## Monthly Archives: April 2020

### Binomial Theorem : Tutorial Problems II: IITJEE Mains practice

I. Find the $(r+1)6{th}$ term in each of the following expansions:

1. $(1+x)^{-\frac{1}{2}}$
2. $(1-x)^{-2}$
3. $(1+3x)^{\frac{1}{3}}$
4. $(1+x)^{-\frac{2}{3}}$
5. $latex(1+x^{2))^{-3}$
6. $(1-2x)^{-\frac{3}{2}}$
7. $(a+bx)^{-1}$
8. $(2-x)^{-2}$
9. $\sqrt[3]{a^{2}-x^{2}}$
10. $\frac{1}{\sqrt{1+2x}}$
11. $\frac{1}{\sqrt[3]{(1-3x)^{2}}}$
12. $\frac{1}{\sqrt[n]{(a^{n}-nx)}}$

Find the greatest term in each of the following expressions:

1. $(1+x)^{-r}$ when $x=\frac{4}{15}$
2. $(1+x)^{\frac{11}{2}}$ when $x=\frac{2}{3}$
3. $(1-7x)^{-\frac{11}{4}}$ when $x=\frac{1}{8}$
4. $(2x+5y)^{12}$​ when $x=8, y=3$
5. $(b-4x)^{-7}$ when $x=\frac{1}{2}$
6. $(3x^{2}=4y^{3})^{-n}$ when $x=9, y=2, n=15$

Find to five places of decimals the value of:

1. $\sqrt{98}$
2. $\sqrt[3]{998}$
3. $\sqrt[3]{1003}$
4. $\sqrt[4]{2400}$
5. $\frac{1}{\sqrt[3]{128}}$
6. $(\frac{601}{50})^{\frac{1}{3}}$
7. $(630)^{-\frac{2}{3}}$
8. $(3128)^{\frac{1}{4}}$

Regards,

Nalin Pithwa.

### Binomial Theorem Tutorial problems I: IITJEE mains practice

I. Expand up to 5 terms the following expressions:

1. $(1+x)^{\frac{1}{2}}$
2. $(1+x)^{\frac{7}{2}}$
3. $(1-x)^{\frac{2}{5}}$
4. $(1+x^{2})^{-2}$
5. $(1-3x)^{\frac{1}{3}}$
6. $(1-3x)^{\frac{-1}{2}}$
7. $(1+2x)^{-\frac{1}{2}}$
8. $(1+\frac{x}{3})^{-2}$
9. $(1+\frac{2x}{3})^{\frac{3}{2}}$
10. $(1+\frac{1}{2}a)^{-4}$
11. $(2+x)^{-2}$
12. $(9+2x)^{\frac{1}{2}}$
13. $(8+12a)^{\frac{3}{2}}$
14. $(9-6x)^{-\frac{3}{2}}$
15. $(4a-8x)^{-\frac{1}{2}}$

II. Write down and simplify:

1. The 8th term of $(1+2x)^{-\frac{1}{2}}$
2. The 11th term of $(1-2x^{3})^{\frac{11}{2}}$
3. The 16th term of $(1+3a^{2})^{\frac{16}{3}}$
4. The 6th term of $(3a-2b)^{-1}$
5. The $(r+1)^{th}$ term of $(1-x)^{-2}$
6. The $(r+1)^{th}$ term of $(1-x)^{-4}$
7. The $(r+1)^{th}$ term of $(1+x)^{\frac{1}{2}}$
8. The $(r+1)^{th}$ term of $(1+x)^{\frac{11}{3}}$
9. The 14th term of $(2^{10}-2^{7}x)^{\frac{13}{2}}$
10. The 7th term of $(3^{8}+6^{4}x)^{\frac{11}{4}}$

Regards,

Nalin Pithwa

### The Greatest Auction Ever Held

Reference: A Beautiful Mind by Sylvia Nasar.

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Washington, D.C., December 1994:

On the afternoon of December 5, 1994, John Nash was riding in a taxi headed to Newark Airport on his way to Stockholm, where he would, in a few days time, receive from the King of Sweden the gold medal engraved with the portrait of Alfred Nobel. At around the same time, a few hundred miles in the south, in downtown Washington, D.C., Vice President Al Gore was announcing with great fanfare the opening of the “greatest auction ever.”

There was, as The New York Times would later report, no fast-talking auctioneer, no banging gavel, no Old Masters. On the auction block was thin air — airwaves that could be used for the new wireless gadgets like telephones, pagers, faxes — worth billions and billions of dollars, enough licenses for every major American city to have three competing cellular phone services. In the secret war rooms and building booths were CEOs of the world’s biggest communications conglomerates — and unlikely group of blue sky economic theoreticians who were advising them.When the auction finally closed the following March, the winning bids totaled more than $7 billion making it the biggest sale in American history of public assets and one of the most successful (and lucrative) applications of economic theory to public policy ever. Michael Rothschild, dean of Princeton’s Woodrow Wilson School, later called it “a demonstration that people thinking hard about a problem can make the world work better…a triumph of pure thought.” The juxtaposition of Gore and Nash, the high-tech auction and the medieval pomp of the Nobel ceremony, was hardly an accident. The FCC auction was designed by young economists who were using tools created by John Nash, John Harsanyi and Reinhard Selten. Their ideas were specifically designed for analyzing rivalry and cooperation among a small number of rational players with a mix of conflicting and similar interests: people, governments, and corporations — and even animal species. The prize itself was a long overdue acknowledgement by the Nobel committee that a sea change in economics, one that had been underway for more than a decade, had taken place. As a discipline, economics had long been dominated by Adam Smith’s brilliant metophor of the Invisible Hand. Smith’s concept of perfect competition envisions so many buyers and sellers that no single buyer or seller has to worry about the reactions of others. It is a powerful idea, one that predicted how free-market economies would evolve and gave policy-makers a guide for encouraging growth and dividing the economic pie fairly. But in the world of mega-mergers, big government, massive foreign direct investment, and whole-sale privatization, where the game is played by a handful of players, each taking into account the others’ actions, each pursuing his own best strategies, game theory has come to the fore. After decades of resistance —- Paul Samuelson used to joke about “the swamp of n-person game theory” —- a younger generation of theorists began using game theory in areas from trade to industrial organization to public finance in the late 1970’s and early 1980’s. Game theory opened up “terrain for systematic thinking that was previously closed.” Indeed, as game theory and information economics have become increasingly entwined, markets traditionally seen as fitting the purely competitive mold have increasingly been studied using game theory assumptions. The latest generation of texts used in top graduate schools today all recast the basic theories of the firm and the consumer, the foundation of economics, in terms of strategic games. “Concepts, terminology and models from game theory have come to dominate many areas of economics,” said Avinash Dixit, an economist at Princeton who uses game theory in work on international trade and is the author of Thinking Strategically. “At last we are seeing the realization of the true potential of the revolution launched by von Neumann and Morgenstern.” And because most economic applications of game theory use the Nash equilibrium concept, “Nash is the point of departure.” The revolution has gone far beyond research journals, experimental laboratories at Caltech and the University of Pittsburgh, and classrooms of elite business schools and universities. The current generation of economic policy-makers — including Lawrence Summers, undersecretary of the treasury, Joseph Stiglitz, chairman of the Council of Economic Advisers, and Vice-President Al Gore — are steeped in the stuff, which they say, is useful for thinking about everything from budget proposals to Federal Reserve policy to pollution cleanups. The most dramatic use of game theory is by governments from Australia to Mexico to sell scarce public resources to buyers best able to develop them. The radio spectrum, T-bills, oil leases, timber, and pollution rights are now sold in auctions designed by game theorists — with far greater success than that of earlier policies. Economists like Nobel Laureate Ronald Coase have advocated the use of auctions by government since the 1950’s. Auctions have long been used in markets where sellers of unusual items — from vintage wines to movie rights — have no idea what bidders are willing to pay. Their basic purpose is to make bidders reveal how much they value the item. But the arguments of Coase and others were stated in abstract, entirely theoretical terms, and little thought was given to how such auctions would actually be conducted. Congress remained skeptical. Before 1994, Washington simply gave away licenses for free. Until 1982, it had been up to regulators to decide which companies deserved the licenses. Needless to say, the process was dominated by political pressures, outrageously expensive paperwork, and long delays. The pace of licensing lagged hopelessly behind market shifts and new technologies. After 1982, Washington awarded licenses using lotteries, with the winners free to resell licenses. Although the reform did speed up the granting of licenses, the process was still hugely inefficient — and unfair. Bidders with no intention of operating an actual telephone business spent millions to get into the game for the purpose of reaping a windfall. Further, although telephone companies were forced to pay the costs of obtaining licenses, Washington (and taxpayers) did not get the benefits of any revenues. There had to be a better way. A young generation of game theorists, including Paul Milgrom, John Roberts, and Robert Wilson at the Stanford B-school, came up with that better way. Their chief contribution consisted of recognizing, as Milgrom said, that “the mere design of some auction was not enough…Getting the auction design right was also critically important.” In particular, they concluded that the most obvious auction designs —- auctioning licenses one by one in sequence using simultaneous sealed bids — was the way least likely to succeed in getting licenses into the hands of corporations that could use them best —- Washington’s stated objective. Game theorists treat an auction like a game with rules and try to evaluate how a given set of rules, taken together, is apt to affect the bidders’ behaviour. They take stock of the options the rules allow, the payoffs to the bidders associated with the options, and bidders expectations about their competitors’ likely choices. Why did these economists conclude that traditional auction formats would not work? Mainly because the value of each individual license to a user depends — as is the case with a Rembrandt or a Picasso — on what other licenses the user is able to obtain. Some licenses are perfect substitutes for one another. That would be the case for similar spectrum bands to provide a given service. But others are complements. That would be the case for licenses to provide paging services in different parts of the country. “To permit the efficient license assignment, an auction must allow bidders to consider various packages of licenses, combining complements and switching among substitutes during the course of the auction. Designing an auction to allow this is quite difficult,” writes Paul Milgrom., one of the economists who designed the FCC auction of which Gore was speaking. A second source of complexity, Milgrom says, is that the purpose of the licenses is to create businesses for new services with unknown technology and unknown consumer demand. Since bidders’ opinions are bound to be wildly divergent, it is possible that license assignment would depend more on bidders’ optimism than on their ability to create a desired service. Ideally, an auction design can minimize that problem. As Congress and the FCC inched closer to the notion of auctioning off spectrum rights, Australia and New Zealand both conducted spectrum auctions. That they proved to be costly flops and political disasters illustrated that the devil really was in the details. In New Zealand, the government ran a so-called second price auction, and newspapers were full of stories about winners who paid far below their bids. In once case, the high bid was NZ$7 million, the second bid was NZ$5000, and the winner paid the lower price. In another, an Otago University student bid NZ$1 for a television license in a small city. Nobody else bid, so he got it for one dollar. The government expected the cellular licenses to fetch NZ$240 million. The actual revenue was NZ$36 million, one-seventh of the advance estimate. In Australia, a botched auction, in which parvenu bidders pulled the wool over the government’s eyes, delayed the introduction of pay television by almost a year.

The FCC’s chief economist was an advocate of auctions, but no game theorists were involved in the first stage of the FCC auction design. The theorists’s phones started ringing only by accident after the FCC issued a tentative proposal for an auction format with dozens of footnotes to the theoretical literature on auctions. That was how Milgrom and his colleague Robert Wilson, leading auction theorists, got into the game. Milgrom and Wilson proposed that the FCC adopt a simultaneous, multiple round auction. In a simultaneous auction, a bunch of licenses are sold at the same time. Multiple rounds means that, after the first round of bidding prices are announced, and bidders have a chance to withdraw or raise one another’s bids. This is repeated round after round until the auction is over. The chief advantage of this format is that it allows bidders to take account of interdependencies among licenses. Just as sequential, closed-bid auctions let sellers discover what bidders are willing to pay for individual items, the simultaneous, ascending-bid auction lets them discover the market value of different groupings of items.

This early proposal —- which the FCC eventually adopted — did not cover seemingly small but critical details. Should there be deposits? Minimum bid increments? Time limits? Should the bidding system be wholly computerized or executed by hand? And so forth. Milgrom, Roberts and another game theorist, Preston McAfee, an adviser to AirTouch, provided proposals on these issues. The FCC hired another game theorist, John McMillan, of the University of Caliofornia, San Diego, to help evaluate the effect of every proposed rule. According to Milgrom, “Game theory played a central role in the analysis of the rules. Ideas of Nash equilibrium, rationalizability, backward induction, and incomplete information, though rarely named explicitly, were the real basis of daily decisions about the details of the auction process.

By late spring 1995, Washington had raised more than USD 10 billion from spectrum auction. The press and the politicians were ecstatic. Corporate bidders were largely able to protect themselves from predatory bidding and were able to assemble an economically sensible set of licenses. It was, as John McMillan said, ” a triumph for game theory.”

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PS: a triumph of pure mathematical thought !:-)

Regards,

Nalin Pithwa