Is a question I thought as dead as a Dodo.  When I came upon it in an undergraduate philosophy of science class  the drums had been muffled and the mourners called. Nevertheless, there are still those who persist in resuscitating the corpse (see here for a recent example) and those, who for noble reasons, indulge them by responding.

There were, and are two good reasons for why this question should be left to rot in peace. The first is that the comparisons made to arrive at a demarcation are problematic. If Science were a country, Physics might be its capital. If one were to ask whether History is a Science, the customary thing to do is to measure the proximity of History to Science’s capital city. Why proximity to the capital and not to one of its outlying settlements like Geology and Archaeology? The second, better reason, is that the question, is X a science?’ is of interest only if we believe that scientific knowledge should be privileged in some way. Perhaps it alone is valid and useful while nonscientific knowledge is not. If that is the case, the correct question should not be whether X is a science, but whether X produces knowledge that is valid and useful. Now we have something interesting to discuss: what constitutes useful or valid knowledge?

One might point to accurate prediction, but this alone cannot be the touchstone. How would we feel about the laws of Newtonian motion if we came upon them via regression? I suspect many of us would find such a theory to be incomplete, not least because of the concern with out of sample prediction. By the way, if you think this outlandish, I first learnt Newton’s laws by sending little carts down inclines with bits of ticker tape attached to them to so that we might, by induction, learn a linear relationship between velocity and acceleration. Truth be told, the Physics was sometimes lost in the enormous fun of racing the carts when the master’s back was turned. What if prediction is probabilistic rather than deterministic? In earlier posts on this blog you will find lengthy discussions of the problems associated with evaluating the accuracy of such predictions. I mention all this to hint at how difficult it is to pin down precisely what constitutes useful, reliable or valid knowledge.

Introduced externalities. The usual examples, pollution, good manners and flatulance. However, I also emphasized an externality we had dealt with all semester: when I buy a particular Picasso it prevents you from doing so, exerting a negative externality on you. I did this to point out that the problem with externalities is not their existence, but whether they are priced’ into the market or not. For many of the examples of goods and services that we discussed in class, the externality is priced in and we get the efficient allocation.

What happens when the externality is not priced in’? The hoary example of two firms, one upstream from the other with the upstream firm releasing a pollutant into the river (That lowers its costs but raises the costs of the downstream firm) was introduced and we went through the possibilities: regulation, taxation, merger/ nationalization and tradeable property rights.

Discussed pros and cons of each. Property rights (i.e. Coase), consumed a larger portion of the time; how would you define them, how would one ensure a perfectly competitive market in the trade of such rights? Nudged them towards the question of whether one can construct a perfectly competitive market for any property right.

To fix ideas, asked them to consider how a competitive market for the right to emit carbon might work. Factories can, at some expense lower carbon emissions. We each of us value a reduction in carbon (but not necessarily identically). Suppose we hand out permits to factories (recall, Coase says initial allocation of property rights is irrelevant) and have people buy the permits up to reduce carbon. Assuming carbon reduction is a public good (non-excludable and non-rivalrous), we have a classic public goods problem. Strategic behavior kills the market.

Some discussion of whether reducing carbon is a public good. The air we breathe (there are oxygen tanks)? Fireworks? Education? National Defense? Wanted to highlight that nailing down an example that fit the definition perfectly was hard. There are degrees’. Had thought that Education would generate more of a discussion given the media attention it receives, it did not.

Concluded with an in depth discussion of electricity markets as it provides a wonderful vehicle to discuss efficiency, externalities as well as entry and exit in one package. It also provides a backdoor way into a discussion of net neutrality that seemed to generate some interest. As an aside I asked them whether perfectly competitively markets paid agents what they were worth? How should one measure an agents economic worth? Nudged them towards marginal product. Gave an example where Walrasian prices did not give each agent his marginal product (where the core does not contain the Vickrey outcome). So, was Michael Jordan overpaid or underpaid?
With respect to entry and exit I showed that the zero profit condition many had seen in earlier econ classes did not produce efficient outcomes. The textbook treatment assumes all potential entrants have the same technologies. What if the entrants have different technologies? For example, solar vs coal. Do we get the efficient mix of technologies? Assuming a competitive market that sets the Walrasian price for power, I showed them examples where we do not get the efficient mix of technologies.

An unintentionally amusing missive by Marion Fourcade, Etienne Ollion and Yann Algan’s discovers that the Economics profession is a self perpetuating oligarchy. This is as shocking as the discovery of gambling in Casablanca. Economists are human and respond to incentives just as others do (see the Zingales piece that makes this point). Are other disciplines free of such oligarchies? Or is the complaint that the Economist’s oligarchy is just order of magnitudes more efficient than other disciplines?

The abstract lists three points the authors wish to make.

1) We begin by documenting the relative insularity of economics, using bibliometric data.

A former colleague of mine once classified disciplines as sources (of ideas) and sinks (absorbers of them). One could just as well as describe the bibliometric data as showing that Economics is a source of ideas while other social sciences are sinks. if one really wanted to put the boot in, perhaps the sinks should be called back holes, ones from which no good idea ever escapes.
2) Next we analyze the tight management of the field from the top down, which gives economics its characteristic hierarchical structure.

Economists can be likened to the Borg, which are described by Wikipedia as follows:

“….. the Borg force other species into their collective and connect them to “the hive mind”; the act is called assimilation and entails violence, abductions, and injections of microscopic machines called nanoprobes.”

3) Economists also distinguish themselves from other social scientists through their much better material situation (many teach in business schools, have external consulting activities), their more individualist worldviews, and in the confidence they have in their discipline’s ability to fix the world’s problems.

If the authors had known of this recent paper in Science they could have explained all this by pointing out that Economists are wheat people and other social scientists are rice people.

Two governments did not survive this week: the Swedish and the Israeli. Here, in Israel, people are interested in the effect of the coming elections on the financial market. The Marker, the most important national daily economics newspaper, published an article on this issue. The chief economist of the second largest investment house, which handles about 30 billion USD, is quoted as saying (my own translation)

“Past experience shows that most of the time, during six months after elections the stock market was at a higher level than before the elections,” emphasized Zbezinsky (the chief economist, ES). The Meitav-Dash investment house checked the performance of the TA-25 Index (the index of the largest 25 companies in the Israeli stock exchange, ES) in the last six elections. They compared the index starting from 6 months before elections up to six months after elections, and the result was that the average return is positive and equals 6%.

To support this claim, a nice graph is added:

Even without understanding Hebrew, you can see the number 25 at the title, which refers to the TA-25 index, the six colored lines in the graph, where the x-axis measures the time difference from elections (in months), and the year in which each elections took place. Does this graph support the claim of the chief economist? Is his claim relevant or interesting? Some points that came up to a non-economist like me are:

1. Six data points, this is all the guy has. And from this he concludes that “most of the time” the market increased. Well, he is right; the index increased four times and decreased only twice.
2. Election is due 17-March-2015, which means three and a half months. In particular, taking as a baseline 6 months before election is useless; this baseline is well into the past.
3. Some of the colored lines seem to fluctuate, suggesting that some external events, unrelated to elections, may have had an impact on the stock market, like the Intifada in 2001 or the consequences of the Lebanon war before the 2009 elections. It might be a good idea to check whether some of these events are expected to occur in the coming nine months and a half.
4. It will also be nice to compare the performance around elections to the performance in between elections. Maybe 6% is the usual performance of the TA-25, maybe it is usually higher, and maybe it is usually lower.

I am sure that the readers will be able to find additional points that make the chief economist statement irrelevant, while others may find points that support his statement. I shudder to the thought that this guy is in charge of some of my retirement funds.

General equilibrium! Crown jewel of micro-economic theory. Arrow and Hahn give the best justification:

“There is by now a long and fairly imposing line of economists from Adam Smith to the present who have sought to show that a decentralized economy motivated by self-interest and guided by price signals would be compatible with a coherent disposition of economic resources that could be regarded, in a well defined sense, as superior to a large class of possible alternative dispositions. Moreover the price signals would operate in a way to establish this degree of coherence. It is important to understand how surprising this claim must be to anyone not exposed to the tradition. The immediate common sense’ answer to the question What will an economy motivated by individual greed and controlled by a very large number of different agents look like?’ is probably: There will be chaos. That quite a different answer has long been claimed true and has permeated the economic thinking of a large number of people who are in no way economists is itself sufficient ground for investigating it seriously. The proposition having been put forward and very seriously
entertained, it is important to know not only whether it is true, but whether it could be true.”

But how to make it come alive for my students? When first I came to this subject it was in furious debates over central planning vs. the market. Gosplan, the commanding heights, indicative planning were as familiar in our mouths as Harry the King, Bedford and Exeter, Warwick and Talbot, Salisbury and Gloucester….England, on the eve of a general election was poised to leave all this behind. The question, as posed by Arrow and Hahn, captured the essence of the matter.

Those times have passed, and I chose instead to motivate the simple exchange economy by posing the question of how a sharing economy might work. Starting with two agents endowed with a positive quantity of each of two goods, and given their utility functions, I asked for trades that would leave each of them better off. Not only did such trades exist, there were more than one. Which one to pick? What if there were many agents and many goods? Would bilateral trades suffice to find mutually beneficial trading opportunities? Tri-lateral? The point of this thought experiment was to show how in the absence of prices, mutually improving trades might be very hard to find.

Next, introduce prices, and compute demands. Observed that demands in this world could increase with prices and offered an explanation. Suggested that this put existence of market clearing prices in doubt. Somehow, in the context of example this all works out. Hand waved about intermediate value theorem before asserting existence in general.

On to the so what. Why should one prefer the outcomes obtained under a Walrasian equilibrium to other outcomes? Notion of Pareto optimality and first welfare theorem. Highlighted weakness of Pareto notion, but emphasized how little information each agent needed other than price, own preferences and endowment to determine what they would sell and consume. Amazingly, prices coordinate everyone’s actions. Yes, but how do we arrive at them? Noted and swept under the rug, why spoil a good story just yet?

Gasp! Did not cover Edgeworth boxes.

Went on to introduce production. Spent some time explaining why the factories had to be owned by the consumers. Owners must eat as well. However it also sets up an interesting circularity in that in small models, the employee of the factory is also the major consumer of its output! Its not often that a firm’s employers are also a major fraction of their consumers.

Closed with, how in Walrasian equilibrium, output is produced at minimum total cost. Snuck in the central planner, who solves the problem of finding the minimum cost production levels to meet a specified demand. Point out that we can implement the same solution using prices that come from the Lagrange multiplier of the central planners demand constraint. Ended by coming back full circle, why bother with prices, why not just let the central planner have his way?

Starr’s ’69 paper considered Walrasian equilibria in exchange economies with non-convex preferences i.e., upper contour sets of utility functions are non-convex. Suppose ${n}$ agents and ${m}$ goods with ${n \geq m}$. Starr identified a price vector ${p^*}$ and a feasible allocation with the property that at most ${m}$ agents did not receiving a utility maximizing bundle at the price vector ${p^*}$.

A poetic interlude. Arrow and Hahn’s book has a chapter that describes Starr’s work and closes with a couple of lines of Milton:

A gulf profound as that Serbonian Bog
Betwixt Damiata and Mount Casius old,
Where Armies whole have sunk.

Milton uses the word concave a couple of times in Paradise Lost to refer to the vault of heaven. Indeed the OED lists this as one of the poetic uses of concavity.

Now, back to brass tacks. Suppose ${u_i}$ is agent ${i}$‘s utility function. Replace the upper contour sets associated with ${u_i}$ for each ${i}$ by its convex hull. Let ${u^*_i}$ be the concave utility function associated with the convex hulls. Let ${p^*}$ be the Walrasian equilibrium prices wrt ${\{u^*_i\}_{i=1}^n}$. Let ${x^*_i}$ be the allocation to agent ${i}$ in the associated Walrasian equilibrium.

For each agent ${i}$ let

$\displaystyle S^i = \arg \max \{u_i(x): p^* \cdot x \leq p^*\cdot e^i\}$

where ${e^i}$ is agent ${i}$‘s endowment. Denote by ${w}$ the vector of total endowments and let ${S^{n+1} = \{-w\}}$.

Let ${z^* = \sum_{i=1}^nx^*_i - w = 0}$ be the excess demand with respect to ${p^*}$ and ${\{u^*_i\}_{i=1}^n}$. Notice that ${z^*}$ is in the convex hull of the Minkowski sum of ${\{S^1, \ldots, S^n, S^{n+1}\}}$. By the Shapley-Folkman-Starr lemma we can find ${x_i \in conv(S^i)}$ for ${i = 1, \ldots, n}$, such that ${|\{i: x_i \in S^i\}| \geq n - m}$ and ${0 = z^* = \sum_{i=1}^nx_i - w}$.

When one recalls, that Walrasian equilibria can also be determined by maximizing a suitable weighted (the Negishi weights) sum of utilities over the set of feasible allocations, Starr’s result can be interpreted as a statement about approximating an optimization problem. I believe this was first articulated by Aubin and Elkeland (see their ’76 paper in Math of OR). As an illustration, consider the following problem :

$\displaystyle \max \sum_{j=1}^nf_j(y_j)$

subject to

$\displaystyle Ay = b$

$\displaystyle y \geq 0$

Call this problem ${P}$. Here ${A}$ is an ${m \times n}$ matrix with ${n > m}$.

For each ${j}$ let ${f^*_j(\cdot)}$ be the smallest concave function such that ${f^*_j(t) \geq f_j(t)}$ for all ${t \geq 0}$ (probably quasi-concave will do). Instead of solving problem ${P}$, solve problem ${P^*}$ instead:

$\displaystyle \max \sum_{j=1}^nf^*_j(y_j)$

subject to

$\displaystyle Ay = b$

$\displaystyle y \geq 0$

The obvious question to be answered is how good an approximation is the solution to ${P^*}$ to problem ${P}$. To answer it, let ${e_j = \sup_t [f_j^*(t) - f_j(t)]}$ (where I leave you, the reader, to fill in the blanks about the appropriate domain). Each ${e_j}$ measures how close ${f_j^*}$ is to ${f_j}$. Sort the ${e_j}$‘s in decreasing orders. If ${y^*}$ is an optimal solution to ${P^*}$, then following the idea in Starr’s ’69 paper we get:

$\displaystyle \sum_{j=1}^nf_j(y^*_j) \geq \sum_{j=1}^nf^*_j(y^*_j)- \sum_{j=1}^me_j$

Here is the question from Ross’ book that I posted last week

Question 1 We have two coins, a red one and a green one. When flipped, one lands heads with probability ${P_1}$ and the other with probability ${P_2}$. Assume that ${P_1>P_2}$. We do not know which coin is the ${P_1}$ coin. We initially attach probability ${p}$ to the red coin being the ${P_1}$ coin. We receive one dollar for each heads and our objective is to maximize the total expected discounted return with discount factor ${\beta}$. Find the optimal policy.

This is a dynamic programming problem where the state is the belief that the red coin is ${P_1}$. Every period we choose a coin to toss, get a reward and updated our state given the outcome. Before I give my solution let me explain why we can’t immediately invoke uncle Gittins.

In the classical bandit problem there are ${n}$ arms and each arm ${i}$ provides a reward from an unknown distribution ${\theta_i\in\Delta([0,1])}$. Bandit problems are used to model tradeoffs between exploitation and exploration: Every period we either exploit an arm about whose distribution we already have a good idea or explore another arm. The ${\theta_i}$ are randomized independently according to distributions ${\mu_i\in \Delta(\Delta([0,1]))}$, and what we are interested in is the expected discounted reward. The optimization problem has a remarkable solution: choose in every period the arm with the largest Gittins index. Then update your belief about that arm using Bayes’ rule. The Gittins index is a function which attaches a number ${G(\mu)}$ (the index) to every belief ${\mu}$ about an arm. What is important is that the index of an arm ${i}$ depends only on ${\mu_i}$ — our current belief about the distribution of the arm — not on our beliefs about the distribution of the other arms.

The independence assumption means that we only learn about the distribution of the arm we are using. This assumption is not satisfied in the red coin green coin problem: If we toss the red coin and get heads then the probability that the green coin is ${P_1}$ decreases. Googling multi-armed bandit’ with dependent arms’ I got some papers which I haven’t looked at carefully but my superficial impression is that they would not help here.

Here is my solution. Call the problem I started with the difficult problem’ and consider a variant which I call the easy problem’. Let ${r=p/(p+\sqrt{p(1-p)}}$ so that ${r^2/(1-r)^2=p/1-p}$. In the easy problem there are again two coins but this time the red coin is ${P_1}$ with probability ${r}$ and ${P_2}$ with probability ${1-r}$ and, independently, the green coin is ${P_1}$ with probability ${(1-r)}$ and ${P_2}$ with probability ${r}$. The easy problem is easy because it is a bandit problem. We have to keep track of beliefs ${p_r}$ and ${p_g}$ about the red coin and the green coin (${p_r}$ is the probability that the red coin is ${P_1}$), starting with ${p_r={r}}$ and ${p_g=(1-r)}$, and when we toss the red coin we update ${p_r}$ but keep ${p_g}$ fixed. It is easy to see that the Gittins index of an arm is a monotone function of the belief that the arm is ${P_1}$ so the optimal strategy is to play red when ${p_r\ge p_g}$ and green when ${p_g\ge p_r}$. In particular, the optimal action in the first period is red when ${p\ge 1/2}$ and green when ${p\le 1/2}$.

Now here comes the trick. Consider a general strategy ${g}$ that assigns to every finite sequence of past actions and outcomes an action (red or green). Denote by ${V_d(g)}$ and ${V_e(g)}$ the rewards that ${g}$ gives in the difficult and easy problems respectively. I claim that

$\displaystyle \begin{array}{rcl} &V_e(g)=r(1-r) \cdot P_1/(1-\beta)+ \\ &r(1-r) \cdot P_2/(1-\beta) + (r^2+(1-r)^2) V_d(g).\end{array}$

Why is that ? in the easy problem there is a probability ${r(1-r)}$ that both coins are ${P_1}$. If this happens then every ${g}$ gives payoff ${P_1/(1-\beta)}$. There is a probability ${r(1-r)}$ that both coins are ${P_2}$. If this happens then every ${g}$ gives payoff ${P_2/(1-\beta)}$. And there is a probability ${r^2+(1-r)^2}$ that the coins are different, and, because of the choice of ${r}$, conditionally on this event the probability of ${G}$ being ${P_1}$ is ${p}$. Therefore, in this case ${g}$ gives whatever ${g}$ gives in the difficult problem.

So, the payoff in the easy problem is a linear function of the payoff in the difficult problem. Therefore the optimal strategy in the difficult problem is the same as the optimal strategy in the easy problem. In particular, we just proved that, for every ${p}$, the optimal action in the first period is red when ${p\ge 1/2}$ and green with ${p\le 1/2}$. Now back to the dynamic programming formulation, from standard arguments it follows that the optimal strategy is to keep doing it forever, i.e., at every period to toss the coin that is more likely to be the ${P_1}$ coin given the current information.

See why I said my solution is tricky and specific ? it relies on the fact that there are only two arms (the fact that the arms are coins is not important). Here is a problem whose solution I don’t know:

Question 2 Let ${0 \le P_1 < P_2 < ... < P_n \le 1}$. We are given ${n}$ coins, one of each parameter, all ${n!}$ possibilities equally likely. Each period we have to toss a coin and we get payoff ${1}$ for Heads. What is the optimal strategy ?

It states that the Minkowski sum of a large number of sets is approximately convex. The clearest statement  as well as the nicest proof  I am familiar with is due to J. W. S. Cassels. Cassels is a distinguished number theorist who for many years taught the mathematical economics course in the Tripos. The lecture notes  are available in a slender book now published by Cambridge University Press.

This central limit like quality of the lemma is well beyond the capacity of a hewer of wood like myself. I prefer the more prosaic version.

Let ${\{S^j\}_{j=1}^n}$ be a collection of sets in ${\Re ^m}$ with ${n > m}$. Denote by ${S}$ the Minkowski sum of the collection ${\{S^i\}_{i=1}^n}$. Then, every ${x \in conv(S)}$ can be expressed as ${\sum_{j=1}^nx^j}$ where ${x^j \in conv(S^j)}$ for all ${j = 1,\ldots, n}$ and ${|\{j: x^j \not \in S^j| \leq m}$.

How might this be useful? Let ${A}$ be an ${m \times n}$ 0-1 matrix and ${b \in \Re^m}$ with ${n > m}$. Consider the problem

$\displaystyle \max \{cx: Ax = b, x_j \in \{0,1\}\ \forall \,\, j = 1, \ldots, n\}.$

Let ${x^*}$ be a solution to the linear relaxation of this problem. Then, the lemma yields the existence of a 0-1 vector ${x}$ such that ${cx \geq cx^* = z}$ and ${||Ax - b||_{\infty} \leq m}$. One can get a bound in terms of Euclidean distance as well.

How does one do this? Denote each column ${j}$ of the ${A}$ matrix by ${a^j}$ and let ${d^j = (c_j, a^j)}$. Let ${S^j = \{d^j, 0\}}$. Because ${z = cx^*}$ and ${b = Ax^*}$ it follows that ${(z,b) \in conv(S)}$. Thus, by the Lemma,

$\displaystyle (z, b) = \sum_{j=1}^n(c_j, a^j)y_j$

where each ${y_j \in [0,1]}$ and ${|\{j: y_j \in (0,1) \}| \leq m }$. In words, ${y}$ has at most ${m}$ fractional components. Now construct a 0-1 vector ${y^*}$ from ${y}$ as follows. If ${y_j \in \{0,1\}}$, set ${y^*_j = y_j}$. If ${y_j }$ is fractional, round ${y^*_j}$ upto 1 with probability ${y_j}$ and down to zero otherwise. Observe that ${||Ay - b||_{\infty} \leq m}$ and the ${E(cy) = cx^*}$. Hence, there must exist a 0-1 vector ${x}$ with the claimed properties.

The error bound of ${m}$ is to large for many applications. This is a consequence of the generality of the lemma. It makes no use of any structure encoded in the ${A}$ matrix. For example, suppose $x^*$ were an extreme point and $A$ a totally unimodular matrix. Then, the number of fractional components of $x^*$ are zero. The rounding methods of Kiralyi, Lau and Singh as well as of Kumar, Marathe, Parthasarthy and Srinivasan exploit the structure of the matrix. In fact both use an idea that one can find in Cassel’s paper. I’ll follow the treatment in Kumar et. al.

As before we start with ${x^*}$. For convenience suppose ${0 < x^*_j < 1}$ for all ${j = 1, \ldots, n}$. As ${A}$ as has more columns then rows, there must be a non-zero vector ${r}$ in the kernel of ${A}$, i.e., ${Ar = 0}$. Consider ${x + \alpha r}$ and ${x -\beta r}$. For ${\alpha > 0}$ and ${\beta > 0}$ sufficiently small, ${x_j + \alpha r_j, x_j - \beta r_j \in [0,1]}$ for all ${j}$. Increase ${\alpha}$ and ${\beta}$ until the first time at least one component of ${x +\alpha r}$ and ${x- \beta r}$ is in ${\{0,1\}}$. Next select the vector ${x + \alpha r}$ with probability ${\frac{\beta}{\alpha + \beta}}$ or the vector ${x- \beta r}$ with probability ${\frac{\alpha}{\alpha + \beta}}$. Call the vector selected ${x^1}$.

Now ${Ax^1 = b}$. Furthermore, ${x^1}$ has at least one more integer component than ${x^*}$. Let ${J = \{j: x^1_j \in (0,1)\}}$. Let ${A^J}$ be the matrix consisting only of the columns in ${J}$ and ${x^1(J)}$ consist only of the components of ${x^1}$ in ${J}$. Consider the system ${A^Jx^1(J) = b - \sum_{j \not \in J}x^1_j}$. As long as ${A^J}$ has more columns then rows we can repeat the same argument as above. This iterative procedure gives us the same rounding result as the Lemma. However, one can do better, because it may be that even when the number of columns of the matrix is less than the number of rows, the system may be under-determined and therefore the null space is non-empty.

In a sequel, I’ll describe an optimization version of the Lemma that was implicit in Starr’s 1969 Econometrica paper on equilibria in economies with non-convexities.

Economists, I told my class, are the most empathetic and tolerant of people. Empathetic, as they learnt from game theory, because they strive to see the world through the eyes of others. Tolerant, because they never question anyone’s preferences. If I had the  talent I’d have broken into song with a version of Why Can’t a Woman be More Like a Man’ :

Psychologists are irrational, that’s all there is to that!
Their heads are full of cotton, hay, and rags!
They’re nothing but exasperating, irritating,
vacillating, calculating, agitating,

Why can’t a psychologist be more like an economist?

Back to earth with preference orderings. Avoided  the word rational to describe the restrictions placed on preference orderings, used consistency’ instead. More neutral and conveys the idea that inconsistency makes prediction hard rather that suggesting a Wooster like IQ. Emphasized that utility functions were simply a succinct representation of consistent preferences and had no meaning beyond that.

In a bow to tradition went over the equi-marginal principle, a holdover from the days when economics students were ignorant of multivariable calculus. Won’t do that again. Should be banished from the textbooks.

Now for some meat: the income and substitution (I&S) effect. Had been warned this was tricky. No shirt Sherlock,’ my students might say. One has to be careful about the set up.

Suppose price vector $p$ and income $I$. Before I actually purchase anything, I contemplate what I might purchase to maximize my utility. Call that $x$.
Again, before I purchase $x$, the price of good 1 rises. Again, I contemplate what I might consume. Call it $z$. The textbook discussion of the income and substitution effect is about the difference between $x$ and $z$.

As described, the agent has not purchased $x$ or $z$. Why this petty foggery? Suppose I actually purchase $x$ before the price increase. If the price of good 1 goes up, I can resell it. This is both a change in price and income, something not covered by the I&S effect.

The issue is resale of good 1. Thus, an example of an I&S effect using housing should distinguish between owning vs. renting. To be safe one might want to stick to consumables. To observe the income effect, we would need a consumable that sucks up a largish’ fraction of income. A possibility is low income consumer who spends a large fraction on food.

Here is the bonus question from the final exam in my dynamic optimization class of last semester. It is based on problem 8 Chapter II in Ross’ book Introduction to stochastic dynamic programming. It appears there as `guess the optimal policy’ without asking for proof. The question seems very natural, but I couldn’t find any information about it (nor apparently the students). I have a solution but it is tricky and too specific to this problem. I will describe my solution next week but perhaps somebody can show me a better solution or a reference ?

We have two coins, a red one and a green one. When flipped, one lands heads with probability P1 and the other with probability P2. We do not know which coin is the P1 coin. We initially attach probability p to the red coin being the P1 coin. We receive one dollar for each heads and our objective is to maximize the total expected discounted return with discount factor β. Describe the optimal policy, including a proof of optimality.