More on Moore’s Law

Everybody on the net got scared of the Conficker worm, and it got much press, including the New York Times.

Bruce Scheier wrote about Conficker, and this quote caught my attention:

Conficker’s April 1st deadline was precisely the sort of event we humans tend to overreact to. It’s a specific threat, which convinces us that it’s credible.

The deadline is an honest signal according to the handicap principle. By mentioning a specific date, the worm is exposing itself. This exposure is more costly in terms of credibility if the worm does not subsequently perform, and therefore adds to its perceived menace.

It is interesting to see Conspicuous Consumption in the light of the Handicap Principle, and how it could lead to more efficient taxing.

Conspicuous consumption is the lavish spending on goods and services acquired mainly for the purpose of displaying income or wealth, as a means of attaining or maintaining social status.

How does it work? Buy a new hand bag for $1,000. Or buy a $200,000 car. Or a $10,000,000 yacht. Whatever it is, make sure your neighbors see it.

In doing so, you are displaying that you can afford a squander $1,000 on a hand bag. If you were poor, or tight on financial resources, you couldn’t. This behavior is costlier to poor individuals, to whom $1,000 represents a much larger portion of their income, than to better off ones. In other words, a behavior that costs more for someone with less of a trait than someone with more of it.

Sound familiar? That’s because conspicuous consumption is an honest signal. Is it a signal for richness, and the social status that accompanies it.

A perfect candidate for taxation

Since cost is not an issue for conspicuous consumption -quite the contrary, cost is a key element- it is a perfect candidate for being taxed. By heavily taxing goods that signal wealth -lets call these luxury goods- you make it even harder to attain, and hence more exclusive and attractive.

Countries like Denmark are very smart to have high taxes on luxury cars, for example. Likewise, the European Union has two rates for VAT, a normal one not to deceed 15%, and a reduced one not to deceed 5%. The reduced rate applies to first necessity goods, and the normal rate to the rest. The US in some states has no or close to no taxes on food, because food represents a large portion or a poor person’s budget, and taxing food taxes them a disproportionate amount.

I would like to see that taken a step further, and see different rates for different categories of purchases. We want to use taxation to improve people’s welfare in general. To do so, we must tax bad behaviors, and subsidize good ones (as a side-note, I’ve often heard that taxes is punishing not sharing. As such, taxing income is punishing people for creating wealth). If we combine this with the other goal of taxing the wealthy more than the poor, we end up with a matrix of items that should be taxed and subsidized differently.

Take alcohol. I think we can agree that drinking alcohol is a bad behavior (antioxidants can be found elsewhere, if beneficial at all). However, a uniform tax on alcohol disproportionately affects poor people over better off ones. But it also happens to be that poor people drink more beer, and wealthy people drink more wine. Therefore a good tax system should tax wine at a higher rate than beer.

Or take food. Some foods are more nutritious than others, while some better for a particular diet. Red meat should be taxed higher than fish (CO2 emissions per kg of red meat > fish), and even within these categories, some products should be taxed higher than others. For instance, salmon should be taxed higher than sardines.

While this system would be quite complex, this is increasingly made feasible as we move towards the computerized and networked economy. Tax revisions could be pushed out to super market chains, then updated on their electronic pricing system. Or why not have an API for value added tax? Maybe a bit too much, but technically feasible.

Another interesting thought is to make the amount someones pays out in taxes an honest signal about wealth.

So Governments should pay attention to goods and services that signal wealth, whether a byproduct (behavior) or a deliberate status-seeking choice, and tax them accordingly.

In this blog post, we study the conditions for which a signal can be trusted when sent to a distrustful party.

The behavior of animals in the wild is often puzzling. Why do babies cry so loud? Why do gazelles jump vertically when they see a predator? It turns out these are primitive forms of communication in the form of signals. Gazelles signal their health and fitness, babies signal their hunger or fear.

But… How do these signals come about?

A branch of mathematics called Game theory provides an insightful framework for understanding how these behaviors come to be, and why they are the way they are. Seen through the lens of Game theory, the previous examples are forms of signaling games.

A signaling game, as defined in Wikipedia, is a dynamic game in which two players, the sender (S) and the receiver (R), interact. The sender has a certain type τ, which is given by nature. The sender knows his own type while the receiver does not know the type of the sender. Based on his knowledge of his own type, the sender chooses to send a message from a set of possible messages M = {m₁, m₂, m₃,…, m_j}. The receiver observes the message but not the type of the sender. Then the receiver chooses an action from a set of feasible actions A = {a₁, a₂, a₃,…., a_k}. The two players receive payoffs dependent on the sender’s type, the message chosen by the sender and the action chosen by the receiver.

An example from nature

Lets consider a lion chasing a gazelle. The observer would notice that in such cases, the gazelle, upon detecting the lion, will start stotting. By doing so, it is signaling its fitness and probable ability to outrun the lion. The lion can then decide not to chase the gazelle, and wait for another (better) opportunity.

Both the lion and the gazelle have an interest in avoiding unsuccessful chases. Both lose energy during the chase, and the gazelle loses out doubly as the time spent running is time not spent grazing. Therefore evolution puts pressure on these species to develop something in order to avoid this particular outcome. This something is a signal-producing capability, that provides orchestration by communication.

Framing the game

In this example, the signal sender S is the gazelle, and the signal receiver R is the lion. The signal is either stotting m₁ or no stotting m₀, so M = {m₀, m₁}. The lion’s feasible actions are chase a₁ or ignore a₀, so A = {a₁, a₂}. The payoffs are chance to catch for the lion, and chance to get caught for the gazelle, which are a function of the gazelle’s fitness τ, the action taken by the lion, and the signal emitted m_i; hence P_lion= f(τ, a_j, m_i), and P_gazelle= g(τ, a_j, m_i). We can now draw the payoff matrix:

figure 1: payoff matrix in a signaling game

Where m₀= 0, and m₁= m.

Conditions for honest signaling

Now that we have framed the example, we can analyze the different strategies available to the players. Three factors influence the payoff outcome as we have seen; the gazelle’s fitness, the signal it chooses to send (as defined by the type of game, and since there would be no use in sending it if it had no impact), and the lion’s reaction.

Lets examine the conditions to which signaling is beneficial to both parties. That is, how does signaling drive out wasteful unsuccessful chases, which use up energy for nothing, through evolutionary pressure?

For this, we must establish relationships between fitness, energy expenditure due to chasing or fleeing, and emitting the signal. We’ll function on the following relationships for the fitness cost of the signal C_signal and the fitness cost of a chase C_chase:

C_signal = h(τ, m) and C_chase = k(τ)

If the signal m_i has an effect on fitness that is not related to τ, then

C_signal = h(τ, m) = h(τ – m)

and

P_gazelle= g(τ, a_j, m_i) = g(h(τ, m_i), a_j)  = g(τ – m_i, a_j)

If we furthermore assume that a₀has no effect on payoff, then

g(τ, a₀) = g(τ) and g(τ – m, a₁) = g(τ – m)

We can then establish the following payoff matrix:

figure 2: gazelle payoff matrix if signal has an invariant effect on payoff

Lion payoff is calculated with function f instead of g.

We can now calculate the difference in fitness between with and without the signal for the lion and the gazelle. Lets assume that f and g are linear functions.

If lion does not chase, then

Δ_fitness= g(τ) – g(τ – m) = g(m)

but if it does, then

Δ_fitness= g(τ  – k(τ)) – g(τ – k(τ) – m) = g(m)

The lion will chase the gazelle if its payoff for chasing is larger than payoff for not chasing

f(τ  – k(τ)) > f(τ)

f(τ  – k(τ)) – f(τ) > 0

f(k(τ)) > 0

The gazelle will emit the signal if its payoff for emitting the signal is larger than for not emitting,

In the case the lion chooses to chase, then

g(τ  – k(τ) – m) > g(τ)

g(τ  – k(τ)) – g(τ) > 0

g(k(τ)) > 0

In the case the lion chooses not to chase, the calculation is very much the same.

As we can see, it does not make sense for the gazelle to send a message in this particular case. This is because we are analyzing a single occurrence of a signaling game. In reality, the lion and gazelle face a repeat game. For example, Tit for Tat is not a strategy for single shot prisoner’s dilemma games, but it is optimal for repeat ones.

What do we learn from this?

That Game theory provides us with a framework for understanding the theoretical underpinning of phenomena observed in nature, and helps build models of prediction for anticipation and comprehension.

First rule of Fight Club (or Marketing in this case) is to have a catchy label.

So I propose Signal Oriented Marketing. It is actually close to the innovation of Object Oriented Programming. The key innovation was the message passing between objects, as much as the objects themselves.

Looking at marketing from a perspective of signal can be extraordinary useful.

Take Market Research. One of the criticisms is that segmentation is often done using attributes that can be gotten at rather than segmentation by care abouts. It is segmentation by demographics rather than by job to be done.

I am proposing a third way: you look at how you are going to signal the attributes of your offering and you segment by that. If you can devise an honest signal to a subset of potential consumers that is what you should use as your segment. If you can not, don’t add the feature set.

Specialized language is often a good avenue.  If I say “ay, there’s the rub” in a discussion, you hone in on it as the crucible of the problem.  We have established that we are both fairly well educated outside the domain at hand.

What does this imply? Don’t shy away from specialized language in advertising. Using plain well understood language is for losers.

Red Bull used this signaling approach in establishing themselves. They deliberately sought out honest signals. Like limiting their product to certain clubs and bartenders that “saw” the benefits. They had the product delivered via special carriers, like FedEx 12 cans sent overnight to a club, etc. Things that are expensive and hard to fake.

The result was that people believed the signal and as such, bought into the benefit of Red Bull. It obvious has an element of the scarcity phenomena as per Cialdini’s Influence, but there is more to it and analyzing this from a perspective of signaling is a better approach.

It can be argued that without signaling, Akerlof’s Market for Lemons would vanish, or at least all innovation and delta quality would be driven out. Akerlof, economics Nobel laureate in 2001, described how:

the interaction between quality heterogeneity and asymmetric information can lead to the disappearance of a market where guarantees are indefinite. In this model, as quality is undistinguishable beforehand by the buyer (due to the asymmetry of information), incentives exist for the seller to pass off low-quality goods as higher-quality ones. The buyer, however, takes this incentive into consideration, and takes the quality of the goods to be uncertain. Only the average quality of the goods will be considered, which in turn will have the side effect that goods that are above average in terms of quality will be driven out of the market. This mechanism is repeated until a no-trade equilibrium is reached.

Why is it that there is a market for lemons, like in used cars, when there are reasons why there shouldn’t? Signaling provides some light to the theory.

In Spence’s winning paper Signaling in Retrospect and the Informational Structure of Markets, education is seen as a signal in the labor market. From his Prize lecture:

The idea behind the job market signaling model is that is there are attributes of potential employees that the employer cannot observe and that affect the individual’s subsequent productivity and hence value to the employer on the job.

He then introduces the basis for the mathematical model subsequently built:

Let us suppose that there are just two groups of people. Group 1 has productivity or value to any employer of 1, and Group 2 has productivity of 2. In this example, these productivity values do not depend on the level of investment in the signal. If there were no way to distinguish between people in these two groups then if both groups stay in the market, the average wage would be 2 – α, where α is the fraction of the population in group 1, and everyone would get that wage. If the higher productivity group through dissatisfaction or for any other reason, exits this labor market, the average productivity and the wage drop to 1. This phenomenon when it occurs is sometimes called the adverse selection problem, a label most commonly applied to insurance markets. It is structurally the same problem that Akerlof described in his famous paper on used cars (lemons).

Why is Education an Honest Signal in this famous Nobel winning paper?

Quite simply, because it is expensive and as such not easily falsifiable.

The job-market model is a way of looking at product pricing and markets. The analogy is clear: Job seekers = Complex product offering, Skill level = Product Utility, and Wages = Price, but Education = What?

Education = Marketing!

What is the purpose and value of Marketing? Marketing is sometimes treated as a necessary evil, something we would all rather not have to truck with and most certainly not something that adds “real” value to the product. Wrong! The value of Marketing seen as attempts of Honest Signaling is huge:

• Branding. Branding is a form of honest signaling. It is a promise that is hard to break. IBM cannot afford to put its name on a dysfunctional product, as this hurts it. You can therefore trust software IBM-branded software.
• Advertising. Advertising has lost most of its credibility, and has equally lost in persuasiveness. Despite this, it can be seen as a mechanism where the signal itself is the value, not the content. The “It must be good since they can afford to blow so much money on advertising this stuff” concept, first introduced by Nelson (1974) who described the idea of advertising signaling quality by dissipating part of the profits, then formalized by Kihlstrom and Riordan (1984), and Milgrom and Roberts (1986). Advertising is a form of honest signaling.
• Analysts. The industry of third party analysts, man in the middle, and honest brokers à la Gartner. More on this later.
• Without signaling, there would be no point in developing better products, as they would not command higher prices. The only rational decision would be to cost reduce.

Marketing -now defined as the signaling body- has developed new signals over time: adverts, public relations, and now social media. All of these are tactics of influence, but more usefully and insightfully defined as attempts at Honest Signaling. This creates a new framework from which we can find better ways to send Honest Signals to the market, and thereby capture this huge value.

Spence’s Job-market model provides an example for opaque products, and how they require a signaling mechanism to provide information. Opaque products require honest signaling, and so need to be marketed with that in mind. This means using analysts, branding, etc. is not a waste of resources. It makes rational sense for players to hire an honest signaler.

So, what is the value of Honest Signaling?

Honest Signaling allows the marketer to provide information on a product’s quality in a trustable manner, where it would otherwise be costly or difficult to do.

For example, how do you demonstrate that your data integration software is good?

• You cannot educate the market. Evaluating such products takes weeks, if not months. Customers do not have time to do thorough research on competing products.
• You cannot advertise. As mentionned before, advertising in its traditional form has lost its credibility, and therefore would not provide significant advantage.

So what options do you have?

• You can put a big brand name on it. A corollary to this is that it can make sense for a large company to purchase a small one in order to re-brand its product. In this specific case, the purchase might in itself be a sufficient Honest Signal, as it would not purchase it if it weren’t good.
• You can hire an analyst firm. The analyst firm then evaluates your product, and you can purchase the evaluation for display. If your software were poor, you couldn’t afford to do so as you would get a negative and detrimental evaluation. And so this is an Honest Signal.
• You can expose yourself to risk. Best example of this is a guarantee. Make an expensive promise and uphold it. If your software were poor, this would be prohibitively costly.
• You can fill the airwaves with advertising. Show that you can afford to squander resources. The key is not content, but volume.

I was visiting my inlaws the other day, and the alarm sticker on their front window caught my attention. It is one similar to the one depicted here.

It got me thinking about the effectiveness of such a measure. How likely is a thief to believe it? If you were one, would you?

For the sake of this analysis, lets assume that we are dealing with a rational thief. This excludes thieves under the influence, junkies, and a couple other categories. The rational thief is confronted with a choice of trusting or calling a bluff when he sees the sticker. If there is no means to verify the presence of a working security system, then he must rely on the signal alone (if he can verify that there is a security system, then he should move on to an easier target).

The Theory

Out of the total population of people displaying the sticker, if A is the number of people with a working security system, and B the number of people who don’t, then the probability that there is one when randomly picking a house is α = A / (A+B). If we furthermore assume that a house with a security system means a certain outcome of being caught by the police and jailed (we will analyze the effect of uncertain outcome in a later post), then the thief can determine his optimum strategy.

Strategies and equilibrium

If α = 0, the thief will never be caught as there is no security system. His strategy should be to always pick the house with a sticker. If α = 1, then he will always be caught. His strategy should be to never pick a house with a sticker.

But somewhere in between, there is an equilibrium. This equilibrium depends on the payoff, of course. To the extreme, you have a payoff so high that it is worth going to jail for, which results in the outcome strategy of always disregard sticker. On the other end, if there is no payoff, then the outcome strategy is never disregard sticker. Furthermore, the payoff is always relative to the thief ($100 worth of stolen goods does not have the same appeal to all thieves), especially true when considering a professional thief and an occasional one.

Sometimes doubt is enough

You could argue that sometimes, introducing doubt into a rational analysis is enough so that the thief prefers to consider an easier prey – a house with no sticker for instance. This is the rationale behind Microsoft’s famous FUD (Fear, Uncertainty, and Doubt) tactics. People will stick to actions of which outcome they can predict rather than those they cannot. Microsoft’s tactic has been the introduction of uncertainty in the outcome of switching operating system from Windows to Linux. Experience shows us that uncertainty does not need to be grounded, only perceived. The company introduced stories of failure and that proved sufficient.

When it comes to our analysis, displaying a security system sticker produces doubt in the thief’s mind. This most likely increases the likelihood of him moving on to an easier target. If you and a friend are chased by a lion, you do not need to outrun the lion: you only need to outrun your friend. Same goes with your neighbor’s. You only need the thief to perceive that your house is better secured than his. This falls outside of the Handicap Principle and consequently Signal Oriented Marketing, however.

Seem familiar?

If you’ve been following this blog, or know a bit about the Handicap Principle / Signal Oriented Marketing, then this analysis probably seems familiar. It involves mutually distrustful parties (the thief and the house owner), that communicate through a signal (the security system sticker).

The signal between the two parties conveys information about the likelihood of being caught by the police, similar to the gazelle’s stotting about the likelihood of the chasing predator not catching it.

How to bring credibility

Through verifiability

In some specific cases, the presence of a security system can easily be made verifiable. In those cases, it should, similar what Stanford suggests webmasters do in article 1 on its website credibility guidelines. Thus a solution would be to make the security system (cameras) visible.

But in other cases, such verifiability is not as easily attained. If it is not possible for a thief to verify the presence of a security system and he must refer solely to the signal (the sticker), than a system in which each sticker has a unique identifier issued by the security system vendor that, when anonymously given by SMS or Internet to the vendor, would retrieve the house address and validity of the sticker.

The combination of address and validity achieves two things. First, it prevents duplication of stickers, and usage on more than one location. Second, it prevents people without a security system from faking the signal. It ties each reference to a unique location, and makes the signal perfectly trustable.

However this system is complex and relies on the thief’s knowledge of the system, as well as him trusting the anonymity of the lookup process, both of which are not a given. In such cases, it is preferable to use Honest Signaling.

When not verifiable

Honest Signaling allows credibility to be established where the quality of an object is hard to ascertain. If it is not possible to verify some quality of an individual or organization, then there must be something about it (the signal) that conveys information on the quality, in a trustable manner. The way nature has solved this, is by creating handicaps. Signals that are costly in relation to the interaction of parties. This means that there is a penalty if the signal isn’t trusted. You show your strength by imposing a handicap on yourself. Like handicaps in Golf.

If you buy into the concept of biomemetics, this translates to a simple tactic. The signal should welcome thieves to “test our security system here”. Or provide directions towards the security cameras. It should give up information that would be useful to the potential thief. It should impose a handicap upon itself.

You may find this behavior in people being boastful: the security guard so confident that the vault is secure, that he openly talks about secret details on the motion detectors, etc. The opposite is telling as well. If he were unsure about the security of the vault, he wouldn’t talk about it at all.

Conclusion

If you (the reader) were to remember one thing about this rather lengthy blog post, it is that Honest Signals are useful a) when you must convey information on the quality of something to someone who does not trust you, and b) when the quality of that something is hard to measure or ascertain. In that specific case, then you should impose a handicap upon yourself, that is costly in relation to the outcome of the interchange.

мебели пловдив

Lets say you’re working on a software project; something new, some never-seen-before app that will change the way things are done.

At some point, you’re going to ask yourself, what features should I add? What should I be working on? You’re going to be determining your priorities.
The common approach is to ask your clients or userbase what they would like to see in the next version. There are many good sites that do this (uservoice.com, ideascale.com, and crowdsound.com are a few), and you’d basically be crowd-sourcing your product management / feature development.

In an enterprise setting, the process is somewhat similar. The sales guy call his lead up and does his sales technique. If the sale goes through, fine, but if not, he tells his boss. His boss then asks him why he didn’t sell, and he turns around and asks the lead why he didn’t land the sale. Lead then responds something along the lines of “your product didn’t have feature X”. Boss then makes a report, and sends it to engineering, which then implements feature X.

This method has a clear fault: leads can and will respond with all sorts of excuses to end the conversation. You then end up building your product on whatever your leads come up with to stop talking to your sales team, and this is how bloatware is born.

There needs to be a counter-force to suggesting a feature. A cost of sorts.

If you start out solving your own problem, then all is well. You only do the essential stuff, what counts to you, because you bear the cost yourself. But after a while, your own problems are fixed, and you move on to fix other people’s problems.

The cost can be anything, but energy expenditure is a good way to determine whether there is cost at all. This energy expenditure can be time. Development resources (sending in a patch), or time working with you on a solution. Or it can be financial. From $10 (or even $1) for individuals, to thousands of dollars for companies.

Since there is a drawback to suggesting a bugfix or creating a new feature, you avoid entirely the stuff nobody wants. And the stuff you really need, you’ll find someone willing to shell out the $10 for the feature. If you have a freemium (free + premium) business model, you may want to suggest going to a higher pricing tier in exchange for development of said feature.

For those who want to know about the theoretical underpinning of this, it is part of my Master’s Thesis research on the Handicap Principle from evolutionary biology applied to Signal Oriented Marketing. It studies how trust emerges among mutually distrustful individuals through specific types of signals. These signals that establish trust are Honest Signals, in the form of handicaps. Like why you would buy a $10 hammer with a lifetime warranty, even though it will never be worth your time sending in a complaint for a refund. Or why witches have long noses, and clowns have round ones. Or why we wear ties at formal gatherings, but bowties in state dinners.

I went to a negotiation competition last Thursday called Les Négociales. We were 120 business school students from the Northern France region.

What was interesting wasn’t so much the competition itself, as the sight of recruiters and head hunters flocking to competition finalists. Competition achievement prowess being an Honest Signal for proficiency at work.

Michael Spence wrote in his Nobel Prize paper ‘Signaling in Retrospect and the Informational Structure of Markets’ on the influence of Education in the Labor Markets. Seen through the lens of Honest Signaling and the Handicap Principle, it would appear that in reality, Education is a Handicap. A handicap that is an honest signal of ability to perform physically and / or intellectually. Or a signal of productive capacity. Likewise, Competitions are a handicap. Both Education and Competitions consume an individual’s resources and produce in themselves no value.

It is interesting to see something everybody knows in a different light.

We have seen in a previous post What the handicap principle is. However an issue arose on the exact meaning of Cost of a Handicap.

A handicap must be Hard to fake, not directly Costly. Cost is only in relation to the interchange.

What is a cheater?

In Handicap Principle jargon, a cheater is an individual that emits the signal despite being unfit. This faking must be difficult to achieve for an equilibrium to be obtained between fakers and honest signalers. Faking is hard and a whole school around self-deception (like Trivers) is based on this.  Deceiving yourself makes it easier to do.

For example, a handicap is only costly when you fake it badly and you get caught. In nature, this is when the Lion chases the Gazelle despite the signal, or when an employer asks for the diplomas you added on your résumé but do not actually have. There is no cost in emitting the signal, only in your bluff being called on.

In Spence’s paper ‘Signaling in Retrospect and the Informational Structure of Markets’, the signal that is education has a cost though. The trait that is signalled is productive capacity, and one signal for it is education. More on this in the post Honest Signaling in Labor Markets.

If you’ve ever dealt with the advertising industry, we probably know about the cognitive threshold under which you do not gain much by advertising, and over which you enter the prospect’s mind. This threshold determines whether the prospect will be able to retain and recall anything from the information advertised.

The handicap principle shines a new light on our understanding of this phenomenon. The threshold can be understood as the minimal expenditure required to become a handicap, and honest signal of quality. The sheer amount of advertising you do. The message is not the content, but the amount. A sort of “if they can afford to spend so much money on advertising, it must be good” school of thought.

According to professor Gilbert in his Lottery winners and Dogs & Pigs on a leash papers, we go back in out memory and try to recall the frequency of images that pertain to what we want to assess. This is why you need to concentrate the message in time, and why a threshold is needed in a marketing campaign.

Model for a Cheat

A corollary to this is that there is a model for a cheat.

In the above advertising example, the cost of advertising is born by the enterprise advertising. In nature, antlers have to be regrown each mating season, gazelles cannot borrow energy from the future. There is a distinct time slot for each signal. No spill over allowed. This is the frequency (amount over time) of advertising, or antler size in a season.

You can break this model if you break this natural time slot. Ponzi schemes like Madoff’s did precisely this. They signalled success by stealing from the future. The reason us humans are vulnerable to this, and so many people get caught in similar Pyramid schemes, is probably that our brains aren’t geared to detect this effectively.

Another example can be taken from my childhood experience in my family’s summerhouse north of Copenhagen, in Denmark. Most of the houses in the neighborhood are heated with an oil furnace. You have an underground tank outside that is filled up on a regular basis. I remember one company that did this service changed its model from filling up when empty, to everyone in the same area on same day. This was very clever as people saw the oil trucks everywhere that day. They concluded that the company had to be doing well and be a good company since there was so many. They forgot they didn’t see any next 2-3 month.

In this case, the time slot is broken. The distribution over time of occurrences is uneven. The cheat is to steal from future occurrences to create high frequency point events.

So bear this in mind both when receiving and emitting honest signals. Ask yourself, where does the cost of the signal come from?

EDIT: I read Dominique Olié Lauga’s PhD thesis titled Essays in Behavioral Industrial Organization, Corruption, and Marketing, in which she argues that advertising has an intrinsic value. She comments that “this is in contrast with the view that advertising is a pure money-burning device”, referring to Nelson (1974) describing the idea of advertising signaling quality by dissipating part of the profits, that Kihlstrom and Riordan (1984) and Milgrom and Roberts (1986) formalize. This is similar to Nobel laureate Michael Spence, who first describes a model in which education does not affect productivity, then adds intrinsic value to it in a more elaborate model.