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.
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.
“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.
Marketing decision support systems (MDSS) incorporate both internal and external data in performing analytics to improve business effectiveness. Weather data have long been considered a crucial external data input in practitioners’ marketing strategy; however, academic research on how weather conditions affect consumer behaviors has been limited. To fill this gap, this research investigates how weather parameters, including sunlight, temperature, and air quality, can be incorporated into MDSS to predict consumers’ variety-seeking in their purchases using public weather data and supermarket panel data for five typical retail products. Our analyses show that weather conditions are associated with greater variety-seeking behavior. The results afford insights into how to exploit weather data for data analytics and employ weather targeting strategies to save promotional expenses and increase profitability.
“소설은 다른 콘텐츠를 소비하는 것과 효과가 다릅니다. 소설을 읽으면, 주인공이 겪는, 색다른 경험을 따라갑니다. 주인공이 나와 다르니 공감이 필요하고, 색다른 경험은 나의 경험이 아니니 당장의 의사결정에 필요한 인지적 종결욕구를 낮추어야 합니다. 결국, 다른 사람을 이해하게 되고, 넓게 보게 됩니다.”
The need for cognitive closure has been found to be associated with a variety of suboptimal information processing strategies, leading to decreased creativity and rationality. This experiment tested the hypothesis that exposure to fictional short stories, as compared with exposure to nonfictional essays, will reduce need for cognitive closure. One hundred participants were assigned to read either an essay or a short story (out of a set of 8 essays and 8 short stories matched for length, reading difficulty, and interest). After reading, their need for cognitive closure was assessed. As hypothesized, when compared to participants in the essay condition, participants in the short story condition experienced a significant decrease in self-reported need for cognitive closure. The effect was particularly strong for participants who were habitual readers (of either fiction or non-fiction). These findings suggest that reading fictional literature could lead to better procedures of processing information generally, including those of creativity.
