Takuro Yamashita is assistant professor at TSE. He holds a PhD degree from Stanford University and he is specialized in mechanism design theory. In 2007, Leonid Hurwicz, Eric Maskin, and Roger Myerson wereawarded the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Novel, for their fundamental contributions in mechanism design theory. In this article, I try to introduce mechanism design theory very briefly. I hope some of you get interested in this theory and grab any textbook to get really introduced. Mechanism design theory is about "optimal design of a mechanism". But, what is a mechanism? A famous example is a "market mechanism". If you have taken a microeconomics course, I believe you encountered the notion of "markets". In a market, consumers and producers announce their demand and supply, and the price is determined to equate the total demand and supply. Everyone trades at this price. This is the "rule" in a market, and any participant must follow this rule to make their transactions. Roughly speaking, a mechanism means a rule of transaction.
Perhaps you have also learned that this particular rule of transaction, the market mechanism, sometimes achieves the efficient allocation of goods, but sometimes not. If the market mechanism does not work well, the rule may be modified (for example, by taxes or subsidies). Or perhaps, completely different rules of transaction than the market mechanism may work better, which may involve more complicated procedures such as bargaining or contracting among some groups of individuals, or even voting. There are plenty of alternative mechanisms. Which one is the best mechanism? This is (one of) the fundamental question of mechanism design theory, and to answer the question, we need to describe the problem more formally. Formally, a mechanism specifies two objects. The set of "messages" for each individual, and the "outcome function". Each individual selects one message in the message set, and send it to the mechanism. You can think of the mechanism as a kind of a computer, in which, each individual inputs a message. Once everyone sends his/her message, then the mechanism outputs an "outcome", based on the outcome function. In the market mechanism, each consumer's message is a demand function, each producer's message is a supply function, and given their messages, the market mechanism outputs the outcome, which is the amount of the goods each individual buys or sells, based on the market- clearing price. To evaluate a mechanism, we need to know how individuals would behave in the mechanism (called a "solution concept"), and which outcomes are induced by their behaviors. Usually, each individual is assumed to have his own preference over the outcomes, and chooses a message to achieve the outcome that is more preferable to him. Of course, the best message for him may depend on which messages the other individuals choose. Thus, often, we adopt a Nash equilibrium behavior (or its generalization such as a Bayesian Nash equilibrium) as the solution concept, i.e., they choose the messages so that each individual's choice is the best choice given the others' choices. The mechanism designer's task is, then, to find a mechanism (i.e., message sets plus an outcome function) in which Nash equilibrium message choices induce desirable outcomes. Or, in other words, a mechanism is carefully designed in order to "incentivize" each individual to choose a message that induces desirable outcomes. For example, consider a good produced by a monopolist. Let the monopolist's marginal cost of production be constant, but suppose that only the monopolist knows the actual value of his marginal cost. To attain the highest possible efficiency, we may want the monopolist to produce more if the marginal cost is lower, and less if it is higher, but the monopolist would do so only when such a choice is consistent with his profit-maximization behavior. Thus, a mechanism must be designed so that the monopolist's choice is aligned with the objective of the mechanism designer (e.g. social planner). So far, I explained mechanisms and mechanism design problems in an environment with "consumers and producers", but we can think of many other examples. Here are a few of them: Auction A seller owns an object. Each bidder has private information about how much he can pay (his "willingness to pay") for the object. In an auction mechanism, each bidder sends a message, and depending on their messages, the winner and the price of the object are determined. The objective of the mechanism designer is, for example, (i) surplus maximization, i.e., to make the bidder with the highest willingness to pay the winner, or (ii) profit maximization, i.e., to make the payment by the bidders as high as possible. The surplus maximization scenario may be more relevant if the mechanism designer is a government who regulates certain auctions to achieve efficient trades. The profit maximization scenario may be more relevant if the mechanism designer is the seller himself. Public good provision A group of PhD students wants to buy a new coffee machine (a public good) in their office, which costs a hundred euro. Each student has private information for his/her willingness to pay. In a public-good mechanism, each student sends a message about how much he/she is willing to pay, and depending on their messages, the decision about the purchase of the coffee machine and and the cost allocation among the students are determined. A mechanism needs to be carefully designed to avoid ``free riding’’ as much as possible, because each student may pretend to be less interested than he/she truly is to save the payment. Team working Workers work together in a company, and the company’s profit is determined by effort levels the workers make. The head quarter wants to design a wage scheme to incentivize each worker to make an effort, but efforts themselves are only observable to the workers. What is the optimal wage scheme that maximizes the profit, through incentivizing high effort choices? These problems have different environments (i.e., a set of individuals, their information structure and preferences, and a set of feasible outcomes) and different criteria for desirable outcomes (e.g., efficiency, profit) to each other. However, they have a fundamental problem in common: whether and how can we incentivize the individuals to choose the self-revealing choices that induce desirable outcomes? Identifying incentives as an important issue in economics was one of the main contributions of mechanism design theory, and especially, of the work of the above mentioned three prize winners. Even though there has been a huge accumulation in the literature, there are still many open questions. Let me describe some of these questions as "puzzles". As explained above, once you specify a mechanism design problem, which consists of an environment, an objective of the mechanism designer (i.e., which outcomes are desirable), and a solution concept (i.e., how individuals would behave in each mechanism), then you can start searching for an optimal mechanism. However, sometimes, the optimal mechanism you find may look "weird": It may look very different from what is typically used in reality, and/or it may look too complicated to understand, etc. The mechanism design literature has been quite successful for some class of problems, but there seem to be cases in which the optimal mechanisms found look "weird". In such a case, one may think that there is something wrong in the way the mechanism design problem is set up. In particular, some recent papers study optimal mechanisms in different solution concepts than the "standard" concepts in the literature (such as the Nash or Bayesian Nash approaches). These attempts have been (and will be) stimulated by recent developments in other fields of economics such as behavioral and experimental economics, and by other disciplines of science such as psychology and computer science, through deeper understanding of human behaviors in decision making. For those who are interested in more detailed explanation of the contribution by the three prize winners, see "The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2007", Nobelprize.org, http://www. nobelprize.org/nobel_prizes/economics/laureates/2007/ press.html. It also has links to some related articles and textbooks.
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Ingela Alger is a TSE researcher and the research director of CNRS. She holds a PhD degree in economics from Université des Sciences Sociales. Her research interests include evolution of preferences, contract theory and industrial organization. When I left Toulouse as a freshly minted Ph.D., I would have been surprised to learn that fifteen years later part of my job would consist in building bridges between economists and biologists. At the time I had no intention whatsoever to make this connection, and if somebody had mentioned it, I would probably have asked myself: What could economists and biologists possibly have in common?
At first glance, the answer is, “Nothing”: for while biologists study cells, plants, animals, and the human body, economists analyze markets, firms, and other institutions created by humans. However, while these two disciplines clearly have distinct objects of study, they share an important goal, namely, to discover laws governing the functioning and behavior of the objects of study. Now, physicists and chemists also seek to uncover such laws; a key difference, however, is that their objects of study are not living beings, while those of biologists and economists are. At second glance, then, the answer is, “Potentially a lot”. Opening a biology journal or textbook would likely bring surprise to many an economist, who would quickly notice that “costs,” “benefits,” and “scarce resources” are mentioned quite frequently. Nonetheless, the currency is not the same: whereas economists use some numeraire good to express costs and benefits, biologists use reproductive success as currency. For biologists, living beings all share one goal, which is to reproduce, and, hence, an individual’s success is measured by his or her reproductive success, or fitness. Many people would certainly find it shocking to reduce the purpose of life to reproductive success, and we economists take pride in our ability to provide models where general results do not necessitate strong assumptions about individual preferences. However, there are strong and compelling arguments in favor of the biologists’ view. By definition, any living individual descends from a long string of ancestors who were all successful at reproducing. By the same token, at any point in time, there are billions of individuals who could have existed, but who failed to do so. Clearly, the ancestors of the former set of individuals must, on average, have followed strategies that led to higher reproductive success than did the ancestors of the latter set of individuals. Hence, by way of “Darwinian revealed preference,” those who are alive today may be expected to be equipped with traits that have been selected for by evolutionary pressure to maximize, or at least favor, reproductive success. Depending on the species and the habitat at hand, surviving and reproducing requires a more or less complex set of traits. These traits come in many forms, including body design, sensory abilities, and behavioral responses. Just like economists, biologists collect data and use mathematical models to produce theoretical predictions. This, then, points to one natural point of connection between economics and biology: using evolutionary logic, what kind of behavioral responses, and preferences triggering these responses, may be expected from first principles, provided that reproductive success is the driving force? In the 1970’s theoretical biologists developed evolutionary game theory, the tool of choice to study this question. Starting about twenty years ago, economists have relied on and further developed evolutionary game theory to model preference evolution. This literature has produced evolutionary foundations for expected and non-expected utility, prospect theory, intertemporal preferences, a host of other-regarding preferences – such as altruism, inequity aversion, spite, and status-seeking – as well as moral values. Importantly, this literature holds the potential for establishing a link between, on the one hand, the preferences that may be expected to arise in a population, and on the other hand, the environment in which the population evolves. Which in turn leads me to how I got into conducting research on preference evolution... Ever since I was a child, I have had the opportunity to observe different cultures from within over long stretches of time. Differences appeared to run deep. At some point I became particularly struck by differences in the amount of helping within families. Broadly speaking, helping within families is less common in developed countries than in developing ones. I soon discovered that biologists had looked into the issue of helping behaviors within the family for decades. Theoretical work published by British biologist William Hamilton in 1964 had shown that, ceteris paribus, the amount of help between relatives should be determined by the degree of relatedness (e.g., helping should be more commonly observed between siblings than between cousins). My work with Jörgen Weibull on the evolution of altruistic preferences builds on and refines Hamilton’s insights. It predicts that the degree of intrafamily altruism selected for by evolutionary forces will typically depend on factors in the environment, where the environment is the set of factors that jointly determine how reproductive success is achieved. In particular, we find that harsher environments may lead to weaker family ties. Hence, while an obvious explanation for the pattern of family ties is that in developed countries formal insurance mechanisms have rendered informal insurance within the family obsolete, this research suggests another hypothesis, namely, that family ties grew weak several centuries ago in regions that are now well developed. Could it be, then, that the formal insurance mechanisms in developed countries are not only the “chicken,” but also to some extent the “egg”? If so, how would such an insight inform our view of economic development? Preference evolution is but one research topic at the border between economics and biology. There are several other natural potential connections. For instance, it is clear that two powerful forces stand out as being ubiquitous not only among humans, but also in a host of other species: competition and cooperation. The cells that make up our bodies all contain the same genetic material, and yet, while some of them produce a liver, others produce a brain: they cooperate in a rather grandiose manner. Anthills are built and maintained by way of teamwork by myriad ants. Among humans cooperation occurs in many different settings, including families, groups of friends, clans, tribes, firms, political parties, government, etc. One can even argue that there is cooperation as long as individuals refrain from killing each other. Competition occurs both between and within species. Often, individuals, or groups of individuals (like firms), that share the same habitat (or the same market) will vie for the same resources. A special but important kind of competition arises in species with sexual reproduction, where individuals of the same sex compete for mates. While cooperation and competition are common among all living beings, economists and biologists may have approached the subject from different angles, and perhaps there are “gains from trade” to be made between the disciplines. Last but not least, building models that recognize that humans are but one species in the dynamic ecosystem that is Earth would likely be useful not only for humans, but also for other species, some of which we depend on to survive. |
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