I was born on December 18, 1951 in the New York City borough of Queens. My parents, Ernest and Lillian, were both public high school teachers of a subject that is probably no longer taught, called Secretarial Studies, which focused on typing and taking dictation via two methods of shorthand stenography, Pitman and Gregg. Their students were young women planning to go directly to work as secretaries after high school.

Not long after I was born, science became very fashionable in the United States, following the launch of the Sputnik satellite by the Soviet Union in 1957. I have an early memory of sitting in class listening to a radio broadcast of the launch of an American satellite, maybe one of the Explorer satellites in early 1958, when I was in first grade. That, plus the fact that my older brother Ted thought we should be scientists, was enough to convince me that science could be a career.

I was fortunate to also follow Ted in attending the Science Honors Program at Columbia University, which offered classes to junior high school and high school students on Saturdays. I thrived there, and I entered Columbia’s engineering school in the Fall of 1968 when I was 16, without having graduated from high school.

At Columbia, I spent a lot of time practicing Shotokan karate, which was very satisfying and which taught me that I could work harder than I had thought. I also found time to study Operations Research, which seemed to me to offer the promise of bringing scientific methods to the organization of at least some parts of human activity. After graduating from Columbia I moved to Stanford University in 1971 to pursue my Ph.D. in what was then Stanford’s Department of Operations Research.

I decided to do research in game theory after taking a class from Michael Maschler, who was visiting Stanford from the Hebrew University of Jerusalem. Bob Wilson agreed to be my advisor and rescued me from having what looked to be a very short academic career after I failed one of my Ph.D. qualifying exams. He was on sabbatical that year, but met with me regularly once a week for an hour. In memory, our meetings followed a kind of script: I would spend a while explaining to him why I hadn’t made progress that week, and then he would spend a while telling me not to be discouraged. Then I would describe some roadblock to further progress, and he would, as we finished our meeting, recommend a paper for me to read. Because his recommendations had always been very much on target, I would go straight from his office to the library and start to read the paper. As I did, I would think, this time Bob made a mistake, this paper has nothing to do with my problem. But then, somewhere in the middle of the paper would be a lemma or remark that helped me get around that roadblock …

My dissertation concerned the then popular models of games with transferable utility, and explored a generalization of the stable sets of outcomes that von Neumann and Morgenstern had proposed as”solutions” of such games. The progress of game theory has included the thorough exploration of a number of dead ends, and this eventually turned out to be one of them. But it gave me some tools (I proved a fixed point theorem on lattices), and an enduring interest in game theory.

I applied for jobs in OR and math departments, and was about to take a job in a math department – I had already decided which one – when, at the last minute, I got a phone call from Bill Zangwill, who was putting together an operations research group in the business school at the University of Illinois at Urbana-Champaign. I was completely relaxed at the campus interview, since I figured I already knew what job I was going to take. While I was visiting Illinois, I suggested that, were I to come, I would like a joint appointment in Economics, since that seemed where the most interesting problems for game theory were to be found. I flew home and found a telegram on my front door, saying that a formal offer was on the way. And so I became at least a nominal part-time economist early in my career.

As I prepared to leave California for Illinois, I paid a visit to Lloyd Shapley at the Rand Corporation in Santa Monica to tell him about my work. I recall a pleasant visit, in which he correctly conjectured how the proof of my fixed point theorem worked, and (if I recall correctly) ended with him driving me to the airport in his station wagon. Around that time, on a visit home to New York, I also visited Oskar Morgenstern at New York University to tell him about my work on von Neumann-Morgenstern solutions. I visited him several more times over the next three years, at NYU and once at his home in Princeton. My discussions with Morgenstern were always non-technical: he would reminisce about people, and I would tell him what I was working on. I sometimes felt a little like Columbus reporting back to Queen Isabella about the new lands that had been discovered in her name; Morgenstern appeared to be entertained by the fact that young people were working on game theory, and pointed out more than once that it had not always been so.

My arrival at Illinois is memorable for two psychologists I met there in my first year. The first, in the first weeks after my arrival, was my colleague Keith Murnighan. We were both new assistant professors in 1974. He had just received his Ph.D. in social psychology from Purdue. One of our senior colleagues suggested we would enjoy talking to each other, and we did, so much so that we decided to do some experiments together, on the kinds of games I had studied in my dissertation. Experiments were newer to me than game theory was to him, but over the course of the next decade we taught each other how to do experiments that would say something useful about game theory. He and I remember our early interactions differently, but we both agree that our first papers took many drafts to converge. Eventually we wrote a dozen papers together, exploring various aspects of game theory including the game theoretic predictions made by theories such as Nash‘s (1950) “solution” to the problem of determining the outcome of two-person bargaining. (Game theory was young, and many things that today would be called models of behavior, or kinds of equilibrium, were optimistically called “solutions,” following von Neumann and Morgenstern.) Keith and I, together with my graduate student Mike Malouf and our colleague Francoise Schoumaker, developed some experimental designs (such as binary lottery games, see Roth and Malouf, 1979, or probabilistically terminated repeated games, see Roth and Murnighan, 1978) that remain in use today. In 1978 I also took a semester leave at the Economics Department at Stanford, where I taught a course whose lecture notes became my first book, Axiomatic Models of Bargaining (Roth, 1978). Axiomatic theories of the kind initiated by Nash were beautiful, and I enjoyed pushing the theory forward, but their failure to account for the kinds of behavior we observed so clearly in experiments convinced me that these too were a dead end for economics.

We published some of those early experiments in psychology journals, which in retrospect reflects my misunderstanding of how experimental economics was going to develop. Psychologists had already done some experiments motivated by game theory, but by and large they had not controlled for the kinds of things that game theorists felt were needed to provide clear tests. I imagined that if we could demonstrate experimental designs that controlled for the things that seemed important to game theorists, psychologists might then proceed to test game theoretic predictions in the laboratory in a way that would be convincing to game theorists. But psychologists have their own agendas, and it quickly became clear that economists could not rely on a division of labor with psychologists, we would have to do for ourselves the experiments whose results we were curious to know.

I had the early opportunity at Illinois to teach a graduate course in game theory, and as I prepared my lectures I realized I didn’t understand as well as I would like many of the things I had been taught. My attempts to understand these things better led not only to my theoretical papers about Nash’s solution, but also to a set of papers about the Shapley value, which had been given an axiomatic foundation when introduced by Shapley (1953). I found that I could not convincingly explain some of the axioms to my students. Instead, I ended up writing a short series of papers in which I explored the idea of formalizing the Shapley value as a utility function for playing a game, so that a decision maker who was a utility maximizer could compare the option of playing a particular position in a given game with other opportunities. The tools for doing this, initiated by Shapley, have not gracefully generalized to the models of games we now mostly consider, but the usefulness of being able to evaluate the prospect of playing a game remains, and how to do that remains an open question. (I also eventually edited a volume of papers on the Shapley value, which we presented to Lloyd at a conference in honor of his 65th birthday and which came out as Roth, 1988.)

Two of my other explorations of models formulated by Shapley began streams of work which have continued throughout my career. In 1974, in the first issue of the first volume of the new Journal of Mathematical Economics, Shapley and Herb Scarf (Shapley and Scarf, 1974) explored a simple abstract model of exchange without money, in which each trader was endowed with a single unit of an indivisible good, which they called a “house,” and each player had preferences over all the houses. They also introduced the “top trading cycles algorithm,” which they attributed to David Gale, for using the preferences of the players to find a set of trades that would result in an allocation in the core: i.e. a set of trades among all the players such that no coalition of players could have done better by going off and trading among themselves. Andy Postlewaite had also arrived as a new assistant professor at Illinois in 1974, and he and I explored the model further (Roth and Postlewaite, 1977), from the point of view of understanding the structure of the core, given the known preferences of the players, a point of view that characterized what was then called cooperative game theory. But I had already begun to think that if one wanted to necessary to elicit the preferences of the players. In a subsequent paper (Roth, 1982a) I showed that the top trading cycles algorithm had the potential to do that in a reliable way, since it would make it safe for players to reveal their true preferences and would never reward a player who mis-stated his preferences.

I had already begun to think about the problem of making it safe for participants to reveal their preferences in connection with the very different kind of clearinghouse that was already in use for organizing the labor market for new American medical doctors. I don’t recall how I first heard about that clearinghouse, but I do remember calling the medical librarian (the medical school was in Chicago) and asking her to send me copies of the documents sent to participants in the clearinghouse, which in those days was called the National Intern and Resident Matching Program (NIRMP). Those documents described briefly how the clearinghouse worked: how residency programs advertised their job descriptions, doctors applied for interviews, residency programs interviewed applicants and rank order lists of residency programs were then collected from doctors, and rank order lists of doctors from residency programs, and combined into a match. The documents made what seemed to me to be a surprising claim, namely that the clearinghouse was designed in such a way that everyone – employers and applicants – did best if they submitted rank order lists that corresponded to their true preferences. As I began to explore the history of this medical match, I found that an early algorithm for turning the rank order lists into a matching of applicants to positions had been replaced after it was noticed that it didn’t have this property. It had been replaced in the early 1950s by an algorithm that I was able to show was essentially equivalent to the ‘deferred acceptance algorithm’ proposed and studied by Gale and Shapley (1962), in their seminal paper “College Admissions and the Stability of Marriage.” Gale and Shapley showed that such an algorithm could always produce a matching that was stable in the sense that no applicant and position would both prefer one another to [one of those to] whom they were matched.

In a paper called “The Economics of Matching: Stability and Incentives,” I showed that there were not any mechanisms that would always both produce a stable matching and make it completely safe for all firms and workers to reveal their true preferences. But the deferred acceptance algorithm could make it completely safe – a “dominant strategy” – for applicantsto submit a rank order list that ranked jobs according to the applicant’s true preferences. Some of these ideas were ‘in the air’ and were also discovered by others: Dubins and Freedman (1981) also showed that the deferred acceptance algorithm could make it safe for applicants to reveal their true preferences and Bergstrom and Manning (1983, unpublished) also showed that no stable matching mechanism could do this for both sides of the market.

Some measure of how much economics has changed in the last decades can be glimpsed from the reaction my paper received when I submitted it to the Journal of Political Economy sometime around 1980. The editor, George Stigler, wrote me a polite letter in which he said that he could see that the paper was interesting and important, but that, except for the title, it wasn’t about economics at all. He didn’t think that a clearinghouse was a marketplace if it didn’t use adjustments in prices/wages to clear the market.

That paper was eventually published in Mathematics of Operations Research (Roth, 1982b), and it marked the beginning of my broader interest in “matching markets” − which are precisely the many important markets, labor markets among them, in which price adjustment alone doesn’t clear the market. Loosely speaking, these are markets that have application procedures, or selection criteria or other institutions, and in which you cannot simply choose what you want (even if you can afford it), but also have to be chosen.

Meanwhile, my relationship with another psychologist was also developing: I met and courted my wife Emilie, who was pursuing her Ph.D. in cognitive psychology at the University of Illinois’ famous psychology department. We met while folk dancing and were married in 1977. When she finished her Ph.D. in 1980 we moved about 25 miles West of Champaign-Urbana to the metropolis of Farmer City, Illinois (population about 2000), which was half way between the University of Illinois and Illinois State University, where she briefly taught. But we were only there for two years, after which the search for two jobs led us to Pittsburgh in 1982.

I began a long and happy sojourn in the Economics Department at the University of Pittsburgh (game theory was beginning to become firmly established in economics, and it had not yet thrived in Operations Research). Emilie began work at the Research and Development Center of the Westinghouse Corporation. The work that she and her colleagues did there gave rise to the new discipline that is today called Cognitive Engineering, which focused on understanding the kinds of information that people need to solve the problems they face, and making sure that this information is available to them, for example in control panels designed to help operate power plants, and deal with emergencies as they arise. Her work has since branched out to the design of many other things, and she likes to point out that I followed her into design.

“Design” is a noun as well as a verb, and in Pittsburgh I continued to look in detail at the design of the medical match, and the conditions in the market for new doctors that had given rise to it in the early 1950s. One of the problems that had plagued the labor market for new doctors in the first half of the 20th century was that competition for good students and for good jobs gradually caused positions to be offered earlier and earlier, until eventually medical students and hospitals were arranging the first post-graduation jobs of medical students two years before they graduated from medical school. Not only did this mean that jobs were being arranged so early that important information on the quality of the match was missing, but job offers were made at different times, and decisions about whether to accept offers were demanded very quickly, so that medical students often had little opportunity to compare different potential positions. The labor market clearinghouse that was organized in the early 1950s solved both these market failures, and my 1984 paper analyzed its design, and pointed to some difficulties it was then facing as a consequence of the growing number of medical students who were married to each other and who wished to find two positions in the same vicinity. This history of that market made clear how a clearinghouse that produced stable matchings had played a critical role in making the market thick, so that lots of potential matches could be considered together, and in dealing with the congestion that had arisen in the less centralized marketplaces that preceded it.

As I began to study the history of other labor markets I found that the “unraveling” of annual markets, in which each year offers are made earlier, and in which there is little opportunity to compare offers, is not so rare. My student Xiaolin Xing and I wrote a paper documenting this in over a dozen markets (Roth and Xing, 1994). It became clear that the timing of transactions was also important in understanding how markets and marketplaces work. Timing is important not just in terms of the date at which transactions take place, but also in terms of how much time they can take, and Xiaolin and I studied the congestion that can result when a marketplace doesn’t give participants enough time to evaluate as many of the transactions as they would like, in connection with difficulties that were being experienced in the market for clinical psychologists which was then run by telephone on a specified day (Roth and Xing, 1997).

While at Pittsburgh I also continued my theoretical and empirical investigation of stable matchings and clearinghouses. I also began to hire postdoctoral fellows, and with one of them, Marilda Sotomayor (who was introduced to me by David Gale) I wrote a monograph (Roth and Sotomayor, 1990) that helped introduce much of what was then known on the subject to a wider audience of economists. On the empirical side, I studied a set of medical clearinghouses that had been organized in different regions of Britain’s National Health Service to deal with the unraveling of their markets for newly graduated doctors (Roth, 1990 and 1991). Those clearinghouses, some of which succeeded and some of which failed, helped provide a kind of natural experiment to clarify how the design of a marketplace could influence the operation of a market.

Pittsburgh became an important center of experimental economics, and my colleague John Kagel (with whom I had earlier edited the Handbook of Experimental Economics (Kagel and Roth, 1995) and I were able to use a laboratory experiment to further illuminate the importance of how the clearinghouses were designed. We compared the performance of the failed clearinghouse design used in Newcastle and Birmingham with the successful design used in Edinburgh, under laboratory conditions that controlled away all the many other differences that exist between those three cities and their medical labor markets (Kagel and Roth, 2000).

It was becoming increasingly clear that how participants learned about the strategic environment would be an important part of market design, and my own understanding of some of the issues was helped by a series of papers that Ido Erev and I wrote about the learning behavior that we observed in experiments (e.g. Erev and Roth, 1995 and 1998). At Pittsburgh I was also able to continue studying bargaining behavior in the laboratory, with Jack Ochs and Bob Slonim (Ochs and Roth, 1989; Slonim and Roth, 1998).

My first chance to put market design into practice came in a phone call from Bob Beran, the director of the medical clearinghouse that was by then called the National Resident Matching Program (reflecting changes in the structure of medical practice that had led “internships” to give way to “residencies” even in the first year of postgraduate employment). The Match was already receiving very able technical assistance from Elliott Peranson, whom I had met previously, and Beran was now calling to ask if I would agree to direct the redesign of the underlying match algorithm, to address an emerging crisis of confidence in a way that would be consistent with changes that had taken place in the underlying conditions in the market for new doctors.

I still recall vividly that my gut reaction was “why me?” as I took that first call. I knew of course why I was a natural for him to call; I had written papers on the medical labor market and the history of the match, and a book on matching. But I also knew that the only things in the book that applied directly to the task I was being asked to undertake were the counterexamples. The book was about simple models of matching, and was full of theorems framed in terms of things that would always happen or could never be achieved: e.g. “the set of stable outcomes is always nonempty,” or “there doesn’t exist any stable matching mechanism that always makes it a dominant strategy for everyone to state their true preferences.” Complications were addressed by counterexamples, such as showing that when there are two-career households in the market, the set of stable matchings could be empty. In my role as a theorist, it had been enough to note that couples therefore presented a hard problem. In my new role as a market designer, they would become my problem.

I worked closely with Peranson, and we together developed the algorithm that continues today to run the “main match” for new residents (Roth and Peranson, 1999). Peranson is an entrepreneurial matchmaker, and the Roth-Peranson algorithm has since been adopted by a number of clearinghouses for more senior medical positions, and for other labor markets in (mostly) health care professions, including the market for clinical psychologists that I had studied as a congested decentralized market. In the course of that collaboration, and in my subsequent collaborations on market design, I have found that my tastes in theory have changed accordingly. I no longer just want to know that some features of the market might cause problems: I am also interested in learning more about how big those problems might be, how frequently they might be encountered, and how to work around them.

In Pittsburgh I taught courses both in experimental economics and in game theory. The 1974 Shapley and Scarf paper was among those I taught regularly in my game theory class, and students always expressed unease that the indivisible goods that were being traded, without the use of money, were called “houses.” It happens that the University of Pittsburgh Medical Center was then one of a small number of very active transplant centers, due in large part to the pioneering efforts of Pitt surgeon Thomas Starzl to overcome immunological barriers to transplantation. And in 1990 kidney transplantation was in the news as another surgeon, Joseph Murray, shared the Nobel Prize in medicine, for having performed the first successful kidney transplant (between identical twins) and for his subsequent work in overcoming immunological barriers between donors and patients. In my class notes from around that time show that I started referring not just to “houses” but to “kidneys” as indivisible goods that could be traded among patient-donor pairs to obtain better matches. I didn’t yet imagine that this would one day be a practical design problem (the first actual kidney exchange was still in the future), but kidney exchange gave me an easy answer to students’ questions about why money could not be used: it had been against American law since 1984 to buy or sell organs for transplantation.

Pittsburgh is also where our sons Aaron and Ben were born, in 1984 and 1991, and the ease of raising a family there made it hard to leave. (A word of advice for parents: pay attention! Childhood moves fast. As I write this in early 2013, our younger son is in graduate school, and our older son is a professor of computer science …) It was hard to leave Pittsburgh, but as our older son was preparing to enter high school (and hence change schools in any event) we moved to Boston in 1998, where I took a position at Harvard, dividing my time equally between the Department of Economics and the Harvard Business School.

At Harvard I occupied two offices and crossed the Charles River twice almost every day, as I would walk from HBS to Economics and then back to get on my bike or in my car for the trip home. It was a short walk, but it sometimes felt like a big change in perspective. As a market designer I was glad to be able to work on both sides of what sometimes seemed like a wide river, between theory and practice, and simple abstraction and messy detail.

One of the first markets I began to study in earnest after arriving at Harvard was the market for top law school graduates who seek prestigious work for a year or two as law clerks to appellate court judges. This is a market that has frequently unraveled (Xiaolin Xing and I had written about it) and just as frequently has tried new rules in an effort to have hiring commence only after law students had completed at least two years of law school. Together with my colleagues Chris Avery and Christine Jolls, and Judge Richard Posner, we surveyed judges and applicants and considered possible solutions (Avery, Jolls, Posner and Roth, 2001 and 2007), as the market struggled and failed to find a home-grown design that would work.

Around that time I also began to study, with Axel Ockenfels, the very different way that timing played a crucial role in the auctions run on eBay. The design of their auctions included an ending rule that elicited a lot of bidding in the final minutes and even seconds of many auctions. We studied, in the field and later (with Dan Ariely) in the lab, how small changes in auction design elicited big changes in bidder behavior (Roth and Ockenfels, 2002; Ariely, Ockenfels and Roth, 2005).

Not long after, Paul Milgrom came for a two year visit, on leave from Stanford, and he and I together taught what may have been the first class in Market Design, in 2000 and again in 2001. Teaching with Paul was a thrilling experience, and helped shaped my views of how the field might develop. Some of those views are in two “manifestos” that I wrote, “The Economist as Engineer …” (Roth, 2002) and “What Have We Learned from Market Design” (Roth, 2008).

My first Ph.D. student at Harvard was Muriel Niederle, who arrived at Harvard the year before I did, and together with fellow student Stefano DellaVigna peppered me with emails urging me to accept Harvard’s offer and assuring me that I’d have no trouble attracting students there. (Indeed, one motivation for moving was the attraction of being able to help educate Harvard students, who would in turn help establish experimental economics and market design more firmly in economics.) She and I began to study the market for gastroenterologists, which was experiencing unraveling like the law clerk market. After studying the gastro market in a series of papers in both economics and medical journals, we were able to help the Yale gastroenterologist Debbie Proctor convince her colleagues to adopt some novel guidelines giving applicants the ability to change their minds if they accepted very early offers, and this helped moderate the pressure to unravel sufficiently so that a successful clearinghouse could operate (see e.g. Niederle and Roth, 2003a,b, 2004 and 2009; Niederle Proctor and Roth, 2006 and 2008). The gastro market underlines what in my experience has been a general rule in market design: for a design to successfully pass from conception to adoption and implementation, it is necessary to have a talented insider who can help us (the economists) understand in detail the market and its problems, and who can take a leading role in explaining and persuading and in implementing institutional changes.

My opportunity to design school choice systems began in 2003 with a phone call from Jeremy Lack at the New York City Department of Education. He knew of my work on the medical match, and wondered if similar efforts might help reorganize the dysfunctional, congested system then used to match students to high schools. Together with Parag Pathak, then a graduate student at Harvard but now a Professor at MIT, and Atila Abdulkadiroğlu, who was then a professor at Columbia, we studied the problem and found ways to adapt a deferred-acceptance clearinghouse that solved the congestion problem and others. It fell to Neil Dorosin, who was director of high school operations at NYC-DOE to implement the new system. He went on to found the nonprofit Institute for Innovation in Public School Choice, which Atila, Parag and I have supported in spreading this technology to a growing number of American cities. But the first city to which we turned our attention after New York was Boston. Atila and Tayfun Sönmez had written a paper about the school choice system used in Boston, and it opened the door for us all to be invited to study the system from the inside, and eventually to redesign the choice algorithm used to implement Boston’s school choice goals (see Abdulkadiroğlu and Sönmez, 2003; Abdulkadiroğlu et al. 2005a, b and 2009).

In 2000, the first kidney exchange in the United States took place at Rhode Island Hospital, and my class notes on top trading cycles started to seem to have practical potential to organize such exchanges. When Utku Ünver (who had been my Ph.D. student at Pitt) visited Harvard in 2002, I suggested that we teach a class on kidney exchange. We posted our notes on the web and Tayfun Sönmez, who was Utku’s colleague at Koç, read them and suggested that we collaborate. The time zone difference between Istanbul and Boston made it seem as if we were working around the clock. When we finished, we had designed an algorithm both for kidney exchange among patient-donor pairs and for integrating these exchanges with non-directed donors, such as deceased donors (and a growing number of living donors) who aren’t part of a pair with a particular intended recipient. We sent a draft of our first paper (Roth, Sönmez and Ünver, 2004) to many surgeons, but initially only one, Frank Delmonico responded. We helped Frank form the New England Program for Kidney Exchange. My colleagues and I (especially, more recently, Itai Ashlagi) have since collaborated with a number other kidney exchange networks, including the Alliance for Paired Donation, founded by an innovative surgeon, Mike Rees, whom we helped to develop new ways of arranging exchanges, including long non-simultaneous chains (Roth et al., 2006 and Rees et al, 2009). While kidney exchange has now helped thousands of patients with incompatible donors receive transplants of life saving compatible kidneys, many other avenues will still need to be explored, as the number of people waiting for kidney transplants has continued to grow. With my student Judd Kessler I have started to explore ways of increasing deceased donation (Kessler and Roth, 2012 and 13a,b)

In thinking about these issues, I have become eager to understand the quite widespread reluctance to consider monetary markets for kidneys, which are illegal almost everywhere in the world. Economists need to better understand popular attitudes towards all sorts of economic transactions. My 2007 paper “Repugnance as a Constraint on Markets” was my first effort in this direction, but it remains a subject well worth exploring, and of importance well beyond medical issues.

At Harvard I was involved in other market design efforts, both of clearinghouses (for Teach for America, and for field assignments for HBS MBA students with Clayton Featherstone, and for freshman and junior seminars for undergraduates with Steve Leider), and for decentralized markets (such as the signaling mechanism for the market for new Ph.D. economists; see Coles et al., 2010). Decentralized marketplaces face design issues just as much as centralized ones do, and I anticipate that this will be a next frontier in market design.

In the summer of 2012 Emilie and I sold our house and moved to California, where I took up a position at Stanford, after 14 years at Harvard. It was a difficult decision, but we were empty-nesters, and we felt ready for a new adventure. I was 60, and to retire gracefully from Harvard my age plus years of service needed to add up to 75, and so it was that when the call came from the Economics Prize Committee it found us in California, at 3:30 in the morning. I was on leave from Harvard but already teaching at Stanford, as a visitor until I turned 61 in December and became the Gund Professor of Economics and Business Administration Emeritus at Harvard and the McCaw Professor of Economics at Stanford. One of the big attractions of coming to Stanford is that I get to be the colleague of several of my students, including Muriel Niederle, Mike Ostrovsky and Fuhito Kojima, whose dissertations I advised at Harvard, and of my advisor Bob Wilson, who remains active as an emeritus professor. Having students, and being one, form some of the important relationships in an academic life.

The call from the Economics Committee was followed by several hectic months, with some difficult decisions involving the allocation of scarce, indivisible resources, since laureates are only allowed to invite fourteen guests to the main festivities. (Allocation of scarce goods is hard; someone should study it …). In addition to family, our guests included both economists and non-economists who had played important roles in some of the markets I had helped design. Assembling the group made it even clearer that market design is an outwardfacing part of economics.

Now, still in the near-aftermath of all that excitement, I am working on getting back to work. The Nobel is a very famous prize indeed, widely covered in the press, and the nicest part of the avalanche of emails and other contacts that result from that has been that many old friends have made contact, some of whom I hadn’t been in touch with since childhood. It turns out that a Nobel is also followed by other recognitions, and perhaps the most unexpected of these is that the Japan Karate Association in Tokyo has now made me an honorary 7th-degree black belt, something that, given my athletic abilities, is even more unimaginable than being an Economic Sciences Laureate.

References

1. Abdulkadiroğlu, Atila, Parag A. Pathak, and Alvin E. Roth, “The New York City High School Match,” American Economic Review, Papers and Proceedings, 95,2, May 2005, 364–367.
2. Abdulkadiroğlu, Atila, Parag A. Pathak, and Alvin E. Roth, “Strategy-proofness versus Efficiency in Matching with Indifferences: Redesigning the NYC High School Match,” American Economic Review, 99, 5 (December), 2009, 1954–1978.
3. Abdulkadiroğlu, Atila, Parag A. Pathak, Alvin E. Roth, and Tayfun Sönmez, “The Boston Public School Match,” American Economic Review, Papers and Proceedings, 95,2, May 2005, 368–371.
4. Abdulkadiroğlu, Atila, and Tayfun Sönmez, “School Choice: A Mechanism Design Approach,” American Economic Review, 93,3, June 2003, 729–747.
5. Ariely, Dan, Axel Ockenfels, and Alvin E. Roth, “An Experimental Analysis of Ending Rules in Internet Auctions,” Rand Journal of Economics, 36, 4, Winter 2005, 891–908.
6. Avery, Christopher, Christine Jolls, Richard A. Posner, and Alvin E. Roth, “The Market for Federal Judicial Law Clerks,” University of Chicago Law Review, 68, 3, Summer 2001, 793–902.
7. Avery, Christopher, Christine Jolls, Richard A. Posner, and Alvin E. Roth, “The New Market for Federal Judicial Law Clerks,” University of Chicago Law Review, 74, Spring 2007, 447–486.
8. Bergstrom, T. and R. Manning (1983), “Can Courtship be Cheatproof?” unpublished, http://escholarship.org/uc/item/5dg0f759
9. Coles, Peter, John H. Cawley, Phillip B. Levine, Muriel Niederle, Alvin E. Roth, and John J. Siegfried, “The Job Market for New Economists: A Market Design Perspective,” Journal of Economic Perspectives, 24,4, Fall 2010, 187–206.
10. Dubins, L.E. and D.A. Freedman (1981), “Machiavelli and the Gale-Shapley algorithm, “American Mathematical Monthly, 88, 485–94.
11. Erev, Ido and A.E. Roth, “Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria,” American Economic Review, 88,4, September 1998, 848–881.
12. Gale, D. and L. S. Shapley (1962), “College admissions and the stability of marriage,” American Mathematical Monthly, 69: 9–15.
13. Kagel, J.H. and A.E. Roth (editors), Handbook of Experimental Economics, Princeton University Press, 1995.
14. Kagel, John H. and A.E. Roth, “The dynamics of reorganization in matching markets: A laboratory experiment motivated by a natural experiment,” Quarterly Journal of Economics, February, 2000, 201–235.
15. Kessler, Judd B. and Alvin E. Roth, “Organ Allocation Policy and the Decision to Donate,” American Economic Review, Vol. 102 No. 5 (August 2012), 2018–47.
16. Kessler, Judd B. and Alvin E. Roth,”Don’t Take ‘No’ For An Answer: An experiment with actual organ donor registration,” working paper, February 2013a.
17. Kessler, Judd B. and Alvin E. Roth, “Organ Donation Loopholes Undermine Warm Glow Giving: An Experiment Motivated by Priority Loopholes in Israel,” working paper, February 2013b.
18. Nash, John F. Jr. (1950), “The Bargaining Problem,” Econometrica 18 (2): 155–162.
19. Niederle, Muriel, Deborah D. Proctor, and Alvin E. Roth, “What will be needed for the new GI fellowship match to succeed?”Gastroenterology, January, 2006, 130, 218–224.
20. Niederle, Muriel, Deborah D. Proctor, and Alvin E. Roth, “The Gastroenterology Fellowship Match – The First Two Years,” Gastroenterology, 135, 2 (August), 344–346, 2008.
21. Niederle, Muriel and Alvin E. Roth, “Relationship Between Wages and Presence of a Match in Medical Fellowships,” JAMA. Journal of the American Medical Association, vol. 290, No. 9, September 3, 2003a, 1153–1154.
22. Niederle, Muriel and Alvin E. Roth, “Unraveling reduces mobility in a labor market: Gastroenterology with and without a centralized match,” Journal of Political Economy, 111, 6, December 2003b, 1342–1352.
23. Niederle, Muriel and Alvin E. Roth, “The Gastroenterology Fellowship Match: How it failed, and why it could succeed once again,” Gastroenterology, 127, 2 August 2004, 658–666.
24. Niederle, Muriel and Alvin E. Roth, “Market Culture: How Rules Governing Exploding Offers Affect Market Performance,” American Economic Journal: Microeconomics, 1, 2, August 2009, 199–219.
25. Ochs, J. and Roth, A.E., “An Experimental Study of Sequential Bargaining,” American Economic Review, Vol. 79, 1989, 355–384.
26. Rees, Michael A., Jonathan E. Kopke, Ronald P. Pelletier, Dorry L. Segev, Matthew E. Rutter, Alfredo J. Fabrega, Jeffrey Rogers, Oleh G. Pankewycz, Janet Hiller, Alvin E. Roth, Tuomas Sandholm, Utku Ünver, and Robert A. Montgomery, “A Non–Simultaneous Extended Altruistic Donor Chain,” New England Journal of Medicine, 360;11, March 12, 2009, 1096–1101.
27. Roth, A.E., Axiomatic Models of Bargaining, Lecture Notes in Economics and Mathematical Systems #170, Springer Verlag, 1979.
28. Roth, A.E., “Incentive Compatibility in a Market with Indivisible Goods,” Economics Letters, Vol. 9, 1982a, 127 132.
29. Roth, A.E., “The Economics of Matching: Stability and Incentives,” Mathematics of Operations Research, Vol. 7, 1982b, 617 628.
30. Roth, A.E., “The Evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory,” Journal of Political Economy, Vol. 92, 1984, 991 1016.
31. Roth, A.E. (editor), The Shapley Value: Essays in Honor of Lloyd S. Shapley, Cambridge University Press, 1988.
32. Roth, A.E., “New Physicians: A Natural Experiment in Market Organization,” Science, 250, 1990, 1524–1528.
33. Roth, A.E., “A Natural Experiment in the Organization of Entry Level Labor Markets: Regional Markets for New Physicians and Surgeons in the U.K.,” American Economic Review, vol. 81, June 1991, 415–440.
34. Roth, Alvin E., “The Economist as Engineer: Game Theory, Experimental Economics and Computation as Tools of Design Economics,” Fisher Schultz lecture, Econometrica, 70, 4, July 2002, 1341–1378.
35. Roth, Alvin E., “Repugnance as a constraint on markets,” Journal of Economic Perspectives, 21 (3), Summer 2007, 37–58.
36. Roth, Alvin E. “What have we learned from market design?” Hahn Lecture, Economic Journal, 118 (March), 2008, 285–310.
37. Roth, A.E. and I. Erev, “Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term,” Games and Economic Behavior, Special Issue: Nobel Symposium, vol. 8, January 1995, 164–212.
38. Roth, A.E. and Malouf, M.K., “Game Theoretic Models and the Role of Information in Bargaining,” Psychological Review, Vol. 86, 1979, 574 594.
39. Roth, A.E. and Murnighan, J.K., “Equilibrium Behavior and Repeated Play of the Prisoners’ Dilemma,” Journal of Mathematical Psychology, Vol. 17, 1978, 189–198.
40. Roth, Alvin E. and Axel Ockenfels, “Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet,” American Economic Review, 92 (4), September 2002, 1093–1103.
41. Roth, A.E. and E. Peranson,”The Redesign of the Matching Market for American Physicians: Some Engineering Aspects of Economic Design,” American Economic Review, 89, 4, September 1999, 748–780.
42. Roth, A.E. and Postlewaite, A. “Weak Versus Strong Domination in a Market with Indivisible Goods,” Journal of Mathematical Economics, Vol. 4, August 1977, 131–137.
43. Roth, Alvin E., Tayfun Sönmez and M. Utku Ünver, “Kidney Exchange,” Quarterly Journal of Economics, 119, 2, May 2004, 457–488.
44. Roth, Alvin E., Tayfun Sönmez, M. Utku Ünver, Francis L. Delmonico, and Susan L. Saidman, “Utilizing List Exchange and Undirected Good Samaritan Donation through ‘Chain’ Paired Kidney Donations,” American Journal of Transplantation, 6, 11, November 2006, 2694–2705.
45. Roth, A.E. and M. Sotomayor, Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis, Econometric Society Monograph Series, Cambridge University Press, 1990.
46. Roth, A.E. and X. Xing, “Jumping the Gun: Imperfections and Institutions Related to the Timing of Market Transactions,” American Economic Review, 84, September 1994, 992–1044.
47. Roth, A.E. and X. Xing, “Turnaround Time and Bottlenecks in Market Clearing: Decentralized Matching in the Market for Clinical Psychologists,” Journal of Political Economy, 105, April 1997, 284–329.
48. Shapley, Lloyd S., “A Value for n-person Games.” In Contributions to the Theory of Games, volume II, by H.W. Kuhn and A.W. Tucker, editors. Annals of Mathematical Studies v. 28, pp. 307–317. Princeton University Press, 1953.
49. Shapley, Lloyd and Scarf, Herbert, 1974. “On cores and indivisibility,” Journal of Mathematical Economics, Elsevier, vol. 1 (1), pages 23–37, March.
50. Slonim, R. and A.E. Roth,”Learning in High Stakes Ultimatum Games: An Experiment in the Slovak Republic,” Econometrica, 66, 3, May 1998, 569–596.

This autobiography/biography was written at the time of the award and later published in the book series Les Prix Nobel/ Nobel Lectures/The Nobel Prizes. The information is sometimes updated with an addendum submitted by the Laureate.