy effect).

For example, consider the following list and try to remember the items in the list.

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It will be observed that you will more reliably recall items in position 1, 2, 6 and 7 than those in the position 3, 4 and 5.

When we recall the first few items on the list, its called the Primary Effect. Primacy effects happen because there is more chance of earlier items being stored in long-term memory (long term memorisation requires rehearsal of the list with less likelihood of remembering the further along the list you go). When lists are rapidly presented, the effect is weaker because there is no time for long-term memory to work. When lists are presented slowly, the effect is stronger as there is more time to store in long-term memory.

Recency effects occur as the last few items in a list are still in working memory and therefore readily available. The strength of this effect is not impacted by the speed of delivery. However, passage of time has a huge effect on the recall of information, weakening the recency effect. If you are distracted by other matters for even 30 seconds, then this effect completely disappears. This is not true on the primacy effect which relies on long-term memory.

What does this have to do with making great content, you ask?

Well, great content starts with great copy, and the arrangement of that copy can dramatically impact how much or how little your viewer will remember. Your content may relay several messages in a list, or you might ask viewers to remember a coupon code, phone number or SMS code. In each of these cases, even a simple modification to how the information is presented can have a significant impact on how much the viewer will remember later.

You can take advantage of this knowledge when presenting information in lists (be it a set of links, your sales pitch, a feature list, client list, etc.)

- Place the least important items in the middle of your lists because these items tend to be stored less frequently in long-term memory and working memory.

- If the viewer’s decision is to be taken long after exposure (> 30 seconds), then place the most important items first. If the decision is to be taken immediately after reading the list, then place the most important item last on the list.

If all the information is equally important, then the best thing to do is group the items in chunks. Presenting long lists of information places significant strain on limited attentional resources and restricted memory systems, especially short-term memory, where it appears only three or four items ‘chunks’ can be maintained at one time. Therefore, you should reduce the strain on viewers by presenting information in small pockets, or chunks, and limit the amount of distraction between presentation of items and recall.

Quite simply, Chunking is a way of arranging information so that your memory has to recall fewer items later. Chunking is the most effective when all of the items in the list are roughly the same “type” and “size” (e.g. numbers versus words versus phrases). That’s where coding comes in. Simply put, Coding is how our brains make things easier to remember by arranging them into groups of like items. By “like items,” I mean practically any grouping that makes the list’s elements seem more similar to each other.

Our brains do chunking and coding automatically as we make our way through the world. However, given how much competition and visual clutter your content may face, you might want to take these few steps to make sure the process is as easy as possible. After all, viewers may only be gracing you with a few seconds of their attention, so you need to make that exposure count.

Oct 30

Andhra Bus Accident: A tale of different narratives

Blog Andhra Bus Accident

A tragic accident involving a Volvo bus happened on the Bangalore – Hyderabad highway today. 44 passengers are feared killed. The bus driver, the helper and five passengers managed to escape death.

So, how did this accident happen? Since morning, we have heard multiple narratives of the accident across different media channels. Initially, it seemed like the bus hit an Oil tanker that resulted in the bus catching fire. Later, a channel reported the bus driver’s statement that the primary cause was a tyre bust which resulted in the bus skidding onto the side of the road.

After a few hours most channels reached a consistent narrative. The bus was being driven at high speeds and while overtaking another car it hit a culvert that impacted the fuel tank. The fuel tank burst into flames which soon swept over the entire bus.

Was the press irresponsible or misleading? Or is there are a larger issue at play?

As humans, we have a high degree of curiosity. We want to know why and how the accident happened when in the immediate term we should be worried about rescue and relief. This curiosity means, we are always searching for a story that makes sense, one thats logical and mostly, one that is a good story. This pushes us towards a narrative which is a significant problem with most investigations. People look for a narrative first rather than going about it objectively and this narrative kills objectivity and truth.

Once people get into a story telling mode, their reporting is influenced by a number of biases. The narratives also tend to be swayed by the stories mentioned by others around. The driver’s themselves tend to explain traffic accidents by reporting circumstances of lowest culpability with credibility (Baker’s Law). In addition, our own interpretation adds another layer of subjectivity. Remember Chinese Whispers?

Using a scientific approach to investigations can help us overcome this issue of subjectivity and reach an accurate diagnosis of the incident. Paul Meehl, an American psychology professor, in his 1954 book “Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence” argued that mechanical methods of data combinations can make more efficient decisions about patients’ prognosis and treatment as compared to clinical or subjective methods. These mechanical methods can use a combination of data, checklists and even clinical judgments to predict the outcome.

The idea is to move from a pure intuition based judgement towards a more objective and systematic way of diagnosing the incident. The system needs to ensure that we come to the narrative at the end of accident investigation rather than lead it. In one of our recent work on Road Safety, we have worked on designing an accident investigation system that avoids bias reporting. The new process captures all the accident elements objectively before arriving to the final prognosis. This system is now being used for future investigations.

We know that media’s job may not be to conduct accident investigations. However, they can also follow simple rules such as not releasing driver’s version immediately and avoid such varied narratives of the same tale.

The key issue is that if we get lost in the narrative and end up drawing wrong inferences, we will be learning wrong things and addressing wrong problems.

Image Source: Here

Oct 15

Chicago transit authority – Right diagnosis. Wrong Prescription

 

 

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We caught this piece of communication developed by Chicago Transit Authority(CTA). The basic point of this is to tell people that the train is faster than you think. There is a scientific backing to this misjudgment of speed. Our brain underestimates the speed of large objects, including trains. Often, while crossing tracks, even after spotting a train, we tend to attempt to cross because the train appears to be moving slower. (Leibowitz hypothesis). Look at this amateur video to see this in action.

 

http://www.youtube.com/watch?v=3taglTyvlxc

Now, how do we solve this problem ? The approach used by CTA is based on the assumption that making people aware of this shortcomings is good enough to solve this problem. The belief is that people will take in this information, process it and put it to use when they are in such situation. The same kind of thinking that automobile companies adopt when it comes to warning about the distance of objects in the rear view mirrors.

This approach in our view is flawed.

For starters, the speed perception or underestimation is a non-conscious activity. It happens through automatic processing. We don’t actually stand next to tracks and carry out an accurate estimation of speed. We are not equipped with such capabilities, which is why we use speed guns and other measurement tools to judge speed accurately. We cannot presume that we can suddenly make this non-conscious process of speed judgement in to a conscious one. And that people have the time, intention and cognitive ability to judge the speed accurately. This is expecting too much. In fact it is likely that most people will not even remember this message while crossing tracks. They are likely to be pre-occupied with many other things and are likely to be in ‘Auto’ mode. This seems like a classic case of right diagnosis, but wrong prescription. Is there a better way to deal with this problem?

If the problem, fundamentally is at a nonconscious level, the solutions should work at this nonconscious level for it to make a definite and quick impact. The solution should make the brain recalibrate the speed of the train in an ‘Auto’ mode where it doesn’t need to deliberate and expend energy. These interventions have to be at the point of action. While it might appear that inside a train is close to being on tracks, mentally these are very different contexts. Being in the train and crossing the tracks on foot are very different contexts.

How can we get the brain to recalibrate the speed and get the judgement right. We can do this by providing stationary reference points. The highly successful ‘Yellow Lines’ intervention is one where the yellow lines act as speed references.

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These are lines painted across the railway tracks either side of crossings. As these yellow lines disappear under the train, the brain can instantly get the speed judgement right and take a decision not to cross the tracks. The beauty with this intervention is that it works at a nonconscious level, has an instant impact and is low cost. Most importantly, it is at the point of action. And this has worked in reducing fatalities significantly in Mumbai Suburban railway network where hundreds of thousands of people trespass across thousands of crossings in Mumbai. At an average of 10 fatalities a day, it is the largest cause of unnatural death in Mumbai city. Yellow lines, coupled with other interventions have reduced the fatalities significantly and this case is well documented. Read the story in The Boston Globe and BusinessWeek
We often have this temptation to believe that making people aware of a problem will solve it. This seems to be the thinking at CTA. However, for us to see impact, we need to make it easier for people to correctly judge the speed of trains. Not by telling them that the train is faster than you think, but by helping them take a right decision quickly and easily when it matters.

Sep 26

Which iPhone are you buying?

Blog iPhone 5s vs 5c pic 2

Yesterday my colleague and I were debating which iPhone version he should buy since he has finally decided to give up his old blackberry. With the launch of the 2 new models, his choice set included the earlier model iPhone 4S, the step-down model iPhone 5C and the most high end version iPhone 5S. While we were comparing the three models, the iPhone 5c quickly went out of favor and the debate centered around the other 2 models.

This anecdotal experience, however, seems to be a representative of the market. While Apple continues to break all the records in iPhone sales, an interesting pattern has emerged. Analytics research suggest that the 5S model is outselling the 5C model by a margin of more than three to one in US. In Australia, this seems to be as high as eleven to one.

Why would a higher price model outshine a cheaper version by such a large extent? Lets try to explain using the concept of Context Effects. People’s preferences are often uncertain and are constructed dynamically in response to a choice set. This dynamic nature can thus be influenced by the format and the manner of the presentation.

There are multiple Context effects that influence consumer’s choice behavior. Attractiveness effect is a key one that is relevant in this situation. In an expanded set of choice, the similarity between certain options can make one of them appear more attractive. This will happen if one option makes the other one look superior. For example, in a choice set with two options X and Z, introducing a third option Y which is similar but inferior to option Z, leads to an increase in the preference of the superior option Z.

The iPhone 5C and 5S models have many technical similarities in terms of display, memory options, ports, thickness etc. iPhone 4S seems more different. In fact, introducing both the new models as part of the iPhone 5 series, ensures that iPhone 4S stands out as a separate evaluation option. The low price difference between 5C and 5S coupled with the faster processor and a slightly better camera in 5S, establishes the superiority of 5S over 5C. As consumers, when we compare the 3 models – 4S, 5C and 5S, 5S comes out as the most favorable option.

iPhone 5C works like a decoy by letting 5S come out as a clear winner. In other words, iPhone 5C may be key to the success of iPhone 5S.

So, which iPhone are you planning to buy?

 

Image Source: Here

Sep 19

Why didn’t the driver stop?

Blog Level Crossing Image

In a a tragic accident between a bus and train in Ottawa, Canada, 6 people died, 8 were critically injured and 30 were hospitalized. The crash happened in the morning rush hour when the bus ran through the guard rail and collided with a train. Passengers in the bus reported screaming at the bus driver while on course but even that did not prevent the accident.

Investigations by the Transportation Safety board to determine the cause of the accident have begun but an obvious question is – Why didn’t the driver see the oncoming train even in broad day light? Did he fall asleep? Were there issues with the brakes? Is this a localized driving problem?

Accidents at level crossing is a universal issue. At least 6000 people die at level crossings every year and there is even an International Level Crossing Awareness Day (ILCAD) dedicated to this cause. Canada alone has seen 257 accidents in the past decade. In India, 63% of railway related fatalities are attributed to accidents at unmanned level crossings.

So what explains this behavior that seems to be prevalent globally? One possible explanation is the negative expectancy of the train by the bus driver. The way we see things is based on how we look for them. It is influenced by what we expect to see. So we are perceptually restricted and tend to pay attention to things that we expect. Psychologists term this phenomena as “Inattentional blindness”. This famous Selective Attention Test cleverly demonstrates our propensity to miss things.

Why do we suffer from this problem? It is actually an evolutionary mechanism to help us cope with the sensory chaos in the world. It helps us makes sense of the infinite visual stimuli that is always present around us and act accordingly. However, in certain cases this mechanism may backfire.

The attention spans of experienced drivers who have driven through a place multiple times tend to be very low due to the similarity and monotony of the environment. The low probability of finding a train also influences their expectation. Thus, breaking this monotony and changing their expectation is key to managing this behavior. This is the focus of our solutions that have been implemented at certain unmanned level crossings in India. Refer this blog post for more details.

Could these solutions prevent this tragedy? We can’t be certain but our research shows they can.

Image Source: Here

Sep 16

Turning cities into living labs. 10 examples from around the world

Rapid urbanisation is resulting many anticipated and unanticipated problems. The nature and intensity of such problems varies rapidly across geographies. There is a need to be creative to solve some of these problems as some of the problems are entirely new and some unresolved in spite of a lot of attempts.

Edward Gardiner, who leads the Behavioural Design Lab wrote a piece in the Guardian recently on turning cities into living labs. 10 really interesting examples cited range from City Science at MIT Media lab, JPAL, London bridge redevelopment. It also features a project executed by Final Mile in minimising collisions at Rail road crossings in India. Read more about the 10 examples here

Aug 09

We are partners in The India Backbone Implementation Network

 

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Implementation has been a major problem in India, particularly in public policies and programs. Planning Commission jointly with India@75 foundation has launched a unique initiative- India Backbone Implementation Network (IBIN) to remove bottlenecks for improving implementation of policies. Pronounced as ‘Ib’+ ‘In’, IbIn combines ‘Ib’ meaning now in a Hindi dialect and ‘In’ for India. It echoes the founding ethos of IbIn: India Now.

Final Mile is proud to be a partner in this ambitious initiative. At the core of the implementation problem is behaviour of individuals, agencies and groups and the lack of coordination between them. An accurate interpretation of the problem will help us design interventions to influence the behaviour of stakeholders involved in policy implementation. Final Mile hopes to bring in its expertise in Behavioural Sciences and Design to drive collaboration and improve coordination among the agencies involved in policy implementation. We understand that its a long journey, but one that we must start now.

MORE ABOUT IBIN

“Citizens in India are fed up with foundation stones strewn across the country by political leaders yearning for the limelight. They want more ‘finishing stones’.  Projects are stuck in tardy processes of approval and snarled in inter- departmental wrangles.

In India, a highly diverse as well as democratic country, consensus is required for all stakeholders to move together, forward and faster. This consensus cannot be commanded. We need another mechanism specifically designed to bring people with different perspectives together: to listen to each other, to distill the essence of their shared aspiration for the country, and define the critical principles they will adhere to in their work as partners in progress. In other words, a backbone capability within the system, that supports collaborative approaches to solving complex/multi-layered issues, is required. The India Backbone Implementation Network (IbIn) will play this role.
The concept of the IbIn has been developed through extensive discussions to determine the root causes for coordination and implementation failures within the country and explore methods of coordination and effective implementation adopted by other countries. The concept of an IbIn has also been incorporated into the 12th Plan.

The architecture of IbIn has been designed along similar principles as the TQM movement of Japan. TQM was a movement of adoption of new techniques by several organizations to improve performance in multiple places in Japan.

The objective of IbIn is to promote widespread capabilities in the country to systematically convert confusion to coordination, contention to collaboration, and intentions to implementation. ” (Excerpts from www.ibinmovement.in)

Aug 07

Cognitive Biases in Evaluating Human Life

One of the greatest feats of the human brain is its ability to filter a vast amount of information into a manageable stream of relevant information. Evolution has sculpted the course of this stream in order to maximize fitness, ensuring that we pay attention to things that are relevant for our survival and reproduction, and filter out irrelevant. Aldous Huxley describes this as a ‘reducing valve’ – our brains funnel the enormous amount of information in the environment in whichever way proved to be most adaptive to our ancestors. This means two things; we have sampled an excruciatingly tiny portion of the buffet of potential experiences our neural hardware is capable of, and we are insensitive to certain environmental information that didn’t confer an adaptive advantage in the ancestral environment.  Developing sensitivity to this information is crucial for rational and ethical behaviour in the modern world.

Cognitive biases can lead the most empathic and conscientious people to behave in ways that could appear as sheer callousness. The source of this seemingly selfish behaviour is not malice or indifference, but more that our brains are not equipped to apprehend reality as it really is. By recognizing our cognitive limitations we can understand why people act in inconsistent and unethical ways and how we can avoid falling into the same trap ourselves.

If people acted in accordance with their espoused egalitarian preferences, they would treat the value of every human life equally. In practice this is not the case. Despite endorsing egalitarian norms studies have shown unconscious cognitive biases can lead to valuation functions that decrease in absolute value as the number of victims increases!

The contributing factors:

1)   Psychophysical Numbing. Contrary to the egalitarian maxim that every life should be valued equally, there is not a linear relationship between number of lives at risk and the size of donations. Instead there is a curvilinear relationship; sensitivity wanes as the number of victims increases. This is known as ‘psychophysical numbing’– diminished sensitivity as the victim number increases, or put differently, people perceive little value in saving an additional person if there are already many lives saved (and if only one person is at risk people value saving that life highly).

 

2)   Scope Insensitivity. People seem to be insensitive to changes in magnitude.  It has been shown that people are willing to donate almost the same amount to save 2,000, 20,000 or 200,000 birds drowning in oil ponds. People are insensitive to numbers but are extremely sensitive to the plight of individuals.

3)   Singularity. People often exhibit a surprising amount of caring for individuals but can be relatively unmoved by catastrophes with a large human cost. A single individual often garners more financial support than a group of victims. It appears the singularity effect is related to the absence of other victims because donations tend to decline when the single victim is part of a group.

4)   Proportion dominance. People often give more weight to the proportion of lives they are saving than to the absolute number of lives saved.  Although participants evaluated helping a higher number of victims as more normative, in actuality their decisions weren’t sensitive to the number of lives saved. This might be because proportions are easier to evaluate than absolute numbers.

5)   Pseudo-inefficacy. Sometimes knowing the number of lives one cannot save leads to a lack of motivation to help those one can save; a feeling of helplessness quashes motivation. But of course, not being able to save everyone does not undermine the lives one can save; in absolute terms the value remains the same. This is important: not being able to solve the whole problem of poverty doesn’t make the good that can be done any less worthwhile.

6)   Identifiability. Identified victims are valued more than statistical victims. Again, people are unaffected by numbers. Making the identifiable victims makes their plight real in people’s minds. The loss of a single identified life may be felt more deeply than many statistical ones. Apathy increases as the victim numbers grow large enough that they cannot be comprehended emotionally, resulting in compassion fatigue.

People are motivated to action not by facts but by feelings. We simply don’t have the brain power to scale up the compassion and empathy we feel for one person’s suffering to the commensurate degree for 1000 people, or even 10 people. Josef Stalin touched on this insensitivity when he said ‘one death is a tragedy, a million a statistic.’ The direct result of this is that good people are complicit in an enormous amount of suffering happening all over the world.

So what can be done?

The first solution is to recognize our own susceptibility to systematic biases that lead us to unsympathetic behaviour; once this has been recognized our rationality can be deployed to alleviate the greatest amount of suffering possible. Recently this effort has been known as ‘Effective Altruism’, which aims to use evidence and reason to evaluate all actions to achieve the greatest positive impact. (Peter Singer gives and excellent overview of Effective Altruism in his TED Talk.) When allocating resources to humanitarian causes, difficult trade offs must be made and the allocation may largely depend on which ethical theory is adopted. This is cognitively demanding which often leads to people not donating at all.

Fortunately there are several expert individuals and organizations that do just this, so not everyone has to go through the cognitive strain. Give Well conducts in-depth charity research to find where money can be best spent to have the greatest positive impact. 80,000 Hours can assist in finding careers that will have the greatest positive impact. The life you can save is an excellent starting point for those who want make an ongoing positive impact.

What exactly is it, then, that motivates people to act compassionately? Psychologists have shown that vivid mental imagery plays a central role in affected responses underlying many decisions; emotional impact is crucial in motivating behaviour. The more concrete the mental imagery the stronger the empathic concern. Increasing the number of victims makes mental imagery more difficult and abstract and therefore less of an emotional response is provoked. It has also been shown that specific features of those in need should be focused on. Presenting individual victims as part of a group tends to reduce affective responses to any single one of them. Priming participants to process information affectively lead to stronger emotions and higher donations.

To conclude, contextual factors that have no normative import can alter how sensitive people are in their valuations of human lives, leading to behaviour wildly at odds with people’s ethical positions. Mechanisms that influence emotional reactions are particularly important in determining the deviations from rationality. How emotions are generated contribute to how people evaluate lives- it is imperative we recognize our cognitive limitations in order to avoid having our moral behaviour dictated arbitrarily by context. In having the awareness of the power of context on evaluations of human life we find the onus is on us to use reason to avoid behaviour that equates to callous indifference, and instead find the actions most aligned with out ethical standpoints and of the greatest positive impact.

Dickert, S., Västfjäll, D., Kleber, J., & Slovic, P. (2012). Valuations of human lives: normative expectations and psychological mechanisms of (ir) rationality. Synthese, 189(1), 95-105.