{"title":"How Corruptible are You? Bribery Under Uncertainty","authors":"Dmitry Ryvkin, Danila Serra","doi":"10.2139/ssrn.1824464","DOIUrl":"https://doi.org/10.2139/ssrn.1824464","url":null,"abstract":"We model corruption in a society as a result of bargaining for bribes between private citizens and public officials. We investigate the role that incomplete information with respect to the intrinsic moral cost of one's potential corruption partner plays out in his or her propensity to engage in bribery, and, consequently, the equilibrium level of corruption in the society. We assume that the cost of engaging in corruption is subject to strategic complementarities, which may lead to multiple corruption equilibria. We find that corruption is lowest when potential bribers and potential bribees are uncertain regarding each other's \"corruptibility\" and have asymmetric bargaining powers. Our uncertainty result provides theoretical support in favor of anti-corruption strategies, such as staff rotation in public offices, aimed at decreasing the social closeness of bribers and bribees. Our bargaining power result suggests that, under uncertainty, monopolistic public good provision has the same corruption-reducing effect as competitive public good provision.","PeriodicalId":359713,"journal":{"name":"ERPN: Other Theory/Field Building (Sub-Topic)","volume":"2020 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120961301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Comparison of Different Bayesian Design Criteria to Compute Efficient Conjoint Choice Experiments","authors":"Jie Yu, P. Goos, M. Vandebroek","doi":"10.2139/ssrn.1288549","DOIUrl":"https://doi.org/10.2139/ssrn.1288549","url":null,"abstract":"Bayesian design theory applied to nonlinear models is a promising route to cope with the problem of design dependence on the unknown parameters. The traditional Bayesian design criterion which is often used in the literature is derived from the second derivatives of the loglikelihood function. However, other design criteria are possible. Examples are design criteria based on the second derivative of the log posterior density, the expected posterior covariance matrix, or on the amount of information provided by the experiment. Not much is known in general about how well these criteria perform in constructing efficient designs and which criterion yields robust designs that are efficient for various parameter values. In this study, we apply these Bayesian design criteria to conjoint choice experimental designs and investigate how robust the resulting Bayesian optimal designs are with respect to other design criteria for which they were not optimized. We also examine the sensitivity of each design criterion to the prior distribution. Finally, we try to find out which design criterion is most appealing in a non-Bayesian framework where it is accepted that prior information must be used for design but should not be used in the analysis, and which one is most appealing in a Bayesian framework when the prior distribution is taken into account both for design and for analysis.","PeriodicalId":359713,"journal":{"name":"ERPN: Other Theory/Field Building (Sub-Topic)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129916163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}