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Showing posts with label imperfect. Show all posts
Showing posts with label imperfect. Show all posts

Saturday 1 July 2023

Never Meet Your Hero

The saying "Never meet your hero" is a cautionary advice that suggests it's best to avoid meeting or getting too close to someone you greatly admire or look up to. The underlying idea is that meeting them in person may shatter the idealized image you have of them, leading to disappointment, disillusionment, or a loss of respect.

Here are a few reasons why this saying holds some truth:

  1. Idealization: When we admire someone from a distance, we tend to create an idealized version of them in our minds. We focus on their achievements, talents, and positive qualities. However, meeting them in person may reveal their flaws, shortcomings, or simply the fact that they are human like everyone else. This contrast between the idealized image and reality can be disheartening.


  2. Unmet Expectations: Meeting your hero can come with high expectations. You might anticipate an extraordinary experience or hope for a deep personal connection. However, in reality, the interaction may not live up to your expectations. They may not meet your assumptions or be as interested in engaging with you as you had hoped. This discrepancy can be disappointing and lead to a sense of letdown.


  3. Human Imperfection: Heroes, like all humans, have their flaws and make mistakes. By meeting them, you become more aware of their imperfections, which can tarnish the pedestal on which you had placed them. You might discover they hold different beliefs, behave in ways that clash with your values, or have made questionable decisions. This revelation can be disillusioning and alter your perception of them.


  4. Loss of Mystery: Part of the allure of heroes lies in the mystery and intrigue surrounding them. When you meet them and learn more about their personal lives, their struggles, and their everyday routines, the enigma may dissipate. This loss of mystery can diminish the charm and fascination you had felt toward them.

It's important to note that while this saying holds some truth, it doesn't mean that meeting your hero will always result in disappointment. Some people have positive experiences and develop deeper admiration and respect for their heroes after meeting them. However, the saying serves as a reminder to be prepared for the possibility that reality may not match your expectations, and it encourages appreciating and respecting people for their accomplishments while acknowledging their humanity.

Sunday 18 June 2023

Economics Essay 89: Imperfect Information

 Explain why imperfect information can lead to market failure.

Imperfect information occurs when buyers and sellers do not possess complete knowledge about the goods, services, or market conditions. It can lead to market failure in several ways:

  1. Adverse Selection: Adverse selection occurs when one party in a transaction has more information than the other, leading to an imbalance of knowledge. In such cases, the party with superior information may take advantage of the other party, resulting in a market failure. For example, in the used car market, sellers may have more information about the condition of the car than buyers, leading to a situation where buyers are hesitant to purchase used cars due to the risk of buying a lemon.

  2. Moral Hazard: Moral hazard occurs when one party alters their behavior after entering into an agreement because they have incomplete information. This can lead to market failure when the party takes risks or engages in actions that are not anticipated by the other party. For instance, in the insurance market, if policyholders know that they are fully covered in case of damage or loss, they may be less careful or take more risks, leading to higher costs for insurance providers and potential market distortions.

  3. Externalities: Imperfect information can also result in market failures related to externalities, which are the spillover effects of economic activities on third parties who are not directly involved in the transaction. When market participants do not have complete information about the external costs or benefits associated with their actions, they may not take them into account when making decisions. This can lead to overproduction or underproduction of goods and services, causing market inefficiencies. For example, if a factory pollutes a nearby river, the cost of environmental damage may not be fully known or accounted for, resulting in an inefficient allocation of resources.

  4. Consumer Misrepresentation: In markets where sellers can misrepresent or manipulate information to deceive buyers, market failures can occur. For instance, sellers may provide false or misleading information about the quality, safety, or performance of their products, leading to a misallocation of resources and harm to consumers.

  5. Information Asymmetry: Information asymmetry occurs when one party in a transaction has more information than the other, creating an imbalance of power. This can lead to market failures, such as unfair pricing, exploitation, or market domination by the party with superior information. For example, in financial markets, when banks or financial institutions possess more information about the risks associated with certain investments than individual investors, it can result in market distortions and inefficiencies.

In all these cases, imperfect information can undermine the efficient functioning of markets, leading to market failures and suboptimal outcomes. Governments and regulatory bodies often intervene to address these information gaps through measures such as mandatory disclosures, consumer protection laws, regulations on advertising and labeling, and enhancing transparency in markets. By reducing information asymmetry and improving information flows, market failures due to imperfect information can be mitigated, allowing for more efficient and fair market outcomes.

Monday 27 June 2022

Don’t date anybody if you only want positive results! Life is poker not chess

Abridged and adapted from Thinking in Bets by Annie Duke





Suppose someone says, “I flipped a coin and it landed heads four times in a row. How likely is that to occur?”


It feels that should be a pretty easy question to answer. Once we do the maths on the probability of heads on four consecutive 50-50 flips, we can determine that would happen 6.25% of the time (0.5 x 0.5 x 0.5 x 0,.5).


The problem is that we came to this answer without knowing anything about the coin or the person flipping it. Is it a two-sided coin or three-sided or four? If it is two-sided, is it a two-headed coin? Even if the coin is two sided, is the coin weighted to land on heads more often than tails? Is the coin flipper a magician who is capable of influencing how the coin lands? This information is all incomplete, yet we answered the question as if we had examined the coin and knew everything about it.


Now if that person flipped the coin 10,000 times, giving us a sufficiently large sample size, we could figure out, with some certainty, whether the coin is fair. Four flips simply isn’t enough to determine much about the coin


We make this same mistake when we look for lessons in life’s results. Our lives are too short to collect enough data from our own experience to make it easy to dig down into decision quality from the small set of results we experience. If we buy a house, fix it up a little, and sell it three years later for 50% more than we paid. Does that mean we are smart at buying and selling property, or at fixing up houses? It could, but it could also mean there was a big upward trend in the market and buying almost any piece of property would have made just as much money. Bitcoin buyers may now wonder about the wisdom of their decisions.


The hazards of resulting


Take a moment to imagine your best decision or your worst decision. I’m willing to bet that your best decision preceded a good result and the worst decision preceded a bad result. This is a safe bet for me because we deduce an overly tight relationship between our decisions and the consequent results. 


There is an imperfect relationship between results and decision quality. I never seem to come across anyone who identifies a bad decision when they got lucky with the result, or a well reasoned decision that didn’t work out. We are uncomfortable with the idea that luck plays a significant role in our lives. We assume causation when there is only a correlation and tend to cherry-pick data to confirm the narrative we prefer.


Poker and decisions


Poker is a game that mimics human decision making. Every poker hand requires making at least one decision (to fold or to stay) and some hands can require up to twenty decisions. During a poker game players get in about thirty hands per hour. This means a poker player makes hundreds of decisions at breakneck speed with every hand having immediate financial consequences. 


It is a game of decision making with incomplete information. Valuable information remains hidden. There is also an element of luck in any outcome. You could make the best possible decision at every point and still lose the hand, because you don’t know what new cards will be dealt and revealed.


In addition, once the game is over, poker players must learn from that jumbled mass of decisions and outcomes, separating the luck from the skill, and guarding against using results to justify/criticise decisions made,


The quality of our lives is the sum of decision quality plus luck. Poker is a mirror to life and helps us recognise the mistakes we never spot because we win the hand anyway or the leeway to do everything right, still lose, and treat the losing result as proof that we made a mistake,


Decisions are bets on the future


Decisions aren’t ‘right’ or ‘wrong’ based on whether they turn out well on any particular iteration. An unwanted result doesn’t make our decision wrong if we had thought about the alternatives and probabilities in advance and made our decisions accordingly. 


Our world is structured to give us lots of opportunities to feel bad about being wrong if we want to measure ourselves by outcomes. Don’t fall in love or even date anybody if you want only positive results.





Friday 25 February 2022

Deception and destruction can still blind the enemy

From The Economist

There are four ways for those who would hide to fight back against those trying to find them: destruction, deafening, disappearance and deception. Technological approaches to all of those options will be used to counter the advantages that bringing more sensors to the battlespace offers. As with the sensors, what those technologies achieve will depend on the tactics used.

Destruction is straightforward: blow up the sensor. Missiles which home in on the emissions from radars are central to establishing air superiority; one of the benefits of stealth, be it that of an f-35 or a Harop drone, lies in getting close enough to do so reliably.

Radar has to reveal itself to work, though. Passive systems can be both trickier to sniff out and cheaper to replace. Theatre-level air-defence systems are not designed to spot small drones carrying high-resolution smartphone cameras, and would be an extraordinarily expensive way of blowing them up.

But the ease with which American drones wandered the skies above Iraq, Afghanistan and other post-9/11 war zones has left a mistaken impression about the survivability of uavs. Most Western armies have not had to worry about things attacking them from the sky since the Korean war ended in 1953. Now that they do, they are investing in short-range air defences. Azerbaijan’s success in Nagorno-Karabakh was in part down to the Armenians not being up to snuff in this regard. Armed forces without many drones—which is still most of them—will find their stocks quickly depleted if used against a seasoned, well-equipped force.

Stocks will surely increase if it becomes possible to field more drones for the same price. And low-tech drones which can be used as flying ieds will make things harder when fighting irregular forces. But anti-drone options should get better too. Stephen Biddle of Columbia University argues that the trends making drones more capable will make anti-drone systems better, too. Such systems actually have an innate advantage, he suggests; they look up into the sky, in which it is hard to hide, while drones look down at the ground, where shelter and camouflage are more easily come by. And small motors cannot lift much by way of armour.

Moving from cheap sensors to the most expensive, satellites are both particularly valuable in terms of surveillance and communication and very vulnerable. America, China, India and Russia, all of which would rely on satellites during a war, have all tested ground-launched anti-satellite missiles in the past two decades; some probably also have the ability to kill one satellite with another. The degree to which they are ready to gouge out each other’s eyes in the sky will be a crucial indicator of escalation should any of those countries start fighting each other. Destroying satellites used to detect missile launches could presage a pre-emptive nuclear strike—and for that very reason could bring one about.

Everybody has a plan until they get punched in the face

Satellites are also vulnerable to sensory overload, as are all sensors. Laser weapons which blind humans are outlawed by international agreement but those that blind cameras are not; nor are microwave beams which fry electronics. America says that Russia tries to dazzle its orbiting surveillance systems with lasers on a regular basis.

The ability to jam, overload or otherwise deafen the other side’s radar and radios is the province of electronic warfare (ew). It is a regular part of military life to probe your adversaries’ ew capabilities when you get a chance. The deployment of American and Russian forces close to each other in northern Syria provided just such an opportunity. “They are testing us every day,” General Raymond Thomas, then head of American special forces, complained in 2018, “knocking our communications down” and going so far as “disabling” America’s own ec-130 electronic-warfare planes.

In Green Dagger, an exercise held in California last October, an American Marine Corps regiment was tasked with seizing a town and two villages defended by an opposing force cobbled together from other American marines, British and Dutch commandos and Emirati special forces. It struggled to do so. When small teams of British commandos attacked the regiment’s rear areas, paralysing its advance, the marines were hard put to target them before they moved, says Jack Watling of the Royal United Services Institute, a think-tank in London. One reason was the commandos’ effective ew attacks on the marines’ command posts.

Just as what sees can be blinded and what hears, deafened, what tries to understand can be confused. Britain’s national cyber-strategy, published in December, explicitly says that one task of the country’s new National Cyber Force, a body staffed by spooks and soldiers, is to “disrupt online and communications systems”. Armies that once manoeuvred under air cover will now need to do so under “cyber-deception cover”, says Ed Stringer, a retired air marshal who led recent reforms in British military thinking. “There’s a point at which the screens of the opposition need to go a bit funny,” says Mr Stringer, “not so much that they immediately spot what you’re doing but enough to distract and confuse.” In time the lines between ew, cyber-offence and psychological operations seem set to blur.

The ability to degrade the other side’s sensors, interrupt its communications and mess with its head does not replace old-fashioned camouflage and newfangled stealth; they remain the bread and butter of a modern military. Tanks are covered in foliage; snipers wear ghillie suits. Warplanes use radiation-absorbent material and angled surfaces so as not to reflect radio waves back to the radar that sent them. Russia has platoons dedicated to spraying the air with aerosols designed to block ultraviolet, infrared and radar waves. During their recent border stand-off, India and China both employed camouflage designed to confuse sensors with a broader spectral range than the human eye.

According to Mr Biddle, over the past 30 years “cover and concealment”, along with other tactics, have routinely allowed forces facing American precision weapons to avoid major casualties. He points to the examples of al-Qaeda at the Battle of Tora Bora in eastern Afghanistan in 2001 and Saddam Hussein’s Republican Guard in 2003, both of whom were overrun in close combat rather than through long-range strikes. Weapons get more lethal, he says, but their targets adapt.

Hiding is made easier by the fact that the seekers’ new capabilities, impressive as they may be, are constrained by the realities of budgets and logistics. Not everything armies want can be afforded; not everything they procure can be put into the field in a timely manner. In real operations, as opposed to PowerPoint presentations, sensor coverage is never unlimited.

“There is no way that we're going to be able to see everything, all of the time, everywhere,” says a British general. “It's just physically impossible. And therefore there will always be something that can happen without us seeing it.” In the Green Dagger exercise the attacking marine regiment lacked thermal-imaging equipment and did not have prompt access to satellite pictures. It was a handicap, but a realistic one. Rounding up commandos was not the regiment’s “main effort”, in military parlance. It might well not have been kitted out for it.

When hiding is hard, it helps to increase the number of things the enemy has to look at. “With modern sensors…it is really, really difficult to avoid being detected,” says Petter Bedoire, the chief technology officer for Saab, a Swedish arms company. “So instead you need to saturate your adversaries’ sensors and their situational awareness.” A system looking at more things will make more mistakes. Stretch it far enough and it could even collapse, as poorly configured servers do when hackers mount “denial of service” attacks designed to overwhelm them with internet traffic.

Dividing your forces is a good way to increase the cognitive load. A lot of small groups are harder to track and target than a few big ones, as the commandos in Green Dagger knew. What is more, if you take shots at one group you reveal some of your shooters to the rest. The less valuable each individual target is, the bigger an issue that becomes.

Decoys up the ante. During the first Gulf war Saddam Hussein unleashed his arsenal of Scud missiles on Bahrain, Israel and Saudi Arabia. The coalition Scud hunters responsible for finding the small (on the scale of a vast desert) mobile missile launchers he was using seemed to have all the technology they might wish for: satellites that could spot the thermal-infrared signature of a rocket launch, aircraft bristling with radar and special forces spread over tens of thousands of square kilometres acting as spotters. Nevertheless an official study published two years later concluded that there was no “indisputable” proof that America had struck any launchers at all “as opposed to high-fidelity decoys”.

One of the advantages data fusion offers seekers is that it demands more of decoys; in surveillance aircraft electronic emissions, radar returns and optical images can now be displayed on a single screen, highlighting any discrepancies between an object’s visual appearance and its electronic signature. But decoy-making has not stood still. Iraq’s fake Scuds looked like the real thing to un observers just 25 metres away; verisimilitude has improved “immensely” since then, particularly in the past decade, says Steen Bisgaard, the founder of GaardTech, an Australian company which builds replica vehicles to serve as both practice targets and decoys.

Mr Bisgaard says he can sell you a very convincing mobile simulacrum of a British Challenger II tank, one with a turret and guns that move, the heat signature of a massive diesel engine and a radio transmitter that works at military wavelengths, all for less than a 20th of the £5m a real tank would set you back. Shipped in a flat pack it can be assembled in an hour or so.

Seeing a tank suddenly appear somewhere, rather than driving there, would be something of a giveaway. But manoeuvre can become part of the mimicry. Rémy Hemez, a French army officer, imagines a future where armies deploy large “robotic decoy formations using ai to move along and create a diversion”. Simulating a build up like the one which Russia has emplaced on Ukraine’s border is still beyond anyone’s capabilities. But decoys and deception—in which Russia’s warriors are well versed—can be used to confuse.

Disappearance and deception often have synergy. Stealth technologies do not need to make an aircraft completely invisible. Just making its radar cross-section small enough that a cheap little decoy can mimic it is a real advantage. The same applies, mutatis mutandis, to submarines. If you build lots of intercontinental-ballistic-missile silos but put icbms into only a few—a tactic China may be exploring—an enemy will have to use hundreds of its missiles to be sure of getting a dozen or so of yours.

Shooting at decoys is not just a waste of material. It also reveals where your shooters are. Silent Impact, a 155mm artillery shell produced by src, an American firm, can transmit electronic signals as if it were a radar or a weapons platform as it flies through the sky and settles to the ground under a parachute. Any enemy who takes the bait reveals the position of their guns.

The advent of ai should offer new ways of telling the real from the fake; but it could also offer new opportunities for deception. The things that make an ai say “Tank!” may be quite different to what humans think of as tankiness, thus unmasking decoys that fool humans. At the same time the ai may ignore features which humans consider blindingly obvious. Benjamin Jensen of American University tells the story of marines training against a high-end sentry camera equipped with object-recognition software. The first marines, who tried to sneak up by crawling low, were quickly detected. Then one of them grabbed a piece of tree bark, placed it in front of his face and walked right up to the camera unmolested. The system saw nothing out of the ordinary about an ambulatory plant.

The problem is that ais, and their masters, learn. In time they will rumble such hacks. Basing a subsequent all-out assault on Birnam Wood tactics would be to risk massacre. “You can always beat the algorithm once by radical improvisation,” says Mr Jensen. “But it's hard to know when that will happen.”

The advantages of staying put

Similar uncertainties will apply more widely. Everyone knows that sensors and autonomous platforms can get cheaper and cheaper, that computing at the edge can reduce strain on the capacity of data systems, and that all this can make kill chains shorter. But the rate of progress—both your progress, and your adversaries’—is hard to gauge. Who has the advantage will often not be known until the forces contest the battlespace.

The unpredictability extends beyond who will win particular fights. It spreads out to the way in which fighting will best be done. Over the past century military thinking has contrasted attrition, which wears down the opponent’s resources in a frontal slugfest, and manoeuvre, which seeks to use fast moving forces to disrupt an enemy’s decision-making, logistics and cohesion. Manoeuvre offers the possibility of victory without the wholesale destruction of the enemies’ forces, and in the West it has come to hold the upper hand, with attrition often seen as a throwback to a more primitive age.

That is a mistake, argues Franz-Stefan Gady of the International Institute for Strategic Studies, a think-tank. Surviving in an increasingly transparent battlespace may well be possible. But it will take effort. Both attackers who want to take ground and defenders who wish to hold it will need to build “complex multiple defensive layers” around their positions, including air defences, electronic countermeasures and sensors of their own. Movement will still be necessary—but it will be dispersed. Consolidated manoeuvres big and sweeping enough to generate “shock and awe” will be slowed down by unwieldy aerial electromagnetic umbrellas and advertise themselves in advance, thereby producing juicy targets.

The message of Azerbaijan’s victory is not that blitzkrieg has been reborn and “the drone will always get through”. It is that preparation and appropriate tactics matter as much as ever, and you need to know what to prepare against. The new technologies of hide and seek will sometimes—if Mr Gady is right, often—favour the defence. A revolution in sensors, data and decision-making built to make targeting easier and kill chains quicker may yet result in a form of warfare that is slower, harder and messier.

Thursday 28 October 2021

Information Asymmetry

From the Economist Schools Brief


 IN 2007 the state of Washington introduced a new rule aimed at making the labour market fairer: firms were banned from checking job applicants’ credit scores. Campaigners celebrated the new law as a step towards equality—an applicant with a low credit score is much more likely to be poor, black or young. Since then, ten other states have followed suit. But when Robert Clifford and Daniel Shoag, two economists, recently studied the bans, they found that the laws left blacks and the young with fewer jobs, not more.

Before 1970, economists would not have found much in their discipline to help them mull this puzzle. Indeed, they did not think very hard about the role of information at all. In the labour market, for example, the textbooks mostly assumed that employers know the productivity of their workers—or potential workers—and, thanks to competition, pay them for exactly the value of what they produce.

You might think that research upending that conclusion would immediately be celebrated as an important breakthrough. Yet when, in the late 1960s, George Akerlof wrote “The Market for Lemons”, which did just that, and later won its author a Nobel prize, the paper was rejected by three leading journals. At the time, Mr Akerlof was an assistant professor at the University of California, Berkeley; he had only completed his PhD, at MIT, in 1966. Perhaps as a result, the American Economic Review thought his paper’s insights trivial. The Review of Economic Studieagreed. The Journal of Political Economy had almost the opposite concern: it could not stomach the paper’s implications. Mr Akerlof, now an emeritus professor at Berkeley and married to Janet Yellen, the chairman of the Federal Reserve, recalls the editor’s complaint: “If this is correct, economics would be different.”

In a way, the editors were all right. Mr Akerlof’s idea, eventually published in the Quarterly Journal of Economics in 1970, was at once simple and revolutionary. Suppose buyers in the used-car market value good cars—“peaches”—at $1,000, and sellers at slightly less. A malfunctioning used car—a “lemon”—is worth only $500 to buyers (and, again, slightly less to sellers). If buyers can tell lemons and peaches apart, trade in both will flourish. In reality, buyers might struggle to tell the difference: scratches can be touched up, engine problems left undisclosed, even odometers tampered with.

To account for the risk that a car is a lemon, buyers cut their offers. They might be willing to pay, say, $750 for a car they perceive as having an even chance of being a lemon or a peach. But dealers who know for sure they have a peach will reject such an offer. As a result, the buyers face “adverse selection”: the only sellers who will be prepared to accept $750 will be those who know they are offloading a lemon.

Smart buyers can foresee this problem. Knowing they will only ever be sold a lemon, they offer only $500. Sellers of lemons end up with the same price as they would have done were there no ambiguity. But peaches stay in the garage. This is a tragedy: there are buyers who would happily pay the asking-price for a peach, if only they could be sure of the car’s quality. This “information asymmetry” between buyers and sellers kills the market.

Is it really true that you can win a Nobel prize just for observing that some people in markets know more than others? That was the question one journalist asked of Michael Spence, who, along with Mr Akerlof and Joseph Stiglitz, was a joint recipient of the 2001 Nobel award for their work on information asymmetry. His incredulity was understandable. The lemons paper was not even an accurate description of the used-car market: clearly not every used car sold is a dud. And insurers had long recognised that their customers might be the best judges of what risks they faced, and that those keenest to buy insurance were probably the riskiest bets.

Yet the idea was new to mainstream economists, who quickly realised that it made many of their models redundant. Further breakthroughs soon followed, as researchers examined how the asymmetry problem could be solved. Mr Spence’s flagship contribution was a 1973 paper called “Job Market Signalling” that looked at the labour market. Employers may struggle to tell which job candidates are best. Mr Spence showed that top workers might signal their talents to firms by collecting gongs, like college degrees. Crucially, this only works if the signal is credible: if low-productivity workers found it easy to get a degree, then they could masquerade as clever types.

This idea turns conventional wisdom on its head. Education is usually thought to benefit society by making workers more productive. If it is merely a signal of talent, the returns to investment in education flow to the students, who earn a higher wage at the expense of the less able, and perhaps to universities, but not to society at large. One disciple of the idea, Bryan Caplan of George Mason University, is currently penning a book entitled “The Case Against Education”. (Mr Spence himself regrets that others took his theory as a literal description of the world.)

Signalling helps explain what happened when Washington and those other states stopped firms from obtaining job-applicants’ credit scores. Credit history is a credible signal: it is hard to fake, and, presumably, those with good credit scores are more likely to make good employees than those who default on their debts. Messrs Clifford and Shoag found that when firms could no longer access credit scores, they put more weight on other signals, like education and experience. Because these are rarer among disadvantaged groups, it became harder, not easier, for them to convince employers of their worth.

Signalling explains all kinds of behaviour. Firms pay dividends to their shareholders, who must pay income tax on the payouts. Surely it would be better if they retained their earnings, boosting their share prices, and thus delivering their shareholders lightly taxed capital gains? Signalling solves the mystery: paying a dividend is a sign of strength, showing that a firm feels no need to hoard cash. By the same token, why might a restaurant deliberately locate in an area with high rents? It signals to potential customers that it believes its good food will bring it success.

Signalling is not the only way to overcome the lemons problem. In a 1976 paper Mr Stiglitz and Michael Rothschild, another economist, showed how insurers might “screen” their customers. The essence of screening is to offer deals which would only ever attract one type of punter.

Suppose a car insurer faces two different types of customer, high-risk and low-risk. They cannot tell these groups apart; only the customer knows whether he is a safe driver. Messrs Rothschild and Stiglitz showed that, in a competitive market, insurers cannot profitably offer the same deal to both groups. If they did, the premiums of safe drivers would subsidise payouts to reckless ones. A rival could offer a deal with slightly lower premiums, and slightly less coverage, which would peel away only safe drivers because risky ones prefer to stay fully insured. The firm, left only with bad risks, would make a loss. (Some worried a related problem would afflict Obamacare, which forbids American health insurers from discriminating against customers who are already unwell: if the resulting high premiums were to deter healthy, young customers from signing up, firms might have to raise premiums further, driving more healthy customers away in a so-called “death spiral”.)

The car insurer must offer two deals, making sure that each attracts only the customers it is designed for. The trick is to offer one pricey full-insurance deal, and an alternative cheap option with a sizeable deductible. Risky drivers will balk at the deductible, knowing that there is a good chance they will end up paying it when they claim. They will fork out for expensive coverage instead. Safe drivers will tolerate the high deductible and pay a lower price for what coverage they do get.

This is not a particularly happy resolution of the problem. Good drivers are stuck with high deductibles—just as in Spence’s model of education, highly productive workers must fork out for an education in order to prove their worth. Yet screening is in play almost every time a firm offers its customers a menu of options.

Airlines, for instance, want to milk rich customers with higher prices, without driving away poorer ones. If they knew the depth of each customer’s pockets in advance, they could offer only first-class tickets to the wealthy, and better-value tickets to everyone else. But because they must offer everyone the same options, they must nudge those who can afford it towards the pricier ticket. That means deliberately making the standard cabin uncomfortable, to ensure that the only people who slum it are those with slimmer wallets.

Hazard undercuts Eden

Adverse selection has a cousin. Insurers have long known that people who buy insurance are more likely to take risks. Someone with home insurance will check their smoke alarms less often; health insurance encourages unhealthy eating and drinking. Economists first cottoned on to this phenomenon of “moral hazard” when Kenneth Arrow wrote about it in 1963.

Moral hazard occurs when incentives go haywire. The old economics, noted Mr Stiglitz in his Nobel-prize lecture, paid considerable lip-service to incentives, but had remarkably little to say about them. In a completely transparent world, you need not worry about incentivising someone, because you can use a contract to specify their behaviour precisely. It is when information is asymmetric and you cannot observe what they are doing (is your tradesman using cheap parts? Is your employee slacking?) that you must worry about ensuring that interests are aligned.

Such scenarios pose what are known as “principal-agent” problems. How can a principal (like a manager) get an agent (like an employee) to behave how he wants, when he cannot monitor them all the time? The simplest way to make sure that an employee works hard is to give him some or all of the profit. Hairdressers, for instance, will often rent a spot in a salon and keep their takings for themselves.

But hard work does not always guarantee success: a star analyst at a consulting firm, for example, might do stellar work pitching for a project that nonetheless goes to a rival. So, another option is to pay “efficiency wages”. Mr Stiglitz and Carl Shapiro, another economist, showed that firms might pay premium wages to make employees value their jobs more highly. This, in turn, would make them less likely to shirk their responsibilities, because they would lose more if they were caught and got fired. That insight helps to explain a fundamental puzzle in economics: when workers are unemployed but want jobs, why don’t wages fall until someone is willing to hire them? An answer is that above-market wages act as a carrot, the resulting unemployment, a stick.

And this reveals an even deeper point. Before Mr Akerlof and the other pioneers of information economics came along, the discipline assumed that in competitive markets, prices reflect marginal costs: charge above cost, and a competitor will undercut you. But in a world of information asymmetry, “good behaviour is driven by earning a surplus over what one could get elsewhere,” according to Mr Stiglitz. The wage must be higher than what a worker can get in another job, for them to want to avoid the sack; and firms must find it painful to lose customers when their product is shoddy, if they are to invest in quality. In markets with imperfect information, price cannot equal marginal cost.

The concept of information asymmetry, then, truly changed the discipline. Nearly 50 years after the lemons paper was rejected three times, its insights remain of crucial relevance to economists, and to economic policy. Just ask any young, black Washingtonian with a good credit score who wants to find a job.


Sunday 24 January 2021

On the Indian Farmers' Agitation for MSP

By Girish Menon


In this article I will try to explain the logic behind the Delhi protests by farmers demanding a Minimum Support Price (MSP).





















If you are a businessman who has produced say 1000 units of a good; and are able to sell only 10 units at the price that you desired. Then it means you will have an unsold stock of 990 units. You now have a choice:


Either keep them in storage and sell it to folks who may come in the future and pay your asking price.


Or get rid of your unsold stock at whatever price the haggling buyers are willing to pay. 


If you decide on the storage option then it follows that your goods are not perishable, it’s value does not diminish with age, you have adequate storage facilities and you have the resources to continue living even when most of your goods are unsold.


If you decide on the distress sale option it could mean that your goods are perishable and/or it’s value diminishes with age and/or you don’t have storage facilities and/or you are desperate to unload your stuff because for you whatever money you get today is important for your survival,


If one were to approach any small farmers’ output, I think such a farmer does not have the storage option available to him. Hence, he will have to sell his output to the intermediary at any price offered. This could mean a low price which results in a loss or a high price resulting in a profit to the farmer.


Whether the price is high or low depends on the volume of output produced by all farmers of the same output. And, no farmer is able to predict the likely future harvest price he would get at the moment he decides what crop to grow.


Thus a subsistence farmer, without storage facilities, is betting on the future price he could get at harvest time. This is a bet that destroys subsistence farmers from time to time when market prices turn really low due to a bumper harvest.


Subjecting subsistence farmers to ‘market forces’ means that some farmers will get bankrupted and be forced to leave their village and go to the city in search of a means of living. In many developed countries, governments have tried to prevent farmer exodus from villages by intervening and ensuring that farmers receive a decent return for their toils,


MSP is a government guarantee of a minimum price that protects farmers who cannot get their desired price at the market, The original draft of the farm law bills passed by the Indian Parliament has no mention of MSP. Also, in Punjab etc., some of these agitating farmers are already being supported with MSP by the state government and they fear that the new bills will take away their protection.


This is a simple explanation of the demand for MSP.


It must also be remembered that:


  • Unlike the subsistence farmer, the middleman who buys the farmers’ output is usually a part of a powerful cartel and who enjoys more market power than the farmer.

  • As depicted in ‘Peepli Live’ destitute farmers, if forced to leave their villages, will add to supply of cheap labour in an era of already high unemployment.

  • These destitute may squat on a city’s scarce public spaces and be an ‘eyesore’ to the better off city dwellers.

  • Some farmers may even contemplate suicide and this will produce less than desirable PR optics for any 'caring' government.



Saturday 15 September 2018

What I learnt from being fired

Robert Armstrong in the FT

The anniversary of Lehman Brothers’ bankruptcy has prompted lots of reflection on the damage done. But there were winners, too. I was one of them: I got fired. A decade ago I was an analyst at a hedge fund. We owned some stocks, and bet against others. At the end of 2008, after five years at the fund and with Wall Street flat on its back, I was let go.

Getting sacked is not the standard way to win in a market collapse. Some will argue that, say, John Paulson, who made $4bn personally betting against mortgages, comes away looking better than I do. This strikes me as a rather conventional take. In any case, the crisis taught me two key things and, more importantly, saved me a lot of embarrassment.

The first thing I learnt is that doing the right thing only goes so far. The two portfolio managers at my fund were smart and despised risk, and they were quick to recognise that something serious was going on. As they only owned stuff that was easy to sell, sell they did, and early on. Their clients lost nothing during the crisis and the fund survived. But the clients pulled their money anyway. They needed it and we, not having lost it, had it. So expenses had to be cut, and I was one of them.

The economy — or, if you prefer, the universe — is a big machine. No matter how smart or careful you are, you might get caught in the gears. Most people learn this eventually. I’m just glad I learnt it by losing a job rather than by, for example, getting struck dead by a meteor. 

Lesson two. In a crisis, nobody knows anything. The writer William Goldman made this point about Hollywood: until you get a movie in front of the audience, no one can say with anything like certainty whether it is good or not. Most of the time, by contrast, finance is a business where the important variables are predictable to within a certain range. But when a storm comes, finance is — no matter what anybody tells you in retrospect — as mysterious and fickle as a popcorn-gobbling rabble. Because that is what, in the final analysis, it is. 

At one point in 2007 I was working with two colleagues on a simple job: determine which of the big European banks have significant exposure to US housing. But the banks were, for some reason, hesitant to answer our questions in very clear terms. In some cases they seemed oddly unwilling to answer the phone.

Nor did the thousands of pages of disclosures from the banks contain anything helpful. The annual reports that had previously appeared to me to be precise quantitative descriptions of businesses looked, suddenly, like a brittle superstructure of certainty erected over a heaving morass of unknowns. 

It is reassuring to think of this like a freshman-philosophy Socrates. Knowing what you don’t know is the beginning of wisdom, and all that. That is a comforting thought when you are sitting in a lecture hall on a crisp autumn afternoon. When you are lost in the woods, knowing what you don’t know is just scary as hell. 

I should note that internalising these two points — that I am not in control, and don’t know much — has cost me money. It has made me much too conservative in how I have my savings invested, and I’ve missed much of the current stock market rally. At the same time, though, I think it has made me a better person and a clearer writer. It is a trade-off I can accept. 

That the effect of the crisis was to make me reflective and over-cautious reveals something else. I probably did not belong in finance in the first place. The low prices of stocks after the crisis made good investors greedy. They made me contemplate the vanity of all human striving.

That is how the crisis shielded me from embarrassment. As it was, I lost my job when a lot of other people did. No one was surprised then and no one asks for an explanation now. It was just one of those things that happens. If there had been no crisis, my guess is that somebody would have got around to firing me for being useless. Which would have been awkward.

Finance, like law, is a profession that attracts a lot of reasonably intelligent, hard-working people who rather like money. People like me. Most of us are not really suited to it, though, and that makes for a lot of unhappy careers. The financial crisis saved me from that, and I am grateful.

Sunday 29 April 2018

Fake five-star reviews being bought and sold online

Dan Box and Sachin Croker BBC Technology

Fake online reviews are being openly traded on the internet, a BBC investigation has found.

BBC 5 live Investigates was able to buy a false, five-star recommendation placed on one of the world's leading review websites, Trustpilot.

It also uncovered online forums where Amazon shoppers are offered full refunds in exchange for product reviews.

Both companies said they do not tolerate false reviews.
'Trying to game the system'

The popularity of online review sites mean they are increasingly relied on by both businesses and their customers, with the government's Competition and Markets Authority estimating such reviews potentially influence £23 billion of UK customer spending every year.

Maria Menelaou, whose Yorkshire Fisheries chip shop is the top-ranked fish and chip shop in Blackpool on several review sites, said the system has replaced traditional advertising.

"It brings us a lot of customers ... It really does make a difference. We don't do any kind of advertising," Mrs Menelaou said.

While three quarters of UK adults use online review websites, almost half of those believe they have seen fake reviews, according to a survey of 1500 UK residents conducted by the Chartered Institute of Marketing and shared with BBC 5 live Investigates.

Some US analysts estimate as many as half of the reviews for certain products posted on international websites such as Amazon are potentially unreliable.

"Sellers are trying to game the system and there's a lot of money on the table," said Tommy Noonan, who runs ReviewMeta, a US-based website that analyses online reviews.

"If you can rank number one for, say, bluetooth headsets and you're selling a cheap product, you can make a lot of money," he said.



'5 star is better for us'

In 2016, Amazon introduced a range of measures prohibiting what it called "incentivised reviews", where businesses offered customers free goods in exchange for positive reviews.

Mr Noonan said this effectively drove the problem underground, leading to the emergence of Facebook groups where potential Amazon customers were encouraged to buy a product and post a review in return for a full refund.

BBC 5 live Investigates identified several of these groups and, within minutes of joining, was approached with offers of full refunds on products bought on Amazon in exchange for positive reviews.

"5 star is better for us" said one person making such an offer, in an exchange of messages with the BBC. "We value our brand, will refund you as we promised ... All my company do in this way."

It was not possible to identify the people making these offers, nor contact the businesses whose products they were seeking reviews for.

"We do not permit reviews in exchange for compensation of any kind, including payment. Customers and Marketplace sellers must follow our review guidelines and those that don't will be subject to action including potential termination of their account," Amazon said in a statement.

Responding to adverts posted on eBay, the BBC was also able to purchase a false 5-star review on Trustpilot, an online review website that describes itself as "committed to being the most trusted online review community on the market".

"Dan Box is one of the most respected professionals I have dealt with. It was a pleasure doing business with him," this review said - word for word as requested by 5 live Investigates.

Trustpilot, whose platform allows anyone to post a review, said they have "a zero-tolerance policy towards any misuse".

"We have specialist software that screens reviews against 100's of data points around the clock to automatically identify and remove fakes," the company said.

In a statement, eBay said the sale of such reviews is banned from its platform "and any listings will be removed".