Category Archives: Strategy

It’s not about flattening the curve. Let’s get rid of it!

17 Tuesday Mar 2020

Posted by in Health, Politics, Strategy

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By now you have surely heard about “flattening the curve” and seen the pretty picture that typically comes with it. A variation even made it into the New York Times last week.

covid_curve

The original argument/idea behind this picture crudely goes as follows. The COVID-19 pandemic will be over once a critical fraction (“the point of herd-immunity”, estimations vary but converge typically around two thirds) of the population develops immunity against the virus. Comparing death rates from regions that were surprised and thus overwhelmed by the virus (e.g. the Wuhan region in China or Lombardy in Italy) with those who were well prepared and hence faced a smaller amount of cases per capita (e.g. South Korea or the remainder of China) teaches us that pushing the number of serious cases below the capacity of the local healthcare system saves a ton of lives.

In order to achieve herd-immunity, however, the fraction of the overall population that gets infected with the virus does not change. This is depicted by the fact that the areas below the curves above are approximately equal. While this sounds like an intriguing argument, it is not realistic. Crude calculations show that it could take years to decades until we reach that point.

What we are (or should be) after, is, in fact, a reduction of the area under the curve itself, not stretching the curve over years. Instead of achieving herd-immunity, our goal should be to eradicate the virus as quickly as possible. While we may hope that summer, a vaccination or medications will put an abrupt end to the pandemic, these are hypotheticals that are far from certain to manifest. China with its rigorous policy of social distancing is the prime example after which other countries should model their response to the virus. The Washington Post published a neat little simulation, misleadingly also referring to flattening of the curve , that exemplifies the argument and generates these neat gifs:

covid_sim Not only does the curve get stretched by means of social distancing but the area under the curve, i.e. the number of overall infected people, diminishes. Since I trust that you neither want to see exorbitant numbers of older people and the odd younger ones die, nor that you want to sit at home for the next fifteen years, let’s give it some effort for a change and stop spreading dubious conspiracy theories.

Waiting for a vaccination or effective medications is a dangerous game.  Social distancing does not only save lives, it also allows to get back to our lives significantly faster. If we want to eradicate the curve, however, governments need to take more decisive (and also painful) action than if we wanted to stretch the curve. In particular, it is insufficient to just provide recommendations and it is certainly counter-productive to propagate herd-immunity. No country should witness world-war like scenes in its hospitals due to the indecisiveness of world leaders.

 

Why Audi is slimming down

27 Wednesday Nov 2019

Posted by in Electric Vehicles, Self-Driving Cars, Strategy

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The car industry and personal mobility is currently subject to more change than it has been over the last century. While the technology underlying cars has constantly evolved, the concept of ownership, fuel and the human’s control over the machine did not. As was true a 100 years ago, a car is till mainly a privately owned vehicle steered by a human being and powered by fossil fuel. We are at the brink of a period, however, that puts the status quo across all these three dimension in question at the same time. Alternative fuel, ride sharing and autonomous vehicles, despite at different stages in their respective development, challenge the very nature of the car industry.

Successfully navigating strategic change requires a firm to deeply understand not only market forces, but above all its own competitive advantage and how its resources and capabilities differ from its current as well as potential future competitors. It is its current strengths that will determine the firm’s optimal strategy when responding to disruptive market events. And this, in fact, is what Audi is attempting to do. If the predominant ownership model changes in response to ride-sharing and results in a relative increase in corporate ownership, Audi is not well-positioned to satisfy the likely nature of such a demand. Neither are they  efficient enough nor is their scale of operations sufficient.

On the other hand, Audi is well known for its craftsmanship and the power of its engines. The company has been a frontrunner in the world of motor sports and when it comes to new trends, be it about style or technology. Its corporate slogan “Vorsprung durch Technik” translates as “Being ahead by means of technology” (and embodies the company’s culture much better than its alternative used in the US until 2016 “Truth in engineering.”) Audi has to focus on next generation’s upscale car customer as opposed to the masses. These customers are likely to demand an electric car that combines an elegant interior with the engine power and overall product quality the company is known for.

This is where employees enter the equation. In order to be a benefit leader focusing on an upscale niche market segment, especially in times of increased automation, does not necessarily require many but the right employees. In a fundamentally changing car industry that quite likely may lead to less car sales for Audi in general, and struggling to keep up with their rivals BMW and Mercedes, it is of utmost importance for Audi to gain market share in this segment by developing the car for tomorrow’s upscale customer. Audi needs engineers that are trained in automation and electrically powered engines and a lean company to be able to offer exceptional cars at competitive prices.

Firing up to 10,000 employees, however, clearly leaves a bad taste, especially in Audi’s home country, Germany, a market that is enormously important for the company, not only in terms of direct sales but also as an indicator or accelerator for other markets. Overall, there is no question that this move represents a gamble for Audi as a company. It appears to be a bet, however, that Audi needs to take. From a loyalty viewpoint one can question the move of firing 10,000 employees while hiring 3,000 new ones and whether employee (re-)training would have been an alternative viable option.

What are the consequences of Google’s $5bn fine?

19 Thursday Jul 2018

Posted by in Antitrust, Strategy

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The European commission dishing out a record fine to Google for anti-competitive behavior appears among other t

hings to be a sign that the EU plans to crack down hard on anti-competitive behavior in the digital economy, an area that historically has been hard to govern due to its fast-paced nature. One deeper question arising from the ruling than the direct impact on Google is how will the EU treat bundling of digital services in the future. If forced bundling itself is interpreted as anti-competitive, then companies like Facebook, Intel and even Apple could be under threat. As of now, the EC fined Google for forcing handheld manufacturers into contracts via their market power that limit competition. While the same cannot be said Apple because of its closed ecosystem, if the EU’s claim would carry over from contracts to forced bundling in general, even Apple would be under threat.

The recent history would suggest that neither the DOJ nor the FTC will follow suit. The US, however, faces a problem or the danger to lose face in this particular case due to its reminiscence of the DOJ against Microsoft case in 1999, where Microsoft was fined heavily for forced bundling of the internet explorer with Windows. Also political forces could see Google as an easier target after the EU’s ruling to boost their reputation in the light of the DOJ’s failed case against the AT&T/Time Warner merger.

The stock market presumably did not react strongly since, even if Google were to change its business plan slightly, the lack of competition in search does not seem to be a major threat to Google. Furthermore, Google is well-positioned also with their own handheld device, the Pixel to respond. Also, I assume that most analysts share my sentiment that the US is unlikely to follow suit. See parts of my AP interview above.

Fake news in sports and how to filter real-world information

21 Thursday Dec 2017

Posted by in Education, Social Dilemma, Strategy

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When obtaining a post-secondary education in Economics and/or Business related fields, one typically spends a considerable amount of time studying various models of decision-making. Arguably two prominent reasons for teaching those models to students not targeting an academic career—most notably the majority of undergraduate students as well budding MBAs—are a) to provide them with frameworks to forecast and foresee the decisions of others and b) to sharpen their ability in making decisions themselves (which, in turn, is closely related to a)).

For their lack of opportunities to make significant decisions themselves while still in training, we typically resort to (real-world) examples featured in the business press or to (fictitious) business cases. In order to provide them with practice, we jointly tackle questions as in “how would you decide in this situation” or “what do you predict as the likely outcome of that scenario” in class. To put it bluntly, we teach them how to read, analyze and understand subject-specific articles. In the age of “fake news” and ubiquitous news coverage, I truly hope we also teach them how not to.

I typically challenge the class to feed each other with interesting and currently published information related to course content. Recently, a student of mine sent me this article published in the Economist linking the National Football League (NFL) with game theory. This student conceivably made a brilliant choice. The major sports leagues in the United States constitute an arena of decision-making commonly beloved by students, game theory is exciting as it adds the strategic component and The Economist is a highly reputable media outlet of business and politics. On the surface, I could not have chosen better myself.

In short, the article describes the contract situation between the (in recent decades) chronically unsuccessful Buffalo Bills and Tyrod Taylor, their current starting quarterback (the one who throws the ball), in early 2017. The Buffalo Bills benched Taylor for their last regular season game on New Year’s eve. At this point, Buffalo had no chance of reaching the playoffs anymore, i.e. it was de facto the last game of their season. After Taylor had played through a groin injury for several weeks, he opted for surgery at the very beginning of the offseason, i.e. 3 days after the last game (in which he did not feature).

The Economist argues that both actions, the Bills benching Taylor for their last game as well as Tyrod opting for surgery were direct consequences of the incentive structure inherent to their contractual situation. While, as claimed in the article, Taylor was an above average (top third) quarterback at an average quarterback salary, the Bills had the option to cut Taylor for negligible expenses until the beginning of March. However, if Mr. Taylor was rendered not fit to perform football services, the Bills would owe him close to $30M if they fired him.

This, for the Economist, constituted a so-called prisoner’s dilemma, a strategic stand-off between multiple parties in which each party has an unequivocally optimal action, which, if employed by everyone relevant to the game results in the socially worst scenario. In other words, the Bills had an incentive to bench Taylor for the last game whereas he should try everything possible not to be fit to play during the offseason (at least until March). What is more, it was stipulated that the Bills overlooked that they were in fact caught in a repeated prisoner’s dilemma with varying actors, that is other players with who they would have to deal in the future.

The main predictions based on their analysis were that, while not explicitly claimed, it was likely that the Bills were either going to fire Tyrod Taylor while injured (a costly endeavor) or opt not to due to the financial penalty of doing so although they would prefer to part ways. In any case, acting according to their best option would somehow sever the bond between the Bills and their quarterback. Moreover, by not realizing the more general implications of crossing a star player, the Bills would significantly reduce their chances of hiring popular players in the future. All of these implications taken together would even prolong and worsen the Buffalo Bills drought and their absence from the playoffs (17 years and counting). Lastly, it was argued that Rex Ryan, the coach of the Bills until their last season game was fired by the club’s owners since he disagreed with benching Taylor for the last season game.

In fact, the Buffalo Bills and Tyrod Taylor did not part way in the offseason of 2017. Instead, they renegotiated Taylor’s contract to keep him as a quarterback on a significantly lower salary. During the season the Bills took Kelvin Benjamin, a highly rated wide receiver (the one who catches the ball) from the Carolina Panthers, under contract. While the Bills traded assets (future draft picks) for the receiver and the trade was not directly Benjamin’s decision, it seems unlikely that the Bills would part with valuable options for today’s college players for an athlete who was not eager to play for the franchise. As of week 15 of the current season (two more games to follow), the Bills show a record of 8 wins and 6 losses and are at the brink of qualifying for the playoffs for the first time in 18 years.

While one time negative results do most certainly not refute the validity of predictions in the first place, a strategic analysis delivers similar results. A football player who opts for surgery only 3 days after the end of the season, a surgery which has an average recovery time of 7 weeks for non-athletes, does not seem to try everything possible to be deemed unfit when March comes around. If Taylor was above average in quality but paid below peers of his level, would it not be optimal for the team if he took care of his injuries as quickly as possible (to ensure his availability for team as long as possible before the start of the next season)? Many teams in various sports bench their star players in games that are inessential to their season goals. Why was this such a particular move in the case of the Buffalo Bills and Tyrod Taylor and why does this move necessarily carry negative consequences for an already injured player? Why, if Tyrod Taylor was overpaid against the league’s standards, did he agree to continue on the team for a substantially lower salary? Is it more likely that the Bills fired their head coach over a singular disagreement regarding the line-up or due the fact that Mr. Ryan did repeatedly not manage to lead a talented team to the playoffs and that he had an abysmal record in close games?

The model-theoretic analysis of the quoted article is as little capable of answering those questions as its predictions coincide with what unraveled after its publication. Bar a very few superstars, the National Football League (NFL) and its teams have tremendous power over their players as most NFL players would be expected to earn only a tiny fraction of their actual salaries if they were not to make it into or would have to exit the league. This fact paired with a salary cap, which ensures talent to the distributed across teams, guarantees teams significant bargaining power.

Dazzled by the institution of a reputable magazine and by the connection of buzz words and sports, I initially forwarded the article to the remainder of my students without any specific comments. After thoroughly reading and reflecting, however, I decided to scrutinize the article in class. In addition to teaching students how to filter business press articles and other relevant outlets for information and conducting an analysis based on these premises, I believe we ought to realize that, in a world characterized by the ubiquity of unfiltered content, teaching them to challenge this very information is the first crucial step towards a successful analytical contribution. While this sounds trivial, it clearly has to begin with questioning the intentions of author and publisher when reading an article. Whether the creators of an article intend to push a dogma, support a particular political view or simply myopically attempt to maximize readership, abstracting from those intentions should be the very first step in the process of filtering information.

Why competing stores always seem to occur next to each other … or why not

19 Friday Aug 2016

Posted by in Strategy

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A recent (seemingly unsuccessful) stint hunting for non-academic articles on spatial aggregation of stores produced curious results. Naturally, many people observe at some point that a.) supposed fierce competitors like Exxon/Mobile and Shell or CVS and Walgreen’s surprisingly frequently appear in tandem, that is to say right next to each other and b.) often even bigger groups of competing stores like car dealerships seem to cluster in certain areas.

Eager for knowledge as human beings (ideally) are, the internet provides massive evidence for users on the quest for the roots of the above described phenomena. Now, the curious part are the answers floating around on the world wide web. Surprisingly, there seems to be no popular trusted media outlet which answers the question (I am thankful if proved wrong and pointed towards the desired direction). Among the attempts showing the most hits is a short SAP article published via Forbes’ BrandVoice feature which can be found here. A few seconds suffice to realize that this article is merely an executive summary of a blog post of author Presh Talwalkar which itself can be found here. These two  pieces are (near) perfect representatives of the majority of (non-academic) justifications of the two above scenarios on the internet.

They, correctly, link scenario a.) above to the basic Hotelling model. Hotelling explains in a simple model why strategic reasoning leads two competing stores to choose an identical location in a local duopoly. In order to stress the old example once more, consider a (linear) beach served by two ice cream stands. Assume the beach to be busy, such that potential customers are distributed uniformly over its entire length. Further assume, for simplicity, that the stands offer the same flavors and quality of ice cream at identical prices and competition is purely executed via location. Since walking over hot sand is uncomfortable, every customer opts to buy ice cream as close as possible to her towel spot. Game theory predicts that both ice cream vendors end up in the middle back to back. Why is that? Suppose one of the vendors to position his stand anywhere north (south) of the middle. Its competitor could swiftly just move an inch south (north) of it to ensure himself more than half of the customers. As a consequence, both stands right next to each other in the middle constitutes the unique Nash equilibrium of the outlined game since in any other scenario at least one of the vendors can make himself better off by unilaterally moving his stand. This is a “strict” Nash equilibrium in the sense that every unilateral deviation causes a vendor to be worse off.

While this simplistic model does not capture the entirety of location choice in duopolies in reality, it is a perfectly reasonable approach to explain why CVS and Walgreen’s can be frequently found across the street of each other, or, as in this special case in Edina, Minnesota, in such close proximity to each other that drive through clerks can wave to each other. In fact, numerous academic articles have claimed that the simple Hotelling approach is a powerful predicition tool in local duopolies. It is, however, crucial to understand that this behavior is nothing specific to representatives of national or global chains as some articles on the internet want us to believe. The simple Hotelling duopoly model applies to strategic competition of national giants CVS and Walgreens in downtown Chicago or San Francisco as much as it does to family operated pharmacies in a rural village in Maine (as long as the village is sufficiently populous to have two pharmacies operate profitably).

The more striking claim to be found in the above referenced articles and the set of pieces they represent is, however, that this logic generalizes to clusters of competing stores like car dealerships. It is straightforward to falsify this claim. Reconsider the above discussed ice cream vendors and bring a third one into play. If this entrant positions himself right in the middle of the beach next to the two incumbents he can expect to serve approximately a third of the beach population drooling over ice cream. Moving slightly north or south of the middle, however, ensures him nearly half the customers. As a result, the basic Hotelling model does not explain why you typically drive by car dealerships for minutes once you passed the first. The logic of the Hotelling model does simply not extend to a scenario featuring a number of competitors in excess of two. Analogously, the same reasoning explains why in a two party system winning the median voter is key, whereas in a multi party system we witness parties positioning significantly to the left or right of each other.

Clustering of car dealerships, furniture stores, etc. is much more likely to be caused by a common effort to reduce customers’ search costs combined with zoning restrictions set by municipalities. Simplified, as car dealerships (to stick to one story) are typically located on the outer fringes of urban areas, it would be mighty costly for a potential customer to drive all over town to compare and test drive vehicles of multiple brands. Thus, she is likely to pre-select a smaller number of manufacturers to visit in the first place. This, in turn, would result in a lower average number of potential customers to enter a car dealership every given day. This reasoning is also consistent with the fact that it has been observed that brands which are closer substitutes to each other are more likely to be found in clusters. Chicago’s northern suburbians for instance may find Subaru, Volkswagen, Mazda, Nissan and Fiat in the Evanston/Skokie area whereas Mercedes, BMW, Infiniti, Volvo and Land Rover are located on the same road in Glencoe.

In the case of dealerships selling bulky and expensive items which require huge parking lots, loading and storing capacity or direct access to roads in the case of cars, regulations passed and incentives provided by local governments seem to further foster clusters. In order to raise their attractiveness to potential customers and residents alike, municipalities might restrict potential storefront locations via zoning or grant tax reliefs to induce the birth of shopping parks. While such factors might also come into play regarding clustering of pharmacies or gas stations, they seem to be less likely the driving forces behind the location coupling of competitors. On the other hand, central strategic positioning predicted in the Hotelling duopoly does not account for suburban clusters of car dealerships as exemplified with three ice cream vendors above.

 

Efficiency vs. distribution in the the medical residents market

24 Thursday Sep 2015

Posted by in Auction, National Resident Matching Program, Strategy

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In my recent post about medical graduates applying for multiple specialties in the National Resident Matching Program (NRMP) I briefly mentioned sloppily “sub-algorithms” of the main resident match. To be more specific, whereas it is true that all residents are matched by means of a single algorithm, possible placement of students is fairly restricted by their choice of specialty. In fact, among all students who (also) applied for -to stress it once more- Neurology in 2013, the average number of specialties student applied to fell short of two. As discussed, there is contradictory advice to students about the optimal strategy in terms of multiplicity of specialties.

On a slightly different note, there might be some evidence for a trade-off between distributional goals and market efficiency related to the strategic problem of students. Consider the following situation: A neurology and a neurosurgery department each have an open position. They compete for two students, each of which has a certain value as a neurologist and as a neurosurgeon. Assume both students chose a single field according to their comparative advantage, i.e. the field in which they are relatively better than the other student. If the abilities of students in their respective fields do not exorbitantly differ firms will be absolutely pleased with this situation since they should be able to hire their only candidate by offering slightly more than an outside option and reap the majority of the output. Society should also be happy since the allocation would be perfectly efficient. Unhappy campers would only be found among the residents who would start their career on moderate wages. In this particular situation both would be better off to register for both specialties to induce firm bidding which leads to increased wages but decreases market efficiency due to uncertain outcomes. The interesting question is: Does this logic generalize?

The endorsement game

21 Monday Sep 2015

Posted by in Strategy, Voting

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Consider the following two-dimensional voting problem: In a two-party system, call the parties A and B, the electorate -divided into a majority and a minority- chooses their preferred candidate along two dimensions. First, voters are more likely to vote for a candidate representing their population subgroup and secondly, they care about the candidates’ political views.

Assume voters to be uniformly distributed over the unit interval in terms of their ideology. The current incumbent from the majority subgroup of the population belongs to party A and supports a median policy. Party B does not have a suitable candidate from the majority subgroup of the population but two promising minority candidates. It is immediate to see that in the simple voting model the minority candidate has no chance of winning the election. What about party B positions its candidates in the primaries to the left and right of the incumbent and has the looser endorse the winner? Assume an endorsement not to guarantee votes but to increase the likelihood of voters initially opting for the endorsing candidate to vote for the endorsed one. Now the primary winner could move for moderation in the main election and reap the benefits of endorsement.

Do we have models which shed light on this scenario?

Applying to multiple specialties in the medical resident match

20 Sunday Sep 2015

Posted by in National Resident Matching Program, Strategy

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The main resident match by the clearinghouse NRMP (National Resident Matching Program) is for several reasons far more complicated than portrayed in the media or stylized in academic models. Simplification in many cases is perfectly acceptable and an art by itself. However, one of those complications is the fact that within the program actually various “sub-algorithms” are run due to the vast number of different clinical specialties, exceeding 40 in 2015. What is more, about half of the students tend to apply for more than one specialty, in general two with a very small number ranking residency programs of more than three specialties. Some sources on the internet designed to help students through the match argue that data seems to suggest that students increase their chances of a match the lower the number of specialties they apply for. In fact, in of all applicants to Neurology -a specialty with rather few openings- for instance the 2013 NRMP matched 63% of students applying for a single specialty, 59% of students with 2, 48% with 3 and 29% with four or more. Outcomes appear to be similar for other specialties independent of the number of vacancies. Based on this evidence it is suggested that students optimize by choosing a single specialty. This is outrageous inference since this claim’s validity depends on various circumstances. Without detailed knowledge of the data I am fairly certain that this statistic is merely caused by an endogenous selection problem. Students with a strong background in possession of excellent reference letters by professors relevant in their field will expect to be matched in their preferred specialty and forbear from applying to different specialties to spare themselves and their advisors the need to explain their dedication to one field over the other. Weaker students though, more uncertain whether they make it or not, will diversify their applications as an insurance policy. It would be interesting if someone were to look closely at the data and attempts to analyze whether fewer specialties are beneficial among weaker candidates. In fact, the opposite might hold true. On the other hand, given limited information about students’ qualities, the number of specialties might actually serve as a signal of quality and confidence and the claim might be correct. This, however, would be obsolete if all students were to apply for a single specialty. Concluding, there is certainly no clear answer to this question without a profound analysis.