COIN course 22 Happimetrics
Knowing what makes you happy will make you happier!
Becoming aware of what and who makes people happy or stressed will increase individual happiness and create blockbuster business performance. Research has clearly shown what creates happy employees – giving them respect, empower them to take their own decisions, and be an empathic, humble leader – but it is awfully hard to actually lead by those principles. This course introduces a proven and tested method for reaching this goal, analyzing individuals’ communication patterns and making them self-aware by mirroring their behavior back to them in a privacy-respecting way. It describes three key steps to build happiness and better performance through entanglement, first starting with how we can communicate better to create a sustainably happy workplace, second how to optimize communication to increase happiness and teamwork by mirroring back the measurements to the individual, and third how to measure groupflow and happiness. This method is based on 20 years of research from our MIT Collaborative Innovation Networks (COIN) project on leadership, creativity, team building, and positive psychology published in over 250 peer-reviewed scientific papers. We take insights from hundreds of industry and research projects our team conducted on individual and organizational creativity and performance, and distill it into an artificial intelligence algorithm for building exceptional teams and organizations, improving individual happiness and organizational performance by establishing and nurturing groupflow.
Groupflow extends the flow concept coined by Hungarian-American psychologist Mihaly Csikszentmihalyi to teams. Flow is the highest state of individual productivity, where an individual is fully immersed into an activity just for the joy of doing it, and not by external motivation such as money, power, or glory. Groupflow enables teams to reach their highest productive and creative state, cooperating above and beyond of what each team member is capable of. Groupflow is the most fruitful mental condition a group of people can achieve, working together to solve a seemingly impossible task. In a state of elation and positive stress, they outperform themselves and the expectations of their peers and deliver a product that is far superior to what each of the team members individually could do.
I – How do emotional reactions reflecting individual morals create entangled tribes?
Part I lays out the foundations of groupflow and how it creates happy high-performing teams. Groupflow arises when people unified by the same morals and thus sharing similar emotional reactions get together to work on a seemingly impossibly hard task. Groupflow is based on the theory of flow defined by Mihaly Csikszentmihalyi, extending it to groups of creative people who develop radically new ideas. Flow in the sense of Csikszentmihalyi consists of operating in positive stress while being fully immersed in a near impossible task. Groups of people get together in creative swarms, sharing similar ethical and moral values, and establish digital virtual tribes. People in the same virtual tribe show similar emotional responses and thus signal to each other membership in the same tribe. They become “synchronized” in entanglement through shared emotional responses to form the collaborative bond – entanglement – that carries them to groupflow.
II – How can measuring emotions and morals create happy and successful teams?
The key to increasing happiness and groupflow is virtual mirroring, showing individuals how they communicate, and how they can do better. Towards that goal, we introduce the inovent process: insight, oversight, entanglement. Creating groupflow through entanglement happens in COINs, Collaborative Innovation Networks, teams of intrinsically motivated people collaborating on a shared creative task to create something radically new. They become part of a virtual organism operating in collective awareness. Their collective consciousness, their entanglement, and their groupflow are supported and reinforced through insight gained by virtual mirroring, by showing them how much they are already synchronized and what they can do to become even more entangled. As has been shown in our research, giving teams access to their virtual mirror greatly improves their performance, creativity, and happiness. This is based on the principle “the best against the rest”, showing people how they are doing compared to the anonymized aggregated communication patterns of their peers together with recommendations for more happiness and groupflow. The virtual mirror also exposes individuals to oversight by integrating feedback from others, leading to stress and pain, as they might discover that they are not as popular, fair, ethical, and collaborative as they think they are. Accepting potential weaknesses, and demonstrating willingness to change will be temporarily painful, but will lead to more groupflow, better performance and thus superior results.
III – How can emotions and morals be measured with AI?
Our work has combined three different approaches to track and mirror human interaction while groups of people collaborate in groupflow: measuring emotions in content, measuring network structure, and measuring interaction dynamics. These methods are enabled through the most recent advances in AI, machine learning, and deep learning. Emotions of individuals can be measured through body signals using smartwatches, through image recognition of body language, through image recognition of facial expressions, and through voice emotion recognition. Most prominently, emotions can also be recognized through word usage. These different emotion recognition systems use different AI algorithms, for instance deep learning for face emotion recognition and body language recognition, or other machine learning algorithms for NLP (natural language processing) to calculate emotions from words. Besides recognizing the emotion of individuals, the network structure of the interaction network can be tracked using graph algorithms and social network analysis, to compute the centrality of individuals in the network, and the distribution of centralities among all members of the network. Finally, network dynamics can be calculated, looking at changes in network structure and position over time. The content of what is being said by whom at what time can also be analyzed, the more the words of an individual in a network differ from the words of the other members of the network, and the faster they are picked up by others, the more influential that individual is. Entanglement measures the rhythm and synchronicity of mutual interaction in the network. The more synchronized the interaction and changes in network position among members of a network are, the more entangled are these individuals.
The course consists of 3 parts
An introductory course on Human Dynamics/Social Network Analysis/machine learning based on the draft book manuscript “Happimetrics”
Coolhunting and coolfarming exercises using the cloud based analysis tools Griffin and Galaxyscope
Project work in teams, doing a large scale project. For projects of the fall term 21/22, see the zoom recordings of the project status meetings
See www.happimetrics.com for an online version of the course materials.