{"title":"机制设计与契约中的鲁棒性","authors":"Gabriel D. Carroll","doi":"10.1146/ANNUREV-ECONOMICS-080218-025616","DOIUrl":null,"url":null,"abstract":"This review summarizes a nascent body of theoretical research on design of incentives when the environment is not fully known to the designer and offers some general lessons from the work so far. These recent models based on uncertainty and robustness offer an additional set of tools in the toolkit, complementary to more traditional, fully Bayesian modeling approaches, and broaden the range of problems that can be studied. The kinds of insights that such models can offer, and the methodological and technical challenges that they confront, broadly parallel those of traditional approaches.","PeriodicalId":47891,"journal":{"name":"Annual Review of Economics","volume":null,"pages":null},"PeriodicalIF":6.8000,"publicationDate":"2019-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-ECONOMICS-080218-025616","citationCount":"56","resultStr":"{\"title\":\"Robustness in Mechanism Design and Contracting\",\"authors\":\"Gabriel D. Carroll\",\"doi\":\"10.1146/ANNUREV-ECONOMICS-080218-025616\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This review summarizes a nascent body of theoretical research on design of incentives when the environment is not fully known to the designer and offers some general lessons from the work so far. These recent models based on uncertainty and robustness offer an additional set of tools in the toolkit, complementary to more traditional, fully Bayesian modeling approaches, and broaden the range of problems that can be studied. The kinds of insights that such models can offer, and the methodological and technical challenges that they confront, broadly parallel those of traditional approaches.\",\"PeriodicalId\":47891,\"journal\":{\"name\":\"Annual Review of Economics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2019-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1146/ANNUREV-ECONOMICS-080218-025616\",\"citationCount\":\"56\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual Review of Economics\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1146/ANNUREV-ECONOMICS-080218-025616\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Economics","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1146/ANNUREV-ECONOMICS-080218-025616","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
This review summarizes a nascent body of theoretical research on design of incentives when the environment is not fully known to the designer and offers some general lessons from the work so far. These recent models based on uncertainty and robustness offer an additional set of tools in the toolkit, complementary to more traditional, fully Bayesian modeling approaches, and broaden the range of problems that can be studied. The kinds of insights that such models can offer, and the methodological and technical challenges that they confront, broadly parallel those of traditional approaches.
期刊介绍:
The Annual Review of Economics covers significant developments in the field of economics, including macroeconomics and money; microeconomics, including economic psychology; international economics; public finance; health economics; education; economic growth and technological change; economic development; social economics, including culture, institutions, social interaction, and networks; game theory, political economy, and social choice; and more.