Skip to main content
Social Sci LibreTexts

2.4: Human Behavior Is Partially Predictable

  • Page ID
    198652
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \) \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)\(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\) \(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\)\(\newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    Learning Objectives

    By the end of this section, you will be able to:

    • Describe human predictability.
    • Explain why human behavior can be predicted in general but not in specific cases.
    • Define motivated reasoning.

    Even though each person is unique, people often act in predictable ways. Given a certain set of rules and a specific set of conditions, it is possible to make pretty good guesses (that is, predictions) about how people will behave. This does not mean that all people act in exactly the same way every time. But it does mean that behavior is not entirely random.

    What Does It Mean to Be Predictable?

    Predictability means that it is possible to guess, with some accuracy, how people will behave in certain situations. In the commercial world, this predictability is what allows, say, Netflix to recommend movies. In short, every time you watch a show, Netflix collects data about what you like and then combines that data with information collected from everyone else using Netflix. If you like the same dozen films as a large number of other people and those people have seen and liked a 13th movie, Netflix predicts that there’s a pretty good chance you’ll enjoy the 13th movie as well.

    Video

    Why Netflix’s Algorithm Is So Binge-Worthy | Mach | NBC News

    Political scientists use data to make predictions about political behavior in much the same way that Netflix uses data about its users’ past viewing habits to make predictions about their future viewing behavior.

    Knowledge about human behavior makes it possible to predict how politicians, public officials, and citizens will respond in various political settings. Political scientists can use knowledge of certain characteristics—for example, an individual’s age, education, and ideology, among many other attributes—to predict how likely it is that they will vote and which party they will vote for. It is possible to predict that politicians will typically seek to increase their political power because they have been observed doing so time and again. One of the central goals of empirical political science is to study how people behave politically in order to make more accurate predictions about how they are likely to behave in the future.

    Show Me the Data
    A line graph shows voting rates by age in the United States.
    Figure 2.12 This figure shows voting rates by age group in United States elections between 1980 and 2016. (credit: United States Census Bureau, Public Domain)

    Political analysts collect data on individuals and groups to understand how, for example, voters have behaved in the past and how they are likely to behave in the future. In the United States, voters’ ages are linked to voter turnout rates (the turnout rate is the proportion of eligible voters that actually cast their votes).

    This graphic shows turnout rates for four voter age categories in presidential elections between 1980 and 2016. During this time, voter turnout rates were highest for voters age 65 and over and lowest for voters age 18 to 29.

    What does this mean? If we know nothing about a voter but their age, we could make a prediction about their likelihood of turning out to cast a vote. Political candidates and their campaigns might use this knowledge as they strategize how best to allocate their resources.

    Which Human Behavior Is Predictable, and Which Is Unpredictable?

    The more information political scientists have about how people have behaved in the past, the better they are able to predict how people will behave in the future—but only within limits, a few of which bear mentioning. When making predictions about what people will do, the typical prediction is usually in the form of “individuals with characteristics like these are likely to do a certain thing,” which is quite different from saying “this individual will do that.” One might predict, for instance, that young, environmentally motivated activists around the world are likely to affiliate with the Green Party of their country. This does not mean that it is possible to accurately predict that Greta Thunberg, perhaps the most famous environmental advocate, is a member of Sweden’s Green Party.67

    Why is it possible to predict political behavior in general but not necessarily in individual cases? One answer is that more and better information results in better predictions: imperfect information leads to imperfect predictions. Just as weather forecasts can be wrong because meteorological relationships are complex and not fully understood, behavior predictions can also be wrong. Moreover, what seems to be the sheer randomness of human behavior should not be discounted.68 An event is random when it cannot be predicted. Consider this: although it is possible to predict the outcomes of a coin flip in general (there is a 50 percent chance of getting heads and a 50 percent chance of getting tails), even the most powerful supercomputers cannot accurately predict the outcome of a single coin flip. Whether a single flip comes up heads or tails is random. If you predict the outcome correctly, you just got lucky.

    In 1962, US intelligence discovered evidence that the Soviet Union was placing nuclear missiles in Cuba—only some 100 miles from American soil. Tensions simmered, and at the peak of what is called the Cuban Missile Crisis, the world was on the brink of nuclear war. US President John F. Kennedy and Soviet Premier Nikita Kruschev had a fateful decision to make: With the fate of the world in their hands, should each side escalate the conflict or pull back? No amount of data would have allowed anyone to predict with certainty what their decisions would be: they could have gone either way. Fortunately, they both chose to de-escalate, and the crisis was resolved. What explains this outcome? As the US Secretary of Defense put it, it was “luck. Luck was a factor. . . . It was just luck that [Kennedy and Kruschev] finally acted before they lost control, and before East and West were involved in nuclear war that would have led to destruction of nations. It was that close.”69

    Despite efforts to better predict political outcomes, political polling, which will be discussed further in Chapter 5: Political Participation and Public Opinion, remains fallible. Poll-based predictions about the outcomes of presidential elections in the United States were substantially off in both 2016 and, though they accurately predicted the eventual winner, in 2020.70 Why? Because the polls ask, “Who are you likely to vote for?” but they cannot precisely measure who will actually show up to vote or how they will actually vote.

    Meet a Professional

    Dennis Quinn, data scientist, writer, researcher, and master’s degree candidate in Yale University’s Public Policy program at the Jackson Institute for Global Affairs

    Please explain what you do for your organization.

    At the moment I’m a graduate student at Yale, where I build data streams to address the humanitarian and national security impacts of climate change. But before this I was on the Pew Research Center’s data science team for about five years. During that time I managed research projects involving data mining, machine learning, or other computational methodologies. The job also required a lot of communication, writing, and public speaking as well.

    How did you get involved in your position?

    In my most recent (paying) job, I joined Pew in 2015 in a pretty standard entry-level position, which I’m pretty sure I was offered because I had skills in Python and R, and they knew they would be expanding into the area soon. That expansion allowed me to build out a role for myself in the new research area—and that’s a big lesson I took from that experience: don’t look for the perfect job. Get the skills you want to use, find a thing you care about, and join an organization that does that thing. Then, try to get them to make a job that you want. It might not work every time, but it’s a solid strategy.

    What advice would you give students who are interested in your line of work?

    A good rule of thumb is that opinions are cheap, data is valuable, and facts are even better—and this is a problem in political science because undergraduate education teaches you to argue really well but not to create original knowledge. And this is an area where you can really differentiate yourself as you get into the working world: learn how to create and communicate useful knowledge in the area you care about. In today’s world this often means using data, but it doesn’t have to. It definitely means learning what the questions are, though, and that’s where political science can do really well. But make sure you take that extra leap into knowledge generation—this can mean a (spoken) language, programming, or certain disciplinary focuses like econometrics. All together, this creates a really strong position for you: knowing where the questions are and having the tools to answer them.

    Consider an extreme example regarding the uncertainty of making predictions and the importance of making accurate ones. During times of war, military leaders have to ask, “Will my soldiers fight, or will they run?” The rational soldier might be tempted to run: What fate could be worse than likely death at the hands of the enemy?71 The expressive soldier might stay to demonstrate his loyalty and bravery. But it is not possible to predict with certainty which soldiers will fight and which will flee. The wise military strategist will adopt the strategies believed to increase the probability—the predictability—that the soldier will remain faithful to their side.

    Three soldiers in uniform and combat helmets kneel in a field holding rifles.
    Figure 2.13 Will they fight, or will they flee? (credit: “Photograph of US Soldiers Crouching in Position in the First Wave of Helicopter Combat Assault” by Department of Defense/National Archives, Public Domain)

    How does the strategic political leader increase the odds that individuals will faithfully follow them? By increasing the incentives—the instrumental reasons—for doing so: by raising the costs of defection and the rewards for fidelity, and by raising the importance of expressive values like solidarity, loyalty, and patriotism.

    It may be tempting to believe that those participating in the political causes you support are seeking the right things or doing so for public-spirited reasons—that they are the good ones—and that those who you see as your political opponents are purely self-interested, if not greedy or corrupt. This can be explained by motivated reasoning, the human tendency to embrace those ideas one wants to believe while rejecting evidence that challenges those beliefs.72 In the United States, for example, a large majority of both Republicans and Democrats believe that the other party is closed-minded, and substantial proportions of both parties believe that members of the other party are lazier or less intelligent, moral, or patriotic than the general public.73 Individuals participate in politics for all sorts of reasons, and there is little justification for the belief that the motivations of those on “our side” are much different from the motivations of those on “their side.” On each side, individuals are likely to have a mix of self-interest and public spirit.


    2.4: Human Behavior Is Partially Predictable is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.

    • Was this article helpful?