Israel is holding an election Tuesday. It’s a multi-party system in Israel, based on proportional representation, and the main battle is between two blocs of parties. We call them the left-wing bloc and the right-wing bloc, and civics textbooks will tell you that this division is about policy: the Arab-Israeli conflict, economics and the role of religion. But let’s face it, nobody cares about public policy anymore. It’s all about Bibi. Parties that intend to form a coalition under Bibi belong to the right-wing bloc and parties that intend to rid the country of him belong to the left wing bloc.

In addition to the battle between the blocs, there is also a contest between the parties in each bloc, especially between one big party and several satellites. Bibi, in particular, is expected to use the final days of the campaign to siphon votes from satellites to his own Likud party, as he did in previous elections. Indeed, in recent days, Bibi and his social media fans said that the number of seats of the Likud party compared to the major opponent (called BlueWhite), and not the size of the blocs, will determine whether Bibi will form the government again. BlueWhite makes the same argument to left-wing voters.

But if Bibi is serious about cannibalizing his bloc, he could use a more fatal argument by appealing to the electoral threshold, that is the minimum share of total votes that a party has to achieve to be entitled to seats in the parliament. The current threshold 3.25%, which translates to roughly five seats in the 120-seat parliament. This sounds like a low threshold, but there are eleven parties in the current parliament, some of them have split into two, there are new parties that are serious contenders, and Likud and BlueWhite together are expected to win about half of the total votes. So many small parties are close to the threshold in the opinion polls. Since the two blocs are almost tied (with a small advantage to the right-wing bloc), the electoral threshold might end up playing a significant role in determining the outcome if many votes are cast to a party that doesn’t cross the threshold.

Enter game theory

It is difficult to do a game-theoretic analysis of anything election because we don’t have a good explanation for what drives voters to stand in line in the voting booth rather than go to the beach. Let me bypass this issue and just assume that you gain some utility from voting to your favorite party. So assume for simplicity that right-wing voters have two options, Likud or New Right (NR), which is one of the new parties in the right-wing bloc, whose leaders Bibi detests. According to the polls, 4% of the voters are New Right supporters. These guys will get utility 2 if they vote New Right and utility 1 if they vote Likud. And here is the twist: you only get this utility if the party you vote to passes the electoral threshold. If it doesn’t, you feel like your vote has been wasted and you get utility 0.

Whether or not an NR supporter will vote her preference depends on what she thinks the other supporter will do. In fact, NR supporters are involved in a population stag hunt game (sometimes called investment game). There are two Nash equilibria in the game: either everyone votes NR or everyone votes Likud. The first equilibrium requires players to take the risky action of voting NR, and if they all go along, they all get high utility 2. I have played variants of the investment game in class several times. Typically, players go for the safe choice, which in this case is Likud. If you do this, you guarantee yourself a utility 1 regardless of what the other players do.

How could the potential voters of NR coordinate on the risky equilibrium which gives a higher utility? I think the public opinion polls have a big role here. In the recent polls, NR has been consistently hovering above the threshold. The polls make it commonly known that there are sufficiently many potential NR voters, and also that they plan to go along with voting NR.

Suppose however that on Monday Bibi hints that his internal polls show that NR does not cross the threshold. What would our NR supporter do? She will likely know that there are no such polls since most Bibi supporters know that he is a lier. But perhaps other players will believe Bibi and switch to voting Likud, which will drive NR below the threshold. Or perhaps other players will worry that some other players will believe him. You see where this is going: to play the risky equilibrium, you need confidence that the other players are playing it too. Bibi can shake this confidence even if he can’t change the fact that NR has enough supporters to cross the threshold if they all collaborate.

By the way, I believe that established parties that are already represented in the parliament are less vulnerable to such an attack, since the confidence of their supporters in the risky equilibrium is stronger, as they played it once already.

Go ahead Bibi, make my day.

Hot on the heels of its new Division of Linear Algebra, Empire State’s President announced a new Institute for Wow. Unlike other centers and institutes on campus that were dedicated to basic research or innovation, this one would focus only on research that would grab attention. “Universities,” she said, “ have tried for centuries to inform and educate. We’ve learnt in the last decade from all the data collected that this just annoys the students, frustrates the professors and bores donors. Instead,” she continued, “we are going to entertain.” She went on to say that the institute would jettison traditional measures of impact and significance and focus on media mentions, likes’ and followers’. The president emphasized that this was in keeping with Empire State’s mission to be not just the best University in the world, but the best University for the world. “On the increasingly long road from birth to death we want to make sure that people are not bored,” she said.

The Institute for Wow will be directed by celebrity academic Isaac Bickerstaff one of the new breed of click-bait’ style scholar that Empire hopes to attract. Bickerstaff first shot to fame with his named cheese’ experiment. He took a large wheel of cheddar cheese and sliced it in two. One half he labeled cheddar cheese’ and the other half he named Partridge Farms cheddar cheese’. He then asked subjects to report their willingness to pay for each kind of cheese and discovered that on average they were willing to pay more for the named’ cheddar. Grey hairs dismissed the work as not accounting for fixed cheddar cheese effects which so incensed Bickerstaff, that he went on to test his hypothesis on Red Leicester, Camembert, Wensleydale, Limburger and Stinking Bishop.

Bickerstaff subsequently went on to test the hypothesis on humans and discovered that papers written by named’ professors were ranked more highly than the same paper written by a professor with no such honorific. On the strength of this Bickerstaff persuaded the dean of Empire State’s business school to raise money to endow a chair for every faculty member in the School. Within two years the publication output of the school had doubled and it had risen ten places in the rankings. The strategy was not without controversy. The University’s  academic senate thought this cheapened the idea of chaired professor. In a compromise, it was decided to call the new positions ottoman rather than chaired professorships.

Professor Bernard Drapier, well known faculty gadfly and guardian of traditions has railed against the Institute for Wow. He says it is yet another example of the University’s subjugation to the military-entertainment complex: “We must guard against the acquisition of unwarranted influence, whether sought or unsought, by the military-entertainment complex. ”

Empire State University today announced a new Division of Linear Algebra and Information. It is the university’s largest program change in decades and helps secure its status among the country’s top Linear Algebra research and training hubs.

“The division will enable students and researchers to tackle not just the scientific challenges opened up by pervasive linear algebra, but the societal, economic, and environmental impacts as well,” the university said.

Empire State is in an elite group with Carnegie Mellon University, MIT, Stanford, and the University of Washington in the caliber and scope of its linear algebra program, said A. N. Other, chief executive of the Plutocrat Institute of Artificial Intelligence, a computer-science professor at the University of Ruritania, and a tech entrepreneur. In creating the new division, Empire State is responding to two issues, Other said. The first is a large, chronic shortage of well-trained linear algebraists. The second is what value a university can add when technical courses are widely available through platforms like Coursera and Udacity. In emphasizing interdisciplinary training among scientists, engineers, social scientists, and humanists, Empire State firmly integrates linear algebra into its prestigious academic offerings, he said.

Empire’s move follows MIT’s announcement last month that it was investing $1 billion in a new college of linear algebra. But leaders at Empire State say their disclosure of the division today was driven by an imminent international search for a director, who will hold the title of associate provost, putting the program on an institutional par with the State’s colleges and schools. They explain that in creating a division rather than a new college, they are reflecting the way linear algebra has become woven into every discipline. Full article at the Chronicle of Higher Ed. Apparently, it is quite the rage to festoon one’s slides with company logos, particularly of the frightful five. At present this is done for free. It suggests a new business. A platform that matches advertisers to faculty. Faculty can offer up their slides and advertisers can bid for the right to place their logos on the slides. This post is only gonna make sense if you read my last-week post on Gittins Index So, why does Gittins’ theorem hold for arms and not for projects ? First, an example. Consider the following two projects A and B. The first period you work on A you have to choose an action Up or Down. Future rewards from working on project A depend on your first action. The (deterministic) rewards sequence is A: 8, 0, 0, 0, … if you played Up; and 5, 5, 5, 5, … if you played Down. Project B gives the deterministic rewards sequence B: 7, 0, 0,… If your discount factor is high (meaning you are sufficiently patient) then your best strategy is to start with project B, get 7 and then switch to project A, play Down and get 5 every period. Suppose now that there is an additional project, project C which gives the deterministic payoff sequence C: 6,6,6,…. Your optimal strategy now is to start with A, play Up, and then switch to C. This is a violation of Independence of Irrelevant Alternatives: when only A and B are available you first choose B. When C becomes available you suddenly choose A. The example shows that Gittins’ index theorem does not hold for projects. What goes wrong in the proof ? We can still define the “fair charges” and the “prevailing charges” as in the multi-armed proof. For example, the fair charge for project A is 8: If the casino charges a smaller amount you will pay once, play Up, and go home, and the casino will lose money. The point where the proof breaks down is step 5. It is not true that Gittins’ strategy maximizes the payments to the casino. The key difference is that in the multi-project case the sequence of prevailing charges of each bandit depends on your strategy. In contrast, in the multi-arm case, your strategy only determines which charge you will play when, but the charges themselves are independent of your strategy. Since the discounts sequence $1,\beta,\beta^2,\dots$ is decreasing, the total discounted payment in the multi-armed problem is maximized if you pay the charges in decreasing order, as you do under the Gittins Strategy. After presenting Richard Weber’s remarkable proof of Gittins’ index theorem in my dynamic optimization class, I claimed that the best way to make sure that you understand a proof is to identify where the assumptions of the theorem are used. Here is the proof again, slightly modified from Weber’s paper, followed by the question I gave in class. First, an arm or a bandit process is given by a countable state space $S$, a transition function $q(s'|s)$ and a payoff function $r:S\rightarrow [0,1]$. The interpretation is that at every period, when the arm is at state $s$, playing it gives a reward $r(s)$ and the arm’s state changes according to $q(\cdot|s)$. In the multi-armed bandit problem, at every period you choose an arm to play. The states of the arms you didn’t choose remain fixed. Your goal is to maximize expected total discounted rewards. Gittins’ theorem says that for each arm there exists a function $\gamma:S\rightarrow [0,1]$ called the Gittins Index (GI from now on) such that, in a multi armed problem, the optimal strategy is to play at each period the arm whose current state has the largest GI. In fancy words, the theorem establishes that the choice which arm to play at each period satisfies Independent of Irrelevance Alternatives: Suppose there are three arms $A,B,C$ whose current states are $a,b,c$. If you were going to start by playing $A$ if only $A$ and $B$ were available, then you should not start with $B$ when $A,B,C$ are available. The proof proceeds in several steps: 1. Define the Gittins Index at state $s$ to be the amount $\gamma$ such that, if the casino charges $\gamma$ every time you play the arm, then both playing and not playing are optimal actions at the state $s$. We need to prove that there exists a unique such $\gamma$. This is not completely obvious, but can be shown by appealing to standard dynamic programming arguments. 2. Assume that you enter a casino with a single arm at some state $s$ with GI $\gamma$. Assume also that the casino charges $\gamma$ every time you play the arm. At every period, you can play, or quit playing, or take a break. From step 1, it follows that regardless of your strategy, the casino will always get a nonnegative net expected net payoff, and if you play optimally then the net expected payoff to the casino (and therefore also to you) is zero. For this reason, this $\gamma$ (the GI of the initial state) is called the fair charge. Here, playing optimally means that you either not play at all or start playing and continue to play every period until the arm reaches a state with GI strictly smaller than $\gamma$, in which case you must quit. It is important that as long as the arm is at a state with GI strictly greater than $\gamma$ you continue playing. If you need to take a restroom break you must wait until the arm reaches a state with GI $\le \gamma$. 3. Continuing with a single arm, assume now that the casino announces a new policy that at every period, if the arm reaches a state with GI that is strictly smaller than the GI of all previous states, then the charge for playing the arm drops to the new GI. We call these new (random) charges the prevailing charges. Again, the casino will always get a nonnegative net expected payoff, and if you play optimally then the net expected payoff is zero. Here, playing optimally means that you either not play at all or start playing and continue to play forever. You can quit or take a bathroom break only at periods in which the prevailing charge equals the GI of the current state. 4. Consider now the multi-arms problem, and assume again that in order to play an arm you have to pay its current prevailing charge as defined in step 3. Then again, regardless of how you play, the Casino will get a nonnegative net payoff (since by step 3 this is the case for every arm separately), and you can still get an expected net payoff $0$ if you play optimally. Playing optimally means that you either not play or start playing. If you start playing you can quit, take a break, or switch to another arm only in periods in which the prevailing charge of the arm you are currently playing equals the GI of its current state. 5. Forget for a moment about your profits and assume that what you care about is maximizing payments to the casino (I don’t mean net payoff, I mean just the charges that the casino receives from your playing). Since the sequence of prevailing charges of every arm is decreasing, and since the discount factor makes the casino like higher payments early, the Gittins strategy — the one in which you play at each period the arm with highest current GI, which by definition of the prevailing charge is also the arm with highest current prevailing charge — is the one that maximizes the Casino’s payments. In fact, this would be the case even if you knew the realization of the charges sequence in advance. 6. The Gittins strategy is one of the optimal strategies from step 4. Therefore, its net expected payoff is $0$. 7. Therefore, for every strategy $\sigma$, Rewards from $\sigma<=$ Charges from $\sigma<=$ Charges from Gittins strategy (First inequality is step 4 and second is step 5) And Charges from Gittins strategy = Rewards from Gittins Strategy (step 6) 8. Therefore, the Gittins strategy gives the optimal possible total rewards. That’s it. Now, here is the question. Suppose that instead of arms we would have dynamic optimization problems, each given by a state space, an action space, a transition function, and a payoff function. Let’s call them projects. The difference between a project and an arm is that when you decide to work on a project you also decide which action to take, and the current reward and next state depend on the current state and on your action. Now read again the proof with projects in mind. Every time I said “play arm $i$”, what I meant is work on project $i$ and choose the optimal action. We can still define an “index”, as in the first step: the unique charge $\gamma$ such that, if you need to pay $\gamma$ every period you work on the project (using one of the actions) then both not working and working with some action is optimal. The conclusion is not true for the projects problem though. At which step does the argument break down? Volume 42 of the AER, published in 1952, contains an article by Paul Samuelson entitled Spatial Price Equilibrium and Linear Programming’. In it, Samuelson uses a model of Enke (1951) as a vehicle to introduce the usefulness of linear programming techniques to Economists. The second paragraph of the paper is as follows: In recent years economists have begun to hear about a new type of theory called linear programming. Developed by such mathematicians as G. B. Dantzig, J. v. Neumann, A. W. Tucker, and G. W. Brown, and by such economists as R. Dorfman, T. C. Koopmans, W. Leontief, and others, this field admirably illustrates the failure of marginal equalization as a rule for defining equilibrium. A number of books and articles on this subject are beginning to appear. It is the modest purpose of the following discussion to present a classical economics problem which illustrates many of the characteristics of linear programming. However, the problem is of economic interest for its own sake and because of its ancient heritage. Of interest are the 5 reasons that Samuelson gives for why readers of the AER should care. 1. This viewpoint might aid in the choice of convergent numerical iterations to a solution. 2. From the extensive theory of maxima, it enables us immediately to evaluate the sign of various comparative-statics changes. (E.g., an increase in net supply at any point can never in a stable system decrease the region’s exports.) 3. By establishing an equivalence between the Enke problem and a maximum problem, we may be able to use the known electric devices for solving the former to solve still other maximum problems, and perhaps some of the linear programming type. 4. The maximum problem under consideration is of interest because of its unusual type: it involves in an essential way such non-analytic functions as absolute value of X, which has a discontinuous derivative and a corner; this makes it different from the conventionally studied types and somewhat similar to the inequality problems met with in linear programming. 5. Finally, there is general methodological and mathematical interest in the question of the conditions under which a given equilibrium problem can be significantly related to a maximum or minimum problem. My students often use me as a sounding board for their new ventures. A sign that the modern University could pass for a hedge fund with classrooms. The request brings a chuckle as it always reminds me of my first exposure to entrepreneurial activity. It happened in the most unlikeliest of places as well as times. A public (i.e. private) school in pre-Thatcherite England. England was then the sick man of Europe and its decline was blamed upon the public schools. Martin Wiener’s English Culture and the Decline of the Industrial Spirit, for example, argued that the schools had turned a nation of shopkeepers into one of lotus eaters. Among the boys was a fellow, I’ll call Hodge. He was a well established source of contraband like cigarettes and pornographic magazines. He operated out of a warren of toilets in the middle of the school grounds called the White City. Why the school needed a small building devoted entirely to toilets was a product of the English distrust of indoor plumbing and central heating. One lesson I learnt from Hodge was never buy a pornographic magazine sight unseen. The Romans call it caveat emptor, but, I think this, more vivid. Hodge was always on the look out for new goods and services that he could offer for a profit to the other boys. One day, he hit upon the idea of buying a rubber woman (it was plastic and inflatable) and renting it out. The customer base consisted of 400 teenage boys confined to a penal colony upon a wind blasted heath. Consider the challenges. How was he to procure one (no internet)? Where would he hide the plastic inamorata to prevent theft or confiscation by the authorities? How would he find customers (no smart phones)? What should he charge? What was to prevent competition? And, of course, what happened? All, I think, best left to the imagination. 17 years ago the Clay Institute announced 7 Millennium problems and offered$1 million for the solution to each.  To date, only one has been solved. It suggests that the supply (of mathematical attention) has not increased to meet demand. Therefore, the value of the prizes are to low. Why might the current value of the  prize prolong the time it takes to obtain a solution? Suppose, two agents, each in possession of an idea that in combination would produce the sought after solution. Neither agent has an incentive to reveal what they know, for fear that the other will build upon this to earn the prize. Pooling their efforts means splitting the prize. Thus, one has a familiar trade-off. Laboring alone delays the reward but it is unshared. Teaming up, accelerates the reward but it must be shared.

There is a test for smarts’ that Sir Peter Medawar was fond of. I often think of it when teaching equilibrium.

If you have ever seen an El Greco, you will notice that the figures and faces are excessively elongated. Here is an example.The eye surgeon Patrick Trevor-Roper, brother to the historian Hugh offered an explanation. Readers of  certain vintage will recall the long running feud between Hugh Trevor-Roper and Evelyn Waugh. Waugh said that the best thing Hugh Trevor-Roper could do would be to change his name and leave Oxford for Cambridge. Hugh Trevor-Roper eventually  became Lord Dacre and left Oxford for Cambridge. But, I digress.

Returning to Patrick, he suggested that El Greco had a form of astigmatism, which distorted his vision and led to elongated images forming on his retina. Medawar’s question was simple: was Patrick Trevor-Roper correct?