Introduction. In Social Physics: How Good Ideas Spread-The Lessons from a New Science, Alex Pentland introduces the readers to a new field of science: social. Social Physics: How Good Ideas. Spread—The Lessons from a New Science ( Penguin Press, ). The event was moderated by Ann R. and. Alex Pentland's new book Social Physics, How Good Ideas Spread - The from a New Science (The Penguin Press, ) will reach a great.
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Social Physics and Rendell et al, Social Learning, Science 4/10 Influence Model & Idea Flow s. 1 . Big Data is good for interpolation epidemic spread. Social Physics book. Read reviews from the world's largest community for readers. From one of the world's leading data scientists, a landmark tour of. Print Get a PDF version of this webpage PDF 'Social Physics: How Good Ideas Spread–The Lessons from a New Science' by Alex Pentland.
Let us begin with the individual first. Creativity and Productivity in Individuals 10a. They use the coffee pot or water cooler to talk to the janitor, the sales guy, and the head of another department.
Being a charismatic connector may come more naturally to some than others; however, it is also something that one can learn—one simply needs to tap into their curious side loc.
Engaging with Close Co-Workers In addition to being curious, and interacting with a wide range of people, the most creative and productive individuals also tend to keep in tight contact with a core group of people whom they work with most closely loc.
In order to see how and why this works, consider the Bell Stars study. In Bell laboratories undertook a study led by Bob Kelly of Carnegie Mellon University to try and determine the characteristics and qualities that separated their most creative and productive workers the star performers from the rest loc.
What the study found is that the single biggest factor in explaining the success of the stars had to do with their social networks and how they interacted with others. First, they maintained stronger engagement with the people in their networks, so that these people responded more quickly and helpfully.
As a result, the stars rarely spent time spinning their wheels or going down blind alleys. Average performers saw the world only from the viewpoint of their job, and kept pushing the same points.
Stars, on the other hand, had people in their networks with a more diverse set of work roles, so they could adopt the perspectives of customers, competitors, and managers.
As we can see from the quote, the star performers not only interacted with a wide range of people which gave them access to many different ideas and viewpoints that could help them , they also kept in close contact with the people they worked with directly. As the quote indicates, the reason this was so important is because doing so made their co-workers well-disposed towards them, and therefore, much more cooperative with them loc. To sum up, then, we may say that the most effective approach when it comes to generating creativity and productivity is a two-pronged one that includes both exploring for new ideas with a wide range of people, and keeping in tight contact with those whom one works with most closely.
Indeed, Pentland has studied a host of different types of groups, organizations, and institutions, and he has found that the most creative and productive groups always have one thing in common: the individual group members maintain high levels of interaction with diverse people outside of the group high exploration , and they also stay in close contact with one another high engagement.
The sociometric data that my research group and I have gathered from many different organizations show that creative output depends strongly on two processes: idea discovery exploration and the integration of those ideas into new behaviors engagement.
Let us take a look at a few examples. In each lab a team of seven members were tracked for their interaction patterns for a period of two weeks using sociometric badges loc. During the study, both the team members themselves and a panel of experts were asked to gauge the creative out-put of the group on a day to day basis, using the standard KEYS creativity assessment loc. In fact, a simple combination of the engagement and exploration measures was able to predict which days were the most creative with Example 2: The Bank of America Study As mentioned, interaction patterns have also been shown to have a large impact on productivity.
For example, Pentland and a team of co-workers conducted a study wherein they investigated the interaction patterns of four teams of call center operators at Bank of America loc. Each of the four teams consisted of about 20 members, and they were tracked using sociometric badges over a period of six weeks loc.
Again, what the study found is that the interaction patterns of the teams went a long way to explaining their relative productivity, and that the teams that showed the most exploration and engagement out-produced the teams that showed less. In this particular case Bank of America asked Pentland and his team what they would recommend in terms of increasing the productivity of their call-center teams.
Pentland came up with the simple measure of ensuring that all of the group members of each team be assigned to take their coffee breaks at the same time previously, the coffee breaks of team members were staggered loc. The measure worked like a charm. The AHT [average call handle] decreased sharply, which means that the employees were much more productive, demonstrating the strong link between interaction patterns and productivity. Example 3. The German Bank Study Pentland and his team have used their insights regarding interaction patterns to help other businesses as well.
For example, one German bank asked Pentland and his team to come in and assess the interaction patters of their workplace, and to offer recommendations as to how these patterns might be improved loc. When Pentland and his team investigated the workplace they found that there were significant holes in the interconnectedness between the various departments—with the customer service department in particular being largely detached from the others loc.
Looking at the arrangement of the office, Pentland and his team noticed that the seating plan was somewhat disjointed and uneven, and they hypothesized that this may in fact have been the main reason behind the lack of interconnectedness. So the team recommended the seating plan be switched up to encourage more interaction. And it worked! First, high levels of out-group interactions with diverse people ensure that the group members have access to a wide range of potentially useful ideas loc.
Second, close connectivity within the group ensures that all of these ideas are shared, which increases the likelihood that the best ideas will rise to the surface loc. Also, and just as important, close connectivity functions to build both norms of behavior and trust within the group, which helps the group members work efficiently and cooperatively with one another loc. And again, the importance of having the right interaction pattern cannot be overstated.
This is not an anomaly. Indeed, Pentland has found that interaction patterns are consistently the most important factor in determining creativity and productivity. With this in mind it begins to make sense why such a simple thing as the seating arrangement in an office, or the break times of team members, has such a big impact on creativity and productivity: increasing exploration and engagement otherwise known as idea flow even a little can make a big difference.
Group Dynamics Up until now we have considered interaction patterns between people in general terms. However, Pentalnd has also studied the specific interaction patterns that make up group dynamics. And here too what Pentland has found is that one type of group dynamic or inter-group interaction pattern tends to outperform others when it comes to creativity and productivity. These findings have been gleaned from studying a wide range of groups.
It is important to note that while the equality dynamic proved to be the most effective in most cases, there were a couple of exceptions to the rule. Specifically, Pentland has found that the equality dynamic tends to do less well in times of acute stress when a decision needs to be made immediately; and also when the issue being discussed is highly contentious and emotions are running high.
When the decision needs to be made now, there may be no time to get all the ideas out and discuss them. A second exception is when the group has a hard time working together and emotions are high; then a leader may have to play the role of facilitator and frequently intervene between contributions by others.
To sum interaction patterns in groups, then, we may say the following: it is best to have an interaction pattern such that the individual group members spend a fair bit of time interacting with people outside the group high exploration , and also a good deal of time interacting with each other high engagement. Also, when it comes to more formal meetings, it is best to ensure that everyone is given roughly equal time to express their views and offer feedback, and that viewpoints and feedback are given in short flourishes.
With regards to promoting these interaction patterns there are certain organizational tweaks that can help as we have seen above.
But over and above that, just assessing the interaction patterns within a group, informing the group members of the interaction patterns that are found, and educating them regarding optimal interaction patterns can make a big difference loc.
Pentland helped create a spin-off company that comes in and assesses the interaction patterns of workplaces. Creativity, Productivity and Crime Rates in Cities Interestingly, the same principles that apply to generating creativity, productivity, and social cohesion in groups also apply to larger collections of people—including communities and whole cities. Indeed, Pentland has found that the amount of exploration and engagement that occurs among the members of communities and cities goes a long way to explaining such things as GDP the main indicator of productivity , the number of patents produced a common indicator of creativity , and crime an indicator of social cohesion.
But just how do you measure the amount of exploration and engagement within a city? Based on such factors as population density, telephone communication patterns, and the downloading behavior of citizens as reflected by credit card records , Pentland and his team are able to gauge just how widely spread-out are the movements and interactions of citizens within a city, and just how much interaction is taking place loc.
Social Physics Summary
These factors then give a fairly accurate representation of the degree of exploration and engagement within the city itself. How Exploration Increases Creativity and Productivity in Cities Now, as we have seen above, the exploration engaged in by individuals and group members tends to increase the stock and diversity of the ideas that these individuals and groups have access to; and this in turn contributes to their creativity and productivity.
And as it turns out the same principle also applies in cities. So, what explains why there is more exploration within some cities than others? Outside of population density, Pentland has found that one of the main factors here is just how efficient the transportation network is. Cities with communities that are isolated from one another due to inefficient traffic flows and public transportation systems tend to exhibit less exploration than cities that have more efficient traffic flows and public transportation systems.
Take Beijing, for example. How Engagement Lowers Crime Rates Of course, creativity and productivity are not the only things we want out of our cities. We also want them to be safe and secure. Here, though, our cities often fail us; for the crime rates in cities are notoriously higher than in smaller communities loc.
Some argue that this is a natural function of the higher populations and population densities that cities have. However, Pentland has found that the crime rates of communities and cities may have more to do with how they are designed than their populations and population densities.
Specifically, the problem with most cities is that they are set up in such a way that discourages engagement among the members of the various communities. Indeed, most cities compartmentalize the different functions of the city in different places, such that citizens must travel far afield in order to satisfy their daily needs.
What this means is that there is less interaction between the members of a community in a city, and thus less engagement. And as the social fabric of the neighborhood is pulled apart the risk of crime goes up loc. In smaller communities, by contrast, the places people need to go to satisfy their daily needs are often clustered together and within walking distance. This means that the members of the community tend to come into more regular contact with one another, are more familiar with one another, and hence there is more trust and less crime loc.
There is good news for cities, though, because we can always redesign them to capture the increased engagement so common in smaller towns and communities—thereby increasing cohesion and lowering crime rates. The key is to design walkable communities centered around a common hub with shops and services loc. Not necessarily. The key is to strike the right balance here. So, in addition to designing tighter communities, it is also important to connect these communities to a vibrant central location where all can meet and mingle.
The best size for such a city can even be calculated… the math of social physics indicates that we get maximum engagement for populations 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 a town center, the schools, and the clinics.
For maximum creative output, though, the business and cultural areas should maximize opportunities for exploration. Social physics makes good use of big data, i. Social Physics Key Idea 2: Social physics has revealed that our peer groups heavily determine our behavior. Would you consider yourself to be pretty independent?
You probably make your own decisions based on your own personal, rational thoughts, right? Most of us believe this to be true, likewise most believe that society is comprised of individuals who make rational choices based on self-interest.
Social physics, however, reveals that things are much more complicated. We often belong to many peer groups at once. For example, you could be a football-loving one peer group police officer another who dropped out another of a particular high school yet another.
Although many of our friends may come from our peer groups, the majority within those groups will be little more than passing acquaintances. Nevertheless, we spend a great deal of our time at the same bars, cafeterias, sports stadiums and other social environments as those in a particular peer group, and thus become heavily influenced by the numerous interactions we have with those around us.
Take this experiment conducted by the author, for example, which showed that we tend to share certain beliefs with those in our peer group. In the experiment, a group of students were given modified smartphones which tracked their communication and their social environments leading up to the US presidential election.
They were asked a range of questions about their political views before the election and then finally were asked for whom they voted, after the election. In short, their beliefs came from observing others and overhearing conversations within their peer groups. Clearly, we have much greater influence on one another than we might otherwise think. Our following book summarys explain why.
Social Physics Key Idea 3: Adopting the behaviors that we observe to be beneficial helps us to thrive in social groups. In order for behavioral change to spread across a peer group, it must match these two criteria: First, it needs to be repeated often over a short period of time; and Second, it needs to demonstrate a positive, beneficial outcome. If those criteria are met, then the chances are greater that the behavior will be influential. Imagine, for example, that during coffee breaks at work, everybody in your team stops drinking coffee in favor of tea.
So why are we prone to adopt behavior in this way? When, for example, capuchin monkeys are deciding where their troop should move, the monkeys at the head of the group will call out when they find a path. The monkeys behind will copy their call in turn to communicate the message to the rest. We still rely on this phenomenon today to determine our behavior. Indeed, aping the positive behaviors of others helps us to get by. Observing decorum at a party, for example, influences us in a way that keeps us from behaving like rude buffoons, and to instead act politely.
These influences thus help society accept the norms that help it run smoothly. Social Physics Key Idea 4: Social physics has shown that social incentives work much better than individual ones. In a department at the US Department of Defense celebrated the 40th anniversary of the creation of the internet by creating what became known as the red balloon challenge: Ten red weather balloons were hidden around the United States, and teams had to utilize the internet and social media to discover their locations.
Of the 4, teams who signed up, most tried to recruit help in their search by utilizing traditional methods of incentivizing people, doing things like offering rewards whenever people could provide the locations of one or more balloons. The second half concerns itself with cities, and it's here I'm torn. Full disclosure: I think cities are the solutions to all of our problems.
So take this with a grain of salt. Primarily, the argument in the latter half of the book is that we need to find a way to use personal data to create a public, open source meta data collection so that citizens can best assess the needs of the city.
While this is accurate, and I'd love it, we run into a few problems here. One, there's a lot of vagueness around how the data privacy is secured. Saying we need a New Deal for Data is fantastic, but achieving it is another step. Secondly, Pentland takes one too many jabs at current solutions to urban problems while having only hopes and promises to go on. As an example, he makes the assertion that congestion pricing is not only bad, it's a system that rewards the rich.
It's beyond me how congestion pricing, which taxes vehicles used within city limits, would punish the poor when they're already likely taking public transit. As for those on the fringe in the middle class, asking them to ride the train instead of paying for parking in the city may be an inconvenience, but it's hardly confiscatory.
Moreover, the externalities congestion creates would be removed, which economically speaking is an additional cost savings. It's arguments like these that appear one-sided intentionally to favor his arguments. Having said all of this, the data does display a side of an argument that is certainly worth looking into, especially if communal, open-source data is achievable in the near future.
Jul 18, Liam rated it liked it. Like sculpting raw clay into a beautiful statue, over time their story becomes more and more compelling. Finally they decide that it is time to act on it, to bring it into the light and test it against reality.
To these people, the practice of harvesting, winnowing, and sculpting ideas feels like play. In fact, some of them call i "The most productive people are constantly developing and testing a new story, adding newly discovered ideas to the story and then trying it out on everyone they meet.
In fact, some of them call it 'serious play. For the buddies that had the most interactions with their assigned target, the social network incentive worked almost eight times better than the standard market approach. If we looked at cities with greater than average rates of exploration in the credit card data, we found that in subsequent years they had a higher GDP, a larger population, and a greater variety of stores and restaurants. It makes sense that more exploration, which results in a greater number of interactions between current norms and new ideas, would be a driver of innovative behavior.
Such a mechanism also allows users to safely grant and revoke data access, to share data anonymously without needing a trusted third party, and to monitor and audit data uses. Feb 17, Rishav Agarwal rated it liked it.
A good place to start for anyone who would like to know more about Sandy's amazing work in understanding social connections. The writing is a bit dry so do treat it as a longish review paper and not a pop science novel. Apr 15, YHC rated it liked it. Very interesting book. Advances the seemingly obvious claim that the increased flow of ideas between and among human agents whether, e. Makes the further claim that idea flow increases as a function of a small number of variables, such as the relative level of engagement of agents with each other, and their relative level of exposure to the innovative id Very interesting book.
Makes the further claim that idea flow increases as a function of a small number of variables, such as the relative level of engagement of agents with each other, and their relative level of exposure to the innovative ideas of other agents especially those outside their networks.
What's most interesting about the book are the empirical claims it makes, and the tests the author, his students, and his colleagues have devised to generate them. Social Physics is not a blueprint for doing so, nor the last word, but it's a hugely suggestive call to arms for specialists and laymen alike to pursue further studies in this area.
My only criticism: Even so, Social Physics strikes me as an essential read. Pentland and friends are clearly on to something Feb 14, Sean Kottke rated it really liked it Shelves: The key take-aways about the ideal conditions for effective idea flow dovetail nicely with other notable recent works on group dynamics and innovation such as The Rainforest.
Organizations that allow their members to be prolific explorers of diverse ideas and provide them with rich opportunities to exchange and expose each other to those ideas will thrive.
Not a surprising finding there, but the research-based insights into how face-to-face and virtual peer networks may be incentivized to acco The key take-aways about the ideal conditions for effective idea flow dovetail nicely with other notable recent works on group dynamics and innovation such as The Rainforest. Not a surprising finding there, but the research-based insights into how face-to-face and virtual peer networks may be incentivized to accomplish these objectives are an important piece of puzzle.
The mathematical models may strike some readers as the epitome of Henri Bergson's definition of comedy as "something mechanical encrusted upon the living," but the real-world experimental research bears it out. However, the biggest deal here is how eerily close the possibilities and policy implications of Social Physics are to the philosophical underpinnings of the true believers in Dave Eggers' The Circle.
Here lie the tools to take us from here to there, whether that excites you or scares the hell out of you. Apr 28, Bett Correa-Bollhoefer rated it it was amazing.
Social Physics is an excellent book on how to create an innovative culture in your organization. The big take-a-ways: Creative people do the following steps 1. Absorb ideas from diverse sources 2. Taking these new ideas that come out of the tests and testing them on more diverse people Ideas can only "flow" by people communicating them with others. Face to face meetings are the strongest way to create a connectio Social Physics is an excellent book on how to create an innovative culture in your organization.
Face to face meetings are the strongest way to create a connection between two people AND ensure that they will share ideas. People need diverse sets of people to share their ideas with. In a meeting when ideas are being shared, each person must be allowed to speak.
If one person hogs the time by talking too much, ideas will not be shared. Conversation turn taking is critical. The more women who attend a meeting the more the meeting will tend to generate creative ideas by ensuring that everyone gets a voice in the discussion.
Star performers build networks and make people feel part of a team. Jun 14, Tom rated it really liked it. This book is different from most social theory books as instead of simply promoting ways of communication and how make society work better cooperatively, it uses big data to prove if methods work or not.
I wouldn't say it's ground breaking, but offers a unique perspective that hasn't been offered before.
Social Physics: How Good Ideas Spread— The Lessons from a New Science
My complaint with the book is that near the end, it felt like a doctorate thesis about looser privacy regulations so he can get better access to individuals data for his own personal research. Th This book is different from most social theory books as instead of simply promoting ways of communication and how make society work better cooperatively, it uses big data to prove if methods work or not. The book could have also explored topics more throughly, as some chapters felt rushed to the point they were briefly explained, then Pentland would conclude with a short description that his data proved otherwise based not a lot of actually data in the chapter but because he said so.
Feb 08, Elly Stroo Cloeck rated it liked it. Combineer de twee en je hebt dit intrigerende boek van Alex Pentland: Sociale Big Data. Niet alleen kan hij met de resultaten gedrag voorspellen predictive analytics , maar ook hoe gedrag zich verspreidt, als de griep zeg maar.
Sterker nog: Ja ja, en je privacy dan? Maar ook daar heeft hij een antwoord op in dit bijna wetenschappelijke boek dat veel details geeft, tot aan de algoritmes aan toe. Dat is het werkgebied van de sociale fysica: Klinkt droog, maar dat is het bepaald niet. De data die worden gebruikt zijn telefoongegevens, pinbetalingen, gps-gegevens, enzovoorts.
Een badge, de zogenoemde sociometer, verzamelt elke 16 miliseconde data over locatie, communicatie, lichaamstaal, spreektoon, wie er in de buurt is, workflow, taken van de drager en nog veel meer.
Dat zou ik weleens willen zien! Dit levert jou en je omgeving meer succes op. Deze relatie heeft Pentland aangetoond in diverse onderzoeken. Dodelijk voor innovatie. Pentland meet die balans door reality mining in organisaties, laat er wat algoritmes op los, en kan daardoor redelijk nauwkeurig succes en innovatie voorspellen. Als we ons niet zo lekker voelen verandert ons gedrag: Als een hele woonwijk of stad dit doet, kan dit dus een hele vroege waarschuwing zijn voor een epidemie.
Super nuttig lijkt me. En wie is daar eigenaar van? Hiermee werd armoede en etnisch geweld onderzocht. In de datapool worden de data bewerkt met algoritmen en alleen de geaggregeerde resultaten worden verstrekt, niet de data zelf. Hierbij werd een juridisch contract gebruikt, waarin het doel en gebruik van de data precies is omschreven.
Pentland voorziet een dergelijke structuur, OpenPDS, voor alle dataverzamelingen, wereldwijd. Het individu is eigenaar en bepaalt voor welk doel hij zijn data ter beschikking stelt.
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De mogelijke nieuwe toepassingen van Big Data, en hoe dit de gezondheid in de wereld kan bevorderen en armoede kan bestrijden, vond ik erg inspirerend. Door de gedetailleerde uitwerking van de onderzoeken raakte ik er ook van overtuigd dat dit allemaal haalbaar is.
Inhoudelijk is dit boek top! Ik had wel wat moeite met de schrijfstijl.
De auteur is een autoriteit op dit gebied en dat laat hij je weten ook. In het begin verleent het wel de nodige geloofwaardigheid aan zijn stellingen, maar door de overdaad vond ik het al snel irritant worden.
Amerikaanse stijl opschepperij die in Nederland minder goed valt, denk ik. Daarnaast is het geschreven in een wat pompeuze stijl en zit er redelijk wat jargon in, als een wetenschappelijk artikel. Daardoor moet je heel geconcentreerd lezen.
De grafieken zijn oorspronkelijk in kleur, maar nu in tinten-grijs afgedrukt, waardoor de interpretatie best lastig is. Tenslotte is de vertaling soms wat onlogisch: We kennen die al met chips voor toegang tot kantoren en de centrale printer, maar ook met tegoeden voor de betaling in de personeelskantine.
Als je door deze uiterlijkheden heen kunt prikken, heb je een bijzonder interessant boek in handen! ISBN 94 8. Maven heeft mij een gratis boek toegestuurd voor deze recensie. Apr 29, Mihai rated it it was amazing. One of the best books on social physics. It follows, using big data, the ways in witch not only good ideas are created but also how societies develop.
Its main objective is to ask what are the roles and evolution of diversity, engagement, social trust, social intelligence and innovation, following both business management and urban development. A must read for those who want to be up to speed on the subject. Alex Pentland is a numbers guy, and this book represents his initial attempts to parlaying to a lay readership his myriad published studies, summaries, and academic analyses of big data-derived computer models.
The book is subdivided into three sections, but near as I can tell, there are really only two big ideas here: What is "Social Physics," exactly, and how is it distinct from social psychology, economics, etc.? At pages , the author finds the concept rooted in folklore. The idea of a collective intelligence that develops within communities is an old one; indeed, it is embedded in the English language. Consider the word "kith," familiar to modern English speakers from the phrase "kith and kin.
These are also the roots for "couth," which means possessing a high degree of sophistication, as well as its more familiar counterpart, "uncouth.
We learn common sense almost automatically, by observing and then copying the common behaviors of our peers. The whole is presented with a great deal of confidence, and I am thus cowed into thinking he may actually know what he's talking about. That said, I found this book extremely slow going and am not sure I come away from it any better enlightened.
Manipulations of statistical models aside, Pentland is not the best communicator. His first main example considers a Swiss energy conservation campaign page In the first experiment, home owners received social feedback on how much electricity they used relative to the average person. When the comparison was between the home owner and all other people in their country, virtually no savings resulted; people behaved the same.
When the comparison was between them and people in their neighborhood, however, things worked better, showing that how closely they identified with the people in the comparison group mattered.
This is a social network effect: Identification with a group of people increases both trust of group members and the social pressure that the group can exert. All praise social physics? I wasn't fully persuaded by this example.
Going With the “Idea Flow”
Another explanation for the difference could be that people receiving the local comparison found the feedback more immediately relevant to them than data from a more distant and less personal environment. In other words, rather than indicating a social network effect per se, the campaign just showed that like a polished mirror, feedback works better the more accurately it appears to reflect its subject.
Another of Pentland's experiments resonated better with me by combining feedback with incentives in a novel way. His team found that dieters were more likely to initiate and sustain their weight loss programs when incentivized by a peer group who encouraged them than by any direct reward see, e. Not that financial or material incentives are misplaced, just that the trick was to offer them up to a subject's peers to promote their participation rather than directly to the subject. It takes a village.
Yet I couldn't make head or tail of the method the author described using to quantify these conclusions. The influence model breaks this overall "company state" into the influence each person c has on a particular other person c' The number of parameters in this model grows relatively slowly with increasing numbers of people and their internal states, making it easy to mathematically model "live" data and use it in real-time applications.
Practically, this means we can determine the influence model parameters -- influence, states, etc.
Also from SAGE Publishing
For almost all of the examples in this book, including the role of social influence on political views, downloading behavior, and health choices, as well as productivity in small groups, departments within companies, and entire cities, we find that using measures of the amount of social interaction -- both direct and indirect -- in order to estimate social influence produces accurate estimates of future behavior.
Leaving aside the details of Pentland's model, if I read that last sentence correctly, it would seem that he expects to move from general principles of the sort that lend greater post hoc understanding to specific behavioral predictions of the sort that might be deliberately manipulated. That's surely the holy grail of public policy, but if that's what he intends to pull off, I'm at a loss to find any place where this assertion is clearly demonstrated or exemplified in his book. Perhaps it appears in the wide range of papers Pentland and his co-authors have published?
Because the author provides URL links to these, as well as to his data sets, and computer implementations of the various equations, the reader needn't just take his word for it, but then, Social Physics doesn't do that great a job in communicating his ideas of findings to a lay audience.
The passage quoted strikes me as a pretty dense way of articulating parametric guesswork, but I guess the ultimate proof of the model lies in the pudding from which it was derived. How consistently does it match patterns observed in the data set? Do the elements of other data sets evincing similar patterns correlate with one another well, or is this more a case of fitting various data to extract a preexisting curve?
Pentland does not seem to address these questions, nor to dispel possible counterexplanations for his various conclusions. If this is so again assuming I understand him correctly , his metric would seem a powerful tool for improving performance and social cohesion.
However, I never was able to understand exactly how this should be operationalized: Does it vary on a case-by-case basis? What other explanations are there for group performance and behavior? His examples don't seem to account for the effects of independent variables, but perhaps it doesn't matter. Just fiddle with seating arrangements, schedules, work assignments, and performance incentives until you have it right. Who needs to triage precise causes and effects?DE: There were so many obvious implications of this work on cities.
Outside of population density, Pentland has found that one of the main factors here is just how efficient the transportation network is. What follows is a full executive summary of Social Physics: This is important because Western society as a whole tends to take the opposite view. Among the many recent books about the new science of networks, this one stands out for the breadth and ingenuity of its data-driven science, and for its commitment to translating scientific exploration into practical knowledge.
It is important to note that while the equality dynamic proved to be the most effective in most cases, there were a couple of exceptions to the rule. The sociometric data that my research group and I have gathered from many different organizations show that creative output depends strongly on two processes: Not necessarily.
Humans are, of course, smarter, independent free-thinking individuals. However, Pentalnd has also studied the specific interaction patterns that make up group dynamics.
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