This chapter uses a range of quotes and findings from the internet and the literature. The key premises of this chapter, which is illustrated with examples, are as follows. First, Big Data requires the use of algorithms. Second, algorithms can create misleading information. Third, algorithms can lead to destructive outcomes. But we should not forget that humans program algorithms. With Big Data come algorithms to run many and involved computations. We cannot oversee all these data ourselves, so we need the help of algorithms to make computations for us. We might label these algorithms as Artificial Intelligence, but this might suggest that they can do things on their own. They can run massive computations, but they need to be fed with data. And this feeding is usually done by us, by humans, and we also choose the algorithms to be used.

Humanity’s situation with climate change is sometimes compared to that of a frog in a slowly boiling pot of water. Most of our climate science takes the form of prediction: telling the frog that in five minutes’ time he will be a little bit warmer. We need more risk assessment: telling the frog that the worst that could happen is he could boil to death, and that this is becoming increasingly likely over time. This approach can give a much clearer picture of the risks of climate change to human health, food security, and coastal cities.

This chapter is devoted to extensive instruction regarding bivariate regression, also known as ordinary least squares regression (OLS).Students are presented with a scatterplot of data with a best-fitting line drawn through it.They are instructed on how to calculate the equation of this line (least squares line) by hand and with the R Commander.Interpretation of the statistical output of the y-intercept, beta coefficient, and R-squared value are discussed.Statistical significance of the beta coefficient and its implications for the relationship between an independent and dependent variable are described.Finally, the use of the regression equation for prediction is illustrated.

The chapter begins with an applied example describing the limitations of bivariate regression and the need to include multiple independent variables in a regression model to explain the dependent variable.The logic of multivariate regression is discussed as it compares to bivariate regression.Running a multivariate regression in the R Commander and interpretation of the results are the main foci of the chapter, with particular attention to the beta coefficients, y-intercept, and adjusted R-squared.Generating the multivariate regression equation from the R Commander output is covered, along with engaging in prediction using this equation.Finally, interpretation of dummy independent variables in a multivariate regression is covered.

Forecasting elections is a high-risk, high-reward endeavor. Today’s polling rock star is tomorrow’s has-been. It is a high-pressure gig. Public opinion polls have been a staple of election forecasting for almost ninety years. But single source predictions are an imperfect means of forecasting, as we detailed in the preceding chapter. One of the most telling examples of this in recent years is the 2016 US presidential election. In this chapter, we will examine public opinion as an election forecast input. We organize election prediction into three broad buckets: (1) heuristics models, (2) poll-based models, and (3) fundamentals models.

Predictions often falter because of human error. Most misses have much more to do with our own human shortcomings than with the technical sophistication of the method at hand. In our experience, forecasting errors occur when we discard or misinterpret evidence right in front of us. The clues are there, but we are blinded by our own filters. This is why it is essential to tackle such biases and discuss corresponding solutions. In this chapter, we’ll look at studies on the forecasting prowess of experts. Then, we’ll focus on cognitive biases that skew predictions. Finally, we’ll present an applied approach to minimize such biases.

In this chapter, a case is made for the inclusion of computational approaches to linguistics within the theoretical fold. Computational models aimed at application are a special case of predictive models. The status quo in the philosophy of linguistics is that explanation is scientifically prior to prediction. This is a mistake. Once corrected, the theoretical place of prediction is restored and, with it, computational models of language. The chapter first describes the history behind the emergence of explanation over prediction views in the general philosophy of science. It’s then suggested that this post-positivist intellectual milieu influenced the rejection of computational linguistics in the philosophy of theoretical linguistics. A case study of the predictive power already embedded in contemporary linguistic theory is presented through some work on negative polarity items. The discussion moves to the competence–performance divide informed by the so-called Galilean style in linguistics that retains the explanatory over prediction ideal. In the final sections of the chapter, continuous methods, such as probabilistic linguistics, are used to showcase the explanatory and predictive possibilities of nondiscrete approaches, before a discussion of the contemporary field of deep learning in natural language processing (NLP), where these predictive possibilities are further amplified.

As literary realism shed earlier providential paradigms, William Thackeray inaugurated a startling interest in alternatives to reality as essential for novels that would be true to life. These “queer speculations” saturate his writing: a child who might have lived, an accident that could have been avoided, a war that would have ended otherwise if only …. Thackeray’s counterfactual imagination matures from occasional stories of the 1840s, through Vanity Fair (1847–48), the Roundabout Papers (1860–63), and Lovel the Widower (1860). His conditionals range from frenzied anticipation to paralyzing regret, developing from wild wagers and total reversals of fortune in the early sketches to a late style where memory, narrative, and writing itself are marked by virtuality. This chapter examines uncertain experience in Thackeray’s oeuvre in relation to historical writing of the eighteenth and nineteenth centuries and accounts of counterfactual reasoning in psychology and narrative theory. Probing uncertainty’s emotional and tonal modulations, Thackeray’s writing widens the space of novelistic realism to include the nonmimetic, hypothetical, improbable, and open-ended – or what he terms the “might-have-beens.”

We explore ethical premises and practical implications of using genetic testing to predict suicide risk. Twin studies indicate heritable components of suicide risk, and associated heritability of mental disorders. Currently, genetics research has abandoned seeking single gene Mendelian determinants, in favour of complex probabilistic epigenetic models. Genome-Wide Association Studies (GWAS) may identify thousands of single nucleotide polymorphisms (SNPs), each contributing very little to the variance in behavioural phenotypes. Since suicide is a behaviour rather than a phenotype, with many different causal aetiologies, it is impossible to predict the behaviours of individuals. We analyse practical and ethical issues that would arise if future research were to identify genetic information that accurately predicts suicide. We examine analytical validity, clinical validity, clinical utility and ethical, legal and social implications. Low sensitivity and specificity for predicting suicide diminish potential advantages and exacerbate risks. We discuss risks of unregulated direct-to-consumer genetic testing services. If someday genetic testing can accurately identify suicide risk in individuals, its use would be contraindicated if we cannot provide effective preventive interventions and mitigate negative impacts of informing people of their suicide risk.

This chapter draws out implications for a defining element of a positivist approach – prediction. Prediction is the primary way positivist scholarship engages the future as well as one of the elements of analysis that define it as “scientific” rather than historical or critical. Presentism demonstrates that while those who do not study history may be doomed to repeat it, those who do study the past are not guaranteed to predict it. The chapter lays out how the presentist move implicates what I call “Disciplinary Prediction,” the predominant way that IR approaches and imagines the future. This chapter evaluates what happens if we divorce the concept of prediction from temporal assumptions that presume continuity and regularity. Once prediction’s temporal scope is allowed to be more limited, contingent, and indeterminate, projects that de-emphasize linearity, actively theorize temporal recurrences, incorporate cycles, and allow for contingency, emergence, and flexibility become increasingly viable. Predictions based on critically informed scholarship also become much more imaginable, enabling a better theorization of the politics of critique as action.