Thaler, R. H. (2018). Nudge, not sludge. Science, 361(6401), 431-431.
For some, the world is becoming increasingly complicated in that there are ever greater responsibilities, from selecting health insurance to figuring out how much to save for retirement. Ten years ago, my friend (and Harvard law professor) Cass Sunstein and I published a book called Nudge: Improving Decisions About Health, Wealth, and Happinessthat offered a simple idea. By improving the environment in which people choose—what we call the “choice architecture”—they can make wiser choices without restricting any options. The Global Positioning System (GPS) technology on smartphones is an example. You decide where you want to go, the app offers possible routes, and you are free to decline the advice if you decide to take a detour. Sunstein and I stressed that the goal of a conscientious choice architect is to help people make better choices “as judged by themselves.” But what about activities that are essentially nudging for evil? This “sludge” just mucks things up and makes wise decision-making and prosocial activity more difficult.
Wisdom has long been suggested as a desired goal of development (see e.g. Clayton and Birren, 1980; Erikson, 1959; Hall, 1922; Staudinger and Baltes, 1994). Questions concerning the empirical investigation of wisdom and its ontogeny, however, are largely still open. It is suggested that besides person characteristics, certain types of experience may facilitate wisdom-related performance. A sample of clinical psychologists (n=36) and highly educated control professionals (n=54) ranging in age from 25 to 82 years responded verbally to two wisdom-related tasks involving life planning and completed a psychometric battery of intelligence and personality measures. Three primary findings were obtained. First, training and practice in clinical psychology was the strongest predictor of wisdom-related performance (26%) and, in addition, showed some overlap with personality variables in this predictive relationship. Second, 14% of the variance in wisdom-related performance was accounted for by standard psychometric measures of personality and intelligence. Personality variables were stronger predictors than variables of intelligence. Important personality predictors were Openness to Experience and a middle-range location on the Introversion–Extraversion dimension. Third, wisdom-related performance maintained a sizable degree of measurement independence (uniqueness). Predictive relationships were consistent with research on naive conceptions of wisdom and our own theoretical account of the ontogenesis of wisdom-related performance.
“사전 부검을 (Premortem) 할 수도 있습니다. 사전 부검이란, 일어날지 모르는 사건이 일어났다고 가정한 뒤에 그 사건과 관련된 주변 정보를 구체화하는 것입니다. 즉, 우리가 시간을 앞서가 있다고 가정을 해 보고, 타임머신을 타고 미래에 가서 현재를 되돌아보는 것입니다”
Research conducted in 1989 by Deborah J. Mitchell, of the Wharton School; Jay Russo, of Cornell; and Nancy Pennington, of the University of Colorado, found that prospective hindsight—imagining that an event has already occurred—increases the ability to correctly identify reasons for future outcomes by 30%. We have used prospective hindsight to devise a method called a premortem, which helps project teams identify risks at the outset.
… Although many project teams engage in prelaunch risk analysis, the premortem’s prospective hindsight approach offers benefits that other methods don’t. Indeed, the premortem doesn’t just help teams to identify potential problems early on. It also reduces the kind of damn-the-torpedoes attitude often assumed by people who are overinvested in a project. Moreover, in describing weaknesses that no one else has mentioned, team members feel valued for their intelligence and experience, and others learn from them. The exercise also sensitizes the team to pick up early signs of trouble once the project gets under way. In the end, a premortem may be the best way to circumvent any need for a painful postmortem.
Tested 3 hypotheses concerning people’s predictions of task completion times: (1) people underestimate their own but not others’ completion times, (2) people focus on plan-based scenarios rather than on relevant past experiences while generating their predictions, and (3) people’s attributions diminish the relevance of past experiences. Five studies were conducted with a total of 465 undergraduates. Results support each hypothesis. Ss’ predictions of their completion times were too optimistic for a variety of academic and nonacademic tasks. Think-aloud procedures revealed that Ss focused primarily on future scenarios when predicting their completion times. The optimistic bias was eliminated for Ss instructed to connect relevant past experiences with their predictions. Ss attributed their past prediction failures to external, transient, and specific factors. Observer Ss overestimated others’ completion times and made greater use of relevant past experiences.
“In 1871, the colony of British Columbia agreed to join the new country of Canada on the condition that a transcontinental railway reach the west coast by 1881. In fact, because of the intervention of an economic depression and political changes, the last spike was not driven until 1885, 4 years after the predicted date of completion. Nearly 100 years later, in 1969, the mayor of Montreal proudly announced that the 1976 Olympics would feature a state-of-the-art coliseum covered by the first retractable roof ever built on a stadium. According to mayor Jean Drapeau, the entire Olympic venture would cost $ 120 million and “can no more have a deficit than a man can have a baby” (Colombo, 1987, p. 269). Because of economic problems, strikes, and other construction delays, the stadium roof was not in place until 1989, 13 years after the predicted date of completion—and cost $120 million by itself! Many people consider the Sydney Opera House to be the champion of all planning disasters. According to original estimates in 1957, the opera house would be completed early in 1963 for $7 million. A scaled-down version of the opera house finally opened in 1973 at a cost of $102 million (Hall, 1980).” (pg. 366)
This study provides the first evaluation of a newly engineered type of commitment device—a temptation bundling device. It shows that in the setting explored, where exercise was bundled with tempting audio novels, this new type of commitment device is valued by a significant portion of the population studied. Further, we find that when temptation bundling is imposed on a population, it can increase gym attendance by 51% at low cost when it is initially instituted, although as in most exercise interventions This study provides the first evaluation of a newly engineered type of commitment device—a temptation bundling device. It shows that in the setting explored, where exercise was bundled with tempting audio novels, this new type of commitment device is valued by a significant portion of the population studied. Further, we find that when temptation bundling is imposed on a population, it can increase gym attendance by 51% at low cost when it is initially instituted, although as in most exercise interventions.
Rapid development and adoption of AI, machine learning, and natural language processing applications challenge managers and policy makers to harness these transformative technologies. In this context, the authors provide evidence of a novel “word-of-machine” effect, the phenomenon by which utilitarian/hedonic attribute trade-offs determine preference for, or resistance to, AI-based recommendations compared with traditional word of mouth, or human-based recommendations. The word-of-machine effect stems from a lay belief that AI recommenders are more competent than human recommenders in the utilitarian realm and less competent than human recommenders in the hedonic realm. As a consequence, importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, and importance or salience of hedonic attributes determine resistance to AI recommenders over human ones (Studies 1–4). The word-of machine effect is robust to attribute complexity, number of options considered, and transaction costs. The word-of-machine effect reverses for utilitarian goals if a recommendation needs matching to a person’s unique preferences (Study 5) and is eliminated in the case of human–AI hybrid decision making (i.e., augmented rather than artificial intelligence; Study 6). An intervention based on the consider-the-opposite protocol attenuates the word-of-machine effect (Studies 7a–b).
“We assessed choice on the basis of the proportion of participants who decided to chat with the human versus AI Realtor by using a logistic regression with goal, matching, and their two-way interaction as independent variables (all contrast coded) and choice (0 = human, 1 = AI) as a dependent variable. We found significant effects of goal (B = 1.75, Wald = 95.70, 1 d.f., p < .000) and matching (B = .54, Wald = 24.30, 1 d.f., p < .000). More importantly, goal interacted with matching (B = .25, Wald = 5.33, 1 d.f., p = .021). Results in the control condition (when unique preference matching was not salient) replicated prior results: in the case of an activated utilitarian goal, a greater proportion of participants chose the AI Realtor (76.8%) over the human Realtor (23.2%;z = 8.91, p < .001), and when a hedonic goal was activated, a lower proportion of participants chose the AI (18.8%) over the human Realtor (81.2%;z = 10.35, p < .001). However, making unique preference matching salient reversed the word-of-machine effect in the case of an activated utilitarian goal: choice of the AI Realtor decreased to 40.3% (from 76.8% in the control; z = 6.17, p < .001). That is, making unique preference matching salient turned preference for the AI Realtor into resistance despite the activated utilitarian goal, with most participants choosing the human over the AI Realtor. In the case of an activated hedonic goal, making unique preference matching salient further strengthened participants’ choice of the human Realtor, which increased to 88.5% from 81.2% in the control, although the effect was marginal, possibly due to a ceiling effect (z = 1.66, p = .097).
Overall, whereas the word-of-machine effect replicated in the control condition when unique preference matching was salient, participants preferred the human Realtor over the AI recommender both in the hedonic goal conditions (human = 88.5%,AI = 11.5%;z = 12.40, p < .001) and in the utilitarian goal conditions (human =59.7%,AI = 40.3%;z = 3.24, p = .001; Figure 3), corroborating the notion that people view AI as unfit to perform the task of matching a recommendation to one’s unique preferences.
These results show that preference matching is a boundary condition of the word-of-machine effect, which reversed in the case of a utilitarian goal when people had a salient goal to get recommendations matched to their unique preferences and needs. The next study tests another boundary condition.” (pp. 99-100)
Artificial intelligence (AI) helps companies offer important benefits to consumers, such as health monitoring with wearable devices, advice with recommender systems, peace of mind with smart household products, and convenience with voice-activated virtual assistants. However, although AI can be seen as a neutral tool to be evaluated on efficiency and accuracy, this approach does not consider the social and individual challenges that can occur when AI is deployed. This research aims to bridge these two perspectives: on one side, the authors acknowledge the value that embedding AI technology into products and services can provide to consumers. On the other side, the authors build on and integrate sociological and psychological scholarship to examine some of the costs consumers experience in their interactions with AI. In doing so, the authors identify four types of consumer experiences with AI: (1) data capture, (2) classification, (3) delegation, and (4) social. This approach allows the authors to discuss policy and managerial avenues to address the ways in which consumers may fail to experience value in organizations’ investments into AI and to lay out an agenda for future research.
This article looks at the trade-offs that gift givers and gift receivers make between desirability and feasibility using construal level theory as a framework. Focusing on the asymmetric distance from a gift that exists within giver-receiver dyads, the authors propose that, unlike receivers, givers construe gifts abstractly and therefore weight desirability attributes more than feasibility attributes. Support for this proposition emerges in studies examining giver and receiver mind-sets, as well as giver and receiver evaluations of gifts. Furthermore, givers do not choose gifts that maximize receiver happiness or other relationship goals even though givers believe they are doing so. Finally, the authors demonstrate that while givers are sensitive to their distance from the receiver, receivers are not sensitive to this distance.
We recruited 425 US-based participants from Amazon.com’s Mechanical Turk. However, 365 were left after removing those who clicked to enter but did not finish the study, failed the IMC, or incorrectly answered whether they were in the giver or receiver condition. Participants were divided into a 2 (participant role: giver vs. receiver) X 2 (perspective: control vs. own preference) between-subjects design. First, participants imagined a specific friend and wrote down that friend’s initials. Then they imagined either giving that friend a gift or receiving a gift from that friend for a birthday occasion. Each participant was asked to imagine a choice between a highly feasible gift (a photo-editing program with few features that was easy to use) and a highly desirable gift (a high-quality photo-editing program that was hard to learn) and to give their relative preference on a 1–7 bipolar scale anchored at “prefer Gift A” and “prefer Gift B,” where Gift B was the high-desirability option. Right before answering, half of the participants were asked to take a moment to think about which software they would prefer for themselves.
왜 사람들은 시간이 부족하다고 이야기할까. 일의 종류와 양이 많아져서 절대적인 시간이 부족하기도 하지만, 하나의 일을 마치기 위해서 필요한 시간이 부족하다고 느끼는 경우도 많다. 즉, 모든 일은 예상보다 오래 걸리지만 사람들은 어떤 일을 미래 시점에 끝내기 위해서 필요한 시간과 비용을 현재 시점에서 과소 예측하는 경향이 있다. 이러한 계획 오류 (Planning fallacy) 때문에, 일을 뒤늦게 시작하고는 결국 시간이 부족하다고 느끼는 경우가 많다. 계획 오류는 정확하게 무엇이며, 어떻게 하면 계획 오류를 극복하고 시간을 잘 활용해서 결국 시간이 부족하다는 느낌에서 벗어날 수 있을까.
정의: 계획 오류란 무엇일까
계획 오류의 유명한 사례는 시드니 오페라하우스 공사다. 1957년에 덴마크 건축가의 설계를 바탕으로 공사를 시작할 때는, 77억 원이 필요하고 6년 후인 1963년에 완공될 것으로 예상했다. 그러나 당초 예상과 달리, 비용은 15배인 1,100억 원이 들었고 시간도 총 16년이 걸려서 결국 1973년에야 공사가 끝났다. 계획 오류는 우리 주변에서도 볼 수 있다. 1994년에 진행된 한 연구에서는 학생들에게 과제물을 내어준 뒤 과제물 작성에 소요될 시간을 예상해 보도록 했다. 연구에 참여한 학생들은 평균 33.9일이 소요될 것으로 예상했으며, 주변 상황이 최상인 경우에는 27.4일 만에, 주변 상황이 최악인 경우에는 48.6일이 소요될 것으로 예상했다. 하지만 실제 소요 시간의 평균은 최악의 상황을 가정한 시간보다도 일주일이 긴, 55.5일이었으며, 오직 30%의 학생들만 본인이 예상한 기간 내에 과제물을 완성했다. (Buehler, Roger; Dale Griffin; Michael Ross (1994). Exploring the “planning fallacy”: Why people underestimate their task completion times. Journal of Personality and Social Psychology. 67 (3): 366–381.)
계획 오류가 발생하는 이유는 과제를 수행하면서 발생할 수 있는 다양한 사건을 사전에 고려하지 않기 때문이다. 시드니 오페라하우스를 건립할 때는 지붕에 필요한 특수 세라믹 타월을 개발하는 데만 3년이 걸리고, 지붕 구조물을 짓는 데는 8년이 걸렸다. 이외에도 자재 수급, 파업, 날씨 등으로 인해서 공사가 지연됐다. 연구에 참가한 학생들도 과제물을 완성하기까지 발생할 수 있는 여러 일을 고려하지 않았다. 흥미롭게도 공사 참가자들이나 연구에 참가한 학생들, 그리고 우리 모두는 하나의 일을 완성하는데 예상보다 시간이 오래 걸린다는 점을 경험상 잘 알고 있다. 하지만 동시에, 과거의 사례를 통해 얻은 경험이 이번에는 일어나지 않을 것이라고 기대한다. 즉, 이번 만큼은 다양한 사건이 일어나지 않고 모든 과정이 순조롭게 진행될 것이라는 근거 없는 낙관을 하게 된다. 이는 우리 인간이 가진 한계를 명확하게 보여준다. 그렇다면, 근거 없는 낙관에서 벗어나 여러 사건을 최대한 고려하여, 결국 시간과 비용을 충분히 투입하고 예정된 시간 내에 과제를 수행하기 위해서는 어떻게 해야 할까.
계획 오류를 극복하는 3가지 방법
첫째, 과제를 수행할 때 발생할 미래의 사건을 쪼개서 구체적으로 생각한다 (Unpack). 당연하면서도 가장 강력한 이 방법은 외출 준비에 걸리는 시간을 예측하는 연구를 통해서 알려졌다. 참가자들에게 처음 만난 이성과 토요일 저녁 7시에 식당에서 만난다면 외출을 준비하는데 시간이 얼마나 걸릴지 질문했다. 절반의 참가자에게는 “외출을 준비한다면” 이라고 단순하게 물어보았고, 나머지 절반에게는 “옷을 선택하고, 샤워를 하고, 머리를 말린다면” 등으로 일을 쪼개어 구체적으로 물어보았다. 단순하게 물어본 조건의 참가자들은 평균 68분이 걸린다고 대답했다. 하지만 과정을 쪼개어 구체적으로 물어본 경우, 참가자들은 평균 89분이 걸린다고 대답했다. (Kruger, J., & Evans, M. (2004). If you don’t want to be late, enumerate: Unpacking reduces the planning fallacy. Journal of Experimental Social Psychology, 40(5), 586–598.)
둘째, 과제를 수행할 때 발생하는 미래의 사건을 구체적으로 생각하기 위해서 예정적 사후 가정을 적용한다 (Prospective hindsight). 이 방법은 인지 심리학에서 제안된 사전부검(Pre-mortem)이라고도 불리는데, 어떠한 계획이 실제로 실행된 뒤에 처참한 실패로 끝난다고 가정하고 어떻게 해서 그런 일이 벌어졌는지 써보는 것과 동일한 방법이다. 즉, 일어날지 모르는 사건이 일어났다고 가정하여 그 사건과 관련된 주변 정보를 구체화하는 것이다. 예정적 사후가정은 1989년에 실험이 검증됐다. 새로 업무를 시작한 직원에 대해서 간략히 묘사한 뒤에 해당 직원이 6개월 후 직장을 그만둘 이유를 생각해 보라고 요청하면 평균 3.5개의 구체적이지 못한 이유를 생각해 내지만, 해당 직원이 6개월 후 직장을 그만두었다고 가정한 뒤 그만둔 이유를 생각해 보라고 요청하면 좀 더 구체적이고 시나리오에 적합한 이유를 평균 4.4개 생각해냈다. (Deborah J. Mitchell, J. Edward Russo, and Nancy Pennington (1989), “Back to the Future: Temporal Perspective in the Explanation of Events,” Journal of Behavioral Decision Making 2: 25–38.)
셋째, 과제를 수행하는 주체를 ‘나’라는 내부자가 아닌 다른 사람이라는 외부자로 바꿔 과제를 수행할 때 발생하는 미래의 사건을 객관적으로 생각한다 (from inside view to outside view). 과제를 내부자의 시각으로 바라보면, 과제가 완성될 것이라는 가정을 먼저 하기 때문에 중도에 여러 사건이 발생할 가능성을 과소평가하게 된다. 그러나 같은 과제를 외부자의 시각으로 바라보면, 과제가 완성된다는 가정이 없어지기 때문에 중도에 발생하는 다양한 사건을 충분히 고려할 수 있다. 시선을 외부자로 돌려서 과제를 객관적으로 본 사례로 반도체 전문가인 앤디 그로브(Andrew Grove) 일화가 있다. 반도체 회사인 인텔은 메모리 반도체로 승승장구하다가 1980년대 중반 일본 반도체 회사들과의 가격 경쟁에서 밀리면서 이익이 2억 달러에서 200만 달러로 급격히 추락했다. 당시 인텔의 대표였던 고든 무어는 메모리 사업부를 매각하는 것을 검토했다. 하지만 메모리 반도체로 회사가 성공했고, 많은 직원이 이 기술에 평생을 바쳤으며, 사내의 이해관계가 수없이 얽혀있었기 때문에 최종 결정을 주저하고 있었다. 이때 인텔의 2인자였던 앤드 그로브는 대표인 고든 무어에게 질문했다. “만약 우리가 쫓겨나 이사회가 새로운 대표를 임명한다면, 그 사람은 무엇을 할 것이라고 생각하나요?” 고든 무어가 대답했다. “회사의 역사를 생각하지 않고 모든 것을 바꿔놓겠지. 아마 메모리 사업에서 철수하겠지.“ 그러자 앤디 그로브가 외부자의 시각을 주입한 해법을 내놓았다. “그럼 우리가 새로 임명된 대표라고 생각하고, 지금 말씀하신 것을 그대로 하는 게 어떨까요?” (Barry M. Staw & Jerry Ross (1987), “Knowing When to Pull the Plug,” Harvard Business Review, March–April 1987: 1–7.)
계획 오류를 극복하는 3가지 결론
사람들은 모든 일이 예상보다 “무척” 오래 걸린다는 점을 너무나 잘 알고 있다. 즉, 계획오류를 너무나 잘 인식하고 있다. 하지만 이번 일은 특별할 것이라는 근거없는 낙관론에 사로잡히기 때문에, 일을 충분히 일찍 시작하지 않고 시간을 효율적으로 사용하지 못한 채 바빠지는 경우가 많다. 결국 계획 오류를 극복하면 시간을 효율적으로 사용하고 바쁘다는 느낌에서도 벗어날 수 있다. 계획 오류를 극복하기 위해서는, 미래의 사건을 구체적으로 쪼개어 생각하거나 (Unpack), 미래의 사건이 이미 일어났다고 가정하거나 (Prospective hindsight), 내가 아니라 남이 일을 수행한다고 생각하는 방법이 있다 (from inside view to outside view). 계획 오류를 극복해서 모든 일이 예상보다 “조금만” 오래 걸리고, 시간을 더욱 효율적으로 사용하기를 기대한다.
주재우 (2024), 계획 오류: 시간편향을 극복하는 세가지 방법, 대학원 신문, https://gspress.cauon.net/news/articleView.html?idxno=30229.
Manufacturers are increasingly producing and promoting sustainable products (i.e., products that have a positive social and/or environmental impact). However, relatively little is known about how product sustainability affects consumers’ preferences. The authors propose that sustainability may not always be an asset, even if most consumers care about social and environmental issues. The degree to which sustainability enhances preference depends on the type of benefit consumers most value for the product category in question. In this research, the authors demonstrate that consumers associate higher product ethicality with gentleness-related attributes and lower product ethicality with strength-related attributes. As a consequence of these associations, the positive effect of product sustainability on consumer preferences is reduced when strength-related attributes are valued, sometimes even resulting in preferences for less sustainable product alternatives (i.e., the “sustainability liability”). Conversely, when gentleness-related attributes are valued, sustainability enhances preference. In addition, the authors show that the potential negative impact of sustainability on product preferences can be attenuated using explicit cues about product strength.
Background Environmentally friendly products are extensively studied and the effect of purchase context on consumers’ preferences for them has been much investigated. However, the effect of product design has been little discussed. Methods In the present work, we conducted two experiments to test whether package color, one component of product design, moderates the effect of purchase context on consumers’ preferences for environmentally friendly products, and obtained two findings. Result First, when purchase context is conspicuous, consumers’ preferences for environmentally friendly products increase. Second, product design moderates the effect of purchase context; when the package color is environmentally friendly (blue), consumers’ preferences for environmentally friendly products increase as the purchase context becomes conspicuous. However, preferences do not increase when the package color is not environmentally friendly (magenta). Conclusions We discuss the academic contribution and managerial implications of our findings to provide insights into product designers as well as marketing practitioners.
광명시청에서 근무하시는 주무관님께서 행동경제학으로 똑똑해지는 공공기관을 알고 싶다고 하시면서 아래의 메시지를 보내오셨다.
“지자체의 존재 이유는 사회 문제를 해결하고, 다수의 시민에게 좋은 정책을 제공하고 그것을 생활 속에 녹아낼 수 있도록 하는 것에 있다고 생각합니다”
광명시청에서 격주로 진행하는 GM 미래 지식포럼에 참가하여, “시민의 행동을 자연스럽게 유도하는 행동경제학” 이라는 이름으로 강연을 진행을 했다. 해당 포럼은, 박승원 광명시장의 주최로, 정순욱 부시장 및 실 국장, 과장 등이 함께 참여하여 광명에 거주하는 시민을 위해 더 나은 정책을 고민한다. 강연에서는 EBS 의 알기 쉬운 행동경제학 에서 짧게 소개된 서울시의 무료 대중교통 정책과 Vancouver의 대중교통 유도 정책을 자세하게 비교하는 것 이외에도 북미, 유럽, 아시아의 공공기관에서 진행된 다양한 프로젝트가 소개되었으며, 강연 이후에는 광명시청에서 이미 추진 중인 정책에 더 많은 시민들이 참여할 수 있는 방안을 함께 모색했다.
GM 미래 지식포럼에서 강연을 진행한 이후, 공무직 직원 분들을 대상으로 같은 내용의 강연을 한번 더 요청하셨다.
“초미세먼지가 증가하면 앱 사용시간은 전반적으로 감소하는데, 휴대폰으로 돈을 버는 캐시 앱 (cash app) 사용 시간은 증가하는 것으로 나타납니다. 공기가 나빠지면 실내에 있는 시간이 늘어나고 시간이 많다고 착각하면서 단위 시간의 금전적 가치를 낮게 계산하기 때문에, 캐시 앱을 평소보다 오래 사용하는 것 같습니다.”
High levels of air pollution in China may contribute to the urban population’s reported low level of happiness 1–3 . To test this claim, we have constructed a daily city-level expressed happiness metric based on the sentiment in the contents of 210 million geotagged tweets on the Chinese largest microblog platform Sina Weibo 4–6 , and studied its dynamics relative to daily local air quality index and PM 2.5 concentrations (fine particulate matter with diameters equal or smaller than 2.5 μm, the most prominent air pollutant in Chinese cities). Using daily data for 144 Chinese cities in 2014, we document that, on average, a one standard deviation increase in the PM 2.5 concentration (or Air Quality Index) is associated with a 0.043 (or 0.046) standard deviation decrease in the happiness index. People suffer more on weekends, holidays and days with extreme weather conditions. The expressed happiness of women and the residents of both the cleanest and dirtiest cities are more sensitive to air pollution. Social media data provides real-time feedback for China’s government about rising quality of life concerns.
