1. Just-in-Time Social Cloud: Computational Social Platform to Guide People’s Just-in-Time Decisions Author:Kwan Hong Lee, Andrew Lippman, Alex S. Pentland, Pattie Maes Speaker: 饒展榕

2. outline INTRODUCTION BACKGROUND SYSTEM ARCHITECTURE APPROACH AND METHOD EVALUATION

3. I.INTRODUCTION Author present the architecture of the just-in- time social cloud and evaluate through two deployments in the real world to assess the impacts of the just-in-time social cloud on people’s real world decisions.

4. The just-in-time social cloud can be incorporated into existing mobile services to help individuals optimize towards their long term goals by exploiting virtual social influences created from other people’s choices.

5. author attempt to design a social platform that can be programmed to benefit human being’s long term goals by mitigating the inter- temporal biases people have towards present.

6. Inter-temporal biases make people discount the future and encourage people to act on impulses for immediate gratification that many times results in trading off their long term rewards.

7. II. BACKGROUND In the current world where people’s choices are broadcast and changes in preferences are shared and consumed in real time, the social cloud is relaying social influences across time and space.

8. Especially when there are uncertainty in justin- time choices, marketers are actively trying to utilize the mobile phones to create multitudinous forces to affect eople’sdecisions and behaviors.

9. A. Inter-temporal Biases When people make decisions in the real world, people use different modes of economizing behavior to reduce energy,time,computational and cognitive resources in making decisions.

10. B. Social Influences Online Mobile devices present virtual peer and social influences that are different from the traditional physical social influences. The effects of such social influence will become more important as many of our online activities migrate from online to the mobile.

11. The combination of mobility and augmentation of contextual information by the mobile devices create choices in the physical world that are assisted by the online world.

12. C. Mobile Phones as Persuasive Interfaces Recent research with mobile phones have allowed us to capture in detail and understand our communication patterns, mobility patterns and to deduce how people behave in aggregate in the real world. Researchers have been using mobile probes to capture and understand people’s shopping behaviors.

13. Bluetooth scanning and location based information from mobile phones have been used to capture people’s social relationships, patterns of activity and their habits in the real world.

14. III. SYSTEM ARCHITECTURE In this section, the core components of the just-in-time social cloud are described including the social repository, the goal, the selection, the presentation and the timing components. Two systems, MealTime and SocialMenu have been implemented to show how the social cloud could be utilized for different applications.

15. The main design goal of the just-in-time social cloud is to embed the social network in the current activity and make them available where and when the decision is being made. Depending on the user’s particular goal, the social cloud is queried with the most influential set of social networks that can guide current decision towards one’s long term goals.

16. In every application, we need the following functional components : 1)Goal 2)Selection 3)Presentation 4)Timing

18. B. Implementations The just-in-time social cloud was realized in different forms through two iPhone applications MealTime and SocialMenu. The MealTime system automatically logged MIT’s Tech- CASH transactions among the participants and their social networks while providing a digital receipt and a view of their friends’purchases.

19. They could comment and rate their purchases through the app.

20. The SocialMenu system captures the choices and browsing behavior of a digital menu at a local restaurant and provides multiple social perspectives using what others have ordered in the past.

21. IV. APPROACH AND METHOD A.MealTime The MealTime application was used to capture people’s near future decisions of deciding on where to go for their meals inside college campus. The real intention of our study was to capture people’s choices and whether people being exposed to other people’s transactions from the phone influenced them differently in their purchase behaviors.

22. B. SocialMenu The goal of the study was to understand how timely, in-place social influences affect choices during uncertainty. When they came to dine and were seated at the table, people were randomly assigned to 1) control, 2) friends, 3) popularity and 4) group of friends experimental groups when they logged-in to the digital menu.

23. V. EVALUATION A.Collected Data The following data were logged at the time of purchase in MealTime. 1)Login times to the mobile application. 2)The latitude/longitude if the phone had location enabled. 3)The views they see, the social cues, and the choices they click through were recorded. 4)Transactions: location name, price and the timestamps from the transaction.

24. The following data were logged by participants in the SocialMenu experiment. 1)Table code given to subjects and their login time. 2)Pre-survey data on participant’s 5 favorite dishes and their tastes.

25. 3)People’s order browsing behavior based on the clicks of different categories and menu items that they considered. 4)Presentation of the social cues: the number of people and the type of social information presented. 5)People’s order duration used to measure uncertainty(captured based on people’s login time and the time when subjects finalized their order).

26. B. Meal Time Results The following lists the summary of the results from the Meal Time study: 1)The diversity index of the first degree social networks are more similar to an individual’s diversity index than the diversity index of the second degree social networks. 2)Places view showing transactions of friends or popularity did not significantly change the diversity index.

27. An individual’s frequency of transactions at each hour of the day is significantly similar to their first degree social network (friends), but not to the second degree networks or random networks.

28. C. Diversity Index The more they visit a particular location, the diversity index is reduced. The diversity index is maximum when they visit each place equal number of times.

29. D. SocialMenu Results The SocialMenu experiment was performed to understand the impacts of different social cloud on people’s just-in-time decisions. 1)Deviation of choices 2)Time of engagement 3)Price factor 4)Individual friends increase engagement