Category: Interdisciplinary


招募我在MetLife团队的商业合作伙伴(储备营业处经理),坐标北京朝阳区 。感兴趣的亲们欢迎用Boss直聘app联系我,或发送简历和自我介绍至8601010791@metlife.com.cn或hu.yuqing@hotmail.com。欢迎转发和分享。
I am looking for business partners (west point agency managers, WAM) to join my team at MetLife Beijing branch . Those who are interested are welcome to contact me via Boss Zhipin app, or send a CV and a cover letter to 8601010791@metlife.com.cn or hu.yuqing@hotmail.com. Please share with anyone who might be interested. Thank you!

 

MetLife成立150年之际,推出“150非凡企业家”全球招募计划。储备营业处经理专享福利有:

  1. 新加坡流明实验室创新培训 (http://lumenlab.sg/)
  2. 日韩深度学习之旅
  3. 美国常春藤名校深造
  4. 150万个人奖金

In its 150 years’ anniversary, MetLife launched its “150 Extraordinary Entrepreneurs” global recruitment program. The West Point Agency Manager (WAM) will have the following exclusive benefits:

  1. Training in Lumen Lab in Singapore (http://lumenlab.sg/)
  2. In-depth travel and study in Japan and South Korea
  3. Study at ivy league schools in the United States
  4. 1.5 million scholarship for each individual

 

职位描述:

  1. 接受公司培训,熟悉以需求为导向的顾问行销方法和流程;了解保险、基金、股权等知识,以及合同法、保险法、继承法等法律法规;根据法律和行规调整,与时俱进
  2. 为客户提供专业而全面的资产安全规划分析,并提供风险控制、养老规划、资产传承等方面的建议
  3. 入职后继续接受组内培训和管理职一对一的辅导
  4. 招募新人;发展自己的团队;带领团队达成业绩目标
  5. 参与并组织晨会、每周例会,月度例会与其他公司活动
  6. 协助和参与公司的宣传与推广工作;塑造良好的营业处氛围和企业文化

Job Description:

  1. Taking MetLife’s training courses, getting familiar with the need-based sales (NBS) methods and processes; Understanding insurance, funds and equity, as well as contract law, insurance law, inheritance law, and other laws and regulations; Staying up-to-date on regulation changes and industry updates
  2. Providing customers with comprehensive and professional analysis in financial security planning, and offering advice on risk control, retirement, asset inheritance, etc.
  3. Constantly receiving group training and one-on-one coaching from mentors
  4. Recruiting new team members; Developing his/her own team; Leading the team to achieve sales goals
  5. Attending and organizing morning meetings, weekly meetings, monthly meetings and other company-level activities
  6. Assisting and participating in MetLife’s promotion activities; Creating good business atmosphere and corporate culture

 

收入及待遇:

  1. 有责任底薪,加上按业绩的提成,收入无上限
  2. 公司内部设各种奖励和竞赛(比如针对新人有“新人王”和“菁英会”项目),每年有多种奖金和多次国内外免费旅游的机会
  3. 有明确的晋升路线,每6个月一次晋升机会;晋升管理职后有增员奖金和管理奖金;最快两年能晋升至营业处经理
  4. 每周工作日从周一到周五,每个月最低要求80%的工作日打卡,工作时间自主有弹性
  5. 有机会参加百万圆桌会(MDRT)等国际寿险会议

Salary and other Benefits:

  1. Basic salary plus commission, without upper limit
  2. Awards from competitions within the company (e.g. “New Sales Kings” and “Elite Clubs” for new members); Bonus based on performance; Opportunities for free travel at home and abroad every year.
  3. A clear promotion route, with one promotion opportunity every 6 months; Team management bonus and recruiting bonus after promotion to a managerial position; Possibility of being promoted to the agency manager (AM) in two years.
  4. Working from Monday to Friday, with minimum 80% of the working days in office per month; Time is flexible
  5. Opportunities to participate in international life insurance conferences such as the Million Dollar Round Table (MDRT)

 

基本要求:

  1. 年龄25-48周岁
  2. 有责任感、乐于学习;乐于助人,具有亲和力;自我驱动,兼具竞争和团结合作意识
  3. 本科学历及以上;专业不限
  4. 过往年收入15万及以上
  5. 无寿险经验
  6. 良好的职业道德;良好的心态

Basic Requirements:

  1. age 25-48
  2. Responsible and willing to learn; Willing to help others and having high level of affinities; Self-driven, competitive and cooperative
  3. Bachelor’s degree and above; Any major is welcome
  4. Average annual income from the past above 150,000
  5. No prior life insurance sales experience
  6. Good professional ethics; Stable mood

 

加分项:

  1. 重点院校毕业生(比如211、985学校)
  2. 硕士及以上学历
  3. 有5年以上(管理)工作经验
  4. 海外留学或工作经历
  5. 团队管理或项目经验
  6. 人脉广,在北京居住两年及以上
  7. 逻辑思维和共情能力;创新能力

Pluses:

  1. Graduated from key point schools (e.g. 211 and 985 schools)
  2. Master’s degree and above
  3. At least 5 years’ (managerial) working experience
  4. Overseas study or working experience
  5. Team management or project experience
  6. Having extensive contacts in Beijing, or living in Beijing for more than two years
  7. Logical thinking and empathy; Creativity

MetLife公司介绍:https://www.metlife.com.cn/about-us/company-info/

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I just finished the final round revision of the book and submitted it to the publisher. Thank all who have helped me along the way. Really looking forward to Glen Weyl’s book tours in China and the RadicalxChange events in Beijing later this year!

今天我终于完成了《激进市场》书稿最后一轮的审校和修改工作。感谢所有老师、朋友和各位编辑们的指导和帮助!Glen Weyl今年下半年会来中国对此书和RadicalxChange(https://radicalxchange.org/)的思想做巡回演讲,大家敬请期待!

一场波澜壮阔的思维实验——《激进市场》译者序

胡雨青

 

第一次见到E·格伦·韦尔(E. Glen Weyl)是在2017年秋季美国国家经济研究局举行的《市场设计》学术研讨会上。格伦友好地冲我打招呼说:“嗨!你好,我叫格伦。”没想到一年后,这位总是穿着V领衬衫的“大男孩”竟然出版了一本极具思考深度的书,并且在西方各大媒体引起了极大反响。《激进市场:为正义社会彻底变革资本主义与民主主义》是一本雄心勃勃的著作。青年经济学家格伦和他的法学家合作者埃里克·A·波斯纳(Eric A. Posner)带领读者领略了一场空前浩大的思维实验。

本书写作的出发点是当今世界的一系列新挑战——尽管近几十年来全球经济有所增长,国与国之间的差距有所缩小,但国家内部的不平等却在加剧,而且社会与政治的分歧与分裂也日趋严重——曾经让西方世界尤其是美国引以为豪的自由主义市场经济面临前所未有的危机。变革迫在眉睫,但眼下比中央计划经济更好的替代品又还没有出现。然而,有幸的是,今天的科技进步,尤其是在大数据背景下的机器学习和人工智能技术的迅速发展,正在悄悄地重新定义社会关系与生产关系。这在一定程度上使得在几十年前可能听起来不切实际的制度设计变得可行,当下棘手的问题在新技术的助力下,可以突破制度藩篱而有了解决方案。

本书分为五章,每一章在不同的领域提出了一种“激进”的方案。对于“激进”可以有两种理解。第一种是十九世纪亨利·乔治时代产生于英国的激进主义以及它所代表的社会变革精神。作者旨在传承并继续探究这种精神并让之在今天重焕光彩,这意味着挑战传统智慧和进行大刀阔斧的改革。第二种代表“彻底”的变革,这是因为“激进”的英文radical与 “根源”(root)同源。这意味着不能再依赖治标不治本的方法,而要透彻领悟社会运作的底层逻辑,通过变革其市场机制来解决问题,只有这样才能从根本上实现社会的持续繁荣与昌盛。

第一章是全书最出彩的部分。它证明了私有财产在本质上具有垄断性,即所有者可以持有财产而不对此加以有效利用,这也阻碍了其他能将此财产加以更有效利用的人获得该财产。但极端的公有化又会造成公地悲剧。作者提出一种新形式的产权在公有和私有间取到一个最优的折中——公有制自评税(common ownership self-assessed tax,简称COST)——来打破所有者对私有财产的垄断控制,同时克服了公有制的低效率问题——利用拍卖的形式把每一种资源都交给最能利用它并投资于它的人,从而实现资源的完全配置效率。

第二章着眼于民主,是作者近五年来主要工作成果的精华。它巧妙地提出了一种二次方投票法(quadratic voting,简称QV),让选民有效地影响对他们最重要的问题,从而克服了“一人一票”下的“多数人的暴政”等问题。在QV下,一个人对一件事如果特别在意可以考虑把更多的发言权积分花在上面,但要付出更高的代价。QV能让少数民族以一种更具影响力的方式表达自己的诉求。除此之外,作者还展示了QV在其他领域中的应用前景,例如与加密货币结合来改善商业中的评级系统。

第三章提议了一种被称之为个人VIP(Visas Between Individuals Program)项目的新移民政策,提倡任何东道国的公民在任何时候都可以担保移民,并从中得到经济收益。VIP不仅使移民和他们的雇主受益,更让每个担保移民的东道国公民受益。这种互惠的关系能够让东道主与移民建立一种积极的关系,从而从政治上缓和对移民的抵触情绪。

第四章关注的是这个时代最主要的垄断形式——投资机构的崛起。投资机构因为拥有多家公司的股权而大幅降低了市场的竞争,甚至制造出一种垄断,尤其是当一家投资机构持有同行业内多家公司的股票时。但目前的反垄断法无法遣散这种垄断力量。作者提议法律可以限制投资机构在行业内部的多样化投资,但可以鼓励在行业之间的多样化。这样的做法能将市场的自由和竞争性从投资机构的控制中解放出来。

第五章提供了一个看待数据的崭新视角。科技巨头从用户的行为中收集数据用于机器学习和人工智能的训练。作者提倡将这些数据视为一种劳动成果,将数据的产生过程视为一种有尊严的工作,并给予用户相应的补偿。在此视角下,人们都将有幸成为推动数字经济运转的数据供应商,而不是被视为数字平台提供的娱乐的被动消费者。这不仅可以建立一个更公平、平等的社会,还可以刺激技术进步和经济增长。

这五章内容相对独立,但每章都背景宏大、引人入胜。读者在阅读的过程有一点需要注意的是,包括作者自己也提到,他们在这里只是提供了一场思维实验——理论模型即使再完美无缺,要把这些激进的想法真正投入实践,还需要考虑更多的因素。例如,需要考虑COST下不稳定的所有权可能导致人们不愿意对物品进行长期投资的情况;除了金钱,文化、安全、幸福感等也是移民政策获得政治支持的重要因素;宗教、文化关怀等有时比金钱更能打动和鼓励人们工作;等等。

但总而言之,这本书非常值得一读。我鼓励所有对未来感兴趣的读者都去读一读,去感受作者细腻的人文关怀和勇敢的激进精神,站在作者的角度去想象未来的画面。因为改变和改善一个社会的,不仅是物质和金钱,也不仅仅是市场制度设计,更是一个民族的胆魄、动力和精神。

最后,感谢格伦在我翻译此书时对书中一些关键背景知识的详细解释和答疑;感谢机械工业出版社的各位工作人员尤其是顾煦副主编、贾萌和王一尘文字编辑的专业而细心的工作;感谢谭国富教授对本书初稿的审校和点评;感谢徐梦玫组织的志愿者团队(唐飞虎、冯惠利、殷越、王萱仲、余舜哲、巫文强、马聪、封雨希、康丹丹、杨山青、王燕婷、汪雪、行走的翻译C、郑捷凯、陈家鸣、姚远、李玥枫、葛如钧、马跃、唐雨萱、陈寒)辛苦而仔细的审读工作;感谢普林斯顿大学出版社的乔·杰克逊(Joe Jackson)主编、萨曼塔·纳德(Samantha Nader)、丽贝卡·本戈西亚(Rebecca Bengoechea)、金伯利·威廉姆斯(Kimberley Williams)和安德鲁·德西奥 (Andrew DeSio)等各位编辑和其他工作人员的组织和协调工作,使本书得以原版引进并顺利面市。

推荐序链接

猪年新春快乐!:) 我翻译的《激进市场》(http://radicalmarkets.com/) 将由机械工业出版社在国内年后出版,大家敬请期待!

推荐序

王健                胡雨青

在纽约市中心,你可以在鳞次栉比的王府井大街上,随着熙熙攘攘的人流,购买到物华天宝,品尝到珍馐美味。但在不远处参差不齐的布鲁克林“黑人区”,你也能见到那些用帆布糊起来的“家”,在那摇摇欲坠的屋檐之下佝偻着衣衫褴褛的身躯,并时时传来充满埋怨而又难懂的外地话。繁华与贫困共存并非纽约独有的风景——不平等是世界的普遍现象,像约热内卢、孟买、墨西哥城,等等。

不少人把贫富差距问题归咎于市场经济,主张限制市场经济的发展。表面上看,自由市场确实往往伴随着收入和财富差距的扩大,而且愈演愈烈的贫富差距升级往往以经济危机、政治分化、社会撕裂甚至军事冲突的惨烈方式收场。上世纪三十年代的全球经济大萧条以及随后的二战和大屠杀就是血淋淋的教训。而2008年全球金融危机过后,世界政治与经济再次陷入了迷局,极端民族主义也出现了令人担忧的复兴迹象:全球经济增长停滞、不平等加剧、民粹主义兴起和精英民主衰落,正一步步把全球各国从合作共赢推向敌视和对抗。例如,美国民粹主义总统川普粉墨登场。他声称为了“让美国再次强大”,必须采用“美国优先”政策;为了让美国“更加安全”,必须在美墨边境修筑一道高墙。然而,这些举措尤其是对移民的排斥和妖魔化宣传与美国自由平等的价值观背道而驰,它们既不能“让美国再次强大”,更无法保证美国的长期安全,甚至连美国的长期盟友都纷纷指责川普政府的这些行为。

除了收入不平等带来的各种冲击,当今世界日新月异的技术进步也飞速影响着传统的社会和经济关系。例如,机器学习和人工智能正在变革社会,使得数据越来越成为一种财富;共享经济正在重新定义人与人以及人与物之间的关系。在贫富差距日益扩大的严峻形势下,面对世界高速变化带来的机遇与挑战,如何化解风险,实现全球经济持续、稳定的繁荣,成为世界诸多学者和政策制定者工作的重中之重。芝加哥大学的埃里克·A·波斯纳(Eric A. Posner)[1]教授和微软首席经济学家E·格伦·韦尔(E. Glen Weyl)[2]在《激进市场》一书中大胆尝试了创新的解决方案,并提出了颠覆传统观念却能够改善社会的操作指南。

本书的两位作者在经济、法律、公共政策和金融市场领域都建树颇丰。其中波斯纳是芝加哥大学法学院教授,美国艺术与科学学院的研究员和美国法律研究所成员。其研究领域包括宪法、金融监管、人权、国际法等。除了《激进市场》,波斯纳还著有《国际人权的曙光》,《气候变化的正义性》,《国际法的限度》,《法律和社会规范》等作品。韦尔是微软的首席经济学家,研究领域包括价格理论、市场设计、产业组织、法律经济学、公共政策等。韦尔同时还是普林斯顿大学朱丽斯-拉比诺维茨公共政策和金融中心与尼奥斯国际关系学院的客座教授,以及社会组织“激进交流”(RadicalxChange)[3]的创始人和主席。

《激进市场》主要涉及五个方面:产权、民主、移民、投资、和大数据。各章分别从不同维度解读当今社会的各种不平等的根源,并提出彻底的变革方案,尝试通过全新的市场和制度设计把经济和政治资源以更有效、更民主和更公平的方式分配给人们。其核心观点认为,当今社会包括贫富差距在内的诸多问题,并不是因为经济过度市场化造成。恰恰相反,它们往往是市场化不充分或者市场被错误利用的后果——在好的市场机制下,经济不仅能实现快速增长,社会也会变得更公平。作者向读者们展示了好的机制如何能够解放市场力量,以及它们如何能够重新唤醒沉睡在十九世纪亨利·乔治时代的自由改革精神,并带来更大的平等、繁荣与合作。

全书写作思维严谨、观念新颖而且具有启发性,让人读起来爱不释手。我们强烈推荐本书给爱好经济和社会问题的广大读者!

[1] 作者主页:http://ericposner.com/。因为其父亲是著名的大法官理査德·A·波斯纳(Richard A. Posner),埃里克·A·波斯纳又被人称为“小波斯纳”。

[2] 作者主页:http://glenweyl.com/

[3] 组织主页:https://radicalxchange.org/

译者序链接

 

My last week in Israel before I come back to Beijing 🙂

https://sites.google.com/site/igtconf2017/

Program

Talks and coffee breaks are in Dan David 001
Dinner and lunches are in Gan Hadkalim (in front of exact sciences building). 
 
Monday, July 10, 2017
 
10:10 — 10:20    Coffee
10:30 — 11:00     Adam Kalai                        Unambiguous communication without a common prior
11:00 — 11:30    Éva Tardos
11:30 — 11:50     Coffee break
11:50 — 12:20     Moshe Tennenholtz        A Game Theoretic Approach to Information Retrieval
12:20 — 12:50    Ariel Rubinstein              Multi-dimensional Reasoning in Games: Framework, Equilibrium and Applications
13:00 — 14:30    Lunch break
14:30 — 15:00    Yuval Salant                   Statistical Inference in Games
15:00 — 15:30    Georgy Egorov               Strategic Communication with Minimal Verification 
15:30 — 16:00    Peter Klibanoff                Incomplete Information Games with Ambiguity Averse Players
16:00 — 16:20    Coffee break
16:20 — 16:50    Peyton Young                 Games, Norms, and Institutions
16:50 — 17:20    David Schmeidler           Desirability
17:20 — 17:50    Robert Aumann              My Ehud
18:30                 Dinner
 
 
Tuesday, July 11, 2017
 
10:10 — 10:20    Coffee
10:30 — 11:00    Tim Roughgarden          On a Theorem of Kalai and Samet
11:00 — 11:30    Michal Feldman              Prophet Inequalities Made Easy: Stochastic Optimization by Pricing Non-Stochastic Inputs
11:30 — 11:50    Coffee break
11:50 — 12:20    Abraham Neyman          Cooperative Strategic Games
12:20 — 12:50    Rann Smorodinsky         Bayesian Learning in Markets with Common Value
13:00 — 14:30    Lunch break
14:30 — 15:00    Leeat Yariv
15:00 — 15:30    Johannes Hörner
15:30 — 16:00    Jonathan Weinstein        Complementarity Revisited
16:00                 Coffee
 
Wednesday, July 12, 2017
 
10:10 — 10:20    Coffee
10:30 — 11:00    Paul Milgrom                    Economics and Computer Science of a Radio Spectrum Reallocation
11:00 — 11:30    TBA, Microsoft Research
11:30 — 11:50    Coffee break
11:50 — 12:20    Rakesh Vohra
12:20 — 12:50    Noam Nisan
13:00 — 14:30    Lunch break
14:30 — 15:00    Yair Tauman                    Coordination games with unknown outside options
15:00 — 15:30    Dov Samet
15:30 — 16:00    Chaim Fershtman           The Competitive Effects of Information Sharing
16:00 — 16:20    Coffee break
16:20 — 16:50    Ehud Lehrer
16:50 — 17:20    Ehud Kalai
17:30                 Reception

Workshop website: http://marketplaceinnovation.net/

It was such a nice stay at Stanford! Thanks for all the feedback 🙂

Yuqing Hu - 1

 

2nd LABEL – IEPR Conference

“Understanding cognition and decision making by children”

 May 4 – 5, 2017

 

 PROGRAM

  Thursday, May 4, 2017
 
 Radisson Los Angeles at USC
Victory AB Room
3540 South Figueroa Street
Los Angeles, California 90007
 
 

8:45 –  9:15   Continental Breakfast

9:15 –  9:30   Introductory remarks  

9:30 – 10:15   “The role of agency in regret and relief in 3- to 10-year-old children”Giorgio Coricelli (USC)

10:15 – 11:00  “Young Children’s Exploration of Alternative Possibilities”.  Henrike Moll (USC)

11:00 – 11:30   Coffee break

11:30 – 12:15   “Navigating Uncertainty: Neural Correlates of Decisions from Experience in Adolescence”. Wouter van de Bos (Max Planck – Berlin)

12:15 – 1:45   Lunch

1:45 – 2:30   “The Costs and Benefits of Growing Up”. Michael Norton (Harvard U.)

2:30 – 3:15   “Using economic experiments to detect drivers of behavior at early ages: Evidence from a large field experiment in Chicago”. Ragan Petrie (Texas A&M)                                        

3:15 – 3:45   Coffee break  

3:45 – 4:30   “Does the little mermaid compete? An experiment on competitiveness with children”Marco Piovesan (U. Copenhagen)

6:30              Dinner (by invitation)

 


 

Friday May 5, 2017
 
Radisson Los Angeles at USC
Victory AB Room
3540 South Figueroa Street
Los Angeles, CA 90007

 

9:00 – 9:30   Continental Breakfast

9:30 – 10:15   “Altruism and strategic giving in children and adolescents”. Isabelle Brocas (USC)

10:15 – 11:00   “Theory of Mind among Disadvantaged Children”. Aldo Rustichini (U. Minnesota)

11:00 – 11:30   Coffee break

11:30 – 12:15   “The development of reciprocal sharing in relation to intertemporal choice”. Felix Warneken (Harvard U.)

12:15 – 1:45   Lunch

1:45 – 2:30   “Group and Normative Influences on Children’s Exchange Behavior”. Yarrow Dunham (Yale U.)

2:30 – 3:15   “The role of social preferences and emotions in children’s decision making -a view from developmental psychology”. Michaela Gummerum (Plymouth U.)

3:15 – 3:45   Coffee Break

3:45 – 4:30   “Socio-cognitive mechanisms of prosocial behavior”. Nadia Chernyak (Boston U.)

Yuqing Hu, Juvenn Woo

GARP, the generalized axiom of revealed preference, is a dichotomous notion. A dataset can either satisfy the theory of a rational consumer or violate it[1]. There are several challenges in testing GARP consistency in an experiment. One challenge comes from the nature of budget sets, as there are more individual-level variations in expenditure than variations in prices. This causes the overlap of two budget sets, upon which the test based could bias towards the satisfaction of GARP, with the extreme case that no violation would occur if the budget sets are nested. Another challenge stems from lack of identities of consumers such that individuals have to be treated as the same for revealed preference tests (Chambers & Echenique, 2016).

 

A few indices have been invented to measure the degree of GARP violations. One is Afriat’s efficiency index (AEI) (Afriat, 1967), which uses the degree of “deflation” of expenditure that is needed to make GARP consistent. Other variations of AEI are Varian’s efficiency index (VEI) (Varian, 1983), and the money pump index (MPI) (Echenique, Lee, & Shum, 2011).

 

There is a modest amount of literature in experimental economics that test GARP consistency, which is summarized below.

 

Battalio et al.(1973) pioneered applying GARP consistency in experimental study. They ran field experiments among psychotic patients to let them exchange tokens for different consumption goods. They induced a variety of different budget sets by changing the value of tokens, and they found that if small measurement errors were allowed, then almost all patients’ behavior was consistent with GARP.

 

Sippel (1997) ran lab experiments in which 42 students were asked to repeatedly choose a bundle of priced entertaining items under different standard budget constraints. Individuals were paid in consumption goods, and were required to actually consume the goods at the experiment. The experiment showed 63% of subjects made choices violating GARP, though the median number of violations over all subjects was only 1 of 45 choices. The number did not change even if the study perturbed demand close to actual demand. Even though the (remarkably) low number of violations for individual indicates the subjects were highly motivated when making choices, a majority of subjects could not be classified as rational in the sense of utility maximization.

 

Harbaugh, Krause and Berry (2001) examined the development of choice behavior for kids. The study conducted simple choice experiments over cohorts of second graders, sixth graders, and undergraduates. They measured and compared number of GARP violations. It found out that for the particular test, about 25% of 7-year-olds, and 60% of 11-year-olds were making choices consistent with GARP respectively, and there is no increase in consistency from 11 to 21-year old. They also found that violations of GARP are not significantly correlated with the results of a test that measures mathematical ability in children.

 

Andreoni and Miller (2002) ran dictator-game experiments to test whether the observed altruistic behavior is utility-maximizing. They found that 98% of subjects make choices that are consistent with utility maximization. Andreoni and Miller went further than most revealed-preference exercises in estimating a parametric function of a utility function accounting for subject’s choices (about half the subjects can be classified as using a linear, CES (constant elasticity of substitution), or Leontief utility).

 

Hammond and Traub (2012) designed a three-stage experiment, where participant advances to next stage of test only if he or she made GARP consistent choices at current one. It had been conducted over 41 (non-economics) undergraduates. The study found that, at the 10% significance level, only 22% of subjects (compared to 6.7% of simulated random choice-maker) passed second-stage tests, and 19.5% passed all three stage tests. The result reinforced Sippel’s study suggesting that human is generally not perfectly rational. It should be noted though conditional on passing second-stage rationality, 62.5% of subjects passed third-stage tests.

 

Choi, Kariv, Müller and Silverman (2014) conducted with CentERpanel survey a comprehensive study on rationality. Measuring GARP consistency by Afriat Critical Cost Efficiency Index (CCEI) and correlation with socio-economic factors, it found that: on average, younger subjects are more consistent than older, men more than women, high-education more than low-education, and high-income subjects more consistent than low-income.

 

Brocas, Carillo, Combs and Kodaverdian (2016) studied choice behaviors in cohorts of young and older adults, varying choice tasks from simple domain to complex domain. They showed that while both young and older adults are about equally well consistent in simple domain, older ones were observed being significantly more inconsistent in complex tasks. Beyond that, by performing working memory and fluid intelligence (IQ) test, further correlation examinations reveal that older adults’ inconsistency in complex domain can be attributed to decline in working memory and fluid intelligence.

[1] One can, however, evaluate the degree of violations.

 

Reference:

 

Afriat, S. N. (1967). The Construction of Utility Functions from Expenditure Data. International Economic Review, 8(1), 67–77.

Andreoni, J., & Miller, J. (2002). Giving According to GARP: An Experimental Test of the Consistency of Preferences for Altruism. Econometrica, 70(2), 737–753.

Battalio, R. C., Kagel, J. H., Winkler, R. C., Edwin B. Fisher, J., Basmann, R. L., & Krasner, L. (1973). A Test of Consumer Demand Theory Using Observations of Individual Consumer Purchases. Western Economic Journal, XI(4), 411–428.

Brocas, I., Carillo, J. D., Combs, T. D., & Kodaverdian, N. (2016). Consistency in Simple vs. Complex Choices over the Life Cycle.

Chambers, C. P., & Echenique, F. (2016). Revealed Preference Theory. Cambridge University Press.

Choi, S., Kariv, S., Müller, W., & Silverman, D. (2014). Who Is (More) Rational? The American Economic Review, 104(6), 1518–1550.

Echenique, F., Lee, S., & Shum, M. (2011). The Money Pump as a Measure of Revealed Preference Violations. Journal of Political Economy, 119(6), 1201–1223.

Grether, D. M., & Plott, C. R. (1979). Economic Theory of Choice and the Preference Reversal Phenomenon. The American Economic Review, 69(4), 623–638.

Hammond, P., & Traub, S. (2012). A Three-Stage Experimental Test of Revealed Preference.

Harbaugh, W. T., Krause, K., & Berry, T. R. (2001). GARP for Kids: On the Development of Rational Choice Behavior. American Economic Review, 91(5), 1539–1545. http://doi.org/10.1257/aer.91.5.1539

Sippel, R. (1997). An Experiment on the Pure Theory of Consumer’s Behaviour. The Economic Journal, 107(444), 1431–1444. http://doi.org/Doi 10.1111/1468-0297.00231

Varian, H. R. (1983). Non-Parametric Tests of Consumer Behaviour. The Review of Economic Studies, 50(1), 99–110. http://doi.org/10.1007/sl0869-007-9037-x

 

 

Bounded: |f(x)|\le M for all x\in \mathbb E.

Uniformly bounded: |f_n(x)|<M for all x\in\mathbb E, n=1, 2, 3,...

Pointwise bounded: |f_n(x)<\phi(x)| where \phi is a finite valued function, and x\in\mathbb E, n=1, 2, 3,...

K is compact, f_n is pointwise bounded and equitcontinuous \Rightarrow f_n is uniformly bounded, and f_n contains a uniformly convergent subsequence.

 

Continuous: f: \mathbb E\subset X\to Y, p\in\mathbb E, \forall \varepsilon>0, \exists \delta>0 s.t. \forall x\in\mathbb E, d_X(x, p)<\delta\Rightarrow d_Y(f(x), f(p))<\varepsilon.

Uniformly continuous: \forall \varepsilon>0, \exists \delta>0 s.t. d_X(p, q)<\delta\Rightarrow d_Y(f(p), f(q))<\varepsilon.

Equicontinuous: \forall \varepsilon>0, \exists \delta>0 s.t. d(x, y)<\delta\Rightarrow |f(x)-f(y)|<\varepsilon.

Equicontinuous \Rightarrow uniformly continuous.

Compact set, uniformly convergence \Rightarrow equicontinuous.

 

Convergent: A sequence \{p_n\} in a metric space X, \exists p\in X s.t. \forall \varepsilon>0, \exists N s.t. n\ge N\Rightarrow d(p_n, p)<\varepsilon.

Uniformly convergent (definition 1): a sequence of functions \{f_n\}, n=1, 2, 3, ... converges uniformly on \mathbb E to a function f if \forall \varepsilon>0, \exists N s.t. n\ge N\Rightarrow |f_n(x)-f(x)|\le \varepsilon for all x\in\mathbb E.

Uniformly convergent (definition 2): \varepsilon>0, \exists N s.t. m\ge N, n\ge N, x\in\mathbb E\Rightarrow |f_n(x)-f_m(x)|\le \varepsilon.

Pointwise convergent: f(x)=\lim_{n\to\infty} f(x) (x\in\mathbb E).

If \{f_n\} is uniformly convergent, then \lim_{t\to x}\lim_{n\to\infty}f_n(t)=\lim_{n\to\infty}\lim_{t\to x}f_n(x).

K is compact, \{f_n\} is continuous and pointwise convergent, f_n\ge f_{n+1} \Rightarrow f_n\to f uniformly.

f_n\to f uniformly, where f_n\in\mathcal R Rightarrow \int_a^b f d_\alpha=\lim_{n\to\infty}\int_a^b f_n d\alpha.

\{f_n\} is differentiable, \{f_n'\} converge uniformly on [a, b]\Rightarrow f_n converges uniformly, and $f'(x)=\lim_{n\to\infty}f_n'(x) (a\le x\le b)$.

\{f_n\} is differentiable, f' is uniformly bounded \Rightarrow f_n is equicontinuous.

 

 

by Calvin Leather, Yuqing Hu

In response to this article: http://www.jneurosci.org/content/36/39/10016

Recent literature in reinforcement learning has demonstrated that the context in which a decision is made influences subject reports and neural correlates of perceived reward. For example, consider visiting a restaurant where have previously had many excellent meals. Expecting another excellent meal, when you receive a merely satisfactory meal, your subjective experience is negative. Had you received this objectively decent meal elsewhere, without the positive expectations, your experience would have been better. This intuition is captured in adaptive models of value, where a stimuli’s reward (i.e. Q-value) is expressed as being relative to the expected reward in a situation, and it has been found that this accurately models activation in value regions (Palminteri et al 2015). Such a model also can be beneficial as it allows reinforcement learning models to learn to avoid punishment, as avoiding a contextually-expected negative payoff results in a positive reward. This had previously been challenging to express within the same framework as reinforcement learning models (Kim et al, 2006).

Alongside these benefits, there has been concern that adaptive models might be confused by certain choice settings. In particular, agents with an adaptive model of value would have an identical hedonic experience (i.e. Q-values in the model) when receiving a reward of +10 units, in a setting where they might receive either +10 or 0 units, and a reward of 0 units, in a setting where they might receive either -10 or 0 units (we will refer to this later as the ‘confusing situation’). With this issue in mind, Burke et. al. (2016) develop an extension to the adaptive model, where contextual information only has a partial influence on reward. So, whereas the previous, fully-adaptive model has a subjective reward (Q-value) of +5 units for receiving an objective reward of 0 in the context where the possibilities were 0 and -10, and an absolute model ignoring context would experience a reward of 0, the Burke model would experience a reward of +2.5. It takes the context into account, but only partially, and accordingly they call their model ‘partially-adaptive’. Burke et. al. compare this partially-adaptive model with a fully-adaptive model, and an absolute model (which ignores context). When subjects were given the same contexts and choices as the confusing situation outlined above, Burke et. al. found that the partially-adaptive model reflects neural data in the vmPFC and striatum better than the fully-adaptive or absolute models.

The partially-adaptive model is interesting, as it has the same advantages as the fully-adaptive model (reflecting subjective experience and neural data well, allowing for avoidance learning), while potentially avoiding the confusion outlined above. Here, we seek to investigate the implications and benefits of Burke et. al.’s partially-adaptive model more thoroughly. In particular, we will consider the confusion situation’s ecological validity and potential resolution, whether it is reasonable that partially-adaptive representations might extend beyond decision (to learning and memory), and the implications of the theory for future work. Before we do this we would like to briefly present an alternative interpretation of their findings.

The finding that the fMRI signal is best classified by a partially-adaptive model does not necessarily entail the brain utilizing a partially-adaptive encoding as the value over which decisions occur. All neurons within a voxel can influence the fMRI signal, so it is possible that the signal may reflect a combination of multiple activity patterns present within a voxel. This mixing phenomenon has been used to explain the success of decoding early visual cortex, where the overall fMRI signal in a voxel reflects the specific distribution of orientation-specific columns within a voxel (Swisher, 2010). Similarly, the partially-adaptive model’s fit might be explained by the average contribution of some cells with a full-adaptive encoding, and other cells with absolute encodings of value (within biological constraints). This concern is supported by the co-occurrence of adaptive and non-adaptive cells in macaque OFC (Kobayashi, 2010). Therefore, more work is needed to understand the local circuitry and encoding heterogeneity of regions supporting value-based decision making.

Returning to the theory presented by the authors, we would like to consider whether a fully-adaptive encoding of value is truly suboptimal. The type of confusing situation presented above was shown to be problematic for real decision makers in Pompilio and Kacelnik (2010), where starlings became indifferent between two options with different objective values, due to the contexts those options appeared in during training. However, this type of choice context might not be ecologically valid. If two stimuli are exclusively evaluated within different contexts, as in Pompilio and Kacelnik, it is not relevant whether they are confusable, as the decision maker would never need to compare them.

Separate from the confusion problem’s ecological validity is the inquiry into its solution. Burke et. al. suggest partially-adaptive encoding avoids confusion, and therefore should be preferred to a fully-adaptive encoding. However, this might only be true for the particular payoffs used in the experiment. Consider a decision maker who makes choices in two contexts. One, the loss context, has two outcomes, L0 (worth 0), and Lneg (worth less than 0), while the other, the gain context, has two outcomes, G0 (worth 0), and Gpos (worth more than 0). If L0-Lneg = Gpos– G0, as in Burke et. al., a fully-adaptive agent would be indifferent between G0 and Lneg (and between Gpos and L0). A partially-adaptive agent, however, would not be indifferent, as the value of G0 would be higher than Lneg.   Now consider what happens if we raise the value the value of Gpos. By doing this, we can raise the average value of the gain context by any amount. Now consider what this does the experienced value (Q-value) of G0. As we increase the average reward of the context, G0 becomes a poorer option in terms of its Q-value. Note that since the only reward we are changing is Gpos, the Q-values for the loss context do not change. Therefore, we can decrease the Q-value for G0 until it is equal to that of Lneg. This is exactly the confusion that we had hoped the partially-adaptive model would avoid. Furthermore, this argument will work for any partially-adaptive model: we are unable to defeat this concern by parameterizing the influence of context in the update equations, and manipulating this parameter.

As mentioned earlier, it is possible that some cells might encode partially-adaptive value, while others might have a fully- or non-adaptive encoding. We should be open to the possibility that even if partially-adaptive value occurs in decision, non-adaptive encodings might be used for storage of value information, and are transformed at the time of decision into the observed partially-adaptive signals. Why might this be reasonable? An agent who maintains partially-adaptive representations in memory faces several computational issues. One is efficiency: storage requirements for a partially-adaptive representation requires S*C quantities or distributions to be stored (one for each of the S stimuli in each of the C contexts). On the other hand, consider an agent who stores non-adaptive stimulus values and the average value of each context, and then adjusts stimulus values using the context values at the time of decision. They could utilize the same information, but only store S+C quantities. Another problem with storage of value information in an adaptive format is the transfer of learning across contexts. If I encounter a stimulus in context A, my experiences should alter my evaluation of that stimulus in context B: getting sick after eating a food should reduce my preference for that food in every context. An agent who stores value adaptively would need to update one quantity for the encountered stimulus in each context, namely C quantities. An agent who stores value non-adaptively only updates a single quantity. So, even if decision utilizes partially-adaptive encoding, non-adaptive representation is most efficient for storage. Furthermore, non-adaptive information is present in the state of the world (e.g. concentration of sucrose in a juice does not adapt to expectations), so this information is available to agents during learning. Accordingly, it must be asked why agents would discard information that might ease learning. While these differences do not necessarily affect the authors’ claims about value during decision, they should be considered when investigating the merits of different models of value.

In sum, while partial adaption is an exciting theory that may provide novel motivations for empirical work, more effort is needed to understand when and where it is optimal. If we can overcome these concerns, the new theory opens up potential investigation into the nature of contextual influence: if we allow a range of contextual influence (via a parameter) in the partially-adaptive model, do certain individuals have more contextual influence, and does this heterogeneity correlate with learning performance? Do different environments (e.g. noise in signals conveying the context) alter the parameter? Do different cells or regions respond with different amounts of contextual influence? As such, the theory opens up new experimental hypotheses that might allow us to better understand how the brain incorporates context in the learning and decision-making processes.

 

 

References

 

Burke, C. J., Baddeley, X. M., Tobler, X. P. N., & Schultz, X. W. (2016). Partial Adaptation of Obtained and Observed Value Signals Preserves Information about Gains and Losses. Journal of Neuroscience, 36(39), 10016–10025. doi:10.1523/JNEUROSCI.0487-16.2016

Kim, H., Shimojo, S., & Doherty, J. P. O. (2006). Is Avoiding an Aversive Outcome Rewarding ? Neural Substrates of Avoidance Learning in the Human Brain. PLoS Biology, 4(8), 1453–1461. doi:10.1371/journal.pbio.0040233

Kobayashi, S., Carvalho, O. P. De, & Schultz, W. (2010). Adaptation of Reward Sensitivity in Orbitofrontal Neurons. Journal of Neuroscience, 30(2), 534–544. doi:10.1523/JNEUROSCI.4009-09.2010

Palminteri, S., Khamassi, M., Joffily, M., & Coricelli, G. (2015). Contextual modulation of value signals in reward and punishment learning. Nature Communications, 6, 1–14. doi:10.1038/ncomms9096

Pompilio, L., & Kacelnik, A. (2010). Context-dependent utility overrides absolute memory as a determinant of choice. PNAS, 107(1), 508–512. doi:10.1073/pnas.0907250107

Swisher, J. D., Gatenby, J. C., Gore, J. C., Wolfe, B. A., Moon, H., Kim, S., & Tong, F. (2010). Multiscale pattern analysis of orientation-selective activity in the primary visual cortex. Journal of Neuroscience, 30(1), 325–330. doi:10.1523/JNEUROSCI.4811-09.2010.Multiscale

Enjoyed the beautiful Chicago summer!!

Frontiers of Economic Theory and Computer Science

August 12–13, 2016

Saieh Hall for Economics, Room 021
ORGANIZERS
BENJAMIN BROOKS, UNIVERSITY OF CHICAGO
MOHAMMAD AKBARPOUR, UNIVERSITY OF CHICAGO

With the advent of the internet economy, there has been a remarkable convergence in lines of inquiry between the fields of economic theory and computer science. The emergence of new markets mediated by information technology has spurred new research on how strategic agents interact with complex institutions, and also on how best to design institutions in the face of limited information and limited computational resources. The disciplines of game theory and complexity theory provide complementary approaches to understanding these issues. This conference brings together leading researchers from these fields to share and discuss exciting new developments. The broader goal is to facilitate a dialogue on how best to advance the common objective of understanding strategic behavior and institutional design in complex environments.

 

 

Program

Friday, August 12

Auctions and Pricing in Markets with Complex Constraints
Stanford University
Discussant: Tim Roughgarden
Combinatorial Auctions via Posted Prices
Blavatnik School of Computer Science at Tel-Aviv University
Perfect Competition in Markets with Adverse Selection
University of Pennsylvania
Games of Incomplete Information Played By Statisticians
Microsoft Research
Discussant: Vasilis Syrgkanis
Multi-dimensional Virtual Values and Second Degree Price Discrimination
Pennsylvania State University
Learning and Efficiency in Games with Dynamic Population
Cornell University

Saturday, August 13

Strong Duality for a Multiple-Good Monopolist
Massachusetts Institute of Technology
Break
Motivational Ratings
Stanford University
Game Abstractions for Counterfactual Predictions in Online Market
Facebook, Inc.
Information Design
Princeton University
Affirmative Action in Assignment Mechanisms
Massachusetts Institute of Technology