subject to

The last constraint comes from the assumption that Uber is committed to ensuring that every rider seeking a ride at the posted price gets one.

Suppose, Uber did not link the payment to driver to the price charged to rider in this particular way. Then, Uber would solve

subject to

The first optimization problem is clearly more restrictive than the second. Hence, the claim that Uber is not profit maximizing. Which raises the obvious puzzle, why is Uber using a revenue sharing scheme?

]]>I came upon Afriat when I learnt about the problem of rationalizability. One has a model of choice and a collection of observations about what an agent selected. Can one rationalize the observed choices by the given model of choice? In Afriat’s seminal paper on the subject, the observations consisted of price-quantity pairs for a vector of goods and a budget. The goal was to determine if the observed choices were consistent with an agent maximizing a concave utility function subject to the budget constraint. Afriat’s paper has prompted many other papers asking the same question for different models of choice. There is an aspect of these papers, including Afriat’s, that I find puzzling.

To illustrate, consider rationalizing expected utility (Eran Shmaya suggested that `expected consumption’ might be more accurate). Let be the set of possible states. We are given a sequence of observations and a single budget . Here represents consumption in state and is the unit price of consumption in state in observation . We want to know if there is a probability distribution over states, , such that each maximizes expected utility. In other words, solves

subject to

The solution to the above program is obvious. Identify the variable with the largest objective coefficient to constraint ratio and make it as large as possible. It is immediate that a collection of observations can be rationalized by a suitable set of non-zero and nonnegative ‘s if the following system has a feasible solution:

This completes the task as formulated by Afriat. A system of inequalities has been identified, that if feasible means the given observations can be rationalized. How hard is this to do in other cases? As long as the model of choice involves optimization and the optimization problem is well behaved in that first order conditions, say, suffice to characterize optimality, its a homework exercise. One can do this all day, thanks to Afriat; concave, additively separable concave, etc. etc.

Interestingly, no rationalizability paper stops at the point of identifying the inequalities. Even Afriat’s paper goes a step farther and proceeds to `characterize’ when the observations can be rationalized. But, feasibility of the inequalities themselves is just such a characterization. What more is needed?

Perhaps, the characterization involving inequalities lacks `interpretation’. Or, if the given system for a set of observations was infeasible, we may be interested in the obstacle to feasibility. Afriat’s paper gave a characterization in terms of the strong axiom of revealed preference, i.e., an absence of cycles of certain kinds. But that is precisely the Farkas alternative to the system of inequalities identified in Afriat. The absence of cycles condition follows from the fact that the initial set of inequalities is associated with the problem of finding a shortest path (see the chapter on rationalizability in my mechanism design book). Let me illustrate with the example above. It is equivalent to finding a non-negative and non trivial solution to

Take logs:

This is exactly the dual to the problem of finding a shortest path in a suitable network (I believe that Afriat has a paper, that I’ve not found, which focuses on systems of the form ).The cycle characterization would involve products of terms like being less than 1 (or greater than 1 depending on convention). So, what would this add?

]]>…. filled with foreboding. Like the Roman, I seem to see “the River Tiber foaming with much blood”.

Much of my energy has been taken up with designing homework problems and a midterm exam on monopoly. Interesting questions are hard to come by. Those lying around make me want to make gnaw my feet off. I started with the assumption, which I may live to regret, that my students are capable of the mechanical and have good memories. The goal, instead, is to get them to put what they have learnt in class to use. Here is an example. Two upstream suppliers, A and B, who each supply an input to a Retailer. The Retailer is characterized by a production function that tells you how much output it generates from the inputs supplied by A and B as well as a demand curve for the final product. Fix the price set by B to, w, say. Now compute the price that A should charge to maximize profit. Its double marginalization with a twist. Suppose the inputs are substitutes for each other. If B raises its price above w what effect will that have on A’s profits? There are two effects. The retailers costs will go up of course, so reducing its output. However, A will retain a larger share of the smaller output. Which will be bigger? Its a question that requires them to put various pieces together. I’ve had them work up to it by solving the various pieces under different guises in different problems. Am I expecting too much? I’ll find out after the midterm. Yet, I cannot see anyway around having questions like this. What is the point of the mathematics we require them to use and know if we don’t ask them to apply it when blah-blah alone is insufficient?

I now have a modest store of such problems, but coming up with them has been devilish hard. Working out the solutions is painful, because it involves actual algebra and one cannot afford errors (on that account I’m behind the curve). To compound matters, one is unable to recycle the exam and homework problems given various sharing sites. I begin to regret not making them do just algebra.

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1) Is the concern for privacy intrinsic or instrumental?

The question matters because an answer would have a profound impact on how one evaluates the welfare consequences of various policies.

2) Property rights over information.

Much of the information about us that is of interest is the result of interactions with others. When I purchase a book from Amazon, who `owns’ the record of that transaction? It could be argued that the record of the transaction is as much Amazon’s as it is mine. The question is not new. It arises, for example, when one writes a biography.

What about when a transaction takes place via an intermediary? What rights does the intermediary have to the record of the transaction?

3) A full specification of property rights would spell out who has the right to disclose what and to whom and under what conditions.

Some of this will involve a balance between the public good and individual harm. Out of court settlements regarding commercial matters whose terms are secret, prevent learning about systemic problems (Akerlof and his lemons, seems like a likely candidate for relabeling).

When is and under what conditions is mandated disclosure warranted? One can also imagine settings where one might wish to prohibit the voluntary disclosure of confidential information. Some professional schools, for example prohibit the disclosure of grades to potential employers (Grossman and Milgrom, anyone?).

4) Compliance. How might one monitor and verify that the specification of property rights have been adhered to?

For example, a promise not to disclose to a third party. In a given setting, are some kinds of promises even feasible? One may promise not to use certain identifying characteristics in the allocation of resources but those characteristics may have good proxies in other `allowed’ characteristics.

Who should bear the costs of such monitoring? (Coase?)As much information of interest is collected by devices, one might think about the `regulation’ of devices as a part of compliance. What standards, if any should devices that collect and transmit information adhere to? Is managing privacy best done through device standards or contracts?

]]>The conclusion of the basic 2-part model suggests charging a high price for razors and a low price for blades. This seems to run counter to the prevailing wisdom. Its an opportunity to solicit reasons for why the conclusion of the model might be wrong headed. We ran through a litany of possibilities: heterogenous preferences (opportunity to do a heavy vs light user calculation), hold up (one student observed that we can trust Amazon to keep the price of ebooks low otherwise we would switch to pirated versions!), liquidity constraints, competition. Tied this to Gillete’s history expounded in a paper by Randall Pick (see an earlier post ) and then onto Amazon’s pricing of the kindle and ebooks (see this post). This allowed for a discussion of the wholesale model vs agency model of pricing which the students had been asked to work out in the homework’s (nice application of basic monopoly pricing exercises!).

The `take-away’ I tried to emphasize was how models help us formulate questions (rather than simply provide prescriptions), which in turn gives us greater insight into what might be going on.

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The formulation of `learning to make predictions’ goes through *merging*, which traces back to Blackwell and Dubins. I will not give Blackwell and Dubin’s definition in this post but a weaker definition, suggested by Kalai and Lehrer.

A Bayesian agent observes an infinite sequence of *outcomes* from a finite set . Let represent the agent’s belief about the future outcomes. Suppose that before observing every day’s outcome the agent makes a probabilistic prediction about it. I denote by the element in which represents the agent’s prediction about the outcome of day just after he observed the outcomes of previous days. In the following definition it is instructive to think about as the true data generating process, i.e., the process that generates the sequence of outcomes, which may be different from the agent’s belief.

Definition 1 (Kalai and Lehrer)Let . Thenmergeswith if for -almost every realization it holds that

Assume now that the agent’s belief is stationary, and let be its ergodic decomposition. Recall that in this decomposition ranges over ergodic beliefs and represents structural uncertainty. Does the agent learn to make predictions ? Using the definition of merging we can ask, does merges with ? The answer, perhaps surprisingly, is no. I gave an example in my previous post.

Let me now move to a weaker definition of merging, that was first suggested by Lehrer and Smorodinsky. This definition requires the agent to make correct predictions in *almost* every period.

Definition 2Let . Thenweakly mergeswith if -almost every realization it holds thatfor a set of periods of density .

The definition of weak merging is natural: patient agents whose belief weakly merges with the true data generating process will make almost optimal decisions. Kalai, Lehrer and Smorodinsky discuss these notions of mergings and also their relationship with Dawid’s idea of calibration.

I am now in a position to state the theorem I have been talking about for two months:

Theorem 3Let be stationary, and let be its ergodic decomposition. Then weakly merges with for -almost every .

In words: An agent who has some structural uncertainty about the data generating process will learn to make predictions in most periods as if he knew the data generating process.

Finally, here are the promised open questions. They deal with the two qualification in the theorem. The first question is about the “-almost every ” in the theorem. As Larry Wasserman mentioned this is unsatisfactory in some senses. So,

Question 1Does there exists a stationary (equivalently a belief over ergodic beliefs) such that weakly merges with for every ergodic distribution ?

The second question is about strengthening weak merging to merging. We already know that this cannot be done for arbitrary belief over ergodic processes, but what if is concentrated on some natural family of processes, for example hidden markov processes with a bounded number of hidden states ? Here is the simplest setup for which I don’t know the answer.

Question 2The outcome of the stock market at every day is either U or D (up or down). An agent believes that this outcome is a stochastic function of an unobserved (hidden) state of the economy which can be either G or B (good or bad): When the hidden state is B the outcome is U with probability (and D with probability ), and when the state is G the outcome is U with probability . The hidden state changes according to a markov process with transition probability , . The parameter is and the agent has some prior over the parameter. Does the agent’s belief about outcomes merge with the truth for -almost every ?.

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Subsequently, multi-product monopolist; substitute and complement. Emphasized this meant each product could not be priced in isolation of the other. Now the puzzle. Why would a seller introduce a substitute to itself? Recalling discussion of durables good monopolist, this seems like lunacy. A bright spark suggested that the substitute product might appeal to a segment that one is not currently selling to. Yes, but wouldn’t that cannibalize sales from existing product? Time for a model! Before getting to model, formally introduced price discrimination.

Day 7, talked briefly about homework and role of mathematics in economic analysis. Recalled the question of regulating the monopolist. Lowering price benefits consumers but harms seller. Do the benefits of customers exceed harm done to seller? Blah, blah cannot settle the issue. Need a model and have to analyze it to come to a conclusion. While we represent the world (or at least a part of it) mathematically, it does not follow that every mathematical object corresponds to something in reality. Made this point by pointing them to the homework question with demand curve having a constant elasticity of 1. Profit maximizing price is infinity, which is clearly silly. Differentiating and setting to zero is not a substitute for thinking.

Went on to focus on versioning and bundling. Versioning provides natural setting to talk about cannibalization and catering to new segment. Went through a model to show how the competing forces play out. Then to bundling.

Discussion of reasons to bundle that do not involve price discrimination. Then a model and its analysis. Motivated it by asking whether they would prefer to have ala carte programming from cable providers. In the model, unbundling results in higher prices which surprises them and was a good note to end on.

]]>Thus far class had been limited to a uniform price monopolist. Pivoted to discussing a multi-product monopolist by way of a small example of a durables good monopolist selling over two periods. Had the class act out out the role of buyers and me the seller cutting price over time. It provided an opportunity to discuss the role of commitment and tie it back to the ultimatum game played Day 1. On day 6 will revisit this with a discussion of JC Penny, which will allow one to get to next item on the agenda: price discrimination.

]]>Double marginalization was followed by computing total consumer surplus by integrating the inverse demand function. Ended on optimal regulation of monopolist, showing that pricing at marginal cost maximizes producer plus consumer surplus. Brief discussion of incentives to be a monopolist if such regulation was in place. Then, asked the class to consider regulating a monopsonist and whether a minimum wage would be a good idea.

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