Today’s research summary is about a paper co-authored by Angela Duckworth, that is at the intersection of psychology and economics. Though I have been following behavioral economics a bit, I still found the paper a bit challenging to read and comprehend and don’t claim to understand all the attached jargon, functions and mathematical formulations. The fact that the paper is 88 pages long wasn’t of help either 🙂 (the saving grace being that 20 or more pages were filled with references alone), so read the rest of the summary at your own peril!
- The paper aims to throw light on how personality affects (socio)economic outcomes and how concepts from personality psychology can be used in economic equations and modeling.
- To start with, an important socioeconomic outcome is success in life. IQ or cognitive ability is well established as a predictor of success in life/job, and slowly but surely, a case is building up for the predictive power of personality traits like conscientiousness to predict success in life/job.
- Its useful to distinguish cognitive factors like Intelligence/IQ from other ‘non-cognitive’ factors like personality traits and motivation.
- Perry Preschool study which enriched the environment (an intervention aimed at increasing IQ) of disadvantaged kids with subnormal IQ, found that IQ gains for treatment group (which shot up initially) and control group became equal at age 10 , though the treatment group continued to be much more successful on many socioeconomic outcomes over their life cycle. This can be only explained if we admit that something other than IQ, maybe personality factors, were changed by the intervention.
- Psychologists use personality, motivation and cognitive factors to explain behavior and success of an agent. Economists however use concepts like preferences, constraints, incentives etc to explain choice/decision/ behavior and ultimately success in life.
- Cognitive factors are defined as ‘‘ability to understand complex ideas,to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought’’. The various tests like IQ tests that measure cognitive ability have led to identification of a general factor ‘g’ of intelligence. The factor structure of Intelligence is hierarchical; as per one conceptualization, the second-order factors are ‘fluid intelligence’ and ‘crystallized intelligence“.
- IQ test are not a pure measure of maximal intellectual performance; for those getting low scores, appropriate incentives can increase their scores. Similarly test anxiety may affect performance; thus IQ measure is affected by factors like motivation and personality.
- Personality factors also have a hierarchical structure; the most common level contains the Big Five factors, below them are specific facets and above them two super factors of plasticity and stability.
- The personality traits of Big Five have been arrived at using factor analysis and are more descriptive in nature, based around clustering of together of traits, adjectives or behaviors. The same can be said of ‘g’ which is again more descriptively arrived at. In contrast, economists prefer measures that have been built based around their predictive power in the real word. MMPI, Hogan personality inventory etc were on the other hand built with the specific aim of predicting real world outcomes.
- Economists, try to estimate preferences of agents and thus predict/explain their behavior etc. Some of the typical preferences studied are time preference, risk aversion, preference for leisure and altruism/ social preferences. Estimating these preferences help explain and predict behavior that deviates from a purely self-interested rational agent.
- Time preference is the preference for immediate reward over future reward. This is measured by the phenomenon of time discounting while making decisions. For example, what would you choose 1 $ today or 2 $ tomorrow? 500 $ this week or 1000 $ next month? based on answers to questions like these (and maybe real world behaviors/ decisions too) economists can infer what is the rate at which you discount future utility for present utility. That function is hyperbolic in nature.
- Its seems “time preference is tri-dimensional, comprising three separate underlying motives: impulsivity, the tendency to act spontaneously and without planning; compulsivity, the tendency to stick with plans; and inhibition, the ability to override automatic responses to urges or emotions”. Its easy to see how the three components of time preference can be related to personality factors. Also important is to note that a person with low future vision or imagination may be constrained on this time preference dimension.
- Risk aversion is the phenomenon, whereby a sure or less uncertain outcome is preferred over an uncertain outcome. For example, what would you choose 1 $ for sure or a 50 % chance of winning 2.2 $? Based on analyzing such decisions, one can again calculate, how risk averse a person is. This paradigm is however prone to framing effects.
- Those who show little risk aversion, also have poor outcomes like indulging in smoking, stealing and not wearing seat belts. The personality trait of sensation seeking, as developed by Zuckerman, is related to this construct.
- Preference for leisure is the preference to use time for relaxation etc over indulging in work or economic activity. Some people are driven to work hard and personality traits like Conscientiousness are really relevant here.
- Social preferences are preferences like inequality aversion where a monkey would not accept cucumber pieces for the same work, if another monkey is getting grapes instead. Doesn’t make sense rationally, but economists can use social preference to get out of this hole!
- The big five as well as IQ are predictive of various life outcomes like leadership, grades, longevity etc
- Most personality traits as well as intelligence measures change with age- they are malleable and follow a pattern. For eg, fluid intelligence decreases while crystallized intelligence increases over the lifespan.
- Environmental factors like parental investment and social roles can be the mechanisms that lead to changes and stability in these traits.
- Preference factors, which are studied by economists, however are not clear as to whether they are stable or change with age and more research needs to be done there.
- The real contribution of this paper is in conceiving psychological traits as constraints under which economic decisions are being made. For eg. low cognitive ability will constrain a person to figure out and get clarity about the issue at hand and he will be forced to choose in uncertainty and his risk aversion maybe causing him to make sub-optimal decisions. The intelligent person has a richer choice set and intelligence is a constrain having real world implications; same is true for personality factors.
- Thus personality traits may be a form of constraints/ preferences and research in either psychology or economics around this shroud inform each other.
- Overall, despite its challenging economics jargon, I found this really useful; as someone interested in personality psychology, this provided a new perspective.
As always, do check out the original paper here.
Ed at Not Exactly Rocket Science has an important post on research by Keizer and colleagues, which found support for the broken windows theory of crime spread. He dos a very good job of describing the broken window theory, the experiments of Keizer et al and how they show that disorder spreads like a virus, so I won’t repeat all that here but urge you to go to his blog to get the complete lowdown.
What I would like to highlight instead is that fact that this Broken window theory was brought to public focus by Malcolm Gladwell in The Tipping Point and subsequently the same theory was thrashed in Frekonomics by Dunbar and surprisingly Malcolm Gladwell had promoted and written an encouraging blurb for Freakonomics. You can read more on the controversy here . I obviously had disliked almost all the explanations in Freakonomics and believe that the book was more on trying to be controversial rather than offering new insights. I , on the other hand, have been sympathetic to Gladwell’s writings and it is heartening to note that new research supports the old position that lawlessness spreads via small acts and it may be more important to take care of small, everyday acts of lawlessness than to focus on a few big problems like the cocaine addicts. I would just end with a brief note on Gladwell’s new book Outliers, which is on my immediate reading list and I am pleased that he shares some of my thoughts about how SES affects outcomes in life (like IQ) and how we are creatures of circumstances.
This is an FYI post about a great article by Bruce Schneier, assessing the psychological issues involved in assessing various security trade-offs. He touches on all aspects of behavioral finance,psychological biases, prospect theory, decision-making etc that are relevant and affect our felling of security vis-a-vis actual and objective security. Although, he is not that strong when it comes to discussing the neurological basis of these, I would highly recommended reading the article in its entirety!
How to maximise your bets : become a schizophrenic or damage your amygdala, the orbitofrontal cortex, or the right insular cortex!
A couple of recent news articles on neuroeconomics, lead to some surprising insights regarding how addictions like Gambling could be self-addictive and how some specific neurological malfunctioning may lead to people fairing better in games of chances and making more ‘rational’ gambles.
The first article in the New Scientist refers to a recent research by Chris Frith et al at University College London, UK in which the authors found that people who had been given dopamine agonists (like L-DOPA) were able to determine the winning strategy involved in a gambling game early then those who were given placebo. The study contained choosing symbols – some of whom were associated with large chances of winning, while others were associated with average chances and still others were associated with financial penalties and should ideally be learned as avoidable symbols.
What they found was that dopamine facilitated the early learning of the symbols that were associated with (monetary) winning outcomes or rewards as compared to controls, but had no effect on the learning of the avoiding or punishment symbols. This, they hypothesize is due to the fact that people get a Dopamine surge whenever ‘rewarded’ and when base dopamine levels are high (it has already been administered prior to the betting game) this leads to greater strength of dopamine reward signal , thus leading to faster learning of the winning strategy. The fact that dopamine does not affect the learning of negative outcomes, confirms that the effect selective and due to the ‘rewarding’ nature of dopamine as opposed to a general improvement in learning due to dopamine administration.
The participants played a computer game in which they were repeatedly shown pairs of unmatched symbols, and had to choose one or the other without being told anything about them beforehand.
Unknown to the participants, one symbol gave them an 80% higher chance of winning £1, whereas another symbol gave them only a 20% higher chance of winning. Other symbols incurred financial penalties.
The volunteers on dopamine prospered because they identified the winning symbols faster than the haloperidol treated patients. And the winning effect was more pronounced if they actually received money in the study.
The dopamine recipients only noticed winning symbols, however. The chemical did not appear to alert recipients to “losing” symbols.
Learning from losing is controlled by other chemicals in the brain, the most dominant probably being serotonin, a chemical linked with depression, Frith concludes.
This brings up some interesting scenarios. If one has started gambling somehow, then as one keeps gambling further, the successive wins would generate more and more dopamine surges (as baseline dopamine increases after a few wins), the gambler would start identifying the winning patterns, and the strength of winning patterns and rewards associated with them would continue to get stronger in the gambler’s mind; there would be no corresponding effect on the learning of negative or losing strategies by him and consequently his learning would be skewed in such a way that winning outcomes would be disproportionately perceived as being rewarding as compared to the losing outcomes – thus in the gamblers mind loses are processed in a ‘normal’ way ; but wins or winning strategies are perceived differently in the sense that they would be learned more strongly, earlier and more persistently – as each win would result in more and more dopamine surge and thus skew the learning in favor of the winning strategy more and more. this is a vicious circle- the gambler is getting more and more dopamine surge and is also becoming better and better at identifying the winning strategies- thus its difficult to convince him otherwise that he is gambling in vain- what he doesn’t realize that he is not attaching a corresponding increased negative outcome to losses or is learning the losing strategies also at the same rate.
The other article is a good review of the field of neuroeconomics in the New Yorker. It touches on many current issues in neuroeconomics, but what is most relevant to us here is the concept of loss aversion, whereby people perceive losses of what they already have as more aversive than a wasted chance of making an equivalent or more gain. To paraphrase from the article:
If you present people with an even chance of winning a hundred and fifty dollars or losing a hundred dollars, most refuse the gamble, even though it is to their advantage to accept it: if you multiply the odds of winning—fifty per cent—times a hundred and fifty dollars, minus the odds of losing—also fifty per cent—times a hundred dollars, you end up with a gain of twenty-five dollars. If you accepted this bet ten times in a row, you could expect to gain two hundred and fifty dollars. But, when people are presented with it once, a prospective return of a hundred and fifty dollars isn’t enough to compensate them for a possible loss of a hundred dollars. In fact, most people won’t accept the gamble unless the winning stake is raised to two hundred dollars.
Further, the article notes that this loss aversion is due to the fact that under ambiguous situations (or situations that involve probabilistic estimates in face of incomplete information to make the probabilistic judgments), our ’emotional’ brain takes precedence over the ‘rational’ brain and prevents us from making ‘rational’ decisions.
In one study, Camerer and several colleagues performed brain scans on a group of volunteers while they placed bets on whether the next card drawn from a deck would be red or black. In an initial set of trials, the players were told how many red cards and black cards were in the deck, so that they could calculate the probability of the next card’s being a certain color. Then a second set of trials was held, in which the participants were told only the total number of cards in the deck.
The first scenario corresponds to the theoretical ideal: investors facing a set of known risks. The second setup was more like the real world: the players knew something about what might happen, but not very much. As the researchers expected, the players’ brains reacted to the two scenarios differently. With less information to go on, the players exhibited substantially more activity in the amygdala and in the orbitofrontal cortex, which is believed to modulate activity in the amygdala. “The brain doesn’t like ambiguous situations,” Camerer said to me. “When it can’t figure out what is happening, the amygdala transmits fear to the orbitofrontal cortex.”
The results of the experiment suggested that when people are confronted with ambiguity their emotions can overpower their reasoning, leading them to reject risky propositions. This raises the intriguing possibility that people who are less fearful than others might make better investors, which is precisely what George Loewenstein and four other researchers found when they carried out a series of experiments with a group of patients who had suffered brain damage.
Further, the article notes that people with orbitofrontal, right insular or amygdala damage, are less fearful or are less able to integrate the fearful or ’emotional’ response of the brain and are thus able to make decisions that are more risky then their normal counterparts. Thus, the counterintuitive conclusion that damages to these areas may make one a better investor/ gambler etc.
Each of the patients had a lesion in one of three regions of the brain that are central to the processing of emotions: the amygdala, the orbitofrontal cortex, or the right insular cortex. The researchers presented the patients with a series of fifty-fifty gambles, in which they stood to win a dollar-fifty or lose a dollar. This is the type of gamble that people often reject, owing to loss aversion, but the patients with lesions accepted the bets more than eighty per cent of the time, and they ended up making significantly more money than a control group made up of people who had no brain damage. “Clearly, having frontal damage undermines the over-all quality of decision-making,” Loewenstein, Camerer, and Drazen Prelec, a psychologist at M.I.T.’s Sloan School of Management, wrote in the March, 2005, issue of the Journal of Economic Literature. “But there are situations in which frontal damage can result in superior decisions.”
If we club the two studies together, one may come to a surprising conclusion that to become a good speculative investor or gambler you may need to temporarily knock out your parts of the brain involved in emotional decision making (one may use TMS here) and also additionally take a dopamine does to learn the rewarding strategies and actions early on. This may be the only way for us to counter the tyranny of loss aversion that nature has imposed on us and move towards that ideal of Homo Economicus.
In the Metaphor related posts, Mixing Memory discusses the 2 main theories for explaining metaphors, the structural mapping and the attributive categorization theories and leaves the third theory related to cognitive linguistic approaches because of Mixing Memories long-time disagreements with the proponent of that George Lakoff:-)
Lakoff/Chomsky stand out as they believe in things like linguistic framing and how that relates to propaganda and have taken political activism related to the same.
While I will be addressing framing and the cognitive linguistic view of conceptual metaphors in a subsequent post, there is a recent Science Daily article reporting on another type of framing– Framing of economic statements in terms of either gains or losses and thereby by invoking the risk-averse cognitive structures leading to different behavioral outcomes, when game theory and mathematical probabilistic behavior would have predicted a same response. In a nutshell, if questions are framed such that out of 50$ I have, I would either have the option of keeping 20 $ for sure or 40% probability of keeping the whole amount (and 60 % probability of losing the whole amount), then my responses of whether I take the gamble or stay with assured amount would be different if the question was framed as I would lose 30 $ for sure or have a 40% probability of keeping the whole amount. In the latter situation, the mere use of word like ‘lose’ is sufficiently powerful to make one averse to that situation and thus wager for the second option viz. of 40% chance of retaining the whole amount.
This is just one example, but many game theoretic experiments are accumulating evidence that framing is important and has real economic consequences.