Instalment 3: Idea Machines (Big Data and Social Physics)

MOOC Summaries - Big Data and Social Physics - Organizations - Business strategy plan concept idea,Light bulb with icons modern business and pencil. Vector illustration layout template designInstalment 3: Idea Machines

“Organizations… Collective Intelligence… Social Behavior Sensing with Sociometric Badges… Understanding How People Work…Meeting Mediator… Time-Critical Social Mobilisation…” 
(Source)

Summaries

  • Intro Video
  • Collective Intelligence
  • Social Behavior Sensing with Sociometric Badges
  • Understanding How People Work
  • Meeting Mediator
  • Time-Critical Social Mobilisation

Intro Video

  • Organizations are idea machines.
  • If the flow of new ideas- interaction- is better, then the organizations work better.
  • Look at patterns of communication; what we found was that the habits of a work group came from this engagement.
  • Engagement within group: accounts for 30%, sometimes 40% of the variation between groups that really work well and groups that work poorly.
  • Another 10% to 20%- and particularly for creative groups- comes from exploration outside the work group.
  • New way using big data communication to better understand organizations.
  • Another example: a big contest at the 40th anniversary to find 10 red balloons somewhere in the United States.
  • And all the traditional intelligence and an emergency people thought it would be impossible to find 10 balloons anywhere in the U.S. in a period of eight hours; 4000 teams signed up.
  • These teams used economic incentives, except for our team, which used a social network incentive: so instead of just paying people for finding the balloon, we paid people for recruiting people to find balloons.
  • What we were able to do in a period of about 24 hours is recruit almost two million people, and we won the award.
  • Different way of thinking; not the normal way, but it works tremendously well.
Chop Chop MOOCs’ summary of Big Data and Social Physics

Collective Intelligence

  • Groups of individuals working together in ways that seem intelligent.
  • People have always done that (e.g. agriculture) but the Internet has created new types of collective intelligence (e.g. Wikipedia).
  • Core question: how can people and computers be connected so that collectively, they become more intelligent.
  • Researching into “organisational genes” that make some organizations intelligent.
  • Examples: Linux, Climate Collaboratorium,
  • Can we think of all the humans and computers in the world be thought of as a global brain?
Chop Chop MOOCs’ summary of Big Data and Social Physics

Social Behavior Sensing with Sociometric Badges

  • Reality mining: using electronic sensors and mobile devices to automatically capture and analyze information about human behavior.
  • Study how people work and collaborate with each other by sensing different kinds of social behaviors contained in different parts of the body and analyze the way people speak to each other.
  • Capture the amount of face to face interaction as we talk to other people through badges worn by people; these are called sociometric badges.
  • Basic question: how can we use sensors to design better interventions that improve how organizations perform?
  • Examples of possible interventions: office layout, real time feedback to teams,  dashboards for organizations and dynamic visualizations etc
  • Eventually we can implement dynamic team reconfiguration strategies to maximize group performance, improving the way people work, communicate, and collaborate.
Chop Chop MOOCs’ summary of Big Data and Social Physics

Understanding How People Work

  • What matters in organizations:how people are interacting and collaborating.
  • Studied a call center e.g. how long does it take people to complete calls?
  • The most predictive feature was how tightly knit the social network was; those with the most tightly knit networks were completing calls in about half the time as people with the least tightly knit networks.
  • How can you actually create these interactions?
  • A simple idea: let people take breaks together instead of individually.
    • There was a decrease in stress levels, increase in cohesion, and productivity went up.
  • Another simple idea: centralising coffee area.
    • Everyone comes to one place, bump into people, meet new people, and those interactions have real economic value.
Chop Chop MOOCs’ summary of Big Data and Social Physics

Meeting Mediator

  • Are there any type of interactions that the social signals are impaired in some ways e.g. over the phone, chatting over text, video conference etc, where some social signals are not communicated (compared to face to face conversations).
  • Example: conversations in a co-located team are more balanced amongst the team members than in a distributed team where someone might dominate the conversation (and another might not participate at all) because the social signals are lacking.
  • Especially interested in looking at the performance of distributed teams because research suggest these usually have much lower trust, lower cooperation, and lower information sharing efficiency.
  • Using the data and giving it as feedback (e.g. visualised in real time) to the distributed team to ensure for example the conversations were more balanced between team members; conversations did in turn become more balanced (on-par with co-located groups), and cooperation and performance went up too.
Chop Chop MOOCs’ summary of Big Data and Social Physics

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