经管之家App
让优质教育人人可得
立即打开
Colin F. Camerer，著名的California Institute of Technology的教授，是行为博弈论的掌门。他撰写的行为博弈论即将由中国人民大学出版社出版
他的主页是：http://www.hss.caltech.edu/~camerer/camerer.html
如果大家看了俺后面贴的他的简介，一定会产生同样的感受：吓死了！
大家在看该贴时，不要忘了浏览后面卡梅瑞教授对行为经济学、实验经济学、行为博弈、神经元经济学、行为政策研究等的介绍。适当的时候俺会写成中文
Behavioral Game Theory: Thinking, Learning, and Teaching
Colin F. Camerer，Teck-Hua Ho，Juin Kuan Chong
The behavioral challenge to economics: Understanding normal people
Aging and Decision Making: A broad comparative study of decision behavior in neurologically healthy elderly and young individuals THE ECONOMETRICS AND BEHAVIORAL ECONOMICS OF ESCALATION OF COMMITMENT: A RE-EXAMINATION OF STAW & HOANG'S NBA DATA
[此贴子已经被作者于2004-7-11 11:58:43编辑过]
Neuroeconomics
Neuroeconomics: How neuroscience can inform economics
这两篇是一个经济学和自然科学紧密结合的崭新学科——神经元经济学的综述和介绍性文章，诺奖得主乔治梅森大学的斯密斯教授曾说，该方向研究将改变经济学的未来，所以他自己也建立了神经元经济学研究小组，把早先的实验经济学进一步推广。
很多国内人认为斯密斯的研究和行为经济学无关，这是多么可笑！
卡梅瑞教授对行为经济学及其相关领域的看法：
1、行为经济学
Behavioral economics Behavioral economics applies psychological principles to economic decisions, in an effort to "reunify" these social sciences. "Literary" economists like Adam Smith, Marshall and Keynes had rich discussions of how people think in behave, but the nuances of these discussions were put aside when the rational choice paradigm emerged. Complete preferences (i.e., utility maximization), equilibrium, perfect competition, and (later) Bayesian updating and rational expectations are undoubtedly useful simplifying assumptions. But most economic models which rest on these foundations can be improved by using psychological regularity to suggest different assumptions which better capture how people actually think and behave, and prove even more useful. For many years economists found assumptions of perfect competition and perfect information to be good approximations; but both idealized cases were eventually replaced by more complicated, and more realistic, models of imperfect competition (e.g., monopolistic competition, and game-theoretic models of corporate behavior), and imperfect information (e.g., signaling). Replacing the useful idealized assumption of perfect rationality with more realistic models, consistent with what is known from psychology, neuroscience, and sociology, is the next natural step in improving economics. To achieve this goal, "behavioral economics" uses evidence from psychological studies of limits on computational ability and willpower, and the influence of emotions like envy, guilt, and moral obligation on economic activity. Behavioral economists study precise mathematical models of how willpower and computational limits and emotions work, and use these models to make predictions about behavior both in the laboratory and in field data, and to suggest governmental policies which make people better off and suggest better ways of organizing exchange and corporate structures ("economic institutions"). George Loewenstein, Mathew Rabin and I recently edited a book of seminal recent readings, Advances in Behavioral Economics (Princeton Press, 2003). Our introductory chapter to the book is a good place to start learning about behavioral economics ("Behavioral economics: Past, present, future"). An article submitted to Journal of Economic Perspectives in May 2004 about how Adam Smith (of "invisible hand" fame) expressed many ideas which reappeared recently in behavioral economics is here.
2、实验经济学
Experimental economics Until relatively recently (the 1970抯), economists thought of economic systems as being like astronomical systems of planets and stars梩hey could only be observed from afar, and not touched or created experimentally. But economic systems can be created in artificial laboratory environments and studied experimentally, as in most older sciences (physics, chemistry, and biology). In an economics experiment, the experimenter specifies 揺ndowments敆what agents start out with? the messages they send and choices they make, and how the messages and choices agents pick determines their economic outcomes. (Usually they are paid substantial sums according to their experimental performance, to be sure they are thinking carefully and acting like agents do in naturally-occurring economic interactions.) The experimenter does not determine how the experimental participants actually behave, because seeing what people do is the whole point of the experiment. (Usually we have one more competing theories about what is likely to happen; an experiment can tell us which of these various theories, which may all sound intuitive, is just wrong.) A good place to learn how to do experiments is from the books by Friedman and Sunder and Davis and Holt. A good place to learn what we found out from economics experiments through 1995 is the Handbook of Experimental Economics, and since then, from the Handbook of Experimental Economics Results (in press). Some of my early experiments concerned how people weigh the chance of winning (probability) and the amount they can win when they choose among risky gambles. (Economists use gambles over money as simple metaphors for risky economic activity like investing in education or assets, starting up a business, buying a house, and so on.) In the 1970抯 and 1980抯 many theories were proposed about how people weight probabilities and value outcomes differently than is proposed in 揺xpected utility theory? My experiments and analyses found that of the new theories that were proposed, prospect theory seems best able to explain patterns in experimental data. Those papers are:
"An Experimental Test of Several Generalized Utility Theories," Journal of Risk and Uncertainty, 2, 1988, 61-104. (Reprinted in J. Hey and G. Loomes (Eds.), Recent Developments in Experimental Economics, Edward Elgar Publishing, Ltd.) "The Predictive Utility of Generalized Expected Utility Theories," with David Harless, Econometrica, 62, 1994, 1251-1290. (Reprinted in J.D. Hey (Ed.), The Economics of Uncertainty, Edward Elgar Publishing Ltd., 2000.) "Violations of the Betweenness Axiom and Nonlinearity in Probabilities," with Teck Ho, Journal of Risk and Uncertainty, 8, 1994, 167-196. People often note that most data evaluating theories of risky choice have been collected by offering subjects simple choices between simple monetary gambles in the lab. But, in fact, many of these models can also be used to understand labor supply, asset pricing, consumer choice, and gambling in field settings which matter for everyday life. Using prospect theory elements to explain interesting patterns in field data is discussed in my paper "Prospect theory in the wild: Evidence from the field," (pp 288-300) in D. Kahneman and A. Tversky (Eds.), Choices, Values, and Frames, 2001. Cambridge: Cambridge University Press. An exciting new development in experimentation is studying unusual, important special populations. A clever undergraduate, Stephanie Kovalchik, with a little coaching from me, John Allman, Dave Grether, and Charlie Plott, studied an amazing sample of 80-year olds and compared them to 20-year old students on a variety of judgment, bargaining, and game theory tasks. The older and younger folks are remarkably similar, except on how much they know about the world and how good their self-knowledge is (i.e., whether they know how much they know): The 80-year olds know more, and know when they don't know and when they do. (Is that a definition of wisdom?) Our paper is here; it's forthcoming (2003-4) in the Journal of Economic Behavior and Organization. Keith Weigelt and I did some early studies on experimental asset markets. In these experiments participants get a valuable asset, which will pay a cash dividend if they hold it at the end of a trading period. We study the prices at which people buy and sell the asset. In 1991 we published the first study on 搃nformation cascades?(which we called 搈irages?梟amely, is it possible for nobody in a market to have 搃nside information?about what an asset is worth, but for some traders to think that price movements mean other people have inside information, which creates a self-fulfilling kind of cascade or 揾erd behavior? The answer is Yes, cascades do occur. But they only occur early in the experiments when traders are inexperienced. After participants trade for a while, they learn to figure out whether other traders have inside information by whether the market is lively or quiet (if it抯 quiet, nobody wants to trade because nobody has inside information) and the cascades stop. That paper is: "Information Mirages in Experimental Asset Markets", with K. Weigelt, Journal of Business, 64, October 1991, 463-493. Keith and I also studied 損rice bubbles? If an asset lives a long time, like a share of stock or a house, prices can go up simply because people think they can sell at a higher price in the future. Financial economists have known about the theoretical possibility of such bubbles for decades, and there are many famous examples like the Dutch tulip bulb bubble in the 1600抯. But it is hard to establish when prices are really in a bubble because we never know the true value of a naturally-occurring asset. In the experiments we create the value of the asset and so we know what it should be worth (and we know that subjects know its 揻undamental value?too). In one experiment we observed many bubbles?in some cases the asset traded for five times its intrinsic value. That paper is "Convergence in Experimental Double Auctions for Stochastically Lived Assets," with K. Weigelt, in D. Friedman & J. Rust (Eds.), The Double Auction Market: Theories, Institutions and Experimental Evaluations, Redwood City, CA: Addison-Wesley, 1993, 355-396. An early survey of ideas about bubbles and fads (well before the 搕ech stock?and Japanese stock bubbles!) is: "Bubbles and Fads in Asset Markets: A Review of Theory and Evidence," Journal of Economic Surveys, 3, 1989, 3-38. (Reprinted in Italian in G. Vaciago and G. Verga (Eds.), La Teoria dei Mercati Finanziari, Italy, Societa Editrice II Mulino.) One of my current research projects, with Roberto Weber is about organizational culture. A culture is a set of values, rules for behaving (搃nstitutions?, and symbols and language. In our experiments subjects get a set of pictures which they must describe to each other, under time pressure, by creating a slang or code. Their code is an element of culture which we can create artificially, and quickly, in the lab and study. We wrote one paper (''Cultural conflict and merger failure: An experimental approach'') on this project and are doing more in 2003. Charlie Hornberger and John Lin developed nice "CultureX" software for studying code development using the kind of picture-naming Rob Weber and I used to study "corporate culture", which we are happy to share. Documentation is here. If you use it please give us feedback. Besides Caltech, there are many universities with labs in experimental economics. This brief list omits many important centers (please email me to correct omissions), but among the most active labs are: Amsterdam; Arizona; Harvard; University College London; Nottingham; Ohio State; Oxford; Pittsburgh;%20Technion; Texas; Texas A&M; Trento; UCLA; Wisconsin; and Zurich.3、神经元经济学
Neuroeconomics What is neuroeconomics? Neuroeconomics is the use of data on brain processes to suggest new underpinnings for economic theories, which explain how much people save, why there are strikes, why the stock market fluctuates, the nature of consumer confidence and its effect on the economy, and so forth. Until recently, economists have always been content to treat the human brain as a "black box" and suggest mathematical equations which simplify what the brain is doing. Most empirical studies of economic behavior have therefore relied on measuring inputs, like prices, and predicting outputs, like how much people will buy, from a simplified theory of brain processes. This approach reflects a bias traceable to the 1880抯, when Jevons wrote 揑 hesitate it is impossible to measure the feelings of the human heart? This 搑ational choice?approach has been enormously successful. But now advances in genetics and brain imaging (and other techniques) have made it possible to observe detailed processes in the brain better than ever before. Brain scanning (ongoing at the new Broad Imaging Center at Caltech) shows which parts of the brain are active when people make economic decisions. This means that we will eventually be able to replace the simple mathematical ideas that have been used in economics with more neurally-detailed descriptions. For example, when economists think about gambling they assume that people combine the chance of winning (probability) with an expectation of how they will value winning and losing (搖tilities?. If this theory is correct, neuroeconomics will find two processes in the brain梠ne for guessing how likely one is to win and lose, and another for evaluating the hedonic pleasure and pain of winning and losing梐nd another brain region which combines probability and hedonic sensations. More likely, neuroeconomics will show that the desire or aversion to gamble is more complicated than that simple model. Research already shows that pathological gamblers tend to lack a certain gene which limits how much pleasure (in the form of the amount of 揹opamine?neurotransmitter that is released when they win) they get from winning. Not getting enough dopamine from everyday pleasures means gamblers need bigger and bigger 揻ixes?to feel stimulated. In our lab at Caltech, we are also investigating the 揻ear of the unknown?or 搕olerance for ambiguity敆how willing are people to gamble, invest, or take a social risk (like going to a party where they don抰 know anybody)? Our hunch is that fear of the unknown is triggered by activity in the 揳mygdala? an almond-shaped region (common to most mammals) which is active in registering very rapid sensations of fear, and in both learning and unlearning what to be afraid of. Understanding the neural basis of investing in the face of unknown odds is important for understanding economic phenomena like entrepreneurship, since entrepreneurs start businesses knowing little about their odds梩hey are economically fearless in a way that most people are not. Another example is discounting future rewards. The standard theory, which was invented in the 1950s, is that people apply a single declining 揹iscount factor?to future rewards when weighing present rewards against future ones. New theories suggest that there are *two* components to 搕ime discounting? not one: One component is the steady discounting of future rewards, and the second is a preference for immediate rewards. The second factor can explain why people struggle with temptations and procrastinate. Using fMRI imaging of brain activity when people choose between immediate and future rewards, we will be able to see whether there are two components of time discounting (as the new theories predict) or just one (as the old theories predict). If we find two components, we will know more about the nature of the preference for immediate reward. It may be an emotional desire, or even a physical instinct like grasping for food that is within your reach. The research could yield dramatic insights in helping people resist temptation, saving more and spending less. A third area we are actively exploring is trust. In a typical 搕rust game?one person has some money, say $10, which they can invest part of, keeping the rest. The amount they invest is tripled (representing the return to a productive investment, like investing in a factory in a rapidly-growing foreign country). But that tripled amount rests in the hands of a second person, or 搕rustee? who is free to repay as much as she likes and keep the rest. To make the game challenging and scientifically interesting, if the second trustee keeps all the money there is nothing the first person can do about it. (Economists call this 搘eak enforcement of property rights?which is often characteristic of less-developed economies with weaker legal systems.) This game enables us to measure loose concepts like trust and trustworthiness in a crisp numerical way. The amount the first person invests is a measure of how much she expects the trustee to repay her. The amount the trustee repays is a measure of trustworthiness, moral obligation, or reciprocity. Trust is important in the economy because even large, complex economies rely largely on trust every day. When you deposit a large sum in a bank ATM, or donate money to a charity assuming it will be spent on good causes, or invest in an overseas business partner you barely know, you are trusting that your money will be safe. Studies show that simple measures of trust, like asking people 揑n general, do you trust people??are highly correlated with how wealthy a society is. (The poorest countries, like many in Asia and Africa, have very low levels of trust, which reflects and tolerates corruption, prevents investment, and causes the most productive workers to emigrate.) So understanding trust in the brain may enable us to understand economic behavior from synapse to society. In early fMRI brain scanning, with collaborators at Baylor Medical Center, we have studied what goes on in peoples?brains when they trust and decide how much to repay. We found a surprising effect of gender. When men decide how much to trust or repay, an area called the 搈edial cingulate sulcus?is active. This is an area used to process potential reward, and calculate numbers. The male brains are just 揹oing the math?and turn off after they have made a decision. The female brains are quite different. After women have decided how much to repay, but before they know how their partner reacted to their decision, areas of the brain active in processing potential reward (ventromedial prefrontal cortex and ventral striatum) and in regulating worry and error-detection (caudate nucleus) are active. The women are worrying, and thinking about the reward consequences, after they have decided how much to repay. The difference in brain activity in the two genders is like the kind of behavior you might see after a couple gets home from a potluck dinner and rehashes the event. The man wants to turn on the TV and catch some sports scores (his medial cingulate is turned off). The woman is more likely to rehash the evening抯 events, and worry about whether she said the right thing and whether the hostess was happy with the dish she brought, and whether plans for having lunch later in the week are genuine. Some other economists are working in this new area producing actual images of peoples' brains as they do economic tasks (like picking between gambles or bargaining), including John Dickhaut, George Loewenstein, Kevin McCabe and Vernon Smith , and Paul Zak. I am collaborating with a team at Baylor headed by Read Montague and with Caltech colleague Steve Quartz on studies of this sort. George, Drazen Prelec and I wrote an overview (''Neuroeconomics: How neuroscience can inform economics'') about this emerging area. A New York Times column addresses this subject, and another New York Times article describes this exciting new field in more detail. A brief perspective on neuroscience and game theory from Science can be found here. A short paper on the promise of neuroeconomics (written for academic economists, but with some examples of general interest) is here. In a collaboration with neuroscientists at Baylor, Emory, and Princeton, we have created a 揾yperscan?consortium that enables linked fMRI scanners to create images of more than one brain at the same time. This is a breakthrough because many aspects of social behavior are not easily understood by looking at just one brain. Would you try to understand a bitter argument by only recording what one person said? Could you understand the ebb and flow of a tennis match by looking only at one player抯 shots (never turning your head)? Of course not. Hyperscanning enables us to see both sides of the equation ?or many sides of the equation, if many people are trading with each other in a marketplace. It sheds light on behaviors that are a property of shared social behavior. A disagreement in bargaining, for example, is a shared activity that can be best understood by seeing the joint activity in two brains at the same time. The two brains might both show simultaneous anger; or they might show that one person is calm and surprised that the other person is angry. A nice picture of two brains is shown here. An exciting conference in "neuromarketing", to be held April, 2004 is described here. An article from the Public Library of Science is here.
4、行为博弈论
Behavioral game theory My specialty in the last few years has been "behavioral game theory", a subfield (or "franchise") of behavioral economics which uses experimental evidence to establish how psychological limits on the ability to make calculations and plan ahead, the way in which people react to fairness, and learning from experience, influence behavior in situations described by "game theory". Game theory is a mathematical analysis of any social situation in which one player梩ypically a person, but possibly a firm or nation梩ries to figure out what other players will do, and choose the best strategy given those guesses about others. Most game theory describes the fictional behavior of an ideal, hypercalculating, emotionless player (like Dr. Spock from Star Trek) and, as a result, is not always a good guide to how normal people who don't plan too far ahead will actually behave. My 2003 book Behavioral Game Theory describes hundreds of different experimental studies which show where game theory predicts well and predicts poorly, and suggests some new kinds of theory. Behavioral game theory gives precise predictions about how people who think only a couple of steps ahead, have both guilt and envy toward others, and learn from experience, are likely to behave (Bibliography for my book.) In work with Teck Ho, Kuan Chong and many talented collaborators, we have created some new theories for explaining how people actually think, learn and "teach" other players in games. Most of our work uses simple statistical theories which are rooted in some psychological principle, and sees how well these theories fit and predict many different types of experimental games. (We also see whether the theories have "economic value", in the sense that a player equipped with these theories, and doing their best assuming other players' behavior was predicted by the theories, could earn more money than the average player does. The answer is that they would.) Here are some of our papers:
"Functional EWA: A one-parameter theory of learning in games." Includes 4 more data sets not in May 2002 version. Ho, Teck-Hua; Colin F. Camerer; and Juin-Kuan Chong. Nov. 2002. May 2002 version. Sophisticated EWA learning and strategic teaching in repeated games," with Teck-Hua Ho and Juin Kuan Chong. Journal of Economic Theory, May 2002, 104 (1), 137-188. "Experience-weighted attraction learning in sender-receiver signaling games," with Chris Anderson, Economic Theory, 2000, 16, 689-718. (Reprinted in Advances in Experimental Markets, T. Cason and C. Noussair (Eds.), Berlin: Springer-Verlag, 2001, 209-238.) "Experience-weighted attraction (EWA) learning in normal-form games," with Teck-Hua Ho, Econometrica, 67, July 1999, 827-874.5、场研究
Field studies: Cabs and basketball Most scientists specialize in either theorizing, looking for patterns in naturally-occurring data, or collecting their own data through surveys or experimentation. But theory, naturally-occurring data, and experimental data are complementary梕ach kind of exploration provokes questions which can be answered with the other kinds of data or theory. To this end, I've also done some studies with field data. Field studies are especially important because many of the phenomena behavioral economists have studied are established in the lab (where it is easier to clearly show that people are making a mistake); so it is crucial to explore whether these phenomena occur in field settings too, especially when people are more experienced and the stakes are high. For example, people are often sensitive to a "reference point" and dislike losing relative to the reference point or falling short of it. To test whether this sensitivity exists in the amount of labor some workers supply, we collected data on how many hours New York City cabdrivers choose to drive on different days, when their daily wage fluctuates (on rainy days, for example, everyone wants a cab, and on weekends in the summer the cab business is slow). The standard theory of labor supply assumes that drivers plan ahead and "intertemporally substitute"梩hey drive a lot on high-wage days and save up so they can quit early on low-wage days (they "make hay when the sun shines"). We found the opposite pattern: Inexperienced drivers act as if they set a daily income target and quit when they reach it. While this pattern seems like a good idea, it means they drive a lot on low-wage days (because it takes a lot of hours to reach their target) and quit too early on high-wage days. (They could have earned about 15% more if they switdhed their hours around and drove longer on high-wage days.) Experienced drivers, however, drive about the same number of days on good and bad days so they earn more than inexperienced drivers. The long version is "Labor supply of New York City cab drivers: One day at a time," with L. Babcock, G. Loewenstein, and R. Thaler, Quarterly Journal of Economics, 112, May 1997, 407-441. A shorter version, published in the book Choices, Values and Frames, is here. A 2003 New York Times column mentions this research, and some follow-up studies, in the context of whether a tax cut will boost productivity. Another study explores whether sports bettors have a good sense of what a random lucky streak is. Psychologists showed that basketball players seem to have streak "hot hand" shooting, and are likely to hit one shot after they hit a couple in a row, but they actually do not. The hot hand just seems to exist because most people expect that a random series will reverse itself or "mean revert"; when it doesn't, they are surprised and come to believe that the sequence of data has momentum or "heat". We discovered that the basketball market makes the same mistake in setting the odds that different teams will win. The point spread on teams on losing streaks is too pessimistic (as if the bettors believe in a mythical "cold hand") and the point spread on teams with winning streaks is too optimistic. That paper is "Does the Basketball Market Believe in the 'Hot Hand'?" American Economic Review, 79, December 1989, 1257-1261. Roberto Weber and I became interested in a phenomenon called "escalation of commitment"梔o people who have sunk money in an investment that seems to be going badly sink even more money or do they cut their losses (treating their costs as "sunk")? Many psychologists have shown this pattern in laboratory studies, but there are almost no conclusive field studies. We replicated a creative study by Staw and Hoang looking at how often NBA basketball players who were picked high and low in the draft actually played. We found that players who were drafted high (a large investment because draft picks are valuable) tended to play more minutes, even controlling for actual performance. This means coaches梠r perhaps owners, or fans梩end to "throw good minutes after bad" and stick to players they had high hopes for even when it is clear their investment was a mistake. But the coaches gradually give up on underperforming draft picks after a couple of seasons. That paper is "The econometrics and behavioral economics of escalation to commitment in NBA draft choices," with Roberto Weber, Journal of Economic Behavior and Organization, 1999.
6、行为经济学的政策研究
Policy applications Since economic theories often have very clear implications for "welfare" (making society as a whole better off), economic analysis has been important in many aspects of guiding government policy. While most of my research tries to document basic facts about how people behave, and suggest theories which organize those facts, sometimes the facts and theories suggest policy ideas or brush up against policy debates. In 1999 I coauthored a National Research Council book with a dozen other people, Pathological Gambling (published by the National Academy of Sciences Press) about the impact of the explosion of gambling opportunities in the US on individuals, families, and communities. About 2% of people who gamble lose control over their gambling in ways that harm their personal and workplace relationships, and sometimes leads to white-collar crime. The main "finding" in our NRC book was that so little is known about pathological gambling that it is hard to guess what impact the rapid growth of gambling opportunities will have. (The economic analyses which suggest gambling is good for a community are also usually badly flawed梩hey don't add up all the "externalities" and also take a local view rather than a national or international one.) Internet gambling is particularly ominous because of the difficulties of monitoring teenage gambling, and of finding the proper legal jurisdiction for cyber-casinos which are located "offshore" (many are in the Caribbean, where regulation and legal reporting is casual) but which are accessed by people around the world. There is also a modest, but disturbing rise in pathological gambling among women, particularly in states where "video poker" and other sorts of private, nonsports gambling are increasingly common. (Women tend to gamble less on cards, dice and other games of "skill" compared to slot machines and lotteries.) I have also attended meetings of the National Institute of Drug Abuse (NIDA) and the National Institute of Alcoholism and Alcohol Abuse (NIAAA), branches of the National Institute of Health, on how behavioral economics can inform the "disease" model of addiction, which guides much public policy toward drugs and alcohol. To an economist, it is surprising that drug policy is largely shaped by a consortium of physicians (who think it is important to investigate the biological bases of addiction桰 agree) and other interest groups, like the military (which inherited a lot of drug-interdiction efforts after the end of the Cold War), and the prison guards' lobby. My personal view is that there is too much spending on restricting supply (particularly for highly addictive drugs like heroin and cocaine) and much too little on basic research and rehabilitation. Prisons are expensive and rehab is cheap. While most rehabilitation leads to only modest rates of success at present (most addicts "relapse"), advances in genetics and other basic research are very promising for improving treatments dramatically梥o the science is worth doing a lot more of, and the economic payoff will probably be huge. A constant question policymakers ask is whether the limits on rationality and willpower we discover in behavioral economics justify policies which are "paternalistic"梩hat is, which seek to limit the choices people have on the grounds that people won't always choose what is good for them. As a Chicago-trained economist, I am not eager to see much broad paternalism without doing more research. At the same time, paternalism is part of the fabric of policy in many ongoing ways桽ocial Security is a kind of "forced saving", minors are prohibited (at least in law) from buying cigarettes and alcohol, people with mental illness have restricted choices, many transfers to the poor are earmarked for certain spending categories (housing, food stamps) rather than given as cash, and so forth. In our Penn Law Review paper, Regulation for conservatives: Behavioral economics and the case for "asymmetric paternalism'', Sam Issacharoff, George Loewenstein, Ted O'Donoghue, Mathew Rabin and I argued for a middle ground you might call "conservative paternalism" (we called it "asymmetric paternalism" in our paper). Conservative paternalism is a set of policies which may help a few people who make judgment mistakes a lot, and impose very little harm on people who behave rationally. Examples include informed consent, disclosing information which profit-motivated firms may not disclose voluntarily (such as nutritional content of food or drug efficacy), mandatory waiting periods (which exist for marriage and divorce), and "cooling-off" laws which allow consumers to break contracts for purchase of certain consumer goods. The Boston Federal Reserve Bank devoted its annual conference in 2003 to behavioral economics, see here.
浏览了一下Camerer的简介，确实有点奇怪。
18岁本科毕业，20岁MBA，22岁博士，似乎是百年不遇的神童，岂料从获得教职到晋升正教授却花了整整10年，又沦为经济学家的一般水平；
从81-04的23年间，在JEP、QJE、AER、Etrica、JPE上发文17篇，加上JET、JEBO、GEB、EER这些准一流杂志，共计24篇，年均一篇，还算不错；不过细看之下，在努力为终身教职拼搏的81-91这十年间，竟只发表了4篇，倒是当上正教授后，从91-04的十三年里，发表了20篇。
再参考其著书、获奖及各种头衔、受邀等情况，也多是在获得正教授职位以后。
由此可见，Camerer的学术经历是行为经济学从上世纪90年代开始的发迹史的一个写照。
以下是引用一刹春在2004-7-11 21:15:58的发言： 社会学里好像把Field studies译成田野调查。
那俺明白了，一直没有确切的理解。可管理学中的现场实验和社会学中的这个原理一样吗？俺还不太明白。
就文章数量而言，卡梅瑞比不上史莱佛，但前者研究的东东比后者玄啊，前者就象西蒙似的，除了研究行为经济学，还研究神经科学和心理学，而且在这方面还发表了很多论文，这些不能用AER等比较。他评教授时间长和他研究的东东不太容易被经济学家接受有关吧。和Thaler类似。
现场实验被叫做field experiment为多，区别对应于laboratory experiment，但我想field study应该也可以，因为霍桑实验就一般被称为Hawthorne Studies。
另外提一句，组织行为学的常用研究方法，除了上述两种experiments以外，还有case study和correlational research.
是不是可以这样理解:社会学中的田野调查样本比行为经济学中的现场实验更大.并且实验设计应该有区别,前者更侧重问卷,后者更重视实验.
案例研究在行为经济学中也是用的,比如行为博弈研究中经常见到.后面那个不知道是什么.
我理解，现场实验讲究真实情境，“真实”意味着对实验者而言确实是在进行真实的工作，实验的成份体现在对工作环境和程序的设计；而社会学中的田野调查不能被称做实验，没有设计成份，调查者不是有意介入到情境中，只是作为一个观察者。
相关性研究的情况俺听学管理信息系统的同学说过，就是根据理论研究，假设一系列自变量和因变量，确定其测度标准，然后通过问卷调查（survey）和现有资料获取测量值，然后考察两组变量之间的相关性。
扫码加好友，拉您进群
全部版块
我的主页
收藏
