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The Evidence for Motivated Reasoning in Climate Change Preference Formation

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The Evidence for Motivated Reasoning in Climate Change Preference Formation REVIEW ARTICLE https://doi.org/10.1038/s41558-018-0360-1 The evidence for motivated reasoning in climate change preference formation James N. Druckman * and Mary C. McGrath Despite a scientific consensus, citizens are divided when it comes to climate change — often along political lines. Democrats or liberals tend to believe that human activity is a primary cause of climate change, whereas Republicans or conservatives are much less likely to hold this belief. A prominent explanation for this divide is that it stems from directional motivated reason- ing: individuals reject new information that contradicts their standing beliefs. In this Review, we suggest that the empirical evidence is not so clear, and is equally consistent with a theory in which citizens strive to form accurate beliefs but vary in what they consider to be credible evidence. This suggests a new research agenda on climate change preference formation, and has implications for effective communication. widely discussed explanation for the public divide in beliefs Our starting point is a standing (or prior) belief. This belief about climate change is that people engage in ‘directional can be any climate change-relevant construct, such as beliefs that A motivated reasoning’1–4. According to this explanation, indi- climate change is occurring, that climate change is anthropogenic, viduals skeptical about climate change reject ostensibly credible about a scientific consensus on climate change, about a conspir- scientific information because it counters their standing beliefs. acy with regard to climate change, about who is responsible for Considering the threat that such a tendency poses to effectual sci- causing and/or addressing climate change, about the efficacy of entific communication, scholars and practitioners have focused on mitigation policies, about risks from climate change, about the identifying conditions that curtail or counteract directional moti- impact of climate-relevant behaviours (such as biking instead of vated reasoning5,6. driving) and about the importance of climate change as a national However, work in this area has overlooked two points that are or global issue8. important to effective climate change communication. First, direc- The updating process has three steps. The first step specifies the tional reasoning is not the only source of erroneous belief: individuals structure of the prior belief. We characterize the prior belief as a who process information in an ‘unbiased’ way can end up with opin- probability distribution regarding the true state of the world. The 2 ions that diverge dramatically from the scientific consensus (belief in structure of this belief is π()μμ~ N(,̂̂00σ ), where μ is a true state of climate conspiracies, for example). Second, there is scant evidence for the world, μ̂0 is the individual’s best guess about the true state of the 2 directional motivated reasoning when it comes to climate change: the world, and σ0̂ is the individual’s uncertainty around that guess (that evidence put forth cannot be distinguished from a model in which is, the individual’s confidence in her guess, or belief strength)9,10. people aim for accurate beliefs, but vary in how they assess the cred- π denotes the function. The ^ is used to indicate a perception, as ibility of different pieces of information. In short, we have little clear opposed to a state of the world7. Say, for example, that μ is the true evidence that can differentiate directional motivated reasoning from impact of human activity on climate change. Then, the individu- an accuracy motivated model—and the distinction between the two al’s belief π (μ ) about the role of human activity in causing climate models matters critically for effective communication. change comprises the individual’s estimate μ̂0 of the actual role of 2 In this Review, we first present a canonical model of how indi- human activity and her confidence σ0̂ in that estimate. viduals update their climate change beliefs in the face of new Second, an individual encounters relevant information in the information using Bayes’ theorem. Next, we discuss two possible guise of an experience (for example, abnormal climate events) or motivations—accuracy and directional—that may influence how communication (such as a statement about what scientists believe). individuals process information about climate change within a We represent this new information, x, as a draw from the distribu- 2 Bayesian framework. We use this framework to understand the evi- tion N(,μσx̂). For now, we assume that the location of the distribu- 2 dence required to support claims of directional motivated reasoning tion is determined by the ‘world’—but note that σx̂ is an individual in the context of climate change beliefs, and to highlight avenues for perception: a person’s evaluation of the credibility of the informa- future research. tion drawn from that distribution. For instance, x could be a mes- sage about a new scientific study showing that humans are causing Introducing the Bayesian framework climate change. The individual receiving the message has some per- Bayesian updating is a theoretical model of the process for incor- ception of its credibility (for example, worthless or highly credible 2 porating new information into prior beliefs to arrive at an updated information) represented by σx̂. Nothing in the Bayesian process belief7. In Box 1, we offer an overview of the Bayesian process that precludes heterogeneity in what people find credible. highlights the notation we use in the following discussion. Further, Third, the individual incorporates the new information with in Table 1, we display a glossary of key terms used throughout the the prior belief to form an updated (posterior) belief, π (µ |x). This Review. Our terminology is partly consistent with previous work4, updating process accounts for how far the new information is from although our approach and argument are distinct. what one previously believed, the strength of one’s prior belief and Department of Political Science, Northwestern University, Evanston, IL, USA. *e-mail: [email protected] NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange REVIEW ARTICLE NATURE CLIMATE CHANGE Box 1 | The Bayesian framework Accuracy motivation. In portrayals of Bayesian updating, it is often assumed that individuals strive to be ‘accurate’—aiming to arrive at a ‘correct’ conclusion4,13. This means that at the evaluation phase, Te Bayesian framework starts with an individual’s prior belief, the individual evaluates information, x, in a way that maximizes π(μ). Tis consists of an individual’s best guess ()μ̂0 about some 2 the likelihood that her updated belief is an accurate estimate of the condition of the world (μ) and her confdence in that guess ()σ0̂. true state of the world. For example, an accuracy-motivated indi- Next, the individual encounters some new information, x. Tis vidual would evaluate a scientific report on human-induced climate information is assumed to bear some relationship to the true change in a manner aimed at arriving at an accurate assessment of condition of the world, but individuals will difer in their percep- the impact of human activity. tions of how closely it relates to the truth (that is, the credibility 2 With an accuracy motivation, the evaluation of x is indepen- of the new information, σx̂). Bayesian updating happens when dent of the individual’s prior belief in question, π (µ ). In this exam- the prior belief is adjusted in light of the new information, taking ple, the individual’s prior belief about human-induced climate into account the individual’s confdence in the new information change has no bearing on whether she evaluates the report as high relative to her confdence in the prior ‘best guess’. or low quality. However, just as nothing in the Bayesian framework Tis incorporation is represented as requires that all people attach the same level of confidence to new information, neither does an accuracy motivation stipulate any 2 22 single level of confidence. For example, accuracy-motivated people σ0̂ σσ0̂̂x πμ()∣~xNμμ̂00+−()x ̂ , may differ in the standing trust they place in scientists. If some- 22 22 σσ0̂ + x̂ σσ0̂ + x̂ one has low confidence in the credibility of the information, that 2 σ0̂ information will be discounted and will carry little weight—but where μ̂00+−()x μ̂ 22 is the individual’s updated best we cannot infer anything about the individual’s motivation from ()σσ0̂+ x̂ guess—the prior best guess adjusted by the distance between the this evaluation. Accuracy-motivated individuals can vary in how prior and new information,22 weighted by relative confidence in much faith they place in a given piece of evidence and thus update σσ0̂̂x new information; and 22 is the individual’s confidence in the in heterogeneous ways. σσ0̂+ x̂ posterior belief. N denotes a normal distribution. Some studies find data consistent with accuracy-oriented updat- Te updating can operate according to an accuracy motivation ing. For example, Ripberger et al. explore how individuals perceive (the updating goal is to arrive at an accurate estimate of µ) or climate anomalies (departures from average precipitation and a directional motivation (the updating goal is to arrive at a temperature) over 11 consecutive seasons14. Here the experienced predetermined conclusion, such as one consistent with the prior anomalies are the new information. The authors find a strong rela- belief π (µ ); see Table 2). Te updating then leads to a posterior tionship between the objective measure of anomalies and respon- belief, π(μ|x). Te strength of the individual’s confdence in dents’ perceptions. Although they find some variations among the new information relative to her strength of confdence in extreme partisans, they note that these effects are small and do “not the prior best guess determines both the extent to which belief overwhelm the Bayesian process whereby both groups incorporate moves in response to the new information, and the strength of feedback… ”14.
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