This weekend, I submitted a talk which considers how semantic technology will help us answer future questions for consideration at SXSW as well as helped edit a chapter on Moral Foundations Theory that contains a section on the future of moral psychology. I have a lot of thoughts on the future of moral psychology, many of which relate to future semantic technology, that probably aren't quite right for the academic audience of that chapter, so I thought I would list them here while I was thinking of them.
What will the future of moral psychology look like? Here are a few trends I see defining the next 10 years.
- Thinking outside academics: I hope that moral psychology, as studied by social psychologists, will start to think of itself as a part of the world rather than as a largely academic exercise. There is a kind of epistemological arrogance/insecurity that exists in any academic discipline (this paper by Paul Rozin points it out in psychology best), which leads one to believe that your methods will point out the truth while others' won't. Philosophers, neuroscientists, anthropologists, and sociologists all study issues of values. Facebook, Google, novelists, and human resources departments all collect data of relevance to moral psychologists and answer questions about the relationship between values and various dependent variables every day. My hope is that these methods will become more compatible, which leads me to my next point.
- Leveraging semantic technology to crowdsource findings: Right now, most findings in psychology exist as text that is impenetrable to machines. The model is one where a small minority from a single institution (academic psychology) are supposed to do all the work, in the form of these papers that are supposed to be definitive, as opposed to more modern models of collaboration made possible with technology. This TED talk by Clay Shirky illustrates it best, where he shows how questions can be answered by the "long tail" of contributors. Rather than a single researcher figuring something out, we need to figure out a way for any individual to be able to contribute data toward an answer and then to be able to aggregate/coordinate that data towards a more robust answer. That is the promise of semantic technologies, which allow study results and data to be combined, in the same way that meta-analyses (the current labor intensive gold standard in psychology) do now, but in a way that anyone can contribute data to an answer and the meta-answer is updated in real-time.
- Open access results: Of course, semantic standards will make little difference as long as results are accessible only to those with expensive institutional access to research. Leveraging the power of collaboration that Shirky talks about requires a level of openness that academics are moving towards, though slowly. Brian Nosek and Yoav Bar-Anan have a great paper on openness that will likely convince many people (also see our forthcoming commentary on that paper). We are publishing our libertarians paper in PLOS One, an open access journal, in part because, all things being equal, we'd rather not lose the copyright to our work and would like others to be able to read it freely. One of my hopes is that it starts a trend where, all things being equal, people choose open access journals.
- Answering real world questions: Openness allows non-academics the chance to contribute and merge their data into datasets that combine variables from moral psychology, business, web analytics, economists, politicians, etc. With this data, we can answer questions that seem to interest me, but are deemed too "applied" for pure academic study such as psychological differences between owners and renters, cell-phone users and landline users, or people who like or dislike public transportation. These are the kinds of real-world variables that (in my opinion) real people care about and where we can make a real contribution to society.
- Creating real-world value: How do we know that physics creates value? Everytime we turn on a light or start our car engine, we prove principles of physics. People use physics. If we want people to really believe in the power of moral psychology, we need to get people to use moral psychology. There is no shortage of organizations out there that want to impact variables of interest to moral psychologists and fail or succeed, we'll learn something. People will really believe in the power of psychology when they see that people are using it to achieve real world outcomes, such as getting people to vote via descriptive, rather than injunctive norms on Facebook. Some of that exists already, but the opening of psychology to non-academics who can contribute their own data and their own variables will vastly speed up this process. Imagine a world where Facebook shows that it's "I Voted" avatar accounted for 3% of the variance in turnout in the Ohio election, tipping the presidential election, and updating the data commons on social influence effect sizes as they pertain to voting, as opposed to other domains. That future isn't that far off.
- Ravi Iyer
In a line of research led by Matt Motyl, at the University of Virginia, we've been exploring ideological differences in preferences for where one lives. This project is informed by a few ideas already out there.
- The observation that cities are getting more and more partisan, as depicted in the Big Sort.
- Richard Florida's ideas about creating people-city matches.
- The observation that satisfying values, rather than material needs, is increasingly what society cares about (also see my SXSW presentation on this).
- Lots of psychological work conceptualizing ideology as a difference that reflects more than just political ideas.
Given these trends, we would expect liberals, conservatives, and libertarians to differ on what traits are most important in choosing a city to live in. To test this, we asked participants to allocate 100 importance points to the 10 (out of 46) most important traits that they would use to judge a city. The idea was to force people to make choices about what is and what is not important as most all of these traits are desirable. The results, based on over 2000 youmorals.org visitors, largely follow common sense and are shown below with the traits preferred by liberals at the top and by conservatives at the bottom. For the statistically minded among you, all correlations of .05 or higher are statistically significant.
Perhaps more interesting are the average number of points allocated by liberals, conservatives, moderates, and libertarians to each of these traits. There is actually a great deal of consensus as to what is important (clean air/water, safety, job opportunities, medical care) even as there are differences (public transportation, family friendly, religiosity). Also interesting is to note aspects of cities for which libertarians score highest (not too noisy, scientific community, many atheists), which dovetails well with our other research on libertarians.
Average points allocated by ideological group:
There are important plusses and minuses of using non-representative samples. However, these results generally conform to popular wisdom about these groups, so while the means may differ in the general population, the overall patterns seem likely to generalize. As with much of our research, the goal isn't to determine which way of being or which city type is best, but rather to help people more explicitly make choices that may align with their value orientation. I'm hopeful that the above lists will prove generative when people search the internet for ideas about where to live, a search which apparently is getting more and more common, according to Google Trends.
- Ravi Iyer
Research has shown that, in the western world, people are increasingly motivated by "post-materialist" values (see work by Inglehart), meaning that physical concerns for safety, security, food, and other physical things is less of a concern. Instead, people are more motivated to satisfy their values (see examples from my 2012 SXSW presentation on this subject). Indeed, one the reasons that I am liberal is likely due to the fact that I don't worry so much about security and physical needs and have a lot of existential angst about what the meaning of life is...for better or worse. Increasingly, as war becomes less common and technology allows us to serve our physical needs better, I believe more and more people will seek post-materialist answers, and much of my research has this theme in it.
Posts in this category:
- Your Values Predict the Stories You Choose
- When is investment banking immoral? A review of Greg Smith’s book, Why I left Goldman Sachs.
- Bill O’Reilly, Sarah Palin and Paul Krugman need to get out of Maslow’s Basement.
- Where to live? Liberal, conservative, & libertarian criteria differ.
- Post-Materialism: People are increasingly motivated by values and higher order psychological needs.
- Big Data Should Measure Value Fit
- The Experiential Economy
When the NY Times or Gallup reports that Obama or Romney has a lead in the polls, how do they know this? Typically, they pay people to randomly call people and they extrapolate from this sample, using established statistical methods, to make generalizations to the population. Some groups won't respond, especially young adults who often have cellphones and screen their calls. Many people I know are like this.
Polling guru, Nate Silver, has written about this issue extensively, and Pew has researched it as well. Cell phone users tend to be younger and more liberal. Pollsters are used to correcting for such selective non-response (e.g. men selectively non-respond more than women) by weighting their answers. However, this critically relies on having a variable that you can use to do this weighting. If cellphone users simply differ on demographic dimensions, weighting should work, but if they differ on other dimensions such as Big 5 personality traits or values, then pollsters will be unable to weight their data.
Do cell phone users differ from landline users on psychological dimensions? The answer is fairly common sense as this is an issue that we all have lots of anecdotal data on. Of course they do. The below chart compares cellphone users to landline users based on visitors to yourmorals.org who answered a question about their phone usage, with traits related to landline use at the top and traits predicting cellphone use at the bottom.
Cellphone users value stimulation, achievement, and hedonism more. They value tradition, conformity, and security less. They are less conscientious, more liberal (especially on social issues), and are younger. Some of these variables are things that pollsters can address by weighting their results (e.g. youth and liberalism), but other variables are things that pollsters do not measure and therefore cannot directly weight for.
Since some of these things vary by ideology, gender and age as well, we can statistically control for these factors and see if we get fewer significant predictors of cellphone usage. Valuing stimulation and achievement are the remaining significant predictors with valuing tradition and being socially conservative as marginally significant predictors. Other psychological variables such as being conscientiousness and valuing hedonism are accounted for by controlling for factors that pollsters likely can weight for. As such, perhaps these psychological variables are less problematic. It is worth noting that valuing stimulation remains by far the best predictor of cellphone usage (after age) in regression analyses controlling for demographic variables (beta = .13, p<.001).
The yourmorals.org sample is not a representative sample, but I think that might be better in this case. Trying to measure characteristics of people who use cellphones, which I would assume correlates with screening calls and generally being less responsive to surveys, might be better done using non-phone means so that your measurement interacts less with what you are measuring. The educated, internet savvy users who tend to answer yourmorals surveys are exactly the kind of people you might want to examine and be unlikely to poll via phone. Further, we aren't interested in whether the overall population has differences between cellphone users and landline users. That could be a function of youth (the biggest predictor here). Rather, we are interested in whether people who have the exact same demographic characteristics and vary only in terms of their cell phone usage may differ in meaningful ways as it is this variance that would confound pollsters. Using a particular non-representative sample can actually be better for answering questions about the relationship between variables, as certain differences are naturally controlled for with the whole sample being generally internet savvy, educated, and white. But certainly these findings (like all social science) need to be replicated by others in other datasets to have more confidence.
The take home message? As noted by Pew and Nate Silver, polls will have to have cellphone samples in order to avoid bias that likely skews against liberal candidates. Second, if my intuition that heavy cell phone users are unlikely to respond regardless is correct, then even pollsters that poll cellphones may have to start thinking about weighting for non-traditional variables that are a proxy for these psychological variables that predict non-response. Silver suggests "urban/rural status, technology usage, or perhaps even media consumption habits". Third, the psychological profile of cellphone users (seeking novelty, being socially liberal and not valuing tradition) suggests that polls might exhibit more bias on social issues such as gay marriage, and other issues which could reasonably be said to correlate with being a novelty seeker. These effects aren't big, but in a world where a few percentage points is big news, they are worth considering when digesting poll results.
- Ravi Iyer
Recently, some colleagues of mine forwarded me this article from the Weekly Standard concerning the use of social science to delegitimize conservatism. There are some valid points in this article that the author uses to question specific studies. However, I think the author fails to understand the breadth of evidence that underlies most social science findings.
Social scientists deal with a far more complex subject than scientists who work with rocks or chemicals. Specifically, human beings have free will. They can decide to do or not do things in response to a stimulus. Further, because we care about human beings in a way that we don't care about rocks, we can't always design studies perfectly, as we have to respect the wishes of others. As such, all social science has problems of sampling and generalizability.
But the fact that all social science research has flaws doesn't mean you should ignore it. For example, presidential polls have flaws, even with the author's preferred sampling method, as question wording, non-response, and weighting to correct for non-response all introduce bias. While each poll is imperfect, each poll still give us some understanding of what is going on in the population. Perhaps more critically, different polls have different flaws, which means that if you aggregate across measures (e.g. see Nate Silver's five thirty eight blog), you can get something close to the truth (the same principle underlies the Wisdom of Crowds). Yes, a survey of yourmorals.org volunteers or undergraduates or mechanical turk participants or randomly selected households who will answer a survey, is imperfect. Yes, artificial experiments, neuroscience correlations, and self-report are all imperfect. But they are all imperfect in somewhat different ways, and if you find the same thing across each of these samples using a variety of different methodologies, then you can be pretty confident of your findings.
Personally, I don't believe any single study or paper, and a I wait to see if there is confirmation across research groups, methodologies, and samples before believing any research. This is true in social science and in other sciences as well. Andrew Ferguson, who wrote the Weekly Standard piece, is capitalizing on an intuition we all likely share, that so many studies out there report so many facts, many of them contradictory (e.g. is alcohol good for your health?), that we can't help but question them. And we should. Individual studies and papers are not proof, and we probably shouldn't report them as such. But much of this research that relates to liberal and conservative differences has many studies using many methodologies and samples behind them, and that is where we can be more confident. It is for this reason that I increasingly find myself drawn to computer scientists and data scientists who work on questions of aggregation, and as technology starts to pervade social science, my guess is that social science will move more towards aggregation and also place less emphasis on individual papers.
I agree with Ferguson that pathologizing the other side isn't helpful, but not because the science is wrong, but because the interpretation often is subject to bias. A lack of empathy can be thought of as an ability to make rational, competent decisions or heartlessness. Loyalty to one's family can be thought of as noble or as nepotism. Reliance on one's intuition can be thought of as indicative of common sense or of ignorance. But the fact that these things differ between liberals and conservatives are indeed facts, with as much evidence behind them as facts like cholesterol causes heart disease. The world's knowledge graph will eventually encompass not just physical facts, but facts like these as well.
- Ravi Iyer
I recently read this article from Fast Company about Father Greg Boyle's work at Homeboy Industries, and just like every other time I've encountered stories of this work, it ended with me in tears. It reminded me that I've been meaning to write about Tattoos On The Heart, which just might be my favorite book ever. It certainly is the most moving book I've ever read.
Since this is a blog that is largely about psychology, I'd like to frame my discussion of the book in terms of one of my favorite psychological theories of personality, Simon Baron-Cohen's Empathizing-Systemizing distinction. Father Boyle is a great empathizer, who seems to "enjoy caring for other people", is able to "predict how someone will feel", and knows "what to do in a social situation" (quotes are from Baron-Cohen's scale). In contrast, he is a fairly mediocre systemizer (e.g. reading "legal documents very carefully"), if we are to infer that trait from the finance side of Homeboy Industries depicted in Fast Company. Luckily, he now has help. This empathizing dimension relates to the two things that I feel are most powerful about Father Boyle. His ability to forgive and his ability to tell stories. From the book:
We had lots of enemies in those early days, folks who felt that assisting gang members somehow cosigned on their bad behavior. Hate mail, death threats, and bomb threats were common...From my office once, I heard a homegirl answer the phone, and say to the caller, "Go ahead and bring that bomb, mutha fucka. We're ready for your ass."..."Uh, Kiddo, um," I tell her, "Maybe we should just say 'Have a nice day and God bless you.'"
Some of the gang members have done terrible things, but one of his favorite things to say to those whom most of society would rather ignore is that "you are so much more than the worst thing that you have done." In the Fast Company article, they give money to a woman who punched their receptionist in the face. Sometimes the generosity seems so without limits as to be insane, yet for these youth who have no fear of prison or death, it seems hard to imagine anything but unconditional love being their salvation. In some ways, Father Greg is giving these youth the unconditional love that many of us take for granted from our parents.
Our YourMorals.org data tells a similar story about the characteristics of empathizers. Empathizers (the blue line) in our dataset, tend to forgive others (as measured by questions like being "understanding of others for the mistakes they've made").
As well, empathizers, in our dataset, also tend to enjoy stories (r=.17, p<.001, N=495), and the second trait that makes Father Boyle unique is his ability to tell stories. Stories are a way for human beings to communicate not just information, but the feelings that go along with that information. Indeed, the most common measure of empathy used in psychology, the Interpersonal Reactivity Index, uses items like "I really get involved with the feelings of the characters in a novel" and "Becoming extremely involved in a good book or movie is somewhat rare for me" (reverse scored) to measure empathy. Stories are powerful things. From the introduction of the book:
I have all these stories and parables locked away in the "Public Storage" of my brain, and I have long wanted to find a permanent home for them. The usual "containers" for these stories are my homilies at Mass in the twenty-five detention centers where I celebrate the Eucharist...After Mass once, at one of these probation camps, a homie grabbed both my hands and looked me in the eye. "This is my last Mass at camp. I go home on Monday. I'm gonna miss your stories. You tell good stories. And I hope....I never have to hear your stories again."
Father Boyle's stories really are good and show the polish of years of curation. They transform me every time I read them, reminding me that while justice may feel good, kindness is far more powerful.
If there is a fundamental challenge within these stories, it is simply to change our lurking suspicion that some lives matter less than other lives.
Food for thought. Please do read the book and I'll be quite shocked if you can read the stories in the book without being similarly moved. I can't recommend it enough.
- Ravi Iyer
I gave a presentation at South by Southwest earlier this month. I appreciate the many people who voted for my idea, who attended my talk, and who gave me feedback via twitter or face to face afterwards. It was a great experience.
It was a great experience, not for the people I met or for the thrill of speaking , both of which were nice, but more so because it forced me to think deeply about what I wanted to say. A famous writer once said that “How do I know what I think until I see what I say?”. My thoughts are still evolving (one person, who was positive about the talk, commented to me after that she could see my thoughts evolve on stage), and if I did the presentation over, I would frame it differently, but what I believe I arrived at, is this: Big data should measure value fit. Or perhaps more generally, the proliferation of data should be used to measure the intangible things that we say are important to us.
Here is more or less what I ended up saying in narrated powerpoint:
I was happy with my talk, but I will try to simplify things a bit the next time I do it. Rather than present more cool findings from psychology, which are endless but ultimately forgotten, I would have focused more clearly on the point I started with: that we need to bridge the gap between the things we say we care about and the things that we measure.
Just as countries are starting to question whether measuring gross domestic product is a good measurement of that which is worthwhile, companies should start to question whether measuring profits/monthly unique visitors/return on investment/facebook likes/valuation, is measuring that which is worthwhile. A recurring theme at South by Southwest was a focus on the importance of values and happiness as evidenced by talks with names like "Go Forth and make Awesomeness: Core Values & Action" or "Why Happiness is the new Currency?". But while companies talk about values and happiness as outcomes, they don’t measure them, perhaps because they feel like they can’t measure the intangible. Moral psychology and positive psychology, which deal with the quantification of values and happiness related constructs, can provide this methodology so that big data can eventually be used to measure the right things.
Once you start to think in this way, you can see this need everywhere. On cue, a friend recently sent me this article from the New York Times, that illustrates the points I make. It’s by a courageous Goldman Sachs employee who quit because of he felt, in the terms of this post, that Goldman was measuring success the wrong way.
How did we get here? The firm changed the way it thought about leadership. Leadership used to be about ideas, setting an example and doing the right thing. Today, if you make enough money for the firm (and are not currently an ax murderer) you will be promoted into a position of influence.
What are three quick ways to become a leader? a) Execute on the firm’s “axes,” which is Goldman-speak for persuading your clients to invest in the stocks or other products that we are trying to get rid of because they are not seen as having a lot of potential profit. b) “Hunt Elephants.” In English: get your clients — some of whom are sophisticated, and some of whom aren’t — to trade whatever will bring the biggest profit to Goldman. Call me old-fashioned, but I don’t like selling my clients a product that is wrong for them. c) Find yourself sitting in a seat where your job is to trade any illiquid, opaque product with a three-letter acronym.
Today, many of these leaders display a Goldman Sachs culture quotient of exactly zero percent. I attend derivatives sales meetings where not one single minute is spent asking questions about how we can help clients. It’s purely about how we can make the most possible money off of them. If you were an alien from Mars and sat in on one of these meetings, you would believe that a client’s success or progress was not part of the thought process at all.
I am sure that Goldman Sachs has sophisticated algorithms to use their giant data sets to predict financial markets and make as much money as possible. I doubt they’ve ever considered measuring the values of their employees. Sometimes what you measure is a reflection of your values.
- Ravi Iyer
ps. I am not short on projects, but if you would like help taking the data you have and using it to measure intangible/psychological things, feel free to email me.
I currently work as both a researcher at USC and as the Director of Data Science at Ranker.com. Some people would consider these two roles to be somewhat tangential, but increasingly, I'm finding that there is a lot of overlap. Technological methods are increasingly of use in social science at the same time as social science methods are being imported into technology companies. Increasingly, companies are trying to create statistical models to predict behavior. As more and more data on human behavior and thought is collected by technology companies, as opposed to university researchers, it seems inevitable that social science itself will be changed.
Technology has not just changed, but disrupted, every other dominant form of information distribution that previously existed, be it the distribution of music (iTunes), news (Huffington Post), books (Amazon), TV (Hulu), gossip (Twitter), jokes (Cheezburger), language (c u l8r), family news (Facebook), and education (TED talks or the Khan Academy). While academia is called the ivory tower for a reason, it seems unlikely that it will escape this wave of change, especially given the fact that the biggest technology companies collect far more data on human thought and behavior in a day than all of academia collects in a year.
Here are five specific ways that I believe technology will change social science:
1. Bigger ecologically valid, data sets - The only thing that separates social science from opinion is the use of data and with more data comes more confident findings. There is currently some debate in social psychology as to methodology that sometimes can lead to false positive results, by taking advantage of chance. For example, statistical significance is defined, in many sciences, as something that has a 95% chance of being correct, which sounds impressive, but if 200 researchers want to prove something, this means that 10 of them will be able to, by sheer chance. As data sets get bigger and bigger, the chance of error will become lower and lower, with standards for "significance" getting more and more stringent. In addition, most of this new data will be collected in real world environments, meaning that there will be less of a logical leap when inferring some real world phenomena that relates to the results of a lab study.
2. Cross-sample Validation - With more data comes the possibility of dividing a dataset into many parts (e.g. by referral URL) and replicating research in many datasets. To do this efficiently will require a technology we use a lot at Ranker, the semantic web. The semantic web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. Right now, researchers cross-validate findings through a painstaking process called meta-analysis, whereby interested parties attempt to reconcile various datasets into a standard format. Of course, because there are no standards, each reconciliation process is a one-time throwaway process, whereby all the extracted data from one researcher is unusable by subsequent researchers. Google scholar is starting the process of taking "dumb text" in papers and creating some metadata as my scholar page contains extractions of dates, authors, and citations from papers I've written. If there were a standard format for describing the data within the papers, there is no reason why those couldn't be extracted as well, allowing us to answer questions like "what is the correlation between openness to experience and ideology in data collected by people before and after 2005?" without having to read all those papers. It would also let people share simple findings like "the correlation between openness to experience and ideology at place X and time Y is Z," which are completely lost now.
3. Adding inter-disciplinary analysis and variables - Right now, social science is balkanized. Every discipline has it's own methodologies and opinions about what is or is not the right way to do things. Personality psychologists care a lot about measurement while political scientists care about sampling. Social psychologists create brilliant artificial controlled lab experiments designed to isolate variables, while technology companies mine free form, uncontrolled data seeking exploratory patterns. Qualitative methods have a richness and depth, that is scoffed at by more quantitative researchers. All of these methodologies have error and the methodologies of any discipline share error, such that they all would be improved by adding the techniques of other disciplines. But as long as there are no standards for data (e.g. the semantic web), reconciling this data would require immense human effort. Further, the lack of standards means that we never have the full picture of human thought and behavior. Psychologists may study risk tolerance and variable A and financial analysts may study risk tolerance and variable B, which might lead to a natural hypothesis as the relationship between A and B. But since psychologists are not interested in B and financial analysts do not care about A, nobody reconciles this data. Real world human behavior usually involves the variables in ALL disciplines, yet each discipline often contents itself with it's own slice of a human being. Semantic technology will eventually allow us to put these slices together.
4. Systems level approaches - Of course, putting together the results of semantic datasets, which combine hundreds of variables and many bi-directional connections, all with varying degrees of confidence arrived at through various methodological and sampling techniques, is not easy using the traditional paper format. The end result of such an approach is often a system or a model, of the type that computer scientists build, rather than a paper. Some psychologists are putting together connectionist models, but the expertise to actually do such things lies in technology circles more than in the social science community.
5. An open knowledge base - The internet hates middlemen, and right now, academic publishers are middlemen who control the flow of information under the outdated idea that people read printed editions of journals devoted to specific limited topics with limited pages. The noble goal of the editorial process is to separate truth from untruth through peer review, which is a laudable, but completely impractical goal, as evidence exists along a continuum instead of being categorically true or untrue. There are so many peer-reviewed journals that anything can get the stamp of "truth". Unlike physics or chemistry, a single paper's worth of evidence, no matter where it appears, is never conclusive in social science. Big controversies exist in social science even about things where there are tons of very well-done papers about the subject, each of which is ostensibly the truth, or else it shouldn't have been published, right? The reality of social science is that best we can do is to sum up all the evidence from all the various data collected, hopefully using various methodologies (again, something the semantic web can solve), and get a bigger picture of how robust any finding is. However, since peer review checks for importance, topicality, novelty, and a host of other subjective factors, not to mention a journal's bias against replications and null findings, the current process actually ends up hiding the true sum of all evidence for any finding. That is how prominent blatantly false findings can exist in the literature for years undetected. Further, since journals require high subscription fees from universities (whose employees do all the work for the journal ironically), only people at first world universities can even see this evidence. Whether you agree with my hypothesis or not, the current system is simply unsustainable given the mountain of data that is coming and the ethos of silicon valley, where publish then review/filter/aggregate is the dominant model. As more and more data on human behavior and thought is published by companies like Hunch, Ok Cupid, Ranker and the Facebook data team, the traditional social science system will necessarily adapt to these methods or become largely irrelevant next to these larger, more ecologically valid, robust, and complex datasets.
In summary, social scientists are incredibly smart about what they do, most moreso than I, and there is a lot that technologists can learn from social science methods. Indeed, on March 11, I'll be giving a talk at SXSW about how much technologists can benefit from social science methods, especially as it relates to serving the intangible needs of employees and customers.
However, there are countless ways that social scientists can benefit from technology as well. Human beings have been studying the human condition for thousands of years, and the idea that a select group of humans can use their special methodology to go off into an ivory tower, figure things out, and then inform the rest of us what the truth is, is an unlikely scenario. Or perhaps more correctly, it is a common scenario that has played out throughout history with no actual impact on our collective understanding. If we really want to make an impact on our collective understanding of ourselves, it will take a collective effort from social scientists and internet professionals, quantitative and qualitative researchers, novelists and political scientists, and including the kid who surveys their 3rd grade class whose data contributes to our collective understanding too. It is my proposition that technology, and specifically the semantic web, may finally allow such a collaboration to occur.
- Ravi Iyer
Almost all social psychologists are smart, but few are wise. I would argue that you can't advance our collective understanding of the human condition by being smart, without also adding some wisdom to give context to what you study.
For example, the most essential paradigm in social psychology is the experiment and the more controlled the experiment is, with fewer extraneous variables, generally the more prestigious the article. However, as these experiments become more and more specific, isolating psychological mechanisms and ruling out alternative hypotheses, they also largely become more divorced from reality. After all, reality is usually uncontrolled and contains more, not fewer variables. Further, most experimenters have an initial hypothesis and will keep working to create the conditions that show their hypothesis to be true. As such, if I show that X causes Y in a lab, it doesn't necessarily follow that X causes Y in society. Often, another researcher will confirm that X does not cause Y using a different paradigm. Since you get to construct the paradigm to show what you want to show in an experiment on humans, what does such a study actually prove? Perhaps a better characterization of the findings of such research is that X can cause Y, rather than the more simplistic X causes Y.
There is something very valuable in showing that X can cause Y. Good social science research performs the same function as a good parable or a good memoir, often illustrating a truth that we know deep down, but often forget. Thinking fast can make you take unwise risks. Being grateful can make you happier. Crying wolf can make people ignore real requests for help. Whether through story or statistics, these examples examples of what can happen are often helpful in considering our daily life.
However, the average person often knows many of these truths already and it takes wisdom to move these examples beyond the realm of the self-evident and into the realm of useful knowledge. This recent New York Times op-ed, by Barry Schwartz, illustrates how one can take parables generated by research (e.g. on how too much of something can be bad) and create something wise. In it he argues that efficiency can make us better off, yet can cause hardship too. I excerpt a bit of it below, but it doesn't do the original article justice, so I hope you read it.
So whereas some efficiency is good, more efficiency may not be better. The psychologist Adam Grant and I published an article last year suggesting that the “too much of a good thing” phenomenon may be more general than commonly thought. Some choice is liberating; too much choice is paralyzing. Some motivation produces excellent performance; too much motivation leads to folding under pressure.
Perhaps we can use the criticism of Bain Capital as an opportunity to bring a little friction [the opposite of efficiency] back into our lives. One way to do this is to use regulation to rekindle certain social norms that serve to slow us down. For example, if people thought about their homes less as investments and more as places to live, full of the friction of kids, dogs, friends, neighbors and community organizations attached, there might be less speculation with an eye toward house-flipping. And if companies thought of themselves, at least partly, as caretakers of their communities, they might look differently at streamlining their operations.
We’d all like a car that gets 100 miles to the gallon. The forces of friction that slow us down are an expensive annoyance. But when we’re driving a car, we know where we’re going and we’re in control. Fast is good, though even here, a little bit of friction can forestall disaster when you encounter an icy road.
Some social scientists think studying human behavior and thought is like physics. If intelligent people spend enough time on it and collect enough data, we experts can figure out all the rules. But research on human beings is inherently messy, especially for those of us who believe in free will. Just imagine how much trouble physicists would have if atoms could decide whether or not to split.
Another view of social science is that it is but one form of evidence, in a conversation about the human condition that has gone on for millions of years and a marketplace of ideas that is far broader than our parochial disciplines and methods. Social scientists provide a unique and important way of thinking about the world, and I'm hopeful the gap between data and knowledge will decrease as data on human behavior is increasingly collected and shared by all sorts of organizations and the wisdom of crowds replaces the intelligence of a very smart few.
- Ravi Iyer
ps. This is part of a series of posts I'm writing to help crystallize my thoughts for a presentation I'm doing at South by Southwest on how moral psychology and big data are converging. Comments that help sharpen my thinking are welcome and please attend my presentation if you will be at SXSW. I'll certainly upload slides/video afterwards.
I feel as if sometime in the early 2000s, society collectively decided that it was better to own a home than rent. Property values went up and it seemed like people were willing to go to great personal difficulty simply for the sake of being an owner. It probably didn't hurt that property values kept going up. Still, I never felt a strong urge to own and the prospect seemed more like a burden (fixing your own things, having trouble being able to move) than a blessing. Of course, that may say more about my personality than about owning or renting.
I thought I'd examine the Big 5 personality traits of people who think owning is "better" (e.g believing that home ownership is important to happiness) vs. those who prefer renting (e.g. believing that renting provides significant advantages compared to owneing a home) using ~800 people who answered these questions at yourmorals.org. I had 7 questions about owning vs. renting (alpha = .87). The Big 5 personality traits are 5 personality dimensions that are deemed most parsimoniously able to characterize people. The dimensions are Agreeableness (e.g. how well do you want to get along with others), Conscientiousness (e.g. how detail oriented and tidy are you), Extraversion (e.g. how outgoing are you), Neuroticism (e.g. how tense are you), and Openness to Experience (e.g. how much do you seek out new experiences).
Predictably, people who prefer owning a home vs. renting are more conscientious (r = .08, p=.016) and less open to new experiences (r = -.08, p=.03), but the differences are quite small.
People who want to be owners also also tend to be more conservative (r=.18, p<.001), older (r=.13, p<.001), and tend to prefer buying material things rather than experiences (r=.13, p<.001). Interestingly, there was no relationship to self described social status or gender. Obviously many of these relationships are small, but they certainly are as I would predict, with perhaps the exception of the lack of relationship with wealth and gender (my guess would have been that women and wealthier people would prefer home ownership).
Got any interesting hypotheses relating to the personalities of those who prefer renting vs. owning? I'd happily try them. I'm eager to examing values with regard to owning/renting next.
- Ravi Iyer
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