All posts by Divya Balakrishnan

Rethinking Behavioural Science Research

The past couple of years have been painful for Social Sciences, with the replicability crisis putting a dent on the credibility of multiple studies in the field – from social priming effects to power poses and will power. An effort to reproduce effects reported in more than 100 cognitive and social psychology studies in three journals, called the Reproducibility Project, has found that findings from around 60 studies do not hold up when retested. Even when effects were replicated, they were weaker than reported in the original studies.

The replicability debate has been focussed, to a large extent, on experimental design and effect sizes. It is suggested that low-power research designs (smaller sample sizes) and lower or weaker effect size studies were more likely unable to be replicated. Additionally, an inherent bias in publication favouring positive results is argued to contribute towards the replication crisis.

An often overlooked part of the discussion seems to be the social context of the experiment and it’s effect on the participants themselves. Currently, academic researchers are sticklers for controlled design, this way the effects of multiple factors on behaviour can be reduced to just one. In view of this, in most universities, the research lab, usually cubicles/ computer laboratory is a heavily controlled, isolated environment. Having a controlled physical environment, however, does not preclude the participants from coming in to the research with their own motivations, dispositions, expectations and emotions. These cannot be dismissed as irrelevant to the study at hand just because the study has been stripped of any context. On the other hand, they exert a large influence on outcomes of the study.

For example, aspects of the experimental setting can influence the participants’ reaction to stimuli presented by the experimenter. Participants in psychology studies get paid, and are motivated to play the role of ‘good’ subjects – ascribe to what they think the experimenter wants – these are termed ‘Demand Effects’.  Participants consciously try to recreate experimenters’ hypotheses using available cues. Any psychology experimenter will attest to this fact. As a student, when I conducted my research on Automatic Priming, I used the same testing protocol – picked solitary computer terminals, used a confederate to trick participants into believing they were engaged in two separate studies – one to deploy the priming intervention (‘professor’ versus ‘hooligan’) and another to study the effect it had on knowledge (IQ test). We did probe participants on what they thought the experiment was about and so on, but at the end of the day, the truth is that most participants had their own hypotheses about what we were trying to prove and played up to their hypotheses. Experimenters themselves unwittingly influence participants with their expectations – which participants want to play up to, dubbed ‘Experimenter Effects’.

Psychology is the study of human behaviour – in our anxiety to ensure that it is a strict science, we are using the same experimental models that we use to study physics to study human behaviour. It is time psychology experiments stop treating participants as passive receptors of stimuli. What we want to study are the motivations, the emotions, the beliefs and dispositions for different contexts – why try to make the participants leave those behind at home (which they won’t anyway). Our research will be richer if we simulate the real-life context that we are trying to study, rather than control for it, so the decisions and outcomes of research will be closer to home.

Research at FinalMile attempts todo just this. With our EthnoLab, we simulate real-life contexts as far as possible – we want the decisions in the Ethnolab to reflect decisions taken in real life, not create an alien context which leads to perceived ‘correct answers’. This might mean recreating the real-life environment – either physically, or virtually. The EthnoLab marries the practicality of a controlled laboratory with the ‘real-life’ness of Ethnography. As Smith and Semin (2004) put it :  “The true strength of the laboratory is not its supposed insulation of behavior from context effects, but its flexibility in allowing experimenters to construct very different types of contexts, suited to test different types of hypotheses.” Welcome to Behavioural research v2.0!

Image Credit:

How Emotional is Behavioral Economics?

Behavioral Economics is garnering more and more attention everyday. As it should be – the brain subscribes heavily to heuristics and mental models in order to process information efficiently. Our preferences are highly malleable and are usually constructed on the fly – which is why any field studying descriptive decision making would be incomplete if it didn’t take into account the effects that the decision context and decision frames have on our choices. Which is all very good for Behavioral Economics.

Daniel Kanhneman, Richard Thaler, Senthil Mullainathan, amongst many others drive this field and are creating a massive shift in thinking across several domains in classical economics – savings, investment, wealth, losses, gains. The definition of Behavioral Economics is wide. “It studies the effects of psychological, social, cognitive, and emotional factors on the economic decisions of individuals and institutions and the consequences.” However, whilst the definition mentions ‘Emotional’, the reality is that the literature on Behavioral Economics falls painfully short when it comes to Emotions. System 1 & System 2, Prospect Theory, Choice Architecture, Choice Bracketing, Heuristics and Biases – whilst talking about how human beings are ‘economically irrational’ and the role of emotions in decision making, barely scratch the surface of the nuances of the Emotional System. Which is a pity, because Emotions do not merely play a role in Decision-Making, they guide the decision-making process. There are no decisions that are devoid of emotions, even ones that might seem extremely calculated and ‘rational’. We’ve written about the omnipotent role of Emotions before, here


The brain is continuously appraising our larger context, the surrounding environment and stimuli, and basis these appraisals – which could be non-conscious, or completely deliberative, the emotional system responds – determining action tendencies, and ultimately actions. These emotional responses clue us in on the values we attach to things and our motivations. People with damaged emotional circuits are severely hampered in their ability to make even the simplest of decisions. The essence is not in the simplicity of emotions as we colloquially understand but in the complex determinants of emotions. In understanding the role that aspects like Individuals Goals, Relevance to Decision Maker, Self-Image, Sense of Control, Ability to deal with the Outcomes play. The handles that these provide in explaining decision making, understanding behavioral outcomes and influencing preference changes are invaluable.

Lets talk about Investing – To Buy or Not is driven by  two dominant emotions that come into play and drive all decisions. The ‘Fear of Losing’ and the ‘Fear of Losing Out. Fear all the same. With all its positives and ramifications when fear has only cognitive underpinnings. Whilst Behavioral Economics talks about this aversion to loss, the emotions behind it – the aversion/avoidance that are driven as a result of Fear and anxiety are not detailed. Whilst felt Emotions are a huge driver of decisions, Anticipated Emotions are an even stronger influencer – anticipation of gains, losses, happiness, sadness, loss of control are very very powerful and are strong elicitors of Preference Reversals. Again, a lot of the Heuristics and Biases that Behavioral Economics talks about are driven fundamentally by Uncertainty – another powerful emotional mediator. Our decisions might not maximise economic utility, but are most often maximising emotional utility.

For Behavioral Economics to become more powerful and impactful, therefore, there is an immediate need to place emotions at the centre of this conversation so one is able to see the source of heuristics that drive our behavior and then work on one or more dimensions of the emotional determinants to influence decision making and behavioral outcomes. At FinalMile, studying these emotions is central to our process. We use insights/learnings from Cognitive Neuroscience as well as Behavioral Economics, to design our EMGRAM framework which allows us to make sense of the Emotions associated with any problem Context.

– Written with Anurag Vaish


Mind, Society and Behaviour – World Development Report 2015

 The World Bank has recently published its flagship World Development Report 2015 titled Mind, Society and Behaviour, its main message being, when it comes to understanding and changing human behavior, we can do better. The report brings together great content from the various disciplines including Neuroscience, Cognitive Science, Psychology, Behavioral Economics, Sociology, Anthropology and Design.


Excerpt :

Research has advanced our understanding of the psychological, social, and cultural influences on decision making and human behavior and has demonstrated that they have a significant impact on development outcomes. This report showcases an impressive set of results. It shows that insights into how people make decisions can lead to new interventions that help households to save more, firms to increase productivity, communities to reduce the prevalence of diseases, parents to improve cognitive development in children, and consumers to save energy. The promise of this approach to decision making and behavior is enormous, and its scope of application is extremely wide.

You can download the report here

As part of the run-up leading to the report, the World Bank blog featured FinalMile (here), in a series discussing ‘Mind and Mindsets’.

Image Source: WorldBank

Beware of Bigger Cars!

A recent news article talks about pedestrians and bikers being the most vulnerable on Mumbai roads. 57% of the deaths on Mumbai roads constitute pedestrians alone. Another 31% is made up of bikers. Together, they contribute to 88% of road fatalities. Strangely absent from the fray, is the top contender for road occupancy – considering there are so many four-wheelers on the road, they constitute less than 15% of the fatalities. However, cars cause 35% of the fatalities. And the more powerful the cars are, the more cause there is for concern. Taxis, surprisingly, cause the least number of accidents.

What explains this poor correlation between the number and the kind of vehicles causing accidents, considering powerful cars and SUVs are lesser in number on Mumbai roads as compared to the innumerable taxis and other smaller cars we see everyday.

blog_use 5.37.24 PM

Consider John Adams’ take on individual risk management – the ‘Risk Thermostat’. According to him, 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, 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. Consider the effect this could have on pedestrians and cyclists/bikers. The safer drivers of cars feel, whether it is thanks to seatbelts or their more expensive, powerful cars with fancy safety features, the more dangerously they drive. And the brunt of this increased risk is being borne by the more vulnerable pedestrians and bikers. We all know that the taxis and auto-rickshaws of Mumbai are infamous for both their age and absence of seat-belts. They are small cars, most of them with a broken down appearance. It is fitting, therefore, that they cause the least number of accidents. Adams also observed a troubling increase in both pedestrian and cyclist deaths immediately following the UK seatbelt law. Delhi enforced the seatbelt rule in February 2002. An exercise performed by Professor Dinesh Mohan, at Indian Institute of Technology, Delhi to see the effectiveness of the seatbelt law concluded that it may have saved at most 11-15 lives per year in Delhi out of almost 2000 fatalities of drivers or front-seat passengers – less than 1% of the total (Chapter 3 of Indianomix)

We’ve noticed this trend not just on roads, but also on the project we carried out to make unmanned level crossings in India safer. The number of cases of the train colliding with bigger cars and SUVs were much higher than for smaller cars and tractors. As people perceive themselves as safer or better equipped against a danger (to navigate to the other side before the train comes in), they are more likely to take more risks. Also, there were fewer accidents at level-crossings where visibility was limited as opposed to when there was clear visibility.

How do we then make our roads safer? The answer does not lie in making cars safer but to redesign traditional traffic safety engineering and legislation, taking into account the vagaries of human behaviour, so that the roads are a safer place to pedestrians, cyclists and bikers as well.

– With inputs from Ram Prasad

Image Source:

Innovation Infanticide

Our CEO, Biju Dominic recently spoke on ‘Innovation Infanticide’ at TEDxXIMB. $1.4 Trillion was spent on R&D in 2012. 1,300,000 research papers are written every year. Why then do we have so little to show when it comes to Innovation? Why is Apple the only name that pops instinctively in or heads when we talk about Innovation? Watch this video to find out.


Warning: The video quality is poor due to bad lighting. However, the audio more than makes up for it