Market theory does not fully explain the economic choices we make. Commentator Wim Hordijk says we must also look to behavioral economics and evolutionary psychology to understand the economy.
Research into behavioral economics has shown, for example, that our assessment of what something is worth to us can be directly, and predictably, influenced. This is the illusion of the free lunch, something humans are known to fall for even when economic theory would clearly suggest we select a more valuable option at a small cost.
Ariely also beautifully elucidates how we sometimes operate on social norms, while other times we fall into market norms. The difference is in whether there is a price attached to something.
If a friend invites you over for dinner, she will probably appreciate it if you bring a nice bottle of wine along (social norms). However, if instead you slap $20 (the price of a nice bottle of wine) in cash on the table and say "thanks for a lovely dinner," she would most likely be offended (market norms). Mixing social norms and market norms inappropriately often leads to irrational behavior and, possibly, even to conflict or misunderstanding.
Our irrational behavior is not just random though. The scientific experiments are repeatable. Each time we are faced with a similar situation, we tend to behave in a similarly irrational way. So, next to the bad news that we are not nearly as rational as we might have thought (or hoped), there is also good news in that we can understand and predict our irrational behavior, at least to some extent. This, in turn, can help us improve our decision making and change our behavior for the better. In other words, we can try to be more rational about our irrationality.
Abstract Decision makers generally feel disconnected from their future selves, an experience that leads them to prefer smaller immediate gains to larger future gains. This pervasive tendency is known as temporal discounting, and researchers across disciplines are interested in understanding how to overcome it. Drawing from recent advances in the power literature, we suggest that the experience of power enhances one’s connection with the future self, resulting in reduced temporal discounting. In Study 1, we show that participants assigned to high-power roles are less likely than others to display temporal discounting. In Studies 2 and 3, we find that priming power reduces temporal discounting in monetary and non-monetary tasks and, further, that connection with the future self mediates the relationship between power and reduced discounting. In Study 4, we show that experiencing a general sense of power in the workplace predicts actual lifetime savings. Implications and future research directions are discussed.
Time travel is a science fiction staple, inspiring the plots of countless books, movies and Star Trek episodes. But while basic physics allows for the possibility of moving through time, practical concerns like the “Grandfather Paradox,” in which a traveler jumps back in time, kills his grandfather and therefore prevents his own existence, seem to stand in the way. Self-described “quantum mechanic” Seth Lloyd looks at an alternate mode of time travel that eliminates any events that could later prove paradoxical, making this phenomenon both theoretically possible and creatively irresistible, whether you’re an astrophysicist or just a daydreamer.
Abstract: This chapter provides an overview of the research literature and the important issues regarding risk perception and risk tolerance. The academic literature reveals that various disciplines provide an assortment of perspectives in terms of how to define, describe, and analyze risk. The behavioral finance perspective encompasses the subjective and objective factors of risk within the domains of risk perception and risk tolerance. Risk perception is the subjective decision-making process that an investor uses when evaluating risk and the amount of uncertainty. Risk tolerance is the degree of risk that an investor is willing to endure in the pursuit of a financial objective. A major problem within the risk tolerance literature is the lack of general agreement about issues such as a standard definition, a uniform theory or model, measurement discrepancies, and the growing number of questionnaires. Academic researchers and practitioners are only now starting to study and understand the long-term effects of the financial crisis in 2007 and 2008 on investor risk-taking behavior.
Cognitive science is partly defined as the study of thought, learning, and mentalorganization, which are all investigable functions of the human brain. Therefore, byunderstanding the principles of the brain, we can take a step forward in holistically knowing whatthe mind is.Neuroscience and ConsciousnessThe brain is comprised of billions of neurons. Neurons are the fundamental cells in thebrain that communicate to perform most bodily functions and higher-level cognitions. The thingthat makes these cells unique is that they are plastic and able to adapt based on the experiencesthey encounter. Scientists' ability to study the connections and specific importances of groups ofneurons across the brain contributes to the understanding of how humans learn, think, andchange.Various behavioral methods like electroencephalography (EEG) and functional magneticresonance imaging (fMRI) allow us to record neural action in the brain during various tasksrelating to cognitive function. By using these techniques, and others, it has been proven that thefrontal lobe of the brain plays a large part in higher-level cognitive functions like analyzinginformation, solving future problems, developing strategies, and controlling purposeful behaviors.This is significant because lower-level primates do not have developed frontal lobes andtherefore are unable to complete these complex actions. This ability to perform higher-levelfunctions, that aren't simply primitive or instinctive responses, is what makes us distinctlyhuman, and ultimately what composes our unique conscious mind.While neuroscience can solve many questions about what it truly means to be aconscious being (like the ability to control instinctive behaviors), it cannot answer them all. Somehuman functions still remain mysterious because neuroscience can't pin down concepts likefree will or behavioral control. In conclusion, the mind is certainly an emergence from the brain,but it isn't necessarily a distinct subject that can be entirely comprehended by science in today’stime.3
Olivier Oullier, a renowned expert in behaviour change and neuroeconomics, explains how research developments in brain sciences and behavioural finance help us comprehend the biases that distort financial and economic decisions and how investors and traders can better understand and cope with such problems.
Each year, advertisers spend very large portions of their budgets on promoting their brands via increasingly more integrated marketing campaigns; however, understanding the return on these investments has always been a challenge.
Constant changes in media, the advance of digital and the plethora of touchpoints that make up potential consumer engagement have simply made the picture more complex. Never before have brands had so many tools at their disposal to engage with consumers, promote their products and interact with their targets. What was once a relatively simple decision in terms of budget allocation and creative optimisation has become an extremely complex puzzle.
System 1 Brand Tracking measures the effectiveness and contribution of each element of a campaign and their impact on brand equity through an innovative approach inspired by the latest advances in behavioural science. Furthermore, it delivers where traditional brand tracking cannot - by revealing the emotionally charged relationship between a consumer and a brand, and delivering key takeaways to foster the emotional bond.
Gabriel Aleixo, Managing Director - LatAm, discusses case studies that demonstrate a more effective approach to brand tracking and measurement of integrated marketing campaigns, and the key driver to brand success - how consumers feel.
When it comes to making decisions, the fact is we think much less than we think we think! Behavioural Economics has shown that our decisions are guided not by our plans or intentions, but by where we are, the people we’re with and the unconscious forces inside us. Summaries of Behavioural Economics offer a collection of delightful anecdotes, but can leave you wondering how on earth to use it. To make sense of it, join Orlando Wood as he talks through BrainJuicer’s Behavioural Model; learn why it’s so important to make buying your brand fun, fast and easy and how the behavioural sciences can inspire better research and marketing.
In PNAS, Fritz et al. (1) follow up their groundbreaking 2012 paper with what will probably be the final nail in the coffin for those who would believe that old musical instruments sound demonstrably better than new instruments. Their study used six prized instruments, Stradivari and Guarneri “del Gesu” violins, and six modern violins. World class violinists who were literally blind to provenance (the violinists wore goggles that dramatically reduced their ability to see) were given two opportunities to play them: in a small salon and in a concert hall. They were allowed to bring a friend to act as a second judge. Their task was to rank order the violins in terms of desirability and to label them as old vs. new. These highly trained and highly discerning musicians utterly failed at detecting old vs. new and showed no consistent preferences.
The study balanced rigor with real-world considerations and represents the most ecologically valid conditions possible while maintaining strict experimental protocols. Yet, intriguingly, the participants themselves remained unconvinced, even after having seen the results with their own eyes (or heard them with their own ears). Said one, “the one thing that you cannot put into a new violin is that it’s been played for 300 years—these instruments change and develop.” Said another, “I would absolutely buy a new instrument, but for a later generation. They need to be broken in” (2).
Why is it that musicians and scientists reach different conclusions when considering the same data? This arises in part due to different ways of knowing things. Scientists know what they know through systematic observation of the external world, mediated by replicable experiments and objective measurement. Artists know what they know through emotional experience, subjectivity, and intuition. When they …
Behavioural insights draws on research into behavioural economics and psychology to influence choices in decision-making. By focusing on the social, cognitive and emotional behaviour of individuals and institutions it suggests that subtle changes to the way decisions are framed and conveyed can have big impacts on behaviour.
In part two of this new series, we present a simple model for intuition that provides a framework for understanding how to take advantage of its strengths and avoid its weaknesses.
Last month, I started a regular column on the power of intuition in investing that inspired many of you to write to me directly to discuss your experiences. While most of you viewed intuition positively, your responses indicated you still weren’t sure how to define it (i.e., that it lacked a central idea). You also expressed some reservations about applying intuition to investing because it is perceived as unreliable. Both these observations stem from the fact that we lack a simple, broadly adopted model of intuition to aid our understanding. With that in mind, here is my model. I hope it provides you with greater clarity.
A Simple Model of Intuition
Intuition is neither System 1 nor System 2 thinking as defined by Daniel Kahneman. Instead, intuition is a different category of mental action that is distinct from both instinct and deliberation. Intuition is a sense exactly like the five standard senses. Here is a simple model for understanding intuition as a sense:
In their experimental markets, Colin Camerer, the Robert Kirby Professor of Behavioral Economics at Caltech, and colleagues found two distinct types of activity in the brains of participants—one that made a small fraction of participants nervous and prompted them to sell their experimental shares even as prices were on the rise, and another that was much more common and made traders behave in a greedy way, buying aggressively during the bubble and even after the peak. The lucky few who received the early warning signal got out of the market early, ultimately causing the bubble to burst, and earned the most money. The others displayed what former Federal Reserve chairman Alan Greenspan called "irrational exuberance" and lost their proverbial shirts. The researchers set up a simple experimental market in which they were able to control the fundamental, or actual, value of a traded risky asset. In each of 16 sessions, about 20 participants were told how an on-screen trading market worked and were given 100 units of experimental currency and six shares of the risky asset.
A trolley is careening toward an unsuspecting group of workers. You have the power to derail the trolley onto a track with just one worker. Do you do it? It might not matter.
"Suppose you're the driver of a trolley car, and your trolley car is hurtling down the track at 60 miles an hour. You notice five workers working on the track. You try to stop, but you can't, because your brakes don't work. You know that if you crash into these five workers, they will all die. You feel helpless until you notice that off to the side, there's a side track. And there's one worker on the side track."
The question: Do you send the trolley onto the side track, thus killing the one worker but sparing the five, or do you let events unfold as they will and allow the deaths of all five? (Most people, for what it's worth, say they would turn.)
Then Sandel asked about a popular variation on the same problem. The same trolley is careening toward unsuspecting innocents, but this time, you're an onlooker on a footbridge, and, "you notice that standing next to you, leaning over the bridge, is a very fat man."
A ripple of laughter rises from the packed auditorium.
"You could give him a shove," he continues. "He would fall over onto the track, right in the way of the trolley car. He would die, but he would spare the five. How many would push the fat man over the bridge?"
A few hands go up, but most of the students just erupt in giggles.
And that's exactly why, some scientists argue, this well-known "trolley dilemma," shouldn't be used for psychology experiments as much as it is.
HANDBOOK OF FINANCE: VOLUME 2: INVESTMENT MANAGEMENT AND FINANCIAL MANAGEMENT, Frank J. Fabozzi, ed., John Wiley & Sons, pp. 85-111, 2008
Abstract: Since the mid-1970s, hundreds of academic studies have been conducted in risk perception-oriented research within the social sciences (e.g., nonfinancial areas) across various branches of learning. The academic foundation pertaining to the "psychological aspects" of risk perception studies in behavioral finance, accounting, and economics developed from the earlier works on risky behaviors and hazardous activities. This research on risky and hazardous situations was based on studies performed at Decision Research (an organization founded in 1976 by Paul Slovic) on risk perception documenting specific behavioral risk characteristics from psychology that can be applied within a financial and investment decision-making context. A notable theme within the risk perception literature is how an investor processes information and the various behavioral finance theories and issues that might influence a person's perception of risk within the judgment process. The different behavioral finance theories and concepts that influence an individual's perception of risk for different types of financial services and investment products are heuristics, overconfidence, prospect theory, loss aversion, representativeness, framing, anchoring, familiarity bias, perceived control, expert knowledge, affect (feelings), and worry.
Humans exhibit a preference for options they have freely chosen over equally valued options they have not; however, the neural mechanism that drives this bias and its functional significance have yet to be identified. Here, we propose a model in which choice biases arise due to amplified positive reward prediction errors associated with free choice. Using a novel variant of a probabilistic learning task, we show that choice biases are selective to options that are predominantly associated with positive outcomes. A polymorphism in DARPP-32, a gene linked to dopaminergic striatal plasticity and individual differences in reinforcement learning, was found to predict the effect of choice as a function of value. We propose that these choice biases are the behavioral byproduct of a credit assignment mechanism responsible for ensuring the effective delivery of dopaminergic reinforcement learning signals broadcast to the striatum.
Quando facciamo una scelta, siamo portati a sopravvalutare i benefici che ne abbiamo ricavato per effetto di uno specifico meccanismo di rinforzo delle connessioni neurali, che avviene in seguito al rilascio del neurotrasmettitore dopamina. Un nuovo studio ha permesso di chiarire che le regioni cerebrali coinvolte in questo fenomeno sono il corpo striato e due diverse porzioni della substantia nigra
A panel of industry experts joined David Wethey to examine how BE can be applied to overcome bottlenecks in client-agency relationships. At the SOCI on Monday 28th February, IPA President, Rory Sutherland introduced David Wethey, BE Think Tank member and Founder of AAI at an exclusive IPA event. Building on the seven key Behavioural Economics principles of 1) loss aversion, 2) the power of NOW, 3) scarcity value, 4) goal dilution, 5) chunking, 6) price perception and 7) choice architecture - David put forward a view on how BE can be applied in the client-agency relationship. In particular he believes that BE can make a critical contribution to decision making in client briefing. He also touched on the potential for Choice Architecture to improve the client-agency relationship in areas like pitching and remuneration.
A recent article in the New York Times highlighting the oft-overlooked malady of “precrastination” gives us an opportunity to discuss some of the downsides of our own intuitions in relation to existing time management systems.
As the article points out, “people appear to be wired to incur a significant physical cost to eliminate a mental burden.” When applied to time management, this often results in what Dan Ariely refers to as “structured procrastination”.
Structured procrastination involves getting little, relatively insignificant things done, in an attempt to “eliminate a mental burden” -- in this case the burden of having many things on your plate. With a standard to-do list, we have a long list of potential activities that vie for our time, and we need to pick and choose which of all these things to take care of first. Not surprisingly, this leads to us checking off those things that are quick and easy to finish (and thus give us the joy of checking them off quicker), as opposed to the things that are difficult but also likely to be important.
On top of the drive to attend first to the small things (and maybe never getting to the large ones), we also generally waste a great deal of time on the maintenance of small tasks -- just writing them down and then ticking them off of our to-do lists. This is of course a fantastic way to feel productive, but it mostly results in putting off the things that we should really be focusing on: the looming project, the awkward phone call, the term paper.
Getting mentally demanding tasks done requires solidifying our commitment to these tasks. Research suggests that simply scheduling things leads to a much higher rate of completion, which is why we developed Timeful’s smart suggestions. We think having a proactive calendar that reminds us about truly important tasks is crucial to developing the behavior that will curb precrastination and procrastination both.
Here’s Dan on the problem of structured procrastination:
RELEARNING ECONOMICS: The Post-Crash Economics Society (PCES) have produced a compelling analysis of the failings in economics education and set out a road map for reform.
“This report addresses a real need, for a more pluralistic and varied approach to the economics curriculum at university level. The mainline of economics from Adam Smith onwards is diverse and often departs from the current mainstream and the way that the subject is taught needs to recognise this. I welcome this contribution to discussion about the way we should explore the living tradition of economic thought and the light it casts on the contemporary world.”
Dr Stephen Davies, Education Director at the Institute of Economic Affairs.
This is a very important Report by the Post-Crash Economics Society. They explore the existing curriculum in careful detail, displaying the narrow theoretical monoculture which characterises not only Manchester’s curriculum but most economics degree courses in the UK and, indeed, the world. They outline what they think the curriculum should be: a programme geared to the problems of the actual economy through history, using the diversity of analyses that have developed through the years. The Report is a landmark: scrupulous, well-informed, passionate, it is required reading for every head of an economics department and highly recommended for everyone interested in the future of economics.
Victoria Chick, Emeritus Professor of Economics at University College London and co-founder of the Post-Keynesian Economics Study Group.
During the last weekend of June, hundreds of students, university lecturers, professors and interested members of the public descended on the halls of University College London to attend the Rethinking Economics conference. They all shared a similar belief: that economics education in most universities had become narrow, insular and detached from the real world.
For a brief period after the financial crisis of 2008, the shortcomings of the economics profession and the way it is taught were recognized. Many economists offered up mea culpas of various kinds and conceded that since they did not foresee the biggest economic event since the Great Depression, there was probably something seriously wrong with the discipline. But as time passed and many economies began to experience gradual, somewhat muted recoveries, the profession regained its confidence.
When I was completing my master’s degree at Kingston University last year, I experienced this firsthand from the more mainstream faculty there. Lecturers offered potted explanations of the crisis using old analytical tools such as supply and demand graphs that cannot incorporate expectations to explain asset price bubbles. The same economists who, just a few years ago, told us that financial markets were the conduits of perfect information began to introduce doublethink phrases in the media such as “rational bubble” (in which investors allegedly act irrationally by bidding up asset prices in full knowledge that prices are heavily inflated but think they can bail out of the market before prices fall) to explain the events of the past few years. There is nothing rational about investors’ acting this way, because they cannot know when the bubble will burst and so cannot time their exit from the market. They cannot know when the herd movement that they are part of will come to an end, so any action that they take to ride the wave will be just as irrational as those of people unaware of the bubble. The entire exercise appeared to be an ad hoc attempt to reinterpret the facts to fit the pet theory — economic agents aware of relevant information act rationally — rather than to alter the theory in light of the facts.
It was difficult not to sense the Soviet-style revisionism that had occurred within the halls of learning: The party had tossed history down the memory hole and introduced a strange, seemingly self-contradictory language that they were busy foisting upon an unwitting public. One Chicago school economist, Ray Ball, argues that the now notorious efficient market hypothesis (EMH), which states that financial markets price in all relevant information, is actually supported by the recent crisis. He argues that the capital flight that led to the bank meltdowns lends support to the EMH because it shows how rapidly financial markets react to new information. But as many will remember, investigations clearly showed that information was not being processed efficiently by market participants in the run-up to the crisis. The most colorful example of this was the Standard & Poor’s employee who, responding to a colleague who said that they should not be rating a mortgage-backed security deal because the estimations of risk were incorrect, said that cows could be estimating the risk of a product and S&P would still rate it.
n part one of my interview with Nobel laureate Robert Engle, we discussed the development of the ARCH model, the global financial crisis, systemic risk, and forecasting liquidity with ARCH models. In this part, we will cover the application of ARCH models in high-frequency trading and how he thinks risk models should be applied in portfolio management.
CFA Institute: Let’s switch gears and talk about some of the most practical ways traders and portfolio managers leverage risk modeling. How about we start with high-frequency trading?
Robert Engle: There is no reason why you can’t calculate value at risk at a millisecond interval, except that we probably need a better volatility model, a volatility model that is specific to the time frame. One way people have done this is sort of what I call the brute force approach, which is you take thousands of observations every day, then you take thousands of observations the next day, put them all together to make them a million observations long, and fit it into the GARCH model. And it turns out that doesn’t work very well because it spends most of its effort predicting time of day shapes. So the decay rates of volatility are like an hour long. It predicts two days ahead. The volatility is not affected by what we know today. And we know that is not true. We need a more complex model.