Sensing, Understanding, and Shaping Social Behavior

Shmueli, Erez; Singh, Vivek K.; Lepri, Bruno; Pentland, Alex

doi:10.1109/tcss.2014.2307438

Cited by 70 publications

(51 citation statements)

References 88 publications

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“…And they analyzed the characteristics of drivers who have an aggressive driving style." Erez Shmueli [4] and his colleagues of MIT(Media Lab.) described, "The ability to understand social systems through the aid of computational tools is central to the emerging field of computational social systems.…”

Section: Related Workmentioning

confidence: 99%

Data Mining by Smartphone and Big Data Analysis for Driver's Subconscious Behavior - An Attempt to Recognize Driver's Characteristics and Realize Safe Driving Techniques

Kubota¹,

Imai²

2016

IJEETDM

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Last few years, some famous types of powerful smart phones have been revitalizing the market in the world and been coming available at every situation and environment. Data mining and Big Data Analysis seem to be very useful and attractive in the information processing domain. Combination of smartphones sensing technology and data mining approach can bring fruitful results to our recognition. This study has been treating with application for smartphones to acquire specific amount of data from the target, manipulation of data set, performance of data mining and visualization normally hidden data characteristics/ relationship through machine learning. Driving characteristics acquired by application of smartphone (iPhone) have been stored as data base. Machine learning (SVM) has been applied to the relevant data for generation of useful identification operator for pattern recognition. And Visualization of subconscious driver's behavior can be demonstrated through such identification operator.

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Section: Related Workmentioning

confidence: 99%

Data Mining by Smartphone and Big Data Analysis for Driver's Subconscious Behavior - An Attempt to Recognize Driver's Characteristics and Realize Safe Driving Techniques

Kubota¹,

Imai²

2016

IJEETDM

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“…Other examples include efforts by utility companies to induce consumers to minimize power wastage by reporting average consumption [14]; and health tracking devices, e.g., Fitbit , Jawbone Up , Nike FuelBand , etc., that all incorporate social nudging to motivate physical activities. The soft control policy using peer pressure is shown to promote cooperation in these and other settings, both in theory and in practice [15–18]. …”

Section: Introductionmentioning

confidence: 99%

“…Soft learning is a class of learning mechanisms that appropriately incentivize agents in a social network to aggregate important information. Examples of soft learning include social sensing and social learning [18–20] in the context of real-time traffic information and online reviews (such as Yelp).…”

Section: Introductionmentioning

confidence: 99%

“…That said, explorers do resemble the asocial innovators and the followers resemble the copying agents in the social learning setting [20]. The confidence level of an agent may be indirectly related to peer pressure [15–18]: The followers experience a higher peer pressure than the explorers, and therefore, set a higher value of β . Also note that h i ( x i ) can be re-written as follows: …”

Section: Introductionmentioning

confidence: 99%

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Soft Regulation with Crowd Recommendation: Coordinating Self-Interested Agents in Sociotechnical Systems under Imperfect Information

2016

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Regulating emerging industries is challenging, even controversial at times. Under-regulation can result in safety threats to plant personnel, surrounding communities, and the environment. Over-regulation may hinder innovation, progress, and economic growth. Since one typically has limited understanding of, and experience with, the novel technology in practice, it is difficult to accomplish a properly balanced regulation. In this work, we propose a control and coordination policy called soft regulation that attempts to strike the right balance and create a collective learning environment. In soft regulation mechanism, individual agents can accept, reject, or partially accept the regulator’s recommendation. This non-intrusive coordination does not interrupt normal operations. The extent to which an agent accepts the recommendation is mediated by a confidence level (from 0 to 100%). Among all possible recommendation methods, we investigate two in particular: the best recommendation wherein the regulator is completely informed and the crowd recommendation wherein the regulator collects the crowd’s average and recommends that value. We show by analysis and simulations that soft regulation with crowd recommendation performs well. It converges to optimum, and is as good as the best recommendation for a wide range of confidence levels. This work sheds a new theoretical perspective on the concept of the wisdom of crowds.

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“…Advances in mobile computing technologies have brought new opportunities for real-time behavior monitoring and behavior change [38]. Small-scale sensors and mobile devices have enabled a variety of measures for everyday life activities, such as step counts [42], emotions [27], sleep quality [24], and mood [34].…”

Section: Introductionmentioning

confidence: 99%

BeUpright

Shin

Kang

Park

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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Research shows the critical role of social relationships in behavior change, and the advancement of mobile technologies brings new opportunities of using online social support for persuasive applications. In this paper, we propose Relational Norm Intervention (RNI) model for behavior change, which involves two individuals as a target user and a helper respectively. RNI model uses Negative Reinforcement and Other-Regarding Preferences as motivating factors for behavior change. The model features the passive participation of a helper who will undergo artificially generated discomforts (e.g., limited access to a mobile device) when a target user performs against a target behavior. Based on in-depth discussions from a two-phase design workshop, we designed and implemented BeUpright, a mobile application employing RNI model to correct sitting posture of a target user. Also, we conducted a two-week study to evaluate the effectiveness and user experience of BeUpright. The study showed that RNI model has a potential to increase efficacy, in terms of behavior change, compared to conventional notification approaches. The most influential factor of RNI model in the changing the behavior of target users was the intention to avoid discomforting their helpers. RNI model also showed a potential to help unmotivated individuals in behavior change. We discuss the mechanism of RNI model in relation to prior literature on behavior change and implications of exploiting discomfort in mobile behavior change services.

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Sensing, Understanding, and Shaping Social Behavior

Cited by 70 publications

References 88 publications

Data Mining by Smartphone and Big Data Analysis for Driver's Subconscious Behavior - An Attempt to Recognize Driver's Characteristics and Realize Safe Driving Techniques

Data Mining by Smartphone and Big Data Analysis for Driver's Subconscious Behavior - An Attempt to Recognize Driver's Characteristics and Realize Safe Driving Techniques

Soft Regulation with Crowd Recommendation: Coordinating Self-Interested Agents in Sociotechnical Systems under Imperfect Information

BeUpright

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