Join discussions in order to build understanding of concepts in service science. Here is our curriculum guide.
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About this site & registering.
Join discussions in order to build understanding of concepts in service science. Here is our curriculum guide.
Follow Jim (@JimSpohrer) on Twitter
About this site & registering.
Companies often ask about IBM’s efforts in the area of Service Science and Big Data Analytics, so here are a few useful pointers:
IBM works with over 500 universities worldwide on service science related courses and programs.
An overview of service science was created with Cambridge University in 2008, and can be downloaded here:
Companies interested in working with IBM (and other companies, universities, government agencies) are encouraged to join ISSIP.org – the International Society of Service Innovation Professionals. Contact Yassi Moghaddam, Executive Director (email@example.com).
Additional information is available here:
Big Data Analytics
IBM works with over 1000 universities worldwide on big data analytics related courses and programs.
An overview of big data analytics applied to enterprise operations can be found in this recent book:
Companies interested in working with IBM (and other companies, universities, government agencies) are encourage to contact the IBM Research – Almaden Accelerated Discovery Lab. IBM Is also active in INFORMS – Operations Research and Management Sciences professional association.
Additional information is available here and here:
The Cognitive Systems Institute, which is a new set of IBM university programs in conjunction with IBM Research and the Watson Business Unit, will focus faculty collaborators on building and evaluating cognitive assistants for every profession. Artificial cognitive systems, or cognitive systems for short, exhibit capabilities and/or perform tasks deemed intelligent by natural cognitive systems, such as people. Professional cognitive assistants are cognitive systems designed to boost the productivity and creativity of professionals. Cognitive systems researchers belong to a special profession, which improves building and evaluating cognitive systems, working on teams with other professionals, such as computer and information research scientists, human factors engineers and ergonomists, sociologists, operations research analysts, mathematicians, statisticians, industrial engineers, and others.
The Cognitive Systems Institute will focus on professional cognitive assistants that exhibit the three L’s – language, learning, and levels. Professional cognitive assistants should interact via natural language, learn by ingesting documents, and make recommendations with confidence levels. For example, the IBM Watson Jeopardy! winning cognitive system answered natural language questions, ingested Wikipedia and other sources to learn, and provided confidence levels with its answers. The IBM Watson group is working on cognitive systems to help doctors, financial planners, researchers, and even chefs. IBM Research is also working on cognitive system that will spar with debaters and politicians to help boost their performance.
The new Cognitive Systems Institute programs will be launched in late August, and will be designed to (1) help prepare faculty to set up Watson/Cognitive aligned courses, with the goal of enabling student teams to develop cognitive apps as part of the Watson Ecosystem, (2) help prepare faculty and their top graduate students to submit aligned collaborative research proposals to funding agencies, with the goal of developing cognitive systems that boost the productivity and creativity of specific types of professions and professional teams (potentially a key part of what NSF calls “smart service systems”), (3) help create linkages between faculty and IBM Researchers to define cognitive system grand challenges with clear measurable business and societal impact that might be achievable in the next 3-5 years, with the goal of further advancing the field of cognitive systems research and increasing aligned national research investments. For example of a grand challenge, IBM’s Watson Jeopardy! system (2011) required close collaboration with seven universities to develop it, and it had a very clear measurable set of performance metrics that focused everyone across organizations and helped make decision-making easier. For this last item, we are also (4) exploring academic interest in having an IBM-Researcher(s)-In-Residence at their universities, with the goal of accelerating collaborative research and achieving measurable grand challenge objectives.
Please let me know which of items 1 – 4 might be of most interest, or if your interests are in some other direction, and we can help guide you to the right IBMers to follow-up (for example, some universities already have data sets ready to be ingested, and investors interested in developing specific applications, so they are exploring the Watson Developer Cloud for Enterprise, as a path to get on site training and developer licenses more quickly). Building cognitive systems to boost the performance of professionals, including research and teaching faculty at universities, is likely to be an important application area, and will have associated research grand challenges.
Jim Spohrer (firstname.lastname@example.org)
Bush (1945) wrote: There is a growing mountain of research. But there is increased evidence that we are being bogged down today as specialization extends. The investigator is staggered by the findings and conclusions of thousands of other workers—conclusions which he cannot find time to grasp, much less to remember, as they appear.
Engelbart (1962) wrote: By “augmenting human intellect” we mean increasing the capability of a man to approach a complex problem situation, to gain comprehension to suit his particular needs, and to derive solutions to problems. Increased capability in this respect is taken to mean a mixture of the following: more-rapid comprehension, better comprehension, the possibility of gaining a useful degree of comprehension in a situation that previously was too complex, speedier solutions, better solutions, and the possibility of finding solutions to problems that before seemed insoluble.
What advances are on the verge of reshaping how we may think and augmenting our intellect? How might these advances contribute to a Moore’s Law for service science and smart service systems? What are the related challenges and opportunities to this vision?
Smart phones are an everyday reality (not quite as envisioned in the 1930s, but close enough)…
Regarding how we may think and augmenting human intellect, my colleagues and I are working on a virtual community called the Cognitive Systems Institute (plan to launch next revision in September 2014) with one important goal being the creation of POVs to boost government and venture funding for cognitive systems research and startup development. “Cogs” (Cognitive Systems/Cognitive Assistants) for boosting regional economic development will be appearing at an accelerating pace. Gartner predicts 10% of all computers will be learning by 2017. Many industries and professions will be disrupted including higher education. The gist of the vision for the Cognitive Systems Institute is to have university researchers building “Cogs” for every profession, for every region and in every language, and with student teams launching startups using the Watson Developer Cloud. IBM’s BlueMix on SoftLayer is the beginning of the API Economy leading to Cognition as a Service, which access to much cognitive componentry including Natural Language Processing on OpenPower and Pattern Recognition on SyNAPSE. “Cogs” become our “Cognitive Bulldozers in the Era of Big Data/Internet of Things” because “Cogs” know individuals (you) and know professions (your job), boosting productivity and creativity (how to measure productivity and creativity for many professions is a challenge as well – I recommend this book). “Cogs” learn, use language to interact more naturally, and have levels of confidence in what they know and do not know – learning, language, and levels. The Forbes Global 2000 companies generate nearly a 1/3 of the GDP of the world in just 2000 publicly traded companies’ revenue…. they have combined about 100M employees. If there was a “Cog” to help each of their employees be twice as productive and creative – you move the needle on the GDP of the planet. People enjoy being more productive and creative so quality-of-life might also be improved, if done right. “Cogs” may well be a key part of a Moore’s Law for Smart Service Systems.
For those interested in helping, after re-reading Bush (1945) and Engelbart (1962), a practical next step would be to read this:
IBM has developed enabling technologies and proof-points, and is investing billions to realize this vision imagined by Bush and Engelbart. Aligning government funding and venture funding will enable university faculty researchers and student entrepreneurs to play a major role in making this vision a reality in the coming decade.
Competing for collaborators is the new normal in a highly interconnected, innovation-driven global economy.
Especially, when it comes to winning the hearts and minds of faculty and students to build startups on industry platforms.
Platforms (sometimes referred to as solution stacks, or HW/SW stacks) include Apple iPhone and Google Android. IBM has platforms as well – including Watson, Big Data Analytics, and Smarter Planet platforms. In the case of Watson, IBM is eager to encourage university start ups to compete to build “Cogs” (cognitive assistants with question answering abilities) on the Watson Developer Cloud, and join the Watson Ecosystem (see http://www.ibm.com/watson, as well as https://www.ibmdw.net/watson/docs/5-steps-powered-watson-application/).
Startups are key to regional economic development, and industry platforms can provide both a starting point and foundation for startups.
Industry is looking for top academic brand partners with courses that are reaching millions of entrepreneurial minded faculty and students globally with research based curriculum. Industry would like one or two lectures in those courses to be geared towards teaching local/regional faculty and student teams about industry platforms, how to be certified as a developer on those platforms, how to develop business plans for startups that build on those platforms, etc.
The easy integration of industry content with globally available top academic brand partners’ courses is what is key.
The KPI from industry perspective is revenue and profit growth driven by successful startups reaching customers with valuable new offerings (in many cases service innovations – hence the connection to ISSIP, the International Society of Service Innovation). The sustainable and viable business model is based on a small percentage of profits going to the cost of including the industry content in the global courses, and helping the newly minted startups be successful.
Competing for collaborators is the new normal, and easy integration of industry content into global available courses is key.
The author of the NYT article, Tyler Cowen, notes:
“Many other examples of automatable jobs are discussed in “The Second Machine Age,” a book by Erik Brynjolfsson and Andrew McAfee, and in my own book, “Average Is Over.” The upshot is that machines are often filling in for our smarts, not just for our brawn — and this trend is likely to grow.”
I especially agree with this point from the NYT article:
“There are unlimited human wants, so there is always more work to be done. The economic theory of comparative advantage suggests that even unskilled workers can gain from selling their services, thereby liberating the more skilled workers for more productive tasks.”
Ricardo’s Law of Comparative Advantage is very profound – and greatly appreciated in the global service science community. The advantages of interaction seems to be mathematically built into the fabric of universe, and certainly society. Not just productivity (via Ricardo, etc.) but also creativity/innovation is enhanced by diverse perspectives and interaction (MIT’s Thomas Malone in his Science article on collective intelligence, and recent formulations by Pentland at MIT - http://service-science.info/archives/3486). This topic will surely be discussed at the National Academies Keck Center in this NSF-sponsored workshop in conjunction with the UIDP – research priorities for service science.
I especially like the perspective in this blog post by @JosieHolford http://t.co/zdlS8g875m — don’t ask kids what they want to be when they grow up (that is 20th Century thinking – implicit a job and profession), ask them what problems they want to work with others to solve (21st Century thinking – implicit a team and purposeful effort) – their type of career may not exist yet.
If the transition to the industrial age is any guide, most of the jobs and types of careers in the age of smart machines do not exist yet. The era of cognitive systems is just beginning to dawn.
Before explaining, consider recipes just a bit…
Recipes are new combinations and configurations of existing ingredients. Some recipes are appealing, and stick around.
In his book “The Coming Prosperity: How Entrepreneurs Are Transforming The Global Economy” Phil Auerswald extends the notion of recipes in several interesting ways, including viewing entrepreneurs as people who bring new innovations to the world by seeking out new combinations and configurations of resources.
In “Technology: What It Is And How It Evolves,” Brian Arthur describes the evolution of technologies as new combinations and configurations of existing technologies, with occasional scientific discoveries expanding the list of ingredients.
Resource integrators are fundamental, and described in the Service-Dominat Logic literature of Vargo and Lusch.
In the emerging service science literature, built on the foundations of the S-D Logic worldview, service systems are defined as dynamic configurations of resources (people, technology, organizations, and information) interconnected by value propositions, for the purpose of value co-creation and capability co-elevation. Service science is the study of the evolving ecology of nested, networked service systems entities, complex systems with capabilities, constraints, rights, and responsibilities.
Smart service systems are an emerging focus area for the National Science Foundation, and a first set of proposals are now under review.
As the era of cognitive computing and smart machines dawns, the people inside service systems will benefit from Cogs. By boosting the productivity and creativity of people in roles in service systems, Cogs will lead to smarter service systems.
So how can a food truck become an example of a smart service system?
IBM has put its cognitive computing system in control of the menu at a food truck feeding attendees at this week’s SXSW festival and the appointment has resulted in some particularly imaginative dishes.
For its “Cognitive Cooking” project, IBM has enlisted the services of four prominent chefs to work with Watson, who is consulting a database of tens of thousands of recipes and ingredient combinations to conceive dishes that the regular foodie has probably never thought of.
“If you were to look inside the system that is running in the IBM cloud you would see a system that is trained on 35,000 different recipes, as if it was digesting a giant cookbook,” said Steve Abrams, Director of Watson Life at IBM. “From reading that cookbook it has learnt an awful lot about different ingredients that are often used in different cuisines, and the ingredients that are often paired together.”
So where is the world of Cogs and Smart Service Systems headed? To get a glimpse of the answer, Ramez Naam has begun to articulate clearly some of the key issues. The notion of “learning” that Naam begins to illuminate is a step in the right direction, especially when he remarks:
And, indeed, should Intel, or Google, or some other organization succeed in building a smarter-than-human AI, it won’t immediately be smarter than the entire set of humans and computers that built it, particularly when you consider all the contributors to the hardware it runs on, the advances in photolighography techniques and metallurgy required to get there, and so on. Those efforts have taken tens of thousands of minds, if not hundreds of thousands. The first smarter-than-human AI won’t come close to equaling them. And so, the first smarter-than-human mind won’t take over the world. But it may find itself with good job offers to join one of those organizations.
A grand challenge research problem in cognitive systems research and service science is to minimize the amount of energy, material , and time required to rebuild more and more capable systems (cognitive systems and service systems). This could mean finding the optimal set of training data to boot-up a Cog that can assist a human in some particular job role (“how many recipes does a Cog need to read and understand before it can help a chef innovate?”). Or it could mean finding the optimal population of Cogs to help a new Cog train up rapidly. Or it could mean, how rapidly can one rebuild the entire societal infrastructure, and ecology of service system entities. These are new fundamental problems in accelerated learning rates, for individuals in isolation (just data stream inputs), for individuals in a society of other entities, and for entire societal boot-ups.
In a few weeks a group of us will convene in Washington DC to develop a proposed research agenda for service science and innovation, with an eye towards how technology advances will enable smart service systems of the future. Empowering the people in service systems to be more productive and creative with Cogs will surely be one of many topics for discussion.
In thinking about the jobs of the future and jobs in the age of smart machines, it is important to think about jobs where there is no end in sight of work to be done. Scientists, artists, ethicists, entrepreneurs, explorers, coaches, and chefs are seven jobs that immediately spring to my mind. Surely, there are many more.
“Cogs” can be thought of as a new species of intelligent agents (“smart machines“) that can learn and communicate with us, and know a person and a person’s history very well. For example, already doctors are starting to recommend healthcare apps to patience, and perhaps soon they will prescribe apps, and insurers will pay. However, “Cogs” will have capabilities far beyond simple apps.
Perhaps like our pets, Cogs know us; but unlike pets, Cogs process natural language and do pattern recognition as smart machines – with capabilities that are on a tremendous improvement trajectory, driven in part by Moore’s Law. Cogs will be one of the key innovations in the era of cognitive computing, or what some have called The Second Machine Age.
The IBM Watson Group is building APIs that will be available in the IBM Cloud (SoftLayer) – and people will be able to build personal Cogs as well as professional Cogs for various applications (each job type or professional will have a GenericCog, that can be customized to know a specific person in a job role).
For example, imagine a ChefCog, it already exists, helping chefs create and prepare amazing new recipes. If a chef has some unique style, then a ChefCog would adapt and help that person be more creative and productive. Or like free-style chess, imagine teams of chefs and their ChefCogs all working together to advance the culinary arts.
One can also imagine DoctorCogs, MedicalEducatorCogs, CustomerServiceCogs, and SalesCogs, all helping people be more creative and productive – and like the ChefCog, early versions of all these types of Cogs already exist.
So you may have a Cog that knows you holistically, and then a separate Cog for each of your roles in life (e.g., various service systems – at home, at work, at school, at the hospital, etc.).
Technically, the set of Cognitive Systems includes entities such as people, pets, and Cogs…. but also larger entities, such as companies that know you and communicate with you and can learn about you like Facebook, Google, etc. as well as nations, states, cities that know you as a citizen and provide service offerings customized to your needs. Any entity that stores information about you and builds up a profile that helps the entity interact with you naturally to co-create value and co-elevate capabilities can be viewed as a type of Cog.
So Cogs are somewhat like a new species, without rights or responsibilities (so not a formal service system entity yet), but definitely with capabilities and constraints.
The relationship: All service system entities are cognitive system entities, but not all cognitive system entities are service system entities. The set of Cogs is a subset of the set of Cognitive Systems, but until Cogs have rights and responsibilities – they are disjoint from the set of Service Systems.
Pentland, A. (2014). Social Physics: How Good Ideas Spread-The Lessons from a New Science. Penguin.
“Most people think in relatively static terms…. I think in terms of social physics: growth processes within networks” p. ix
“…research program to develop a rigorous intellectual framework that extends current individual-centric economic and policy thinking by including social interactions. It posits social learning and social pressure as primary forces that drive the evolution of culture and govern much of the hyperconnected world.” p. ix
“But as we know all know, academic papers are, well, academic. So I’ve also helped… creating half a dozen start-up companies…” p. x
Chapter 1: From ideas to Actions
“Where do new ideas come from? How do they get put into action? How can we create social structures that are cooperative, productive, and creative?” p. 1
“Suddenly our society has become a combination of humans and technology that has powers and weaknesses different from any we have ever lived in before.” p. 2
“Adam Smith himself understood that it is our social fabric… In his book, Theory of Moral Sentiments he argued that it was human nature to exchange not only goods but also ideas, assistance, and favors our of sympathy.” p. 3
“The goal of this book is to develop a social physics that extends economic and political thinking by including not only competitive forces byt also exchanges of ideas, information, social pressure, and social status in order to fully explain human behavior.” pp. 3-4.
“Social physics is a quantitative social science that describes reliable, mathematical connections between information and idea flow on the one hand and people’s behavior on the other. Social physics helps us understand how ideas flow from person to person through the mechanism of social learning and how this flow of ideas ends up shaping the norms, productivity, and creative output of our companies, cities, and society.” p. 4.
“Just as the goal of traditional physics is to understand how the flow of energy translates into changes in motion, social physics seeks to understand how the flow of ideas and information translates into changes in behavior.” p. 5
“The ultimate test of a practical theory, of course, is whether or not it can be used to shape outcomes… create better companies, cities, and social institutions.” p. 7
“The engine that drives social physics is big data… by analyzing patterns of human experience and idea exchange within the digital bread crumbs we all leave behind us as we move through the world… These data tell the story of everyday life by recording what each of us has chosen to do…. reality mining…” p. 8
“During the past decade, my students and I have developed the ability to build and deploy such living labs, measuring entire social organisms – groups, companies, and whole communities – on a second-by-second basis for up to years at a time.” p. 9
“To accomplish this I have developed legal and software tools to protect the rights and privacy of the people in the labs to insure they are fully informed about what is happening to their data and that they maintain the right to opt out at any time.” p. 9
“… enabling us to build some of the first practical ‘socioscopes.’ These new tools give a view of life in all its complexity…” p. 10
“Figure 1: Qualitative overview of social science observations and experiments, with the horizontal axis showing data collection duration (duration of observation from minutes to years) and vertical axis showing richness of the information collected (measurements per person per minute from one to hundreds).” p 11.
“Just a brief examination of Figure 1 makes it easy to see that these social physics data sets are many orders of magnitude richer than previous social science data sets.” p. 12
“In support of this book, I have placed several of the world’s largest and most detailed living lan data sets onto the Web.” p. 13
“Friends and Family: Roughly eighteen months of data from a small community of young families…” p. 13
“Social Evolution: Nine months of data from a university dormitory…” p. 13
“Reality Mining: Nine months of data from graduate students at two university laboratories…” p. 13
“Badge Data Set: One month within a white-collar workplace…” p. 14
“Data for Development… These data are now all available from http://www.d4d.orange.com/home.” p. 14
“Idea flow within social networks, and how it can be separated into exploration (finding new ideas/strategies) and engagement (getting everyone to coordinate their behavior).” p. 15
“Social learning, which is how new ideas become habits, and how learning can be accelerated and shaped by social pressure.” p. 15
“Social physics also shares some surface resemblance to other academic domains, such as cognitive sciences…. rather than focus on individual thoughts and emotions, social physics focuses on social learning as the major driver of habits and norms.” p. 16
“The social physics that is emerging brings together branches of economics, sociology, and psychology, along with network, complexity, decision, and ecology sciences and fuses them together using big data.” p. 17
“It shows how we can begin to build a society that is better at avoiding market crashes, ethnic and religious violence, political stalemates, widespread corruption, and dangerous concentration of power. The first steps are to being setting scientific, reliable policies for growth and innovation, and to institute information and legal architectures for the protection of privacy and public transparency…. This vision of a data-driven society implicitly assumes that the data will not be abused. … I have called this the New Deal on Data … ” p. 17
“While these changes will help protect citizens from companies, they do little to protect against the government itself.” p. 18
“Language – engagement, exploration, idea, idea flow, information, interaction, social influence, social learning, social network incentive, social norms, social pressure, society, strategy, trust, value.” p. 19-21
Part One – Social Physics
Chapter 2 – Exploration
“The most consistently creative and insightful people are explorers. They spend an enormous amount of time seeking out new people and different ideas, without necessarily trying very hard to find the ‘best’ people or ‘best’ ideas. Instead, they seek out people with ‘different’ views and ‘different’ ideas.” p. 26
“The main work of science, art, or leadership is the same: developing a compelling story about the world and then deciding to test it against reality.” p. 27
“Social Learning – Harvard Business Review article ‘Beyond the Echo Chamber.’” p. 29.
“What Kelly found was that star producers engage in ‘preparatory exploration’; that is, they develop dependable two-way streets to experts ahead of time…” p. 35
“Second, start performers’ networks were also more diverse.” p. 35
“What we found was that individuals who adopted an energetic, engaging interaction style that created more interactive conversations ended up being more important to idea flow in social networks.” p. 36
“This echo chamber overconfidence effect is a source of fads and financial bubbles.” p. 37
“For example, what can be done when the flow of ideas becomes either too sparse and slow or too dense and fast? … As a result of this tuning we were able to increase the profitability of all the social traders by 6 percent, this doubling their profitability.” p. 38
“…created a spin-off company called Athena Wisdom that is now tuning financial and decision-making networks around the world.” p. 39
“Diversity is important… Contrarians are important…” p. 49
“In summary, people act like idea-processing machiens combining individual thinking and social learning from the experiences of others. Success depends greatly on the quality of your exploration and that, in turn, relies on the diversity and independence of your information and idea sources.” p. 41.
“Utilizing these questions, we can reliably predict what individuals will choose to do and how good their outcomes will be in situations ranging from companies (Part II of this book), to cities (Part III), to entire countries (Part IV).” p 42
Chapter 3 – Idea Flow
“…it is the rates of idea flow – or the barriers to idea flow – that we must understand if we are to work well together.” p. 44
“Idea flow is the spreading of ideas, whether by example or story, through social networks – be it a company, family, or a city… It facilitates the transfer of habits and customs from person to person and from generation to generation” p. 44
“… some psychologists refer to us as Homo imitans.” p. 45
“By harvesting from the parts of our social networks that touch other streams, that is, by crossing what sociologist Rob Burt called the ‘structural hole’ within the fabric of society, we can create innovations.” p. 45
“The bottom line: In these three example – health habits, political preferences, and consumer consumption – exposure to the behavior of peers, both direct and indirect, predicted idea flow.” p. 45
“Perhaps this is because learning from surrounding example behaviors is much more efficient than learning solely from our own experiences. Mathematical models of learning in complex environments suggest that the best strategy for learning is to spend 90 percent of our efforts on exploration, i.e., finding and copying others who appear to be doing well. The remaining 10 percent should be spent on individual experimentation and thinking things through.” p. 54
“Figure 4 (adapted from Kahneman’s Nobel Prize lecture): Humans have two ways of thinking: an older capability based on associations and experience (“fast”) and a new capability based on attentive, rule-based thinking (“slow”).” p. 56
“Psychological studies have shown that the snap judgments of people are more altruistic and cooperative than the decisions made slowly and thoughtfully.” p. 57
“As Nobel Laureate Herb Simon put it, our rational, conscious thinking is the program that invokes the habits of action that take care of all the details of daily life, just as computer programs have subroutines that handle frequently used computations.” p. 58
“Learning and reinforcing the social contract is what enables a group of people to coordinate their actions effectively.” p. 59
Chapter 4 – Engagement
“The ability to work together, though, goes beyond simple idea flow within a community; it also includes striking a bargain between individuals to adopt behaviors that are synchronized and compatible.” p. 63
“Some evolutionary theorists think that this type of ‘social voting’ process could be the most common type of decision making in social animals, in part because it is very good at accounting for the cost-benefit trade-offs of everyone in the group.” p. 63
“Average performers thought teamwork meant doing their part on the team. Star performers, however, saw things differently: They pushed everyone on the team toward joint ownership of goal setting, group commitments, work activities, schedules, and group accomplishments.” p. 63
“Similarly, business research has shown that this sort of engagement – repeated cooperative interactions among all members of the team – can improve the social welfare of the group, and promotes the trustworthy cooperative behavior conducive for successful business relationships.” p. 64
“What our grandmothers would have known, though, was that nearly all the social influence occurred between close friends who had a face-to-face relationship.” p. 65
“The Facebook voting example suggests that information by itself is a rather weak motivator… that seeing members of our peer groups adopting a new idea provides a very strong motivation to join in and cooperate with others.” p. 65
“But social physics tells us that there is another way: by providing incentives aimed at people’s social networks rather than economic incentives or information packets that are aimed at changing the behavior of individuals.” p. 66
“On average it turned out that the social network incentive scheme worked almost four times more efficiently than a traditional individual-incentive market approach… The number of direct interactions that people had with their buddies was an excellent predictor of how much their behavior would change. Similarly, the number of times people had direct interactions with each other gave a surprisingly accurate prediction of the trust they expressed in each other.” pp. 68-69
“The social physics approach to getting everyone to cooperate is to use social network incentives rather than to use individual market incentives or to provide additional information… Engagement – repeated cooperative interactions among members of the community – brings movement toward cooperative behavior.” p. 69
“This social network incentive caused electricity consumption to drop 17 percent, twice the best result seen in earlier energy conservation campaigns and more than four times more effective than the typical energy reduction campaign.” p. 72
“… examined the growth and performance of more than one thousand companies’ internal digital social networks.” p. 72
“In other words, engagement build culture.” p. 74
“Social physics tells us that we must include not only economic exchanges, but also exchanges of information, ideas, and the creation of social norms in order to fully explain human behavior.” p. 75
“If the majority of interactions were instead exploitative, then each interaction would serve to destroy trust.” p. 76
“Engagement requires interaction. Engagement requires cooperation. Building trust.” p. 77-78
“…idea flows, i.e., the spreading of new behaviors through a social network, may be conceptualized as exploration to harvest new ideas followed by engagement with peers to sift through those ideas and convert the good ideas into habits.” p. 79
“The Mathematics of Social Influence.” pp. 80-84
Part Two – Idea Machines
Chapter 5 – Collective Intelligence
“Groups of people, as well as communities, also have a collective intelligence that is different from the individual intelligence iof each group member. Moreover, this group intelligence is about as important a factor in predicting group performance as IQ is in predicting individual performance.” p. 87
“The largest factor in predicting group intelligence was the equality of conversational turn taking; groups where a few people dominated the conversation were less collectively intelligent than those those with a more equal distribution of conversational turn taking. The second most important factor was social intelligence… Women tend to do better at social signals…” p. 88
“What these sociometric data showed was that the patten of idea flow by itself was more important to group performance than all other factors… ” p. 89
“The characteristics typical of the highest performing groups included: 1) a large number of ideas… 2) dense interaction… 3) diversity of ideas… ” p. 89
“Figure 6: (a) an unproductive pattern of interaction, (b) a good pattern of interaction.” p. 89
“One exception to using these patterns of interaction as a guide is performance in times of stress… A second exception is when… emotions are high… ” p. 90
“The sociometric data from these small working groups highlight that teams are operating as idea-processing machines in which the pattern of idea flow is the driving factor in performance.” p. 90
“…spin-off company, Sociometric Solutions… ” p. 92
“Harvard Business Review article ‘The New Science of Building Great Teams’ …” p. 93
“As a result of this simple change, the call center management converted the break structures of all their call centers to this new system and forecast a $15 million per year productivity increase.” p. 95
“Our sociometric badges were deployed in this Chicago-area data-serve sales firm for a period of one month… collecting roughly a billion measurements about who talked to whom, their body language, and even their tone of voice… http://realitycommons.media.mit.edu)” p. 95
“Remember that engagement is defined as idea flow within a work group…” p. 96
“The solution suggested by other social species, such as ape troops and bee colonies, is to alternate between exploration for idea discovery and engagement with others for behavior change.” p. 97
“Figure 7: Exploration and engagement networks. (a) Exploration is when team members interact with other teams. (b) Engagement is when they interact with each other.” p. 98
“Qualitatively, this is what the Bell Stars study of Chapter 2 and 3 found: Star performers became familiar with different perspectives on their work. Senior management, customers, sales, and manufacturing groups all have different views, and the combination of their ideas with those already in their work group were a major source of useful creative thinking.” p. 99
“In fact, a simple combination of the engagement and exploration measures was able to predict which days were the most creative with 87.5 percent accuracy.” p. 102
“To use Herb Simon’s phrasing, if there is a consensus about an idea, it is then integrated into the team’s store of ‘action habits’ to use for their fast thinking.” p. 102
“Because fast thinking uses associations rather than logic, it can make intuitive leaps more easily by finding creative analogies.” p. 103
Chapter 6 – Shaping Organizations
“This makes the pattern of idea flow the single biggest peformance factor that can be shaped by leadership, and yet today there isn’t a single organization in the world that keeps track of both face-to-face and electronic interaction patterns. And, as we all know, what isn’t measured can’t be managed.” p. 106
[[the first unwritten law of service science is "whatever is measured can be gamed or corrupted, and will surely lose its value over time, (requiring new dimensions to be created and put into quasi-balance with existing dimensions for the ecology to remain viable and growing)." ]]
“The goal is to increase the social intelligence of both work groups and the entire organization, and so increase their performance.” pp. 106-107
“When we instrument a typical organization in order to visualize interaction patterns, both managers and employees wear our specially designed sociometric badges (see the Reality Mining appendix for more detail).” p. 107
“The most useful visualizations convey the levels of engagement and exploration within the organization…: We have found that engagement levels predict up to half the variation in group productivity, independent of content, personality, or other factors. Exploration is how much the members of a group of a group bring in new ideas from the outside; that in turn predicts both innovation and creative output.” p. 107
“Good idea flow is difficult in some kinds of groups, for example, in both widely dispersed and mix-language groups.” p. 108
[[[one reason IBM is so amazing]]]
“Figure 9: The Meeting Mediator system consists of (a) a sociometric badge (left) to record the interaction patterns of groups, and a mobile phone (right) to display them as real-time feeback.” p. 109
“While the mathematical measure of idea flow between a work group and people outside it is probably the best way to measure exploration, we have found that it is usually adequate to simply count the number of outside interactions.” p. 113
“…came up with what he calls Bayesian truth serum, which is a way of figuring our who has genuinely new bits of information that might make a difference. One might also call this the wise guys solution to the problem of insufficient diversity in idea flow. In the wise guy method, we look for individuals who can accurately predict how other people will act but whose own behavior is different. The logic is that if a person can predict othr people’s actions, then they already know the common knowledge. But if their behavior is also different from everyone else’s, then they must know something the others don’t. The behavior of such wise guys, then, can be counted as an independent bit of information.” p. 115
“In practical applications, I have found that this third method, estimating the amount of social influence between people, is the easiest and works quite well.” p. 116
“The charismatic connectors are not just extroverts of life of the party types. Rather, they are genuinely interested in everyone and everything… They tend to drive the conversations, asking about what is happening in people’s lives, how their projects are doing, how they are addressing a problem, etc. …People can teach themselves to be charismatic connectors – they are made, not born.” pp. 117-118
Chapter 7 – Organizational Change
“Because the social sciences, including economics, have had to work with such impoverished data, it has been difficult for scientists to understand the process of change.” p. 120
“Now let us examine the Red Balloon Challenge, a case in which my research team and I were able to use social network incentives to build a worldwide organization and accmplish a difficulty task in only a few hours, beating hundreds of competing teams to win the prize money. The strategy we took to accomplish this feat was so novel and effective that our approach was published in the journal Science and later expanded upon in the Proceeedings of the National Academy of Science.” p. 121
“As a result of using this social network incentive strategy, our research team correctly identified the location of all ten balloons in just 8 hours, 52 minutes, and 41 seconds.” p. 124
“Nevertheless, during the last century this sort of hierarchical crowdsourcing has been exactly the model of most corporations. Workers sit in cubicles doing independent tasks, adn then their outputs are routed to anonymous others for the next stage of processing. ” p. 126
“This connection between engagement, trust, and people’s ability to act cooperatively is perhaps the main point of Robert Putnam’s classic book Bowling Alone, which highlights the relationship between civic engagement and health of society. We are trading in ideas, good, favors, and information and not simply the competitors that traditional market thinking would make us. In each area of our lives, we develop a network of trusted relationships and favor those ties over others.” p. 130
“Understanding ourselves this way could have dramatic effect on the character of our society. Because idea flow creates culture, supports productivity, and enables creativity we should place greater value on professions that enhance idea flow: teachers, nurses, ministers, and policemen, along with doctors and lawyers who work for charities, as public defenders, or for inner city hospitals.” p. 130
“My goal is to imagine what a data-driven city might look like and how we can use big data and social physics to create more productive and creative cities. And then in the last section, I will discuss what changes need to be made to privacy, management, and government in order to create a brighter, safer future.” p. 131
“Each of these signals has roots in the biology of our nervous system. Mimicry is believed to be related to cortical mirror neurons, part of a distributed brain structure that seems to be unique to primates and is especially prominent in humans. For example, mirror neurons react to other people’s actions and provide a direct feedback channel between people. One result of this is the surprising ability of human newborns to mimic their parents’ facial movements despite their general lack of coordination… Indeed, these signaling patterns are so clear that they are now used commercially to screen for mental health conditions such as depression and to monitor patient engagement during treatment. For more details see http://cognitocorp, an MIT spin-off company that I cofounded.” p. 134
Part Three – Data-Driven Cities
Chapter 8 – Sensing Cities
“But these century-old solutions are increasingly obsolete. We have cities jammed with traffic, worldwide outbreaks of diseases that are seemingly unstoppable, and political institutions that are deadlocked and unable to act. In addition, we face the challenges of global warming, uncertain energy, water, and food supplies, and a rising population that will require building one thousand new cities of a million people each in order to stay even.” p. 137-138
“Rather than static systems that are separated by function – water, food, waste, transport, education, energy, and so on – we must consider them as dynamic and holistic systems. We need networked, self-regulating systems that are driven by need and preferences of the citizens instead of ones focused only on access and distribution.” p. 138
“Right now, the most important generator of city data is a familiar tool: the ubiquitous mobile phone. These devices are, in effect, personal sensing devices that are becoming more powerful and sophisticated with each product iteration. In addition to deriving information on user locations and call patterns, we can map social networks, and even gauge people’s moods by analyzing the digital chatter that has become so pervasive. ” p. 138-139
“Networks will become faster, devices will have more sensors, and techniques for modeling human behavior will become more accurate and detailed.” p. 139
“Many of the sensing and control elements required to build a digital nervous system are already in place. What is missing, though, are two critical items: The first is social physics, specifically dynamic models of demand and reaction that will make the system function correctly, and the second is the New Deal on Data, an architecture and legal policy that guarantees privacy, stability, and efficient government.” p. 139
“The proliferation of mobile phones makes it possible to leap beyond demographics to directly measure human behavior.” p. 141.
“These data, created by an MIT spin-off company, Sense Networks (which I co-founded), allow us to analyze movement and purchasing behaviors of tens of millions of people in real time.” p. 141
“… the process of social learning and the development of social norms within cities is driven by the observation of peer behavior, that is, by people trying to fit in with their chosen peer groups.” p. 142
“For most people, the primary pattern is the workday, that is, going to work and coming home, usually along the same path day after day. The second most pronounced pattern is the weekend and days off, often with the characteristic behavior of sleeping in and spending that night out in a location besides the home or work. Perhaps surprisingly, the places we go and things we do during our free time are almost as regular as our work patterns. The third pattern, however is a wild card: days spend exploring, usually a shopping trip or an outing. This last is distinguished by it lack of structure. Together these three patterns typically account for 90 percent of most of out behavior.” p 142-143
“As we will see in the next few sections, these data-driven forecast allow us to prepare for peaks in demand and manage them better. It also means that we can react better to emergencies or disasters, because we can know who is likely to be where and when.” p. 143
[Why does the Atlanta snow storm (late Jan or early Feb 2014) come to mind?]
“A simple example consists of basically crowdsourcing dangerous conditions. If other cars have just recently gone down the road you are driving on and had emergency breaking events, then you are at significant risk of an accident. If you are traveling faster than other cars were, then you are in real danger. Warnings based on this sort of big data could be used to reduce accidents rats dramatically.” p. 144
[New features in cars, even before driverless cars, are intelligent following behavior in traffic jams. This is significant. Productivity boost from freeing up medical, emergency, police, repair shop, insurance company, etc. resources from needless accidents. An what a disruption to families - accidents need to be avoided.]
“Perhaps the most interesting idea is to use transportation networks to increase the productivity and creativity of cities. We can use data about people’s habits to structure public transportation networks to increase the productivity and creativity of cities: We can use data about people’s habits to structure transportation to promote more exploration within cities.” p. 145
[Bill Gates once told a group of us at the San Jose Tech Museum that if we could use technology to make it seem like there was suffering next door to us in our neighborhoods, especially the wealthiest and most segregated of us "so-called elite folks," it would cause the most dramatic drop in human suffering on the planet in human history, because people are basically good and have empathy for the unnecessary suffering of others. I recall thinking - "I like this guy Bill Gates" - when he said that. He also mentioned he was going slightly schizophrenic between "make-lots-of-profit Bill Gates" and "give-away-lots-of-money Bill Gates"- got a good laugh from the crowd, then he added that his wife was helping him remain somewhat sane through the transition. Shortly afterBill Gates made these comments at San Jose Tech Museum, Warren Buffet aligned his charitable efforts with the Gates Foundation. Nice, there is a lot of good in the universe... But helping people who behave irrationally, and making peace with that, is explored in the eccentric yet powerful writings of ... Anthony Galambos 'Unto the stars (sit et astra)' - unfortunately I don't think the Tea Party has read this work of a paranoid of idea-theft, philosophically-minded aerospace engineer of the 1960's, though if their leadership did, it might provide some foundation for their hopes for society. Perhaps Gene Roddenbury read it though. Oh well, who really knows what influences what in history... not me that's for sure, mere speculation. I probably read too much any way. I doubt anyone will read these remarks, so I am safe.]”
“With reported sore-throat and cough symptoms we found that people’s normal patterns of socialization were disrupted, and they began to interact with more and different people (good for the virus, bad for humans [especially their productivity and creativity]).” p. 146
“This idea is underpinning another of my group’s spin-off companies, Ginger.io, that I helped co-found…” p. 147
“Moreover, using financial incentives privileges the rich. As an example, consider congestion pricing… This is particularly worrisome because exploration results in innovation… There are three types of interventions that are naturally suggested by the social physics perspective. …Social Mobilization: As used in the Red Balloon Challenge… Tuning the Social Network: …To solve the problem of both insufficient diversity and echo chambers… Leveraging social engagement: … Facebook ‘get out the vote’ campaign in 2010 targeted 61 million people… ” p. 150-152
“The main barriers to achieving these goals are privacy concerns and the fact that we don’t yet have any consensus around the trade-offs between personal and social values.” p. 153
Chapter 9 – City Science
“Urban areas use resources more efficiently and produce more patents and inventions with fewer roads and service per capita than rural areas.” p. 155-156
“Cities are idea machines in the same way that companies are idea machines.” p. 156
“The difference, however, is that social physics conceptualizes cities and companies as idea factories, so the focus is on the flow of ideas rather than the flow of goods.” p. 157
[Yep, Service-Dominant Logic by Vargo and Lusch - service science and social physics are cousins.]
“As the remainder of this chapter will explain, what really matter is the flow of ideas, and not classes or markets.” p. 157
“That is, when we look at all of out interactions we see that people have many social roles (e.g., mother, coworker, citizen, jazz enthusiast, etc.) and each role engages a different set of people, so that the functions of engagement and exploration are combined across all of a person’s social networks.” p. 160
“Figure 16. A typical shopping pattern, with the size of each circle indicating the frequency of places visited…” p. 161
“This suggests that when people have abundant resources, it is their curiosity and social motivations that drive their exploratory behavior and not the desire to find cheaper prices or better product.” p. 164
“That is, they used their extra money to increase their exploration.” p. 164
“In fact, the relationship between the amount of disposable income and amount of exploration is very predictable.” p. 164
“Figure 17: The model of idea flow along social ties accurately predicts GDP per square mile.” p. 166
“Because of the dependence of idea flow on transportation efficiency, the idea flow equations can be turned around and GDP can be used to calculate the average commuting distance.” p. 166-167
[Amos Hawley's "Human Ecology" work comes to mind.]
“Designing Better Cities: Traditional theories of city growth emphasize markets and classes, suggesting that specialization in industry or new categories of highly trained workers as generative models of city development. In contrast, the social physics approach provides a plausible and empirically grounded model that does not require the presence of these social structures. Instead, it relies only on the fine-grain characteristics of human social interaction: the distribution of social ties, the flow of ideas along those ties, and the means by which those ideas are converted into new behaviors and new social norms by engagement with peer groups.” p. 167
“The failure of most city zoning is that if cities segregate by function, then exactly the wrong change in the structure of social ties occurs: Engagement decreases locally… What we want is the opposite: self-contained towns in which people meet each other regularly and there are many friends of friends. As famous urban advocate Jane Jacons argued, a healthy city has complete, connected neighborhoods.” p. 168
“The best size for a city can even be calculated: If within each peer group everyone is a friend of a friend, then the math of social physics indicates that we get maximum engagement for populations of up to roughly one hundred thousand people. This suggests that the best solution is small-to-medium-sized towns in which everyone is within walking distance of town center, the stores, the schools, the clinics.” p. 168
“This is the approach planners in Detroit are trying, by working to create a tiny hot new city inside the decaying sprawl of the original one.” p. 170
“There is no need to appeal to assumptions about social hierarchies, specialization, or other special social constructs in order to explain how GDP, research and development, and crime grow with increasing city population.” p. 170
“… we have seen that today’s digital technology is not as good at spreading new ideas as are face-to-face interactions.” p. 171
“The recommendations about city structures that come from social physics are similar to those of famous urban advocate Jane Jacobs, but what social physics has added is a quantitative, mathematical basis for recommendations. By understanding cites as idea engines, we can use the equations of social physics to being to tune them for better performance.” p. 172
“Digital Networks Versus Face-To-Face” p. 172
PART FOUR: Data-Driven Society
Chapter 10 – Data-Driven Society
“We have seen that the digital bread crumbs we leave behind provide clues about who we are and what we want. That makes these personal data immensely valuable, bot for public good, and for private companies. As European consumer commissioner Meglena Kuneva said recently, ‘Personal data is the new oild of the Internet and the new currency of the digital world.’ This new ability to see the details of every interaction, however, can be used for good or for ill.” p. 177
“A successful data-driven society must be able to guarantee that our data will not be abused – and perhaps especially that government will not abuse the power conferred by acces to such fine-grained data. To achieve the positive possibilities of a data-driven society we require what I have called the New Deal on Data – workable guarantees that the data needed for public goods are readily available while at the same time protecting the citizenry.” p. 178
“These data must not remain the exclusive domain of private companies, because then they are less likely to contribute to the common good. This, these privagte organizations must be key players in the the New Deal on Data’s framework for privacy and data control. Likewise, these data should not become the exclusive domain of the government, because this will not serve the public interest of transparency, and we should be suspicious of trusting the government with such power.” p. 179
“… I will discuss what may be the world’s first large-scale digital commons, and explain how a resource such as this can be used to help build a better society.” p. 179
“We need to recognize personal data as a valuable asset of the individual that is given to companies and government in return for service.” p. 180
“In 2007, I first proposed the New Deal on Data to the World Economic Forum. Since then, this idea has been run through various discussions and eventually helped shape the 2012 Consumer Data Bill of Rights in the United States, along with a matching declaration on Personal Data Protection in the EU.” p. 181
“A system like this has made the interbank money transfer system among the safest systems in the world, but until recently such technology was only for the big guys. … the Institute for Data Driven Design (co-founded by John Clippinger and myself) have helped build openPDS (open Personal Data Store), a consumer version of this type of system, and we are now testing it with a variety of industry and government partners.” p. 182
“… Data Liberation Front (www.dataliberation.org), a group of Google engineers who mission statement says that ‘users should be able to control the data they store in any of Google’s products’ and whose goal is to ‘make it easier to move data in and out.’” p. 184
“Until we have a solid, well-proven, and quantitative theory of social physics, we won’t be able to formulate and test hypotheses in the simple, clear-cut manner that today allows us to reliably design bridges or test new drugs… We need to construct living laboratories… …”open data city” I have just help launch within the city of Trento in Italy… More details on this living lab can be found at http://www.mobileterrioriallab.eu.” p. 186-187
“Some people react negatively to the phrase social physics, because they feel it implies that people are machines with free will and without the ability to move independently of our role in society.” p. 189
“The fact that most of our attitudes and thoughts are based on integrating experiences of others is the very basis for both culture and society. It is why we can cooperate and work together toward common goals.” p. 191
“To accomplish this change we need a language and logic that everyone can understand and that has proven to be more useful thank the old language of markets and classes. I believe the language of social physics – exploration, engagement, social learning, and measurement of idea flows – has the potential to serve this role.” p. 192
Chapter 11 – Design for Harmony
“Competition versus cooperation. … In fact, the main source of competition in society may not be among individuals but rather among cooperating groups of peers.” p. 194-195
“Figure 18: (a) a classical market, (b) an exchange network. An exchange network is a market where trade options are limited to connections within the social network. Trust and personalized service is much more likely to develop with an exchange network.” p. 197
“Natural Law: Exchanges, Not Markets – Modern society is based on the idea that markets can distribute resources efficiently and on the assumption that humans are relentless competitors. But as we have seen, this is simply not a good description of how our society lives and functions.” p. 199
“In other words, many early societies operated much more like an exchange network than a market.” p. 200
“The central reason that exchange networks are better than markets is trust.” p. 200
“In markets, one must usually rely on having access to an accurate reputation mechanism that rates all the participants, or to an outside referee to enforce the rules.” p. 201
“Because we are not just economic creatures, our models must include a broader range of human motivations, such as curiosity, trust, and social pressure.” p. 203
“I believe there are three design criteria for our emerging hypernetworked societies: social efficiency, operational efficiency, and resilience.” p. 203
“Such examples give hope that we can build human-machine systems that very quickly configure both economic and social incentives to assemble entire systems, products, and services on the fly. We need to think more broadly, however, than simply how to rebuild damaged systems. We also need to think about the resilience of the entire social design.” p. 210
“Consequently, to survive systemic risks we need to have a diverse set of systems rather than one so-called best system.” p. 210
“These results and others like them are available at http://www.d4d.orange.com/home.” p. 214
“All around the world governments and universities are beginning to take a look at how cities are organized and governed, motivated by rapid increase in city populations and the number of new cities that are being created.” p. 215
“… as codirector of MIT Media Lab’s City Science initiative (see http://cities.media.mit.edu) I am now working with a variety of cities to improve idea flow.” p. 215
Appendix 1 – Reality Mining
“In recent years, the social sciences have been undergoing a digital revolution, heralded by the emerging field of computational social science. In our 2009 Science paper, David Lazer and I, together with more than a dozen endorsing colleagues, describe the potential of computational social science to increase our knowledge of individuals, groups, and societies by use of data with an unprecedented breadth, depth, and scale.” p. 217
“Figure 19: A standard design for a sociometric badge, courtesy of Sociometric Solutions, Inc.” p. 220
“Social media activity, credit card activity, and other sorts of individual information can also be recorded. It is available for Android mobile phones at httP://www.funf.org.” p. 224
Appendix 2 – OpendPDS
“Personal data – digital information about users’ locations, calls, Web searchers, and preferences – have been called the oil of the new economy and what I have seen reinforces this comparison.” p. 225
“Owning a personal data store (PDS) would allow the user to view and understand how the data collected might be used, as well as to control the flow of data and to manage fine-grained data access.” p. 227
Appendix 3 – Fast, Slow, and Free Will
Psychologist Daniel Kahneman and artficial intelligence pioneer Herb Simon, both Nobel Prize winners, each embraced a model of a human with two ways of thinking. In Kahneman’s formulation, one way of thinking is fast, automatic, and largely unconscious mode, and the second way of thinking is a slow, rule-based, and largely conscious mode. A thumb-nail sketch of gfast thinking is that it drives habits and intuitions, largely by using associations among personal experiences and experiences learned by observing others. In contrast, the slow mode of thinking uses reasoning, combining beliefs in order to reach new conclusions.” p. 235
“The best capsule summary is that habits and gut instinct are based on fast thinking which uses engagement with others to integrate their experiences with our own, and thus form our habits of action. Exploration and guiding our attention to help figure things out seem to be the core functions of slow thinking, which is supported by observation of events, context, and correlation that are learned both personal perception and language. Understanding that humans have two ways of thinking that work quite differently transform many of the classic disputes in philosophy, anthropology, and sociology. … emphasize how the structure of society shapes the behavior of the individual… …emphasize free will and how individual cognitive processes shape individual behavior. …it tells us that both sides of the free will versus social context debate are right, but neither is right about all human behavior all of the time.” p. 239
Appendix 4 – Math
The concept of influence is extraordinarily important in the natural sciences. The basic idea of influence is that an outcome in one entity can cause an outcome in another. Flip over the first domino, and the second one will fall. If we understand exactly how two dominos interact – how one domino influences another – and we know the initial state of the dominos and how they are situated relative to one another, then we can predict the outcome of the whole system.” p. 241-242
“An entity’s state is affected by its network neighbors’ states and changes accordingly. Each entity in the network has specifically defined strength of influence over every other entity and equivalently, each relationship can be weighted according to their strength.” p. 243
“The state of each entity is not directly observable. As in a hidden Markov model (HHM), however, each entity emits a signal… ” p. 245
“The number of parameters grows quadratically with respect to the number of entities C and the latent space size S. This largely relieves the requirement for large training sets and reduces the changes of model overfitting, making the influence model scalable to larger social systems.” p. 247
fyi… A very mechanistic view that I have seen before in Gilberts work “Human Competence: Engineering Worthy Performance”
However, big data makes it fresh… more data makes it quite interesting… still mechanistic, but interesting…
Dr. James (“Jim”) C. Spohrer
Director, IBM University Programs (IBM UP) and Cognitive Systems Institute
IBM Research – Almaden, 650 Harry Road, San Jose, CA 95120 USA
email@example.com 408-927-1928 (o) 408-829-3112 (c)
Innovation Champion (http://www.service-science.info/archives/2233)
Hello! I am serving as a co-chair of the “Decision Analytics, Mobile Services and Service Science” track of the upcoming 48th Hawaii International Conference on System Sciences (HICSS) (http://www.hicss.hawaii.edu/hicss_48/apahome48.htm). My co-chair, Christer Carlsson, and I are writing to internationally renowned scholars such as you with expertise in various areas of analytics, mobile systems and service science in hopes that you will consider submitting a paper to our track. We have been receiving a great deal of interest, and your contributions would help us improve the quality of the sessions. The deadline for submitting papers to HICSS-48 is June 15, 2014 (less than 4 months). Please consider submitting your work if it is related to any of the specific topics listed and/or if you feel it addresses visions of the future of this track. We expect a range of concepts, tools, methods, philosophies and theories to be discussed. We thank you, in advance, for your valuable contribution to HICSS-48. Please let us know if you have any questions or need additional information. We look forward to receiving your submission!
HICSS-48 CALL FOR PAPERS
January 5-8, 2015 – Grand Hyatt, Kauai
Additional detail may be found on HICSS primary web site:
The Decision Analytics, Mobile Services and Service Science Track
(http://www.hicss.hawaii.edu/HICSS_48/Tracks/DecisionAnalytics.htm) focuses on emerging managerial and organizational decision-making and innovation strategies, processes, tools, technologies, services and solutions in the Digital Age. This track has four interrelated themes. Analytics focuses on decision making processes, models, tools and technologies. Mobile Services work with the development and delivery of data, information and services with mobile technology platforms. Challenges and issues of emerging service systems, and service-orientation and -transformation of strategies, pro-cesses, organizations, systems and technologies are covered in Service Science. In this track, we also discuss innovative approaches of decision making for/with Critical and Emerging Solutions in a number of high-impact areas.
This track includes the following 17 mini-tracks:
1. Big Data Analytics: Concepts, Methods, Techniques & Applications
2. Data, Text & Web Mining for Business Analytics
3. Decision Making in Production Processes
4. Decision Support For Sustainability
5. ICT Enabled Services
6. Intelligent Decision Support for Logistics & Supply Chain Management
7. Interactive Visual Decision Analytics
8. Mobile Value Services
9. Multi-Criteria Decision Analysis & Support Systems
10. Network Decision Support Systems
11. New Economic Models of the Digital Economy
12. Open Data Services
13. Service Analytics
14. Service Science
15. Smart Service Systems: Analytics, Cognition & Innovation
16. Soft Computing
17. The Internet Of Things & Big Data Analytics
- June 15 – Submit full manuscripts for review. The review is double-blind; therefore this submission must be without author names.
- Receive acceptance notification by August 15.
- Revise your manuscript to add author names. If required, make other changes.
- Submit Final Paper for Publication by September 15.
Haluk Demirkan (firstname.lastname@example.org)
- Associate Professor of Service Science, Information Systems & Supply Chain Management, Milgard School of Business, University of Washington – Tacoma
- Founder & Executive Director of Center for Information Based Management Focused on Analytics & Service Innovation
- Co-Founder & Board of Director, International Society of Service Innovation Professionals (www.issip.org)
- Track Chair for Analytics, Mobile & Service Science at HICSS (www.hicss.hawaii.edu/)
The Cognitive Systems Institute is being established to explore answers to two questions with both technology and pubic policy dimensions:
How can cognitive systems be used to improve the productivity and creativity of:
(1) individual researchers and their teams?
(2) research universities and their regions?
More specifically, the Cognitive Systems Institute works with cognitive systems researchers (1) to understand and improve their productivity and creativity, and (2) thereby understand and transform research university-driven development (learning, discovery, and engagement) in smart regions. A public policy purpose for improved cognitive systems and social networks is to augment individual and collective intelligence to benefit business and society, and improve quality-of-life in the “nested, networked service systems” that we all belong to, depend on, and co-create as we live, work, learn, and play.
The Cognitive Systems Institute is being established as a global, virtual community-of-interest by IBM first and foremost to understand and collaboratively work to improve the productivity and creativity of multi-disciplinary cognitive systems researchers. For example, how can improved access to data (including test and training data sets, corpora of literature, patents, start-up proposals, etc.), tools (both open and proprietary), grand challenge problems and cognitive sport tournaments, and other shared resources and events improve the productivity and creativity of cognitive systems researchers and problem-solving professions?
Second, the Cognitive Systems Institute will foster the creation of diverse point-of-views (POVs) documents on how cognitive systems will likely transform education systems and sustainable regional development, lifelong learning, skills and employment. For example, public policy related to personal data, quantified-self, learning, discovery, and sustainable development. Cognitive systems will augment individual intelligence and augment collective intelligence of organizations and institutions (“smart service systems”).
Please contact (email@example.com) if you are a cognitive systems researcher interested in learning more about the Cognitive Systems Institute.
Goal 1: Cognitive Systems will improve the productivity and creativity of Cognitive Systems Researchers, as well as other problem-solving professionals. What are the most relevant articles, blog posts, books, etc. related to improving Cognitive Systems Researchers productivity and creativity?
Vattam, S., Wiltgen, B., Helms, M., Goel, A. K., & Yen, J. (2011). DANE: fostering creativity in and through biologically inspired design. In Design Creativity 2010 (pp. 115-122). Springer London.
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on (pp. 248-255). IEEE.
Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., … & Van Alstyne, M. (2009). Life in the network: the coming age of computational social science. Science (New York, NY), 323(5915), 721.
Goal 2: In addition to augmenting individual intelligence, cognitive systems in conjunction with social networks will increasingly augment collective intelligence of families, businesses, and cities. What are the most relevant articles, blog posts, books, etc. related to the transformational impact of cognitive systems on regional educational systems and regional economic development?
We are also planning a “Handbook of Cognitive Systems” that will provide a snap-shot on these two questions from multiple disciplinary, sectoral, and cultural perspectives. Researchers will be invited to contribute chapters to the handbook from diverse areas of research, including Artificial Intelligence, Cognitive Science, Machine Learning, Pattern Recognition, Planning, Robotics, Computer Vision, Neural Networks, Energy Efficient New Computing Architectures, Big Data Social Physics, Quantified Self, Regional Economic Development, Transformation of Education Systems, Transformation of Professional Systems and Associations, Urban Science, Data Science, Service Science, Public Policy, etc.
Please contact me for more information:
Dr. James (“Jim”) C. Spohrer
Director, IBM Global University Programs (IBM UP) and Cognitive Systems Institute
650 Harry Road, IBM Research – Almaden, San Jose, CA 95120 USA
firstname.lastname@example.org 408-927-1928 (o) 408-829-3112 (c)