Behavioural Economics

Darwin goes to Wall Street

Since the financial crash of 2008 the field of economics has been in a state crisis, where a paradox lies at the heart of the issue.

On the one hand markets are remarkably rational and efficient, displaying allocation and foresight that prediction scientists are only beginning to fully grasp. Efficient markets are powerful, practical tools for aggregating information, and they do it more quickly and cheaply than any known alternative.

However markets also prone to wild swings of irrationality, and can fail spectacularly. Indeed, soul searching in the wake of the financial crisis has led to calls for the field of economics to be revolutionised. Behavioural economics has since risen in prominence, challenging the orthodoxy of neoclassical economic theory by outlining how predictably irrational people can be.

What can we make of this apparent contradiction?

In Adaptive Markets, MIT finance professor Andrew Lo ambitiously calls for a paradigm shift in financial economics, and urges us to reconsider financial markets from an evolutionary perspective. Lo argues by understanding the evolutionary forces that have shaped human behaviour and spurred financial innovation we can reconcile these paradoxical findings, and successfully address the problems facing the financial world.

It Takes a Theory to Beat a Theory

One passage from the book that stuck with me is where Andrew tells the story of taking his son to the National Zoo in Washington DC, who was then a toddler. “As expected, my son was delighted with the Great Ape House, but for me, this visit was nothing less than transformational.”

Lo describes his family standing in front of a group of orangutans, where the alpha male of the group came surprisingly close to the iron-bar fencing separating the great apes from the visitors. In response, Andrew pulled his son away from the fence to keep him out of danger. This startled the alpha male’s companion, who instantly moved in front of the younger orangutan to repel Andrew’s advance- in essence mirroring each other’s behaviour.

The human brain works in mysterious ways. At that precise moment, I saw clearly how the Efficient Markets Hypothesis and its behavioural critique could be reconciled. In fact I understood two things that should have been obvious to me all along, but which I had never thought about until then.

The first insight Andrew had was just how instinctive our primate behaviour is and the the legacy of our shared ancestry, where 97% of our DNA is identical to that of chimpanzees. However Andrew notes the large gulf between our species, where one of these primates carried on their life as usual held in captivity, where the other would go on to write a book detailing the experience and resolving a longstanding academic controversy. Lo argues it is this 3% of the human genome that separates us from other primates, which enables cultural innovation and human ingenuity, and permits advanced economic activity and efficient trading.

The core argument presented in Adaptive Markets is that financial markets do not follow the laws of economic theory. Rather, financial markets are the product of human evolution, and follow the laws of biology instead.

Undoubtedly, behavioural economists have helped improve our understanding of economic decision-making. However, what behavioural economists neglected to answer is the ultimate question: why do people possess these psychological dispositions? Answering ultimate questions leads one to evolution, as the human brain has been honed by the forces of natural selection.

As stated by Lo:

Economic behavior is one aspect of human behavior, and human behavior is the product of biological evolution across eons of different environments. Competition, mutation, innovation, and especially natural selection are the building blocks of evolution. All individuals are vying for survival- even if the laws of the jungle are less vicious on the African Savannah than on Wall Street. It’s no surprise, then, that economic behavior is often best viewed through the lens of biology.

A key concept Lo uses to explain economic behaviour is an evolutionary mismatch:  traits selected for in our ancestral past which were once advantageous, which become maladaptive when the environment changes. For example, Lo argues evolution can help explain loss aversion and people’s inclination to ‘probability match’ in financial settings, which can result in sub-optimal investing.

Financial behavior that may seem irrational now is really behavior that hasn’t had sufficient time to adapt to modern contexts. An obvious example from nature is the great white shark, a near- perfect predator that moves through the water with fearsome grace and efficiency, thanks to 400 million years of adaptation. But take that shark out of the water and drop it onto a sandy beach, and its flailing undulations will look silly and irrational. It’s perfectly adapted to the depths of the ocean, not to dry land.

For the past 50 years, academic finance has been dominated by highly mathematical models and methods that derived from physics. These sophisticated quantitative techniques spawned a wave of financial innovation, and triggered an evolutionary change within the world of finance. However, Lo argues that despite the advantages these advanced quantitative techniques provided, this mass ‘mathematization’ of finance has significant flaws.

Finance isn’t physics, despite the similarities between the physics of heat conduction and the mathematics of derivative securities, for example. The difference is human behavior and the role of evolution in its development… The financial crisis showed us that investors, portfolio managers, and regulators do have feelings, even if those feelings were mostly disappointment and regret during the last few years. Financial economics is much harder than physics.

The Financial Crisis

Much of Adaptive Markets is dedicated to the 2008 financial crisis. Although there are numerous causes of the crisis, Lo argues that at the most fundamental level the primary cause of the crash was greed overpowering fear.

The Adaptive Markets Hypothesis tells us that, at the most basic level of the financial crisis, greed overwhelmed fear. Ignoring the changing environment, people at all levels of the system created a narrative that greed was good. The pushback against the warnings about the oncoming crisis was stronger than the warnings themselves- until it was too late. 

Rather soberingly, Lo details how economists such and Robert Shiller and Raghuram Rajan raised the alarm of the American housing market showing signs of a bubble, which could potentially lead to a catastrophic meltdown of the financial system. However, these concerns were largely ignored by the wider financial community. With the benefit of hindsight, such warnings were remarkably prescient.

Lo’s own research on the hedge fund industry also showed early warning signs of the financial crisis. Back in 2005, journalist Mark Gimein published an overview of Lo’s research in the New York Times. The last paragraph reads; “The nightmare script for Mr. Lo wold be a series of collapses of highly leveraged hedge funds that bring down major banks or brokerage firms that lend to them”.

Apparently Lo’s warning seemed ridiculous at the time, yet in hindsight various hedge funds collapsed at the start of the crisis “and the fact that Bear Stearns and Lehman Brothers both experienced their first wave of losses through their hedge funds, it wasn’t too far off the mark.”

Hedge funds are described by Lo as the ‘Galapagos Islands of finance’, being a novel species of finance that exploit market anomolies which are highly vulnerable to changing environments. According to Lo, increased complexity combined with tighter coupling and a new spawn of financial gigantism increased the odds of a catastrophic meltdown.

Lo also takes the opportunity to challenge the narratives we share to explain the financial crisis. For example, one common theme is that the financial crisis was the result of the unscrupulous practices of financial elites. Although there is much truth to this story, Lo argues many determinants of the crisis were systemic and were not caused by ethical lapses. However, this claim is contested by leaders in the field.

Finance Behaving Badly

Not only does Adaptive Markets cover the basics of evolutionary psychology, it also makes reference to the cultural evolution. Lo makes clear that culture, a domain which is frequently deemed beyond the realm of biology, is itself the product of evolution. That is, subject to the processes of variation, selection and replication. A core argument in Adaptive Markets is that an evolutionary perspective of culture can help us identity and prevent financial scandals.

Long before the days Gordon Gekko became a cultural icon, social psychologists have studied what leads ordinary people to behave like monsters. The infamous Milgram and Zimbardo Stanford prison experiments were conducted during the 1960s and 1970s respectively, and demonstrated in shocking and graphic detail how blind obedience to authority can lead ordinary people to commit atrocities.

Lo notes how little people were paid to participate in these experiments, and yet still were complicit in such unthinkable acts. Milgram paid his participants roughly today’s equivalent of $36, where Zimbardo paid his subjects roughly $90 in today’s money.

Imagine a situation in which you were instructed to engage in questionable financial practices- actions that aren’t nearly as gut-wrenching as delivering electrical shocks- by a managing director or vice president in a suit and tie, and you’re given tremendous financial incentives, like a multi-million dollar year-end bonus, to do so. In light of the Lucifer Effect, it’s not hard to understand how context and culture can lead to even caring and ethical individuals to do reprehensible things to unsuspecting clients. This is the Gekko Effect. 

One possible critique of Adaptive Markets is that it emphases the importance of the environment, yet it does not adequately address the environmental forces shaping financial institutions. However, Lo’s reflections on regulation and corporate fraud is a clear exception.

Although the data is confined to the United States and the time series is rather small (especially when considering evolutionary time-scales), Lo provides evidence that financial scandals and scams are cyclical. That is, historically financial scandals have increased as stock markets rose, and declined once market conditions deteriorated.

 

Lo Dyck, Morse and Zingale's (2013, figure 1) estimates of the percentage of large corporations starting and engaging in fraud

Estimates of the percentage of large corporations starting or engaging in fraud, from 1996-2004 (Dyck, Mores & Zingales, 2013)

Deason et al (2015) Frequency of SEC- prosecuted Ponzi schemes by calendar quarter from 1988 to 2012

Frequency of SEC prosecuted Ponzi schemes by calendar quarter from 1988 to 2012 (Deason, Rajgopal & Waymire, 2015)

These findings may seem counter-intuitive. However, the researchers investigating ponzi schemes make it clear that such large-scale fraud is harder to sustain during deteriorating market conditions, as was the case with Madoff. Lo also notes that regulatory budgets increase after financial bubbles burst.

Lo states the unravelling of Madoff also revealed the biases and blind spots of financial regulators. Although social scientists have much to learn about the behaviour of auditors and financial regulators in the lead up to such scandals, Lo argues loss aversion may help explain why regulators don’t react quicker to signs of disturbance. That is, being more concerned about being wrong and causing a public scandal than investigating cases of potential large-scale fraud.

Fixing Finance 

Lo argues that if we wish to change financial culture and tackle corporate malfeasance, we first have to understand the broader contextual and environmental forces that have shaped such cultures over time and across circumstances.

As a professional working in the field, I’ve observed increasing activity to monitor aspects of organisational culture within the banking industry. However, I believe we’re only beginning to grapple with this challenge, and that various issues need to be addressed. To elaborate, what do we mean exactly when we refer to ‘culture’, how do we measure it, and which aspects of culture can actually be changed and should be prioritised?

Lo provides some excellent advise here, and demonstrates the importance of framing.

The first step requires a subtle but important shift in our language. Instead of seeking to “change culture”, which seems naive and hopelessly ambitious, suppose our objective is to engage in “behavioural risk management” instead… Despite the fact we’re referring to essentially the same goal, the latter phrase is more concrete, feasible, and- this is important- unassailable from a corporate board’s perspective.

To conclude, a core theme of Adaptive Markets is that the financial system is more similar to an ecosystem of living organisms than a machine, and that we must manage the system accordingly. This is a very different perspective compared to traditional approaches of financial regulation, however Lo notes the list of prominent economists who have reached similar conclusions.

Despite certain reviews of the book arguing that the Adaptive Markets Hypothesis offers little practical value, this theoretical insight arguably has many practical implications.

For one, it shows investors how markets are not wholly efficient and can be beaten, and makes clear that specific investment strategies can either succeed or fail depending on the broader financial environment.

Lo shares some big ideas on how large-scale investment funds can be designed to address pressing social problems, such as the high cost of developing new cancer drugs.

For regulators, the adaptive toolkit provides ways of addressing the cultural roots of corporate maleficence, which could help prevent the next Bernie Madoff from succeeding.


Written by Max Beilby for Darwinian Business

Click here to buy a copy of Adaptive Markets

Why anti-corruption strategies may backfire

One of the defining attributes of humans is that we are champion cooperators, surpassing levels of cooperation far beyond what is observed in other species across the animal kingdom. Understanding how cooperation is sustained, particularly in anonymous large-scale societies, remains a central question for both evolutionary scientists and policy makers.

Social scientists frequently use behavioural game theory to model cooperation in laboratory settings. These experiments suggest that ‘institutional punishment’ can be used to sustain cooperation in large groups- a set up analogous to the role governments play in wider society. In the real-world however, corruption can undermine the effectiveness of such institutions.

In July’s edition of the journal Nature Human Behaviour, Michael Muthukrishna and his colleagues Patrick Francois, Shayan Pourahmadi and Joe Henrich published an experimental study which rather cleverly incorporated corruption into a classic behavioural economic game.

Corruption worldwide remains widespread, unevenly distributed and costly. The authors cite estimates from the World Bank, stating US$1 trillion is paid in bribes alone each year. However, levels of corruption vary considerably across geographies. For example, estimates suggest that in Kenya 8 out of 10 interactions with public officials require a bribe. Conversely, indices suggest Denmark has the lowest level of corruption, and the average Dane may never pay a bribe in their lifetime.

Transparency International state that more than 6 billion people live in countries with a serious corruption problem. The costs of corruption range from reduced welfare programmes, to death from collapsed buildings. In other words, corruption can kill.

Michael Muthukrishna’s work suggests that corruption is largely inevitable due to our evolved psychological dispositions; the challenge is apparently to find the conditions where corruption and its detrimental impacts can be minimised. As Muthukrishna is quoted saying in an LSE press release for the paper:

Corruption is actually a form of cooperation rooted in our history, and easier to explain than a functioning, modern state. Modern states represent an unprecedented scale of cooperation that is always under threat by smaller scales of cooperation. What we call ‘corruption’ is a smaller scale of cooperation undermining a larger-scale.

Playing Bribes

What follows is an overview of the studies’ experimental design and results. If this is of little interest, I suggest skipping to the section titled ‘Backfire effect’.

To model corruption, the authors modified a behavioural economic game called the ‘institutional punishment game’. The participants were anonymous, and came from countries with varying levels of corruption. Overall, 274 participants took part in the study. The participants were provided with an endowment, which they could divide between themselves and a public pool. The public pool is multiplied by some amount and then divided equally among the players, regardless of their contributions.

The institutional punishment game is designed so that it is in every player’s self-interest to let others contribute to the public goods pool, whilst contributing nothing oneself. However, the gain for the group overall is highest if everybody contributes the maximum possible. Each round one group member is randomly assigned the leader, who can allocate punishments using taxes extracted from other players.

The ‘bribery game’ that Muthukrishna and his colleagues developed is the same as the basic game, except that each player had the ability to bribe the leader. Therefore, the leader could see both each players’ contributions to the public pool, and also the amount each player gave to them personally. The experimenters manipulated the ‘pool multiplier’ (a proxy for economic potential) and the ‘punishment multiplier’ (the power of the leader to punish).

For each player’s move, the leader could decide to do nothing, accept the bribe offered, or punish the player by taking away their points. Any points offered to the leader that he or she rejected were returned to the group member who made the offer. Group members could see only the leader’s actions towards them and their payoff, but not the leader’s actions towards other group members.

Compared to with the basic public goods game, the addition of bribes caused a large decrease in public good provisioning (a decline of 25%).

Leaders with a stronger punishment multiplier at their disposal (referred to as ‘strong leaders’) were approximately twice as likely to accept bribes and were three times less likely to do nothing (such as punish free-riders). As expected by the authors, more power led to more corrupt behaviour.

Having generated corruption, the authors introduced transparency to the bribery game. In the ‘partial transparency’ condition, group members could see not only the leader’s actions towards them, but also the leader’s own contributions to the public pool. However, they did not see the leader’s actions to other group members. In the ‘full transparency’ condition, information on each member and the leader’s subsequent actions was made fully available (that is, individual group members contributions to the pool, bribes offered to the leader, and the leader’s subsequent actions in each case).

Although the costs of bribery were seen in all contexts, the detrimental effects were most pronounced in the poor economic conditions.

The experiments demonstrated that corruption mitigation effectively increased contributions when leaders were strong or the economic potential was rich. When leaders were weak (that is, their punitive powers were low and economic potential was poor), the apparent corruption mitigation strategy of full transparency had no effect, and partial transparency actually further decreased contributions to levels lower than that of the standard bribery game.

Backfire effect

The study indicates that corruption mitigation strategies help in some contexts, but elsewhere may cause the situation to deteriorate and can therefore backfire. As stated by the authors; “[…] proposed panaceas, such as transparency, may actually be harmful in some contexts.”

The findings are not surprising from a social psychological perspective, and support a vast literature on the impacts of social norms on behaviour. Transparency and exposure to institutional corruption may enforce the norm that most people are engaging in corrupt behaviours, and that such behaviour is permissible (or that one needs to also engage in such dealings to succeed). Why partial transparency had a more detrimental impact than full transparency when leaders were weak is not made clear however.

Remarkably, the authors found that participants who had grown up in more corrupt countries were more willing to accept bribes. The most plausible explanation presented is that exposure to corruption whilst growing up led to these social norms being internalized, which manifested in these individuals’ behaviour during the experiments.

It’s important to note that this is only one experimental study looking into anti-corruption strategies, and that caution is required when extending these research findings to practice. As stated by the authors; “Laboratory work on the causes and cures of corruption must inform and be informed by real-world investigations of corruption from around the globe.”

This aside, the authors’ research challenges widely held assumptions about how best to reduce corruption, and may help explain why the ‘cures for corruption’ which may prove successful in rich nations may not work elsewhere. To paraphrase the late Louis Brandeis, ‘sunlight is said to be the best of disinfectants, yet this may depend on climatic conditions and the prevalence of pathogens’.

Written by Max Beilby for Darwinian Business

Click here to read to full paper.

 

References

Muthukrishna, M., Francois, P., Pourahmadi, S., & Henrich, J. (2017). Corrupting cooperation and how anti-corruption strategies may backfire. Nature Human Behaviour.

Milinski, M. (2017). Economics: Corruption made visible. Nature Human Behaviour.

When Less is Best (LSE, 2017); Available here

Corruption Perceptions Index 2015 (Transparency International, 2015); Available here 

 

Image credit: George Marks/Getty Images.

 

The Knowledge Illusion, by Steven Sloman & Philip Fernbach

Most things are complicated, even things that appear rather simple.

Take the toilet as an example. As a thought experiment, would you be able to explain to someone else how a toilet works?

If you’re fumbling for an answer– you’re not alone. Most people cannot either.

This not just a party trick. Psychologists have used several means to discover the extent of our ignorance. For example, Rebecca Lawson at the University of Liverpool presented people with a drawing of a bicycle which had several components missing. They were asked to fill in the drawing with the missing parts.

Sounds easy, right? Apparently not.

Nearly half of the participants were unable to complete the drawings correctly. Also, people didn’t do much better when they were presented with completed drawings and asked to identify the correct one.

Four badly drawn bikes

Four badly drawn bikes (Lawson, 2006)

To a greater or lesser extent, we all suffer from an illusion of understanding. That is, we think we understand how the world works when our understanding is rudimentary.

In their new book The Knowledge Illusion, cognitive scientists Steven Sloman and Philip Fernbach explore how we humans know so much, despite our individual ignorance.

Thinking is for action

To appreciate our mental limitations, we first need to ask ourselves: what is the purpose of the human brain? Answering this question ultimately leads to evolution, as the human brain has been honed by the forces of natural selection.

The authors note there is no shortage of of explanations of what the human mind evolved for. For example, there are those who argue the mind evolved to support language, or that it is adapted for social interactions, hunting, or acclimatising to changing climates. “[…] [T]hey are all probably right because the mind actually evolved to do something more general than any of them… Namely, the mind evolved to support our ability to act effectively.”

This more general explanation is important, as it helps establish why we don’t retain all the information we receive.

The reason we’re not all hyperthymesics is that it would make us less successful at what we’ve evolved to do. The mind is busy trying to choose actions by picking out the most useful stuff and leaving the rest behind. Remembering everything gets in the way of focusing on the deeper principles that allow us to recognize how a new situation resembles past situations and what kind of actions will be effective.

The authors argue the mind is not like a computer. Instead, the mind is a flexible problem solver that stores the most useful information to aid survival and reproduction. Storing superficial details is often unnecessary, and at times counterproductive.

Community of knowledge

Evidently, we would not do very well if we relied solely on our individual knowledge. We may consider ourselves highly intelligent, yet we wouldn’t survive very long if we found ourselves alone in the wilderness. So how do we survive and thrive, despite our mental limitations?

The authors argue the secret of our success is our ability to collaborate and share knowledge.

[…][W]e collaborate. That’s the major benefit of living in social groups, to make it easy to share our skills and knowledge. It’s not surprising that we fail to identify what’s in our heads versus what’s in others’, because we’re generally- perhaps always- doing things that involve both. Whether either of us washes dishes, we thank heaven that someone knows how to make dish soap and someone else knows how to provide warm water from a faucet. We wouldn’t have a clue.

One of the most important ingredients of humanity’s success is cumulative culture— our ability to store and transmit knowledge, enabled by our hyper-sociality and cooperative skills. This fundamental process is known as cultural evolution, and is outlined eloquently in Joe Henrich’s book The Secret of Our Success

Throughout The Knowledge Illusion, the metaphor of a beehive is used to describe our collective intelligence. “[…][P]eople are like bees and society a beehive: Our intelligence resides not in individual brains but in the collective mind.” However, the authors highlight that unlike beehives which have remained largely the same for millions of years, our shared intelligence is becoming more powerful and our collective pursuits are growing in complexity.

Collective intelligence

In psychology, intelligence has largely been confined to ranking individuals according to cognitive ability. The authors argue psychologists like general intelligence as it’s readily quantifiable, and has some power to predict important life outcomes. For example, people with higher IQ scores do better academically and perform better at their jobs.

Whilst there’s a wealth of evidence in favour of general intelligence, Sloman and Fernbach argue that we may be thinking about intelligence in the wrong way. “Awareness that knowledge lives in a community gives us a different way to conceive of intelligence. Instead of regarding intelligence as a personal attribute, it can be understood as how much an individual contributes to the community.”

A key argument is that groups don’t need a lot of intelligent people to succeed, but rather a balance of complimentary attributes and skill-sets. For example to run a company, you need some people who are cautious and others who are risk takers; some who are good with numbers and others who are good with people.

For this reason, Sloman and Fernbach stress the need to measure group performance, rather than individual intelligence. “Whether we’re talking about a team of doctors, mechanics, researchers, or designers, it is the group that makes the final product, not any one individual.”

A team led by Anita Woolley at the Tepper School of Business have begun devising ways of measuring collective intelligence, with some progress made. The idea of measuring collective intelligence is new, and many questions remain. However, the authors contend that the success of a group is not predominantly a function of the intelligence of individual members, but rather how well they work together.

Committing to the community

Despite all the benefits of our communal knowledge, it also has dangerous consequences. The authors argue believing we understand more than we do is the source of many of society’s most pressing problems.

Decades worth of research shows significant gap between what science knows, and what the public believes. Many scientists have tried addressing this deficit by providing people with more factual information. However, this approach has been less than successful.

For example, Brendan Nyhan’s experiments into vaccine opposition illustrated that factual information did not make people more likely to vaccinate their children. Some of the information even backfired– providing parents stories of children who contracted measles were more likely to believe that vaccines have serious side effects.

Similarly, the illusion of understanding helps explains the political polarisation we’ve witnessed in recent times.

In the hope of reducing political polarisation, Sloman and Fernbach conducted experiments to see whether asking people to explain their causal understanding of a given topic would make them less extreme. Although they found doing so for non-controversial matters did increase openness and intellectual humility, the technique did not work on highly charged political issues, such as abortion or assisted suicide.

Viewing knowledge as embedded in communities helps explain why these approaches don’t work. People tend to have a limited understanding of complex issues, and have trouble absorbing details. This means that people do not have a good understanding of what they know, and they rely heavily on their community for the basis of their beliefs. This produces passionate, polarised attitudes that are hard to change.

Despite having little to no understanding of complicated policy matters such as U.K. membership of the European Union or the American healthcare system, we feel sufficiently informed about such topics. More than this, we even feel righteous indignation when people disagree with us. Such issues become moralised, where we defend the position of our in-groups.

As stated by Sloman and Fernbach (emphasis added):

[O]ur beliefs are not isolated pieces of data that we can take and discard at will. Instead, beliefs are deeply intertwined with other beliefs, shared cultural values, and our identities. To discard a belief means discarding a whole host of other beliefs, forsaking our communities, going against those we trust and love, and in short, challenging our identities. According to this view, is it any wonder that providing people with a little information about GMOs, vaccines, or global warming have little impact on their beliefs and attitudes? The power that culture has over cognition just swamps these attempts at education.

This effect is compounded by the Dunning-Kruger effect: the unskilled just don’t know what they don’t know. This matters, because all of us are unskilled in most domains of our lives.

According to the authors, the knowledge illusion underscores the important role experts play in society. Similarly, Sloman and Fernbach emphasise the limitations of direct democracy– outsourcing decision making on complicated policy matters to the general public. “Individual citizens rarely know enough to make an informed decision about complex social policy even if they think they do. Giving a vote to every citizen can swamp the contribution of expertise to good judgement that the wisdom of crowds relies on.”

They defend charges that their stance is elitist, or anti-democratic. “We too believe in democracy. But we think that the facts about human ignorance provide an argument for representative democracy, not direct democracy. We elect representatives. Those representatives should have the time and skill to find the expertise to make good decisions. Often they don’t have the time because they’re too busy raising money, but that’s a different issue.”

Nudging for better decisions

By understanding the quirks of human cognition, we can design environments so that these psychological quirks help us rather than hurt us. In a nod to Richard Thaler and Cass Sunstein’s philosophy of libertarian paternalism, the authors provide some nudges to help people make better decisions:

1. Reduce complexity

Because much of our knowledge is possessed by the community and not by us individually, we need to radically scale back our expectations of how much complexity people can tolerate. This seems pertinent for what consumers are presented with during high-stakes financial decisions.

2. Simple decision rules

Provide people rules or shortcuts that perform well and simplify the decision making process.

For example, the financial world is just too complicated and people’s abilities too limited to fully understand it.

Rather than try to educate people, we should give them simple rules that can be applied with little knowledge or effort– such as ‘save 15% of your income’, or ‘get a fifteen-year mortgage if you’re over fifty’.

3. Just-in-time education

The idea is to give people information just before they need to use it. For example, a class in secondary school that reaches the basics of managing debt and savings is not that helpful.

Giving people information just before they use it means they have the opportunity to practice what they have just learnt, increasing the change that it is retained.

4. Check your understanding 

What can individuals do to help themselves? A starting point is to be aware of our tendency to be explanation foes.

It’s not practical to master all details of every decision, but it can be helpful to appreciate the gaps in our understanding.

If the decision is important enough, we may want to gather more information before making a decision we may later regret.


Written by Max Beilby for Darwinian Business

Click here to buy a copy of The Knowledge Illusion

References

Fernbach, P. M., Rogers, T., Fox, C. R., & Sloman, S. A. (2013). Political extremism is supported by an illusion of understanding. Psychological Science, 24(6), 939-946.

Haidt, J. (2012) The Righteous Mind: Why good people are divided by politics and religion. Pantheon.

Henrich, J. (2016). The Secret of Our Success: How culture is driving human evolution, domesticating our species, and making us smarter. Princeton University Press.

Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance: Can one construct predict them all? Journal of Personality and Social Psychology, 86(1), 148-161.

Lawson, R. (2006). The science of cycology: Failures to understand how everyday objects work. Memory & Cognition, 34(8), 1667-1675.

Nyhan, B., Reifler, J., Richey, S., & Freed, G. L. (2014). Effective messages in vaccine promotion: a randomized trial. Pediatrics, 133(4), e835-e842.

Sunstein, C., & Thaler, R. (2008). Nudge: Improving decisions about health, wealth and happiness. New Haven.

Thaler, R. H. (2013). Financial literacy, beyond the classroom. The New York Times.

Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), 686-688.