“It is often said that no person is an island and it takes a village to raise a child, but psychology has largely lacked the scientific evidence to quantify and characterize these aphorisms,” wrote MIT professor Alex (Sandy) Pentland in Contextualizing Human Psychology, - an article published in the August, 2020 issue of Technology, Mind and Behavior, a journal of the American Psychological Association. “As a result, experimental focus is usually on more easily quantifiable individual traits and behaviors.”
“In the last decade, however, digital data from online interactions, cell phones, and credit cards have allowed us to precisely quantify large-scale social behavior at a very fine level of detail. The little data breadcrumbs that we leave behind as we move around in the world are enabling new ways of understanding human behavior, giving rise to the emerging discipline of computational social science. “[T]hese new tools can help relate individual traits to the surrounding social context and thus better explain life outcomes and societal characteristics.”
The article illustrated the advances and challenges of data-driven predictions by discussing a 2017 mass collaboration experiment. The experiment asked each of 160 academic teams to predict six life outcomes, - such as a child’s grade point average and whether a family would be evicted from their home, - by analyzing development data of over 4.200 at-risk children from the Fragile Families and Child Wellbeing Study. These data had been collected by interviewing primary caregivers over more than 15 years, as well as other assessments including early childhood education and the children’s scores on a variety of standard tests. Additional information was provided on the parents, including medical, employment, and incarceration histories, religion, and child care practices. Almost 13,000 measurements were made for each child and their family.
The academic teams competed by using the data to predict life outcomes of these children using any models of their choice. They were judged on the accuracy of their predictions, whose actual values were only available to the challenge organizers. Pentland’s MIT research team was one of the academic groups that participated in the experiment. His team won first place by producing the most accurate predictions in three of the categories and second place in a fourth category.
Overall, the predictions were disappointing regardless of the method used by any of the 160 research teams, including MIT’s team. “Despite the rich data set and state-of-art statistical methods, however, our best predictions for these life outcomes were not very accurate and in fact were only slightly better than those from a simple benchmark,” wrote Pentland. “The uncomfortable conclusion is that you cannot predict children’s life outcomes from any of the standard tests or interview methods applied to either the children or their families.”
But, while not doing well in predicting individual life outcomes, the models were able to identify aggregate properties, - e.g., the effect of education on earnings and racial differences in school performance. As it turns out, “you can predict at least some life outcomes from data about the neighborhood in which the children and their families live.”
“Many of the large-scale data analyses using the tools of computational social science provide evidence that when seeking to understand how behavior traits affect life outcomes, it is best to conceive of humans as a species who are on a continual search for new opportunities and ideas and that the surrounding social networks serve as a major, and perhaps the greatest, resource for finding opportunities,” wrote Pentland. “Humans are like every other social species: our lives consist of a balance between the habits that allow us to make a living by exploiting our environment and exploration to find new opportunities. In the animal literature this is known as foraging behavior.”
What accounts for such universal behavioral principles? The answer most likely lies in evolutionary biology. Survival is clearly a key evolutionary imperative. And surviving in a changing environment requires a combination of social learning and new ideas. Humans have thus evolved with the drive to learn from each other. But, at the same time, mutations and innovations will vary among different groups, with natural selection favoring those human groups better able to adapt to changing conditions by exploring their environment.
“One interpretation of the Fragile Family results that is consistent with these and similar results in the computational social science literature is that very early social learning establishes children’s foraging pattern. It is useful to think of this type of ‘social programming’ in relation to fast and slow thinking, as proposed by psychologist Daniel Kahneman.”
In the 1970s, the prevailing view among social scientists was that people were generally rational and in control of the way they think and make decisions. But, the pioneering work of Princeton Professor Emeritus Daniel Kahneman and his long time collaborator Amos Tversky, - who died in 1996, - challenged these assumptions. In his 2011 bestseller Thinking, Fast and Slow, Kahneman explained the research they conducted over the past several decades that have led to our current understanding of judgement and decisions making, - for which he received the 2002 Nobel Prize in Economics.
The book’s central thesis is that our mind is composed of two very different systems of thinking, System 1 and System 2. System 1 is the intuitive, fast and emotional part of our mind. Thoughts come automatically and very quickly to System 1 without us doing anything to make them happen. System 2 is the slower, logical, rational part of the mind. It’s where we evaluate and choose between multiple options, because only System 2 can think of multiple things at once and shift its attention between them. System 1 typically works by developing a coherent story based on the observations and facts at its disposal. This helps us deal efficiently with the myriads of simple situations we encounter in everyday life.
Research has shown that the intuitive System 1 is actually more influential in our decisions, choices and judgements than we generally realize. System 1 is shaped by both evolutionary biology and social context. We’re born with the ability to learn from and adapt to our tribe, - a kind of social imprinting. That’s why babies quickly learn to recognize cats and other animals from a relatively few examples whereas it takes huge amounts of data to similarly train a machine learning algorithm. While the impact of social context weakens as we get older and System 2 develops, Systems 1 continues to play an important role throughout our life.
“Computational social science suggests that the fast mind is the repository of cultural norms, a sort of tribal mind constructed largely unconsciously by integrating observations about how other people behave with biological constraints and tendencies,” wrote Pentland. “In contrast, slow thinking is built on beliefs gained by individual reasoning and observations that seem interesting - facts and behaviors that might someday prove useful. Because slow thinking is rule-based and reflective, it provides a safe way to conjecture new ideas and norms without direct evidence. Language and slow-thinking are tightly coupled and so memorable stories can act as a sort of social ‘virtual reality’ that allows us to learn useful facts and behaviors without having to observe them directly.”
“In the Fragile Families example, it seems that very early experience sets the basic structure for the children’s fast-thinking norms and habits. Characteristics such as the tendency to explore versus hide, to persevere versus give up, and to assume personal agency seem to be established very early, by observation of and interaction with both other children and adults. Slow-thinking faculties mature on top of this foundation and have only limited ability to modify it. Habits are hard to break even when they obviously cause harm and changing social foraging habits is even more difficult because the disadvantages of a flawed fast-thinking repertoire are usually quite subtle and difficult to focus upon.”
“What computational social science suggests is that the ‘rational individual’ model refers mostly to our slow-thinking mind and is a poor description of how people incorporate new actions and habits into their everyday, fast-thinking behavior. The key failure is not limitations on rationality; it is that the fast-thinking mind does not maximize for the needs of the individual. Instead, our fast-thinking mind, which is responsible for most of our everyday behaviors, is culture-bound, maximizing according to social norms, group benefit, and biological constraint, often against the interests of the individual.”
“The idea that fast thinking is primarily culture bound, instead of being driven by individual thought and reflection, means that fast thinking is collectively rational rather than individually rational. Humans continually engage in exploratory behavior to find new adaptive behaviors and most of these new behaviors come from mimicry of other people. As the Fragile Families, diversity, and similar studies illustrate, it seems to be the breadth of a person’s exploratory behavior, and not their individual cognitive traits, that usually dominate life outcomes and the evolution of social characteristics.”
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