Fairness and equity considerations in the allocation of social goods and the development of algorithmic systems pose new challenges for decision-makers and interesting questions for the EC community. We overview a list of papers that point towards emerging directions in this research area.
{"title":"Fairness and equity in resource allocation and decision-making","authors":"F. Monachou, Ana-Andreea Stoica","doi":"10.1145/3572885.3572891","DOIUrl":"https://doi.org/10.1145/3572885.3572891","url":null,"abstract":"Fairness and equity considerations in the allocation of social goods and the development of algorithmic systems pose new challenges for decision-makers and interesting questions for the EC community. We overview a list of papers that point towards emerging directions in this research area.","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"20 1","pages":"64 - 66"},"PeriodicalIF":1.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43550153","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}
Please send solutions to the author by e-mail, with the title of this puzzle in the subject header. The best solution will be published in the next issue of SIGecom Exchanges, provided that this solution is of sufficiently high quality. Quality is judged by the author, taking into account at least soundness, completeness, and clarity of exposition. (Incidentally, there is another birthday puzzle for which we still need a solution [1]!)
{"title":"Puzzle","authors":"Vincent Conitzer","doi":"10.1145/3572885.3572894","DOIUrl":"https://doi.org/10.1145/3572885.3572894","url":null,"abstract":"Please send solutions to the author by e-mail, with the title of this puzzle in the subject header. The best solution will be published in the next issue of SIGecom Exchanges, provided that this solution is of sufficiently high quality. Quality is judged by the author, taking into account at least soundness, completeness, and clarity of exposition. (Incidentally, there is another birthday puzzle for which we still need a solution [1]!)","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"20 1","pages":"73 - 73"},"PeriodicalIF":1.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48354016","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}
This is an annotated reading list on papers in the intersection of economics and computation and behavioral economics.
这是一份关于经济学、计算和行为经济学交叉领域论文的注释阅读列表。
{"title":"Economics and computation meets cognitive biases","authors":"Sigal Oren","doi":"10.1145/3572885.3572892","DOIUrl":"https://doi.org/10.1145/3572885.3572892","url":null,"abstract":"This is an annotated reading list on papers in the intersection of economics and computation and behavioral economics.","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"12 1","pages":"67 - 69"},"PeriodicalIF":1.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41298090","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}
Hoda Heidari, Solon Barocas, J. Kleinberg, K. Levy
Many policies allocate harms or benefits that are uncertain in nature: they produce distributions over the population in which individuals have different probabilities of incurring harm or benefit. Comparing different policies thus involves a comparison of their corresponding probability distributions, and we observe that in many instances the policies selected in practice are hard to explain by preferences based only on the expected value of the total harm or benefit they produce. In cases where the expected value analysis is not a sufficient explanatory framework, what would be a reasonable model for societal preferences over these distributions? Here we investigate explanations based on the framework of probability weighting from the behavioral sciences, which over several decades has identified systematic biases in how people perceive probabilities. We show that probability weighting can be used to make predictions about preferences over probabilistic distributions of harm and benefit that function quite differently from expected-value analysis, and in a number of cases provide potential explanations for policy preferences that appear hard to motivate by other means. In particular, we identify optimal policies for minimizing perceived total harm and maximizing perceived total benefit that take the distorting effects of probability weighting into account, and we discuss a number of real-world policies that resemble such allocational strategies. Our analysis does not provide specific recommendations for policy choices, but is instead interpretive in nature, seeking to describe observed phenomena in policy choices.
{"title":"On modeling human perceptions of allocation policies with uncertain outcomes","authors":"Hoda Heidari, Solon Barocas, J. Kleinberg, K. Levy","doi":"10.1145/3572885.3572889","DOIUrl":"https://doi.org/10.1145/3572885.3572889","url":null,"abstract":"Many policies allocate harms or benefits that are uncertain in nature: they produce distributions over the population in which individuals have different probabilities of incurring harm or benefit. Comparing different policies thus involves a comparison of their corresponding probability distributions, and we observe that in many instances the policies selected in practice are hard to explain by preferences based only on the expected value of the total harm or benefit they produce. In cases where the expected value analysis is not a sufficient explanatory framework, what would be a reasonable model for societal preferences over these distributions? Here we investigate explanations based on the framework of probability weighting from the behavioral sciences, which over several decades has identified systematic biases in how people perceive probabilities. We show that probability weighting can be used to make predictions about preferences over probabilistic distributions of harm and benefit that function quite differently from expected-value analysis, and in a number of cases provide potential explanations for policy preferences that appear hard to motivate by other means. In particular, we identify optimal policies for minimizing perceived total harm and maximizing perceived total benefit that take the distorting effects of probability weighting into account, and we discuss a number of real-world policies that resemble such allocational strategies. Our analysis does not provide specific recommendations for policy choices, but is instead interpretive in nature, seeking to describe observed phenomena in policy choices.","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"20 1","pages":"47 - 54"},"PeriodicalIF":1.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44973386","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}
Emily Diana is a rising fifth year Ph.D. student in Statistics and Data Science at the Wharton School, University of Pennsylvania, where she is advised by Michael Kearns and Aaron Roth. Her research focuses on the intersection of ethical algorithm design and socially aware machine learning, and she is honored to have been recognized as both a Rising Star in EECS by MIT and a Future Leader in Data Science by the University of Michigan. Before Penn, she received a B.A. in Applied Mathematics from Yale and an M.S. in Statistics from Stanford, and she spent two years as a software developer at Lawrence Livermore National Laboratory. Mingzi Niu is a rising fifth year Ph.D. student in Economics at Rice University, where she is advised by Mallesh Pai and Hülya Eraslan. Her research interest are primarily in microeconomic theory, with a focus on mechanism design, information theory and behavioral economics. Before Rice, she received a B.A. in Finance and Banking and a B.S. in Mathematics and Statistics at Peking University, and a M.A. in Economics at Duke University. Georgy Noarov is a rising third year PhD student in Computer and Information Science at the University of Pennsylvania, advised by Michael Kearns and Aaron Roth. Previously, he graduated from Princeton University with a B.A. in Mathematics. His research interests span across the fields of uncertainty quantification, online learning, fairness in machine learning, and algorithmic game theory.
{"title":"SIGecom winter meeting 2022 highlights","authors":"Emily Diana, Mingzi Niu, Georgy Noarov","doi":"10.1145/3572885.3572886","DOIUrl":"https://doi.org/10.1145/3572885.3572886","url":null,"abstract":"Emily Diana is a rising fifth year Ph.D. student in Statistics and Data Science at the Wharton School, University of Pennsylvania, where she is advised by Michael Kearns and Aaron Roth. Her research focuses on the intersection of ethical algorithm design and socially aware machine learning, and she is honored to have been recognized as both a Rising Star in EECS by MIT and a Future Leader in Data Science by the University of Michigan. Before Penn, she received a B.A. in Applied Mathematics from Yale and an M.S. in Statistics from Stanford, and she spent two years as a software developer at Lawrence Livermore National Laboratory. Mingzi Niu is a rising fifth year Ph.D. student in Economics at Rice University, where she is advised by Mallesh Pai and Hülya Eraslan. Her research interest are primarily in microeconomic theory, with a focus on mechanism design, information theory and behavioral economics. Before Rice, she received a B.A. in Finance and Banking and a B.S. in Mathematics and Statistics at Peking University, and a M.A. in Economics at Duke University. Georgy Noarov is a rising third year PhD student in Computer and Information Science at the University of Pennsylvania, advised by Michael Kearns and Aaron Roth. Previously, he graduated from Princeton University with a B.A. in Mathematics. His research interests span across the fields of uncertainty quantification, online learning, fairness in machine learning, and algorithmic game theory.","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"20 1","pages":"3 - 23"},"PeriodicalIF":1.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47715772","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}
Here we provide an overview of an important issue in online field experiments: spillover effects. We include a reading list for researchers in both academia and industry who are interested in this topic.
{"title":"Spillover effects in online field experiments","authors":"Yuan Yuan, T. Liu","doi":"10.1145/3572885.3572893","DOIUrl":"https://doi.org/10.1145/3572885.3572893","url":null,"abstract":"Here we provide an overview of an important issue in online field experiments: spillover effects. We include a reading list for researchers in both academia and industry who are interested in this topic.","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"20 1","pages":"70 - 72"},"PeriodicalIF":1.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42063732","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}
In many data analysis problems, we only have access to biased data due to some systematic bias of the data collection procedure. In this letter, we present a general formulation of systematic bias in data as well as our recent results on how to handle two very fundamental types of systematic bias that arise frequently in econometric studies: truncation bias and self-selection bias.
{"title":"Analyzing data with systematic bias","authors":"M. Zampetakis","doi":"10.1145/3572885.3572890","DOIUrl":"https://doi.org/10.1145/3572885.3572890","url":null,"abstract":"In many data analysis problems, we only have access to biased data due to some systematic bias of the data collection procedure. In this letter, we present a general formulation of systematic bias in data as well as our recent results on how to handle two very fundamental types of systematic bias that arise frequently in econometric studies: truncation bias and self-selection bias.","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"20 1","pages":"55 - 63"},"PeriodicalIF":1.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47097668","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}
The theory of algorithmic fair allocation is at the center of multi-agent systems and economics in recent decades due to its industrial and social importance. At a high level, the problem is to assign a set of items that are either goods or chores to a set of agents so that every agent is happy with what she obtains. In this survey, we focus on indivisible items, for which exact fairness as measured by envy-freeness and proportionality cannot be guaranteed. One main theme in the recent research agenda is designing algorithms that approximately achieve fairness criteria. We aim at presenting a comprehensive survey of recent progress through the prism of algorithms, highlighting the ways to relax fairness notions and common techniques to design algorithms, as well as the most interesting questions for future research.
{"title":"Algorithmic fair allocation of indivisible items","authors":"H. Aziz, Bo Li, H. Moulin, Xiaowei Wu","doi":"10.1145/3572885.3572887","DOIUrl":"https://doi.org/10.1145/3572885.3572887","url":null,"abstract":"The theory of algorithmic fair allocation is at the center of multi-agent systems and economics in recent decades due to its industrial and social importance. At a high level, the problem is to assign a set of items that are either goods or chores to a set of agents so that every agent is happy with what she obtains. In this survey, we focus on indivisible items, for which exact fairness as measured by envy-freeness and proportionality cannot be guaranteed. One main theme in the recent research agenda is designing algorithms that approximately achieve fairness criteria. We aim at presenting a comprehensive survey of recent progress through the prism of algorithms, highlighting the ways to relax fairness notions and common techniques to design algorithms, as well as the most interesting questions for future research.","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"20 1","pages":"24 - 40"},"PeriodicalIF":1.0,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47135653","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}
We study the communication complexity of incentive compatible auction-protocols between a monopolist seller and a single buyer with a combinatorial valuation function over n items [Rubinstein and Zhao 2021]. Motivated by the fact that revenue-optimal auctions are randomized [Thanassoulis 2004; Manelli and Vincent 2010; Briest et al. 2010; Pavlov 2011; Hart and Reny 2015] (as well as by an open problem of Babaioff, Gonczarowski, and Nisan [Babaioff et al. 2017]), we focus on the randomized communication complexity of this problem (in contrast to most prior work on deterministic communication). We design simple, incentive compatible, and revenue-optimal auction-protocols whose expected communication complexity is much (in fact infinitely) more efficient than their deterministic counterparts. We also give nearly matching lower bounds on the expected communication complexity of approximately-revenue-optimal auctions. These results follow from a simple characterization of incentive compatible auction-protocols that allows us to prove lower bounds against randomized auction-protocols. In particular, our lower bounds give the first approximation-resistant, exponential separation between communication complexity of incentivizing vs implementing a Bayesian incentive compatible social choice rule, settling an open question of Fadel and Segal [Fadel and Segal 2009].
{"title":"The randomized communication complexity of revenue maximization","authors":"A. Rubinstein, Junyao Zhao","doi":"10.1145/3505156.3505165","DOIUrl":"https://doi.org/10.1145/3505156.3505165","url":null,"abstract":"We study the communication complexity of incentive compatible auction-protocols between a monopolist seller and a single buyer with a combinatorial valuation function over n items [Rubinstein and Zhao 2021]. Motivated by the fact that revenue-optimal auctions are randomized [Thanassoulis 2004; Manelli and Vincent 2010; Briest et al. 2010; Pavlov 2011; Hart and Reny 2015] (as well as by an open problem of Babaioff, Gonczarowski, and Nisan [Babaioff et al. 2017]), we focus on the randomized communication complexity of this problem (in contrast to most prior work on deterministic communication). We design simple, incentive compatible, and revenue-optimal auction-protocols whose expected communication complexity is much (in fact infinitely) more efficient than their deterministic counterparts. We also give nearly matching lower bounds on the expected communication complexity of approximately-revenue-optimal auctions. These results follow from a simple characterization of incentive compatible auction-protocols that allows us to prove lower bounds against randomized auction-protocols. In particular, our lower bounds give the first approximation-resistant, exponential separation between communication complexity of incentivizing vs implementing a Bayesian incentive compatible social choice rule, settling an open question of Fadel and Segal [Fadel and Segal 2009].","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"19 1","pages":"75 - 83"},"PeriodicalIF":1.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42437273","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}
We provide an overview of our recent work that studies the market for fake product reviews on Amazon.com where reviews are purchased in large private internet groups on Facebook and other sites. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook is associated with a significant but short-term increase in average rating and number of reviews. We exploit a sharp but temporary policy shift by Amazon to show that rating manipulation has a large causal effect on sales. Finally, we examine whether rating manipulation harms consumers or whether it is mostly used by high-quality or young products in a manner akin to advertising. We find that after firms stop buying fake reviews, their average ratings fall and the share of one-star reviews increases significantly, particularly for young products, indicating rating manipulation is mostly used by low-quality products and is deceiving and harming consumers.
{"title":"Exploiting social media for fake reviews","authors":"Sherry He, Brett Hollenbeck, Davide Proserpio","doi":"10.1145/3505156.3505164","DOIUrl":"https://doi.org/10.1145/3505156.3505164","url":null,"abstract":"We provide an overview of our recent work that studies the market for fake product reviews on Amazon.com where reviews are purchased in large private internet groups on Facebook and other sites. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook is associated with a significant but short-term increase in average rating and number of reviews. We exploit a sharp but temporary policy shift by Amazon to show that rating manipulation has a large causal effect on sales. Finally, we examine whether rating manipulation harms consumers or whether it is mostly used by high-quality or young products in a manner akin to advertising. We find that after firms stop buying fake reviews, their average ratings fall and the share of one-star reviews increases significantly, particularly for young products, indicating rating manipulation is mostly used by low-quality products and is deceiving and harming consumers.","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"19 1","pages":"68 - 74"},"PeriodicalIF":1.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42670584","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：