Moshe Hoffman , Erez Yoeli , and Carlos David Navarrete
The Evolution of Morality
Part of the series Evolutionary Psychology pp 289-316
Here is an excerpt:
The key result for evolutionary dynamic models is that, except under extreme conditions, behavior converges to Nash equilibria. This result rests on one simple, noncontroversial assumption shared by all evolutionary dynamics: Behaviors that are relatively successful will increase in frequency. Based on this logic, game theory models have been fruitfully applied in biological contexts to explain phenomena such as animal sex ratios (Fisher, 1958), territoriality (Smith & Price, 1973), cooperation (Trivers, 1971), sexual displays (Zahavi, 1975), and parent–offspring conflict (Trivers, 1974). More recently, evolutionary dynamic models have been applied in human contexts where conscious deliberation is believed to not play an important role, such as in the adoption of religious rituals (Sosis & Alcorta, 2003 ), in the expression and experience of emotion (Frank, 1988 ; Winter, 2014), and in the use of indirect speech (Pinker, Nowak, & Lee, 2008).
Crucially for this chapter, because our behaviors are mediated by moral intuitions and ideologies, if our moral behaviors converge to Nash, so must the intuitions and ideologies that motivate them. The resulting intuitions and ideologies will bear the signature of their game theoretic origins, and this signature will lend clarity on the puzzling, counterintuitive, and otherwise hard-to-explain features of our moral intuitions, as exemplified by our motivating examples.
In order for game theory to be relevant to understanding our moral intuitions and ideologies, we need only the following simple assumption: Moral intuitions and ideologies that lead to higher payoffs become more frequent. This assumption can be met if moral intuitions that yield higher payoffs are held more tenaciously, are more likely to be imitated, or are genetically encoded. For example, if every time you transgress by commission you are punished, but every time you transgress by omission you are not, you will start to intuit that commission is worse than omission.
The book chapter is here.
Showing posts with label Learning. Show all posts
Showing posts with label Learning. Show all posts
Tuesday, May 30, 2017
Tuesday, May 9, 2017
Inside Libratus, the Poker AI That Out-Bluffed the Best Humans
Cade Metz
Wired Magazine
Originally published February 1, 2017
Here is an excerpt:
Libratus relied on three different systems that worked together, a reminder that modern AI is driven not by one technology but many. Deep neural networks get most of the attention these days, and for good reason: They power everything from image recognition to translation to search at some of the world’s biggest tech companies. But the success of neural nets has also pumped new life into so many other AI techniques that help machines mimic and even surpass human talents.
Libratus, for one, did not use neural networks. Mainly, it relied on a form of AI known as reinforcement learning, a method of extreme trial-and-error. In essence, it played game after game against itself. Google’s DeepMind lab used reinforcement learning in building AlphaGo, the system that that cracked the ancient game of Go ten years ahead of schedule, but there’s a key difference between the two systems. AlphaGo learned the game by analyzing 30 million Go moves from human players, before refining its skills by playing against itself. By contrast, Libratus learned from scratch.
Through an algorithm called counterfactual regret minimization, it began by playing at random, and eventually, after several months of training and trillions of hands of poker, it too reached a level where it could not just challenge the best humans but play in ways they couldn’t—playing a much wider range of bets and randomizing these bets, so that rivals have more trouble guessing what cards it holds. “We give the AI a description of the game. We don’t tell it how to play,” says Noam Brown, a CMU grad student who built the system alongside his professor, Tuomas Sandholm. “It develops a strategy completely independently from human play, and it can be very different from the way humans play the game.”
The article is here.
Wired Magazine
Originally published February 1, 2017
Here is an excerpt:
Libratus relied on three different systems that worked together, a reminder that modern AI is driven not by one technology but many. Deep neural networks get most of the attention these days, and for good reason: They power everything from image recognition to translation to search at some of the world’s biggest tech companies. But the success of neural nets has also pumped new life into so many other AI techniques that help machines mimic and even surpass human talents.
Libratus, for one, did not use neural networks. Mainly, it relied on a form of AI known as reinforcement learning, a method of extreme trial-and-error. In essence, it played game after game against itself. Google’s DeepMind lab used reinforcement learning in building AlphaGo, the system that that cracked the ancient game of Go ten years ahead of schedule, but there’s a key difference between the two systems. AlphaGo learned the game by analyzing 30 million Go moves from human players, before refining its skills by playing against itself. By contrast, Libratus learned from scratch.
Through an algorithm called counterfactual regret minimization, it began by playing at random, and eventually, after several months of training and trillions of hands of poker, it too reached a level where it could not just challenge the best humans but play in ways they couldn’t—playing a much wider range of bets and randomizing these bets, so that rivals have more trouble guessing what cards it holds. “We give the AI a description of the game. We don’t tell it how to play,” says Noam Brown, a CMU grad student who built the system alongside his professor, Tuomas Sandholm. “It develops a strategy completely independently from human play, and it can be very different from the way humans play the game.”
The article is here.
Monday, May 8, 2017
Raising good robots
Regina Rini
aeon.com
Originally published April 18, 2017
Intelligent machines, long promised and never delivered, are finally on the horizon. Sufficiently intelligent robots will be able to operate autonomously from human control. They will be able to make genuine choices. And if a robot can make choices, there is a real question about whether it will make moral choices. But what is moral for a robot? Is this the same as what’s moral for a human?
Philosophers and computer scientists alike tend to focus on the difficulty of implementing subtle human morality in literal-minded machines. But there’s another problem, one that really ought to come first. It’s the question of whether we ought to try to impose our own morality on intelligent machines at all. In fact, I’d argue that doing so is likely to be counterproductive, and even unethical. The real problem of robot morality is not the robots, but us. Can we handle sharing the world with a new type of moral creature?
We like to imagine that artificial intelligence (AI) will be similar to humans, because we are the only advanced intelligence we know. But we are probably wrong. If and when AI appears, it will probably be quite unlike us. It might not reason the way we do, and we could have difficulty understanding its choices.
The article is here.
aeon.com
Originally published April 18, 2017
Intelligent machines, long promised and never delivered, are finally on the horizon. Sufficiently intelligent robots will be able to operate autonomously from human control. They will be able to make genuine choices. And if a robot can make choices, there is a real question about whether it will make moral choices. But what is moral for a robot? Is this the same as what’s moral for a human?
Philosophers and computer scientists alike tend to focus on the difficulty of implementing subtle human morality in literal-minded machines. But there’s another problem, one that really ought to come first. It’s the question of whether we ought to try to impose our own morality on intelligent machines at all. In fact, I’d argue that doing so is likely to be counterproductive, and even unethical. The real problem of robot morality is not the robots, but us. Can we handle sharing the world with a new type of moral creature?
We like to imagine that artificial intelligence (AI) will be similar to humans, because we are the only advanced intelligence we know. But we are probably wrong. If and when AI appears, it will probably be quite unlike us. It might not reason the way we do, and we could have difficulty understanding its choices.
The article is here.
Tuesday, March 7, 2017
Experiments suggest dogs and monkeys have a human-like sense of morality
Bob Yirka
Phys.org
Originally posted February 15, 2017
A team of researchers from Kyoto University has found that dogs and capuchin monkeys watch how humans interact with one another and react less positively to those that are less willing to help or share. In their paper published in the journal Neuroscience & Biobehavioral Reviews, the team describes a series of experiments they carried out with several dogs and capuchin monkeys and what they discovered about both species social preferences.
The article is here.
Target Article:
James R. Anderson et al, Third-party social evaluations of humans by monkeys and dogs, Neuroscience & Biobehavioral Reviews (2017).
DOI: 10.1016/j.neubiorev.2017.01.003
Phys.org
Originally posted February 15, 2017
A team of researchers from Kyoto University has found that dogs and capuchin monkeys watch how humans interact with one another and react less positively to those that are less willing to help or share. In their paper published in the journal Neuroscience & Biobehavioral Reviews, the team describes a series of experiments they carried out with several dogs and capuchin monkeys and what they discovered about both species social preferences.
The article is here.
Target Article:
James R. Anderson et al, Third-party social evaluations of humans by monkeys and dogs, Neuroscience & Biobehavioral Reviews (2017).
DOI: 10.1016/j.neubiorev.2017.01.003
Wednesday, January 25, 2017
The candy diet
Seth Godin
Seth's Blog
Originally posted January 4, 2017
The bestselling novel of 1961 was Allen Drury's Advise and Consent. Millions of people read this 690-page political novel. In 2016, the big sellers were coloring books.
Fifteen years ago, cable channels like TLC (the "L" stood for Learning), Bravo and the History Channel (the "History" stood for History) promised to add texture and information to the blighted TV landscape. Now these networks run shows about marrying people based on how well they kiss.
And of course, newspapers won Pulitzer prizes for telling us things we didn't want to hear. We've responded by not buying newspapers any more.
The decline of thoughtful media has been discussed for a century. This is not new. What is new: A fundamental shift not just in the profit-seeking gatekeepers, but in the culture as a whole.
"Everything should be made as simple as possible, but no simpler."*
[*Ironically, this isn't what Einstein actually said. It was this, "It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience." Alas, I've been seduced into believing that the shorter one now works better.]
Is it possible we've made things simpler than they ought to be, and established non-curiosity as the new standard?
The blog post is here.
Seth's Blog
Originally posted January 4, 2017
The bestselling novel of 1961 was Allen Drury's Advise and Consent. Millions of people read this 690-page political novel. In 2016, the big sellers were coloring books.
Fifteen years ago, cable channels like TLC (the "L" stood for Learning), Bravo and the History Channel (the "History" stood for History) promised to add texture and information to the blighted TV landscape. Now these networks run shows about marrying people based on how well they kiss.
And of course, newspapers won Pulitzer prizes for telling us things we didn't want to hear. We've responded by not buying newspapers any more.
The decline of thoughtful media has been discussed for a century. This is not new. What is new: A fundamental shift not just in the profit-seeking gatekeepers, but in the culture as a whole.
"Everything should be made as simple as possible, but no simpler."*
[*Ironically, this isn't what Einstein actually said. It was this, "It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience." Alas, I've been seduced into believing that the shorter one now works better.]
Is it possible we've made things simpler than they ought to be, and established non-curiosity as the new standard?
The blog post is here.
Monday, December 19, 2016
Normality: Part descriptive, part prescriptive
Adam Bear and Joshua Knobe
Cognition
Published October 24, 2016
Abstract
People’s beliefs about normality play an important role in many aspects of cognition and life (e.g., causal cognition, linguistic semantics, cooperative behavior). But how do people determine what sorts of things are normal in the first place? Past research has studied both people’s representations of statistical norms (e.g., the average) and their representations of prescriptive norms (e.g., the ideal). Four studies suggest that people’s notion of normality incorporates both of these types of norms. In particular, people’s representations of what is normal were found to be influenced both by what they believed to be descriptively average and by what they believed to be prescriptively ideal. This is shown across three domains: people’s use of the word ‘‘normal” (Study 1), their use of gradable adjectives (Study 2), and their judgments of concept prototypicality (Study 3). A final study investigated the learning of normality for a novel category, showing that people actively combine statistical and prescriptive information they have learned into an undifferentiated notion of what is normal (Study 4). Taken together, these findings may help to explain how moral norms impact the acquisition of normality and, conversely, how normality impacts the acquisition of moral norms.
The article is here.
Cognition
Published October 24, 2016
Abstract
People’s beliefs about normality play an important role in many aspects of cognition and life (e.g., causal cognition, linguistic semantics, cooperative behavior). But how do people determine what sorts of things are normal in the first place? Past research has studied both people’s representations of statistical norms (e.g., the average) and their representations of prescriptive norms (e.g., the ideal). Four studies suggest that people’s notion of normality incorporates both of these types of norms. In particular, people’s representations of what is normal were found to be influenced both by what they believed to be descriptively average and by what they believed to be prescriptively ideal. This is shown across three domains: people’s use of the word ‘‘normal” (Study 1), their use of gradable adjectives (Study 2), and their judgments of concept prototypicality (Study 3). A final study investigated the learning of normality for a novel category, showing that people actively combine statistical and prescriptive information they have learned into an undifferentiated notion of what is normal (Study 4). Taken together, these findings may help to explain how moral norms impact the acquisition of normality and, conversely, how normality impacts the acquisition of moral norms.
The article is here.
Monday, May 16, 2016
Inside OpenAI
Cade Metz
Wired.com
Originally posted April 27, 2016
Here is an excerpt:
OpenAI will release its first batch of AI software, a toolkit for building artificially intelligent systems by way of a technology called “reinforcement learning”—one of the key technologies that, among other things, drove the creation of AlphaGo, the Google AI that shocked the world by mastering the ancient game of Go. With this toolkit, you can build systems that simulate a new breed of robot, play Atari games, and, yes, master the game of Go.
But game-playing is just the beginning. OpenAI is a billion-dollar effort to push AI as far as it will go. In both how the company came together and what it plans to do, you can see the next great wave of innovation forming. We’re a long way from knowing whether OpenAI itself becomes the main agent for that change. But the forces that drove the creation of this rather unusual startup show that the new breed of AI will not only remake technology, but remake the way we build technology.
The article is here.
Wired.com
Originally posted April 27, 2016
Here is an excerpt:
OpenAI will release its first batch of AI software, a toolkit for building artificially intelligent systems by way of a technology called “reinforcement learning”—one of the key technologies that, among other things, drove the creation of AlphaGo, the Google AI that shocked the world by mastering the ancient game of Go. With this toolkit, you can build systems that simulate a new breed of robot, play Atari games, and, yes, master the game of Go.
But game-playing is just the beginning. OpenAI is a billion-dollar effort to push AI as far as it will go. In both how the company came together and what it plans to do, you can see the next great wave of innovation forming. We’re a long way from knowing whether OpenAI itself becomes the main agent for that change. But the forces that drove the creation of this rather unusual startup show that the new breed of AI will not only remake technology, but remake the way we build technology.
The article is here.
Thursday, March 10, 2016
Robots could learn human values by reading stories, research suggests
By Alison Flood
The Guardian
Originally published February 18, 2016
Here is an excerpt:
The system was named Quixote, said Riedl, after Cervantes’ would-be knight-errant, who “reads stories about chivalrous knights and decides to emulate the behaviour of those knights”. The researchers’ paper sees them argue that “stories are necessarily reflections of the culture and society that they were produced in”, and that they “encode many types of sociocultural knowledge: commonly shared knowledge, social protocols, examples of proper and improper behaviour, and strategies for coping with adversity”.
“We believe that a computer that can read and understand stories, can, if given enough example stories from a given culture, ‘reverse engineer’ the values tacitly held by the culture that produced them,” they write. “These values can be complete enough that they can align the values of an intelligent entity with humanity. In short, we hypothesise that an intelligent entity can learn what it means to be human by immersing itself in the stories it produces.”
Riedl said that, “In theory, a collected works of a society could be fed into an AI and the values extracted from the stories would become part of its goals, which is equivalent to writing down all the ‘rules’ of society.”
The researchers see the Quixote technique as best for robots with a limited purpose that need to interact with humanity.
The article is here.
The Guardian
Originally published February 18, 2016
Here is an excerpt:
The system was named Quixote, said Riedl, after Cervantes’ would-be knight-errant, who “reads stories about chivalrous knights and decides to emulate the behaviour of those knights”. The researchers’ paper sees them argue that “stories are necessarily reflections of the culture and society that they were produced in”, and that they “encode many types of sociocultural knowledge: commonly shared knowledge, social protocols, examples of proper and improper behaviour, and strategies for coping with adversity”.
“We believe that a computer that can read and understand stories, can, if given enough example stories from a given culture, ‘reverse engineer’ the values tacitly held by the culture that produced them,” they write. “These values can be complete enough that they can align the values of an intelligent entity with humanity. In short, we hypothesise that an intelligent entity can learn what it means to be human by immersing itself in the stories it produces.”
Riedl said that, “In theory, a collected works of a society could be fed into an AI and the values extracted from the stories would become part of its goals, which is equivalent to writing down all the ‘rules’ of society.”
The researchers see the Quixote technique as best for robots with a limited purpose that need to interact with humanity.
The article is here.
Thursday, February 4, 2016
Empathy can be learned by sharing positive experiences
Yahoo News
Originally published December 28, 2015
A study by researchers at the University of Zurich indicates that empathy towards strangers can be learned and that positive experiences with others influence empathic brain responses.
According to a recent Swiss study, we are all capable of feeling empathy towards strangers. By repeating positive experiences with strangers, our brain learns and develops empathic responses.
The article is here.
Originally published December 28, 2015
A study by researchers at the University of Zurich indicates that empathy towards strangers can be learned and that positive experiences with others influence empathic brain responses.
According to a recent Swiss study, we are all capable of feeling empathy towards strangers. By repeating positive experiences with strangers, our brain learns and develops empathic responses.
The article is here.
Sunday, December 6, 2015
The Effects of Social Context and Acute Stress on Decision Making Under Uncertainty
Oriel FeldmanHall, Candace M. Raio, J. T. Kubota, M. G. Seiler, & E. A. Phelps
Psychological Science, November 5, 2015
doi: 10.1177/0956797615605807
Abstract
Uncertainty preferences are typically studied in neutral, nonsocial contexts. This approach, however, fails to capture the dynamic factors that influence choices under uncertainty in the real world. Our goal was twofold: to test whether uncertainty valuation is similar across social and nonsocial contexts, and to investigate the effects of acute stress on uncertainty preferences. Subjects completed matched gambling and trust games following either a control or a stress manipulation. Those who were not under stress exhibited no differences between the amount of money gambled and the amount of money entrusted to partners. In comparison, stressed subjects gambled more money but entrusted less money to partners. We further found that irrespective of stress, subjects were highly attuned to irrelevant feedback in the nonsocial, gambling context, believing that every loss led to a greater chance of winning (the gamblers’ fallacy). However, when deciding to trust a stranger, control subjects behaved rationally, treating each new interaction as independent. Stress compromised this adaptive behavior, increasing sensitivity to irrelevant social feedback.
The entire article is here.
Psychological Science, November 5, 2015
doi: 10.1177/0956797615605807
Abstract
Uncertainty preferences are typically studied in neutral, nonsocial contexts. This approach, however, fails to capture the dynamic factors that influence choices under uncertainty in the real world. Our goal was twofold: to test whether uncertainty valuation is similar across social and nonsocial contexts, and to investigate the effects of acute stress on uncertainty preferences. Subjects completed matched gambling and trust games following either a control or a stress manipulation. Those who were not under stress exhibited no differences between the amount of money gambled and the amount of money entrusted to partners. In comparison, stressed subjects gambled more money but entrusted less money to partners. We further found that irrespective of stress, subjects were highly attuned to irrelevant feedback in the nonsocial, gambling context, believing that every loss led to a greater chance of winning (the gamblers’ fallacy). However, when deciding to trust a stranger, control subjects behaved rationally, treating each new interaction as independent. Stress compromised this adaptive behavior, increasing sensitivity to irrelevant social feedback.
The entire article is here.
Sunday, October 18, 2015
Haunts or Helps from the Past: Understanding the Effect of Recall on Current Self-Control
Hristina Nikolova, Cait Lamberton, and Kelly L. Haws
Journal of Consumer Psychology
Available online 30 June 2015
Scientific Abstract
Conventional wisdom suggests that remembering our past, and particularly, the mistakes we have made, will help us make better decisions in the present. But how successful is this practice in the domain of self-control? Our work examines how the content of consumers' recollections (past self-control successes versus failures) and the subjective difficulty with which this content comes to mind (easily or with difficulty) jointly shape consumers' self-control decisions. When successes are easy to recall, we find that people display more self-control than when they have difficulty recalling successes. However, recalling failures prompts indulgence regardless of its difficulty. We suggest that these differences in behavior may exist because recalling failures has substantially different affective and cognitive consequences than does recalling successes. Consistent with this theory, we demonstrate that self-certainty moderates the effects of recall on self-control. Taken together, this work enhances our understanding of self-control, self-perceptions, and metacognition.
Layperson interpretation can be found here.
Professional article can be found here.
Journal of Consumer Psychology
Available online 30 June 2015
Scientific Abstract
Conventional wisdom suggests that remembering our past, and particularly, the mistakes we have made, will help us make better decisions in the present. But how successful is this practice in the domain of self-control? Our work examines how the content of consumers' recollections (past self-control successes versus failures) and the subjective difficulty with which this content comes to mind (easily or with difficulty) jointly shape consumers' self-control decisions. When successes are easy to recall, we find that people display more self-control than when they have difficulty recalling successes. However, recalling failures prompts indulgence regardless of its difficulty. We suggest that these differences in behavior may exist because recalling failures has substantially different affective and cognitive consequences than does recalling successes. Consistent with this theory, we demonstrate that self-certainty moderates the effects of recall on self-control. Taken together, this work enhances our understanding of self-control, self-perceptions, and metacognition.
Layperson interpretation can be found here.
Professional article can be found here.
Monday, March 30, 2015
The Kids Are All Right
Have we made our children into moral monsters?
By Daniel Engber
Slate.com
Originally published March 16, 2015
We’re in the middle of a moral panic—a panic over morals. An op-ed posted Monday in the New York Times warns that our children don’t believe in moral facts. They don’t get that things are right or wrong; they don’t accept the inner truth of values. Should a person lie or cheat or kill to get ahead? Kids today, these kids today, they couldn’t say for sure. I mean, aren’t ethics just, like, a matter of opinion? Everyone totally gets to have their own opinions, amirite?
This Village of the Damned scenario, laid out by Fort Lewis College philosopher Justin McBrayer, has clearly struck a chord. As of Thursday, it sat among the Times’ five most emailed, viewed, and shared articles of the week, and has inspired more than 1,800 comments. McBrayer’s most alarming claim—the one that pushed his essay into Facebook feeds and Twitter timelines—tells us how the nation’s youth came to be so dissolute. If millennials haven’t learned respect for ethics, it’s because we grown-ups let them down.
The entire article is here.
By Daniel Engber
Slate.com
Originally published March 16, 2015
We’re in the middle of a moral panic—a panic over morals. An op-ed posted Monday in the New York Times warns that our children don’t believe in moral facts. They don’t get that things are right or wrong; they don’t accept the inner truth of values. Should a person lie or cheat or kill to get ahead? Kids today, these kids today, they couldn’t say for sure. I mean, aren’t ethics just, like, a matter of opinion? Everyone totally gets to have their own opinions, amirite?
This Village of the Damned scenario, laid out by Fort Lewis College philosopher Justin McBrayer, has clearly struck a chord. As of Thursday, it sat among the Times’ five most emailed, viewed, and shared articles of the week, and has inspired more than 1,800 comments. McBrayer’s most alarming claim—the one that pushed his essay into Facebook feeds and Twitter timelines—tells us how the nation’s youth came to be so dissolute. If millennials haven’t learned respect for ethics, it’s because we grown-ups let them down.
The entire article is here.
Wednesday, March 18, 2015
Validating vignette and conjoint survey experiments against real-world behavior
Jens Hainmueller, Dominik Hangartner, and Teppei Yamamoto
Validating vignette and conjoint survey experiments against real-world behavior
PNAS 2015 112 (8) 2395-2400; doi:10.1073/pnas.1416587112
Abstract
Survey experiments, like vignette and conjoint analyses, are widely used in the social sciences to elicit stated preferences and study how humans make multidimensional choices. However, there is a paucity of research on the external validity of these methods that examines whether the determinants that explain hypothetical choices made by survey respondents match the determinants that explain what subjects actually do when making similar choices in real-world situations. This study compares results from conjoint and vignette analyses on which immigrant attributes generate support for naturalization with closely corresponding behavioral data from a natural experiment in Switzerland, where some municipalities used referendums to decide on the citizenship applications of foreign residents. Using a representative sample from the same population and the official descriptions of applicant characteristics that voters received before each referendum as a behavioral benchmark, we find that the effects of the applicant attributes estimated from the survey experiments perform remarkably well in recovering the effects of the same attributes in the behavioral benchmark. We also find important differences in the relative performances of the different designs. Overall, the paired conjoint design, where respondents evaluate two immigrants side by side, comes closest to the behavioral benchmark; on average, its estimates are within 2% percentage points of the effects in the behavioral benchmark.
Significance
Little evidence exists on whether preferences about hypothetical choices measured in a survey experiment are driven by the same structural determinants of the actual choices made in the real world. This study answers this question using a natural experiment as a behavioral benchmark. Comparing the results from conjoint and vignette experiments on which attributes of hypothetical immigrants generate support for naturalization with the outcomes of closely corresponding referendums in Switzerland, we find that the effects estimated from the surveys match the effects of the same attributes in the behavioral benchmark remarkably well. We also find that seemingly subtle differences in survey designs can produce significant differences in performance. Overall, the paired conjoint design performs the best.
The entire article is here.
Editor's note: This research is significant when ethics educators use vignettes to help psychologists learn about their ethical decision-making process and moral beliefs.
Validating vignette and conjoint survey experiments against real-world behavior
PNAS 2015 112 (8) 2395-2400; doi:10.1073/pnas.1416587112
Abstract
Survey experiments, like vignette and conjoint analyses, are widely used in the social sciences to elicit stated preferences and study how humans make multidimensional choices. However, there is a paucity of research on the external validity of these methods that examines whether the determinants that explain hypothetical choices made by survey respondents match the determinants that explain what subjects actually do when making similar choices in real-world situations. This study compares results from conjoint and vignette analyses on which immigrant attributes generate support for naturalization with closely corresponding behavioral data from a natural experiment in Switzerland, where some municipalities used referendums to decide on the citizenship applications of foreign residents. Using a representative sample from the same population and the official descriptions of applicant characteristics that voters received before each referendum as a behavioral benchmark, we find that the effects of the applicant attributes estimated from the survey experiments perform remarkably well in recovering the effects of the same attributes in the behavioral benchmark. We also find important differences in the relative performances of the different designs. Overall, the paired conjoint design, where respondents evaluate two immigrants side by side, comes closest to the behavioral benchmark; on average, its estimates are within 2% percentage points of the effects in the behavioral benchmark.
Significance
Little evidence exists on whether preferences about hypothetical choices measured in a survey experiment are driven by the same structural determinants of the actual choices made in the real world. This study answers this question using a natural experiment as a behavioral benchmark. Comparing the results from conjoint and vignette experiments on which attributes of hypothetical immigrants generate support for naturalization with the outcomes of closely corresponding referendums in Switzerland, we find that the effects estimated from the surveys match the effects of the same attributes in the behavioral benchmark remarkably well. We also find that seemingly subtle differences in survey designs can produce significant differences in performance. Overall, the paired conjoint design performs the best.
The entire article is here.
Editor's note: This research is significant when ethics educators use vignettes to help psychologists learn about their ethical decision-making process and moral beliefs.
Saturday, June 30, 2012
Can Doctors Learn Empathy?
By Pauline W. Chen, MD
The New York Times - Well
Originally published June 21, 2012
Empathy has always been considered an essential component of compassionate care, and recent research has shown that its benefits go far beyond the exam room. Greater physician empathy has been associated with fewer medical errors, better patient outcomes and more satisfied patients. It also results in fewer malpractice claims and happier doctors.
The New York Times - Well
Originally published June 21, 2012
Empathy has always been considered an essential component of compassionate care, and recent research has shown that its benefits go far beyond the exam room. Greater physician empathy has been associated with fewer medical errors, better patient outcomes and more satisfied patients. It also results in fewer malpractice claims and happier doctors.
A growing number of professional accrediting and licensing agencies have taken these findings to heart, developing requirements that make empathy a core value and an absolute “learning objective” for all doctors. But even for the most enthusiastic supporters of such initiatives, the vexing question remains: Can people learn to be empathetic?
A new study reveals that they can.
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